Psychophysical system and method

Abstract

A method of determining an optimised visual field test activation sequence. The method comprises: receiving visual field loss data that is associated with one or more patterns of visual field loss; generating modified visual field loss data using the visual field loss data, the modified visual field loss data comprising a plurality of modified visual field loss values, each modified visual field loss value being associated with a respective visual field spatial reference of a set of visual field spatial references; and determining an optimised visual field test activation sequence, based at least in part on the modified visual field loss data.

Description

[0001]PSYCHOPHYSICAL SYSTEM AND METHOD TECHNICAL FIELD This disclosure relates to a psychophysical system and method. In particular, this disclosure relates to a system and method for determining an optimised visual field test activation sequence. This disclosure also relates to a system and method for performing a visual field test. This disclosure also relates to a system and method for generating and rendering a visual field test output. BACKGROUND Visual field tests are used to evaluate the range of vision of a patient being tested and to detect any loss or abnormalities in the patient’s visual field. In a visual field test, a patient will focus on a central target and respond to visual stimuli that appear in their visual field. The patient’s responses to the visual stimuli are used to prepare a map of the patient’s visual field. Visual field tests help identify vision impairments that may not be noticeable through standard eye exams, including peripheral vision loss, blind spots, or areas of reduced sensitivity. They assist with the identification of, and monitoring of conditions such as glaucoma, retinal diseases, and neurological disorders that can affect vision. By mapping out how well a patient can see in different parts of their visual field, these tests aid in assessing the extent of vision loss and guiding appropriate treatment strategies. It is to be understood that, if any prior art publication is referred to herein, such reference does not constitute an admission that the publication forms a part of the common general knowledge in the art, in Australia or any other country. SUMMARY OF THE DISCLOSURE In some embodiments, there is provided a method of determining an optimised visual field test activationsequence (^^). The method may comprise receiving visual field loss data (^^, ^^) that is associated withone or more patterns of visual field loss. The method may comprise generating modified visual field lossdata (^^′, ^^) using the visual field loss data (^^, ^^). The modified visual field loss data (^^′, ^^) maycomprise a plurality of modified visual field loss values ൫^^^ᇱ,^൯. Each modified visual field loss value ൫^^^ᇱ,^൯ may be associated with a respective visual field spatial reference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯. The method may comprise determining an visual field test activationsequence (^^), least in part on the modified visual field loss data (^^′, ^^). The optimised visualfield test activation sequence (^^) may define an order in which emission units associated with respective visual field spatial references൫^^^^൯are to be activated in a visual field test. The order may be determined based on a value of at least one information metric determined for each visual field spatial reference ൫^^^^൯,using the modified visual field loss data (^^′, ^^).In some embodiments, the visual field loss data (^^, ^^) comprises a plurality of visual field loss maps(^^^^). The visual field loss data (^^, ^^) may comprise a list (^^) of weights (^^^). Each visual field loss map(^^^^) may be associated with a respective weight (^^^) of the list (^^) of weights (^^^). In some embodiments, each visual field loss map (^^^^) comprises a plurality of visual field loss values൫^^^,^൯. In some embodiments, each visual field loss value൫^^^,^൯is associated with a respective visual reference ൫^^^^൯ of the set(^^)of visual field references ൫^^^^൯. In some the modified visual field loss data comprises applying athresholding function ^^ to the visual field loss data (^^, ^^). The modified visual field loss data (^^′, ^^)may comprise a plurality of modified visual field loss maps(^^ᇱ^^). The modified visual field loss data(^^′, ^^) may comprise the list (^^) of weights (^^^). Each modified visual field loss map (^^ᇱ^^) may be associated with a respective weight (^^^) of the list (^^) of weights (^^^). In some embodiments, one or more modified visual field loss map (^^ᇱ^^) may be generated from a respective visual field loss map (^^^^). In some embodiments, each modified visual field loss map (^^ᇱ^^) may be generated from a respective visual field loss map (^^^^). In some embodiments, one or more modified visual field loss map (^^ᇱ^^) may comprise the plurality of modified visual field loss values൫^^^ᇱ,^ ൯. In some embodiments, each modified visual field loss map (^^ᇱ^^) may comprise the plurality of modified visual field loss values൫^^^ᇱ,^ ൯. In some embodiments, one or more modified visual field loss value൫^^^ᇱ,^ ൯may be associated with a respective visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯. In some embodiments, each modified loss value ൫^^^ᇱ,^൯ may be associated with a respective visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references൫^^^^൯. In some embodiments, one or more modified visual field loss value ൫^^^ᇱ,^൯ is generated using a respective visual field loss value ൫^^^,^൯ of the visual field loss map(^^^^)from which the respective modified visual field loss map (^^ᇱ^^) is generated. In some embodiments, one or more modified visual field loss value൫^^^ᇱ,^ ൯is associated with the same visual field spatial reference൫^^^^൯as that of the visual field loss value ൫^^^,^൯ from which the modified visual field loss value ൫^^^ᇱ,^൯ is generated. In some embodiments, each modified visual field loss value ൫^^^ᇱ,^൯ is generated using a respective visual field loss value ൫^^^,^൯ of the visual field loss map (^^^^) from which the respective modified visual field loss map (^^ᇱ^^) is generated. In some embodiments, each modified visual field loss value ൫^^^ᇱ,^൯ is associated with the same visual field spatial reference ൫^^^^൯ as that of the visual field loss value ൫^^^,^൯ from which the modified visual field loss value ൫^^^ᇱ,^is In some embodiments, one or more modified visual field loss map(^^ᇱ^^)is associated with the weight(^^^)that is associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) is generated. In some embodiments, each modified visual field loss map (^^ᇱ^^) may be associated with the weight(^^^)that is associated with the visual field loss map(^^^^)from which the modified visual field loss map (^^ᇱ^^) is generated.In some embodiments, applying the thresholding function ^^ to the visual field loss data (^^, ^^) comprisesapplying the thresholding function ^^ to each visual field loss map (^^^^). In some embodiments, applying the thresholding function to one of the visual field loss maps (^^^^) comprises: comparing each of the visual field loss values൫^^^,^൯of the respective visual field loss map (^^^^) to one or more visual loss thresholds; and generating the modified visual field loss൯for each of the visual field loss values൫^^^,^൯based on the comparison. In some embodiments, the method further comprises receiving initial psychophysical testing data (^^). The initial psychophysical testing data (^^) may comprise a plurality of activation intensity hierarchical data structures ൫^^^^൯. In some each activation intensity hierarchical data structure ൫^^^^൯ is associated with a respective visual field spatial reference ൫^^^^൯ of the set (^^) of visual references ൫^^^^൯. In some embodiments, the method comprises generating modified psychophysical testing data (^^ᇱ), based at least in part on the initial psychophysical testing data (^^). In some embodiments, generating the modified psychophysical testing data(^^ᇱ)comprises iteratively: comparing differences in values of sibling nodes of the activation intensity hierarchical data structures൫^^^^൯to an intensity difference criterion; and pairs of sibling nodes that satisfy the intensity difference criterion from the respective activation intensity hierarchal data structures ൫^^^^൯, thereby generating a plurality of pruned activation intensity hierarchical data structures൫^^ᇱ^^൯, psychophysical testing data (^^ᇱ) comprising the pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯. In some embodiments, the optimised visual field test activation sequence (^^) comprises an ordered set of the visual field spatial references൫^^^^൯. In some embodiments, an order of the visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence (^^) defines a sequence in which the emission units are to be activated. In some embodiments, the method further comprises defining, for each visual field spatial reference ൫^^^^൯ of the set(^^)of visual field spatial references ൫^^^^൯, a plurality of groups ൫^^^^,^^൯. Each group ൫^^^^,^^൯ may comprise modified visual field loss maps (^^ᇱ^^) that have the same field ൫^^^ᇱ,^൯ associated with the visual field spatial reference ൫^^^^൯ associated with ൫^^^^,^^൯. In some embodiments, defining the plurality of groups ൫^^^^,^^൯ comprises group assignment function to each modified visual field loss map (^^ᇱ^^). In some embodiments, determining the optimised visual field test activation sequence (^^) comprises defining a weight dataset ൫^^^^൯, the weight dataset ൫^^^^൯ comprising the list (^^) of weights (^^^). In some embodiments, the optimised visual field test activation sequence(^^)comprises iteratively performing a plurality of operations. In some embodiments, the plurality of operations that are iteratively performed comprise removing a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯. In some embodiments, the plurality of are iteratively performed comprise determining a plurality of modified visual field loss value weighted sums ൫^^^,^൯. In some embodiments, each modified visual field loss value weighted sum൫^^^,^൯is associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps(^^ᇱ^^). In some embodiments, each modified visual field loss value weighted sum ൫^^^,^൯ is determined based on the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^). In some embodiments, each modified visual field loss value weighted sum൫^^^,^൯is a sum of the weights(^^^)associated with the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯. In some embodiments, the plurality of operations that are iteratively performed comprise computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums൫^^^,^൯associated with that visual field spatial reference൫^^^^൯. In some embodiments, the plurality of operations that are iteratively performed comprise determining an optimised information metric value ^^^. In some embodiments, the optimised information metric value ^^^is associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined. In some embodiments, the plurality of operations that are iteratively performed comprise adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence(^^). In some embodiments, the plurality of operations that are iteratively performed comprise adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^). In some embodiments, the plurality of operations that are iteratively performed comprise comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯. In some embodiments, the plurality of operations that are iteratively performed comprise: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weighting the weights (^^^) of the list൫^^^^൯that are associated with the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯; adding the modified lists ൫^^^^,^^൯ of (^^^) to an end of the weight dataset ൫^^^^൯; if the count ^^^is less than the maximum depth of the pruned activation data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯. In some embodiments, the plurality of operations that are iteratively performed comprise discarding the list ൫^^^^൯ of weights (^^^) removed from the weight dataset ൫^^^^൯, if the count ^^^is equal to or greater than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯. In some embodiments, determining the optimised visual field test activation sequence(^^)comprises iteratively: removing a list (^^^) of weights (^^^) from the weight dataset൫^^^^൯; determining a plurality of modified visual field loss value sums ൫^^^,^൯, wherein each modified visual field loss value weighted sum൫^^^,^൯is: associated with a visual field spatial reference ൫^^ ^^൯ and one of ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); determined based on the modified visual field loss maps(^^ᇱ ^^)of the group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); and a sum of the weights(^^^)associated with the modified visual field loss maps respective group ൫^^^^,^^൯; computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field spatial reference൫^^^^൯; determining an optimised information metric value ^^^, the optimised information metric value ^^^associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined; adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence(^^); adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^); and comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯; if count ^^^is less than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weighting function to the weights(^^^)of the list ൫^^^^൯ that are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯; adding the modified lists ൫^^^^,^^൯ of weights(^^^)to an end of the weight dataset ൫^^^^൯; and if the count ^^^is equal to or greater than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯, discarding the list ൫^^^^൯ of(^^^)removed from the weight dataset ൫^^^^൯. In some the method further comprises terminating the when the weight dataset ൫^^^^൯ is empty at an end of an iteration. In some embodiments, there is provided a method. The method may comprise performing a visual field test. The method may comprise generating a visual field test output based at least in part on visual field test data stored during the visual field test. Performing the visual field test may comprise iteratively activating one or more emission unit of a visual field test apparatus in accordance with an optimised visual field test activation sequence(^^); and storing visual field test data associated with the activation. Each emission unit may be associated with a respective visual field spatial reference ൫^^^^൯ of a set(^^)of visual field spatial references ൫^^^^൯. An intensity of a respective activation may be defined by a value of a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is also associated with the visual field spatial reference ൫^^^^൯ of the activated emission unit. In some embodiments, performing the visual field test comprises, for at least one iteration, determining an updated reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯, based on the stored visual field test data associated with the activation, if a reference node of the activation is not a leaf node. In some embodiments, performing the visual field test comprises, for at least one iteration, updating the optimised visual field test activation sequence (^^) if a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ associated with the activation is a leaf node, to remove subsequent activations of the respective emission unit from the optimised visual field test activation sequence (^^). In some embodiments, generating the visual field test output comprises determining an output value for each visual field spatial reference ൫^^^^൯, based on the stored visual field test data. In some embodiments, the method further comprises rendering a graphical output determined based on the visual field test data on a display. In some embodiments, the optimised visual field test activation sequence (^^) is determined in accordance with any one of claims 1 to 33. In some embodiments, there is provided a computing system. The computing system may comprise at least one processor. The computing system may comprise memory. The memory may store program instructions accessible by the at least one processor. The program instructions may be configured to causethe at least one processor to receive visual field loss data (^^, ^^) that is associated with one or morepatterns of visual field loss. The program instructions may be configured to cause the at least oneprocessor to generate modified visual field loss data (^^′, ^^) using the visual field loss data (^^, ^^). Themodified visual field loss data (^^′, ^^) may comprise a plurality of modified visual field lossvalues ൫^^^ᇱ,^൯. Each modified visual field loss value ൫^^^ᇱ,^൯ may be associated with a respective visual field spatial reference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯. The program instructions may be configured the at least one processor to determine an visual field test activationsequence (^^), based in part on the modified visual field loss ^^). The optimised visualfield test activation sequence(^^)may define an order in which emission units associated with respective visual field spatial references ൫^^^^൯ are to be activated in a visual field test. The order may be determined based on a value of at least metric determined for each visual field spatial reference ൫^^^^൯,using the modified visual data (^^′, ^^).In some embodiments, the visual field loss data (^^, ^^) comprises a plurality of visual field loss maps(^^^^). In some embodiments, the visual field loss data (^^, ^^) comprises a list (^^) of weights (^^^). Eachvisual field loss map (^^^^) may be associated with a respective weight (^^^) of the list (^^) of weights (^^^). In some embodiments, each visual field loss map (^^^^) comprises a plurality of visual field loss values൫^^^,^൯. Each visual field loss value൫^^^,^൯may be associated with a respective visual field spatial ൫^^^^൯ of the set (^^) of spatial references ൫^^^^൯. In some embodiments, generating the modified visual field (^^′, ^^) comprises applying athresholding function ^^ to the visual field loss data (^^, ^^). The modified visual field loss data (^^′, ^^)may comprise a plurality of modified visual field loss maps(^^ᇱ^^). The modified visual field loss data(^^′, ^^) may comprise the list (^^) of weights (^^^). Each modified visual field loss map (^^ᇱ^^) may be associated with a respective weight(^^^)of the list (^^) of weights(^^^). In some embodiments, one or more modified visual field loss map(^^ᇱ^^)is generated from a respective visual field loss map (^^^^). In some embodiments, one or more modified visual field loss map (^^ᇱ^^) comprises the plurality of modified visual field loss values ൫^^^ᇱ,^൯. Each modified visual field loss value ൫^^^ᇱ,^൯ may be associated with a respective visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯. In some embodiments, each modified visual field loss map(^^ᇱ^^)is generated from a respective visual field loss map (^^^^). In some embodiments, each modified visual field loss map (^^ᇱ^^) comprises the plurality of modified visual field loss values ൫^^^ᇱ,^൯. Each modified visual field loss value൫^^^ᇱ,^ ൯may be associated with a respective visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references ൫^^^^൯. In some embodiments, one or more modified visual field loss value ൫^^^ᇱ,^൯ is generated using a respective visual field loss value൫^^^,^൯of the visual field loss map (^^^^) from which the respective modified visual field loss map(^^ᇱ^^)is In some embodiments, each modified visual field loss value ൫^^^ᇱ,^൯ is generated using a visual field loss value൫^^^,^൯of the visual field loss map (^^^^) from which the respective modified visual field loss map (^^ᇱ^^) is In some embodiments, one or more modified visual field loss value ൫^^^ᇱ,^൯ is associated with field spatial reference ൫^^^^൯ as that of the visual field loss value ൫^^^,^൯ from which the modified visual field loss value ൫^^^ᇱ,^൯ is generated. In some embodiments, each visual field loss value ൫^^^ᇱ,^൯ is associated with the same visual field spatial reference൫^^^^൯as visual field loss value൫^^^,^൯from which the modified visual field loss value ൫^^^ᇱ,^൯ is generated. In some embodiments, one or more modified visual field loss map(^^ᇱ^^)is associated with the weight(^^^)that is associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) is generated. In some embodiments, each modified visual field loss map (^^ᇱ^^) is associated with the weight (^^^) that is associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) is generated.In some embodiments, applying the thresholding function ^^ to the visual field loss data (^^, ^^) comprisesapplying the thresholding function ^^ to each visual field loss map(^^^^). In some embodiments, applying the thresholding function to one of the visual field loss maps(^^^^)comprises: comparing each of the visual field loss values൫^^^,^൯of the respective visual field loss map (^^^^) to one or more visual loss thresholds; and generating the modified visual field loss value ൫^^^ᇱ,^൯ for each of the visual field loss values ൫^^^,^൯ based on the comparison. In some embodiments, the program instructions are further configured to cause the at least one processor to receive initial psychophysical testing data (^^). The initial psychophysical testing data (^^) may comprise a plurality of activation intensity hierarchical data structures ൫^^^^൯. In some embodiments, each activation intensity hierarchical data structure ൫^^^^൯ is associated with a respective visual field spatial reference ൫^^^^൯ of the set(^^)of visual field references ൫^^^^൯. In some embodiments, the program instructions are further configured to cause the at least one processor to generate modified psychophysical testing data (^^ᇱ), based at least in part on the initial psychophysical testing data (^^). In some embodiments, generating the modified psychophysical testing data (^^ᇱ) comprises iteratively: comparing differences in values of sibling nodes of the activation intensity hierarchical data structures ൫^^^^൯ to an intensity difference criterion; and pairs of sibling nodes that satisfy the intensity difference criterion from the respective hierarchal data structures൫^^^^൯, thereby generating a plurality of pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯, psychophysical testing data (^^ᇱ) comprising the pruned activation intensity hierarchical data ൫^^ᇱ^^൯. In some embodiments, the optimised visual field test activation sequence(^^)comprises an ordered set of the visual field spatial references ൫^^^^൯. In some embodiments, an order of the visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence (^^) defines a sequence in which the emission units are to be activated. In some embodiments, the program instructions are further configured to cause the at least one processor to define, for each visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references൫^^^^൯, a plurality of groups ൫^^^^,^^൯, each group ൫^^^^,^^൯ comprising modified visual field loss maps (^^ᇱ^^) that have the same modified loss value൫^^^ᇱ,^ ൯associated with the visual field spatial reference൫^^^^൯associated with that group ൫^^^^,^^൯. In some embodiments, defining the plurality of groups൫^^^^,^^൯comprises applying a group assignment function to each modified visual field loss map (^^ᇱ^^). In some embodiments, determining the optimised visual field test activation sequence (^^) comprises defining a weight dataset൫^^^^൯, the weight dataset൫^^^^൯comprising the list (^^) of weights (^^^). In some embodiments, determining the optimised visual field test activation sequence (^^) comprises iteratively performing a plurality of operations. In some embodiments, the plurality of operations that are iteratively performed comprise removing a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯. In some embodiments, the plurality of are iteratively performed comprise determining a plurality of modified visual field loss value weighted sums ൫^^^,^൯. In some embodiments, each modified visual field loss value weighted sum൫^^^,^൯is associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^). In some embodiments, each modified visual field loss weighted sum ൫^^^,^൯ is determined based on the modified visual field loss maps (^^ᇱ^^) of the respective group൫^^^^,^^൯of modified visual field loss maps (^^ᇱ^^). In some embodiments, each modified visual field loss value weighted sum ൫^^^,^൯ is a sum of the weights(^^^)associated with the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯. In some embodiments, the plurality of operations that are iteratively performed computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums൫^^^,^൯associated with that visual field spatial reference൫^^^^൯. In some embodiments, the plurality of operations that are iteratively performed comprise determining an optimised information metric value ^^^. In some embodiments, the optimised information metric value ^^^is associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined. In some embodiments, the plurality of operations that are iteratively performed comprise adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence (^^). In some embodiments, the plurality of operations that are iteratively performed comprise adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence(^^). In some embodiments, comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯. In some embodiments, of operations that are iteratively performed comprise: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weighting function to the weights (^^^) of the that are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group൫^^^^,^^൯; adding the modified lists ൫^^^^,^^൯ of weights end of the weight dataset ൫^^^^൯; if the count ^^^is less than the maximum depth of the pruned activation data structure ൫^^ᇱ൯ tha ^^t is associated with the respective visual field spatial reference In some the plurality of operations that are iteratively performed comprise discarding the list ൫^^^^൯ of weights (^^^) removed from the weight dataset ൫^^^^൯, if the count ^^^is equal to or greater maximum depth of the pruned activation data structure ൫^^ᇱ^^൯ that is with the respective visual field spatial In some embodiments, determining the optimised visual field test activation sequence (^^) comprises iteratively: removing a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯; determining a plurality of modified visual field loss value sums ൫^^^,^൯, wherein each modified visual field loss value weighted sum ൫^^^,^൯ is: associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); determined based on the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); and a sum of the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) of the respective group൫^^^^,^^൯; computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums൫^^^,^൯associated with that visual field spatial reference ൫^^^^൯; determining an optimised information metric value ^^^, the optimised information metric value ^^^being associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined; adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence (^^); adding one to a count ^^^associated visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence(^^); and comparing the count ^^^to a maximum depth of the pruned activation hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯; if count ^^^is less than the maximum depth of the pruned activation hierarchical data structure that is associated with the respective visual field spatial for each group൫^^^^,^^൯, generating a modified list൫^^^^,^^൯of weights (^^^) by applying a down- function (^^^) of the list ൫^^^^൯ that are associated with the modified visual field loss maps (^^ᇱ^^) of the group൫^^^^,^^൯; adding the modified lists to an end of the weight dataset ൫^^^^൯; and if the count ^^^is equal to or greater maximum depth of the prunedᇱ hierarchical data structure ൫^^^^൯ that is associated with the respective visual field spatial ൫^^^^൯, discarding the list ൫^^^^൯ (^^^) removed from the weight dataset ൫^^^^൯. In some the program instructions are further configured the at least one processor to terminate the iterations when the weight dataset ൫^^^^൯ is empty at an end of an iteration. In some embodiments, there is provided a visual test apparatus. The visual field test apparatus may comprise one or more emission units. The visual field test apparatus may comprise at least one visual field test apparatus processor. The visual field test apparatus may comprise visual field test apparatus memory. The visual field test apparatus memory may store visual field test apparatus program instructions. The visual field test apparatus program instructions may be accessible by the at least one visual field test apparatus processor. The visual field test apparatus program instructions may be configured to cause the at least one visual field test apparatus processor to perform a visual field test. The visual field test apparatus program instructions may be configured to cause the at least one visual field test apparatus processor to generate a visual field test output based at least in part on visual field test data stored during the visual field test. Performing the visual field test may comprise iteratively: activating the one or more emission units in accordance with an optimised visual field test activation sequence(^^); and storing visual field test data associated with the activation. Each emission unit may be associated with a respective visual field spatial reference ൫^^^^൯ of a set(^^)of visual field spatial references ൫^^^^൯. An intensity of a respective activation may be defined by a value of a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is also associated with the visual field spatial reference ൫^^^^൯ of the activated emission unit. In some embodiments, performing the visual field test comprises, for at least one iteration, determining an updated reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯, based on the stored visual field test data associated with the activation, if a reference node of the activation is not a leaf node. In some embodiments, performing the visual field test comprises, for at least one iteration, updating the optimised visual field test activation sequence (^^) if a reference node of a pruned activation intensity hierarchical data structure൫^^ᇱ^^൯associated with the activation is a leaf node, to remove subsequent activations of the respective emission unit from the optimised visual field test activation sequence(^^). In some embodiments, generating the visual field test output comprises determining an output value for each visual field spatial reference ൫^^^^൯, based on the stored visual field test data. In some embodiments, the visual field test apparatus program instructions are further configured to cause the at least one visual field test apparatus processor to render a graphical output determined based on the visual field test data on a display. In some embodiments, the optimised visual field test activation sequence (^^) is determined in accordance with method described herein. In some embodiments, there is provided a system. The system may comprise the computing system described herein. The system may comprise the visual field test apparatus described herein. In some embodiments, there is provided a method comprising performing a visual field test, wherein performing the visual field test comprises activating one or more emission units of a visual field test apparatus in an order defined in an optimised visual field test activation sequence (^^) determined in accordance with the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS Notwithstanding any other forms which may fall within the scope of the methods as set forth in the Summary, specific embodiments will now be described, by way of example only, with reference to accompanying drawings in which: Figure 1 shows an example of a light sensitivity map that can be generated from a visual field test; Figure 2 shows a smoothed grey scale light sensitivity map that can be generated from the light sensitivity map of Figure 1; Figure 3 is a block diagram of a system, according to some embodiments; Figure 4 shows a display of a visual field monitoring system, according to some embodiments; Figure 5 is a process flow diagram of a method, according to some embodiments; Figure 6 shows a visual representation of a modified visual field loss map, according to some embodiments; Figure 7 shows an example of a plurality of modified visual field loss maps, one or more of which are associated with a psychophysical condition, according to some embodiments; Figure 8 shows an example of an activation intensity hierarchical data structure, according to some embodiments; Figure 9 shows left and right eye graphical outputs generated using visual field test data, according to some embodiments; Figure 10 shows further left and right eye graphical outputs generated using visual field test data, according to some embodiments; Figure 11 shows further left and right eye graphical outputs generated using visual field test data, according to some embodiments; and Figure 12 shows further left and right eye graphical outputs generated using visual field test data, according to some embodiments. DETAILED DESCRIPTION Specific embodiments of the disclosed methods will now be described by way of example only. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the disclosed method. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to pertaining the disclosed methods. Existing visual field testing methods can be effective at testing patient visual fields; however, the accuracy of the tests can be affected by the patient’s cooperation, attention and ability to respond correctly. Commercial visual field tests are designed to be run to completion, fully thresholding upwards of 50 locations in a patient’s central visual field. Patient fatigue, distraction or cognitive issues can impact the results and accuracy of visual field tests. Visual field testing can take extended periods of time to perform, during which patients may lose focus or become restless. Fatigue can lead to diminished performance, where the patient might not respond accurately to visual stimuli. This can create inconsistencies in the results which may therefore not accurately reflect the patient’s true visual field status. Further, cognitive and psychological factors can also impact the efficacy of visual field testing. For patients with cognitive impairments or mental health conditions, following complex instructions or maintaining the required level of concentration throughout the test can be challenging. Anxiety, stress, or other emotional states may affect their performance during the test, potentially leading to unreliable results. In the event that a patient is unable or unwilling to complete a full visual field test, it is typically necessary to restart the test. Visual field testing involves the activation of one or more emission unit that emits light intended to be viewed using a particular area of the patient’s visual field, at intensities that vary over time. The emission units are either positioned at, or emit light at, one or more of a plurality of positions on a display of a visual field test apparatus. Each of these positions may be associated with a respective visual field spatial reference of a set of visual field spatial references. A visual field spatial reference may be considered to be a reference to a particular portion of both the display of the visual field test machine, and the visual field of the patient. That is, each position on the display of the visual field testing machine corresponds to a particular part of the patient’s visual field, with both of these areas being mappable to notional visual field spatial references. In this way, a visual field spatial reference may both be associated with a particular position on the display of a visual field test apparatus and a particular part of the patient’s visual field. The patient provides an input each time they notice one of these emissions, with their inputs being used to generate light sensitivity maps reflecting their visual field sensitivity. Figure 1 shows an example of a light sensitivity map 10 that can be generated from a visual field test. Figure 2 shows a smoothed grey scale light sensitivity map 20 that can be generated from the light sensitivity map 10 of Figure 1. Clinicians can use light sensitivity maps such as those of Figures 1 and 2 to identify and / or monitor various psychophysical conditions. It will be appreciated that an ophthalmological condition may be considered a psychophysical condition. Further, a neurological condition may be considered a psychophysical condition. A condition that affects vision may be considered a psychophysical condition. The present disclosure relates to a method of determining an optimised visual field test activation sequence. The optimised visual field test activation sequence is such that higher priority areas of the patient’s visual field are tested earlier. Particular areas of the patient’s visual field are tested in a systematic order that enables useful information to be derived from the results of the test, even in the case where the patient stops providing useful input prior to the end of the activation sequence. In this way, a detailed map of the patient’s visual field can be generated in the case where the patient completes the full visual field test; however, in addition to this, a useful, whilst not necessarily complete map of the patient’s visual field can be determined in cases where the test is only partially completed. The determination of, and performance of, the optimised visual field test activation sequence described herein can allow for visual field maps to be determined for patients that would otherwise be unable or unwilling to complete a full visual field test. System 100 Figure 3 is a block diagram of a system 100, according to some embodiments of the present disclosure. The system 100 may be referred to as a visual field testing system. The system 100 may be referred to as a psychophysical testing system. The system 100 may be referred to as an ophthalmological system. The system 100 may be referred to as an ophthalmological testing system. The system 100 may be referred to as a neurological system. The system 100 may be referred to as a neurological testing system. Computing System 102 The system 100 comprises a computing system 102. The computing system 102 comprises at least one processor 104. In some embodiments, the computing system 102 comprises a plurality of processors 104. The processor(s) 104 of the computing system 102 may be referred to as computing system processors. The computing system 102 may therefore be said to comprise at least one computing system processor. The computing system 102 comprises memory 106. Memory 106 may be referred to as computing system memory. The computing system 102 may therefore be said to comprise computing system memory 106. Memory 106 may comprise or be in the form of one or more non-transitory computer readable storage medium. Memory 106 stores program instructions 108. The program instructions 108 may be referred to as computing system program instructions. The memory 106 may therefore be said to store computing system program instructions. The computing system 102 comprises a network interface 110. The network interface 110 may be referred to as a computing system network interface. The program instructions 108 are accessible by the at least one processor 104. The at least one processor 104 is configured to execute the program instructions 108. In particular, the at least one processor 104 is configured to execute the program instructions 108 to cause the computing system 102 to function as described herein. That is, when executed, the program instructions 108 cause the at least one processor 104 to function as described herein. In some embodiments, the program instructions 108 are in the form of instruction program code. The at least one processor 104 comprises one or more microprocessors, central processing units (CPUs), application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs), tensor processing units (TPUs) or other processors capable of reading and executing program instructions. It will be appreciated that the at least one processor 104 may be a distributed processor. That is, one or more processor of the at least one processor 104 may be physically separated from one or more other processor of the at least one processor 104. In some embodiments, the computing system 102 may be said to comprise a plurality of processors 104. Where functionality is described herein as being performed by the at least one processor 104, it will be understood that the relevant functionality may be performed by one or more, or a plurality of processors 104 of the computing system 102. Memory 106 may comprise one or more volatile or non-volatile memory types. For example, memory 106 may comprise at least one of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Memory 106 may comprise one or more computer-readable storage medium. A computer-readable storage medium can be any medium that can tangibly contain or store computer-executable instructions, such as the program instructions 108, for use by or in connection with the computing system 102. In some examples, the storage medium is a transitory computer-readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and / or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Memory 106 is configured to store the program instructions 108. The program instructions 108 are accessible by the at least one processor 104. The program instructions 108 may be referred to as computer-executable instructions. Memory 106 stores executable program code modules. The program code modules are configured to be executable by the at least one processor 104. The executable program code modules, when executed by the at least one processor 104, cause the computing system 102 to perform certain functionality, as described herein. The program instructions 108 may comprise the program code modules. In the illustrated embodiment, the computing system 102 comprises a sequence determiner 109. The program instructions 108 comprise a sequence determiner 109. The sequence determiner 109 is configured to determine an emission unit activation sequence. In particular, the sequence determiner 109 determines the emission unit activation sequence based on input data. The sequence determiner 109 is configured to determine the optimised visual field test activation sequence (^^) described herein. In some embodiments, the sequence determiner 109 is a software module. That is, the sequence determiner 109 is a program code module. The at least one processor 104 may execute the sequence determiner 109 to determine the emission unit activation sequence based on input data. That is, the at least one processor 104 may execute the sequence determiner 109 to determine the optimised visual field test activation sequence (^^). In other words, the at least one processor 104 may run the sequence determiner 109 to perform one or more of the steps of a method described herein. The network interface 110 facilitates communication between the computing system 102 and one or more other components of the system 100. The network interface 110 may comprise a combination of network interface hardware and network interface software suitable for establishing, maintaining and facilitating communication over a relevant communications network. Examples of a suitable communications network include a cloud server network, wired or wireless internet connection, Bluetooth™ or other near field radio communication, and / or a physical network such as a wired Universal Serial Bus (USB) network or an Ethernet network. The computing system 102 comprises a user interface 111. The user interface 111 is configured to enable a user of the computing system 102 to provide an input to the computing system 102. That is, one or more user(s) of the user interface 111 can submit requests to the computing system 102 via the user interface 111. The computing system 102 can provide outputs to the user. In other words, the user interface 111 is configured to enable the computing system 102 to provide one or more outputs to a user. The user interface 111 may comprise one or more user interface components, such as one or more of a display device, a touch screen display, a keyboard, a mouse, a camera, a microphone, buttons, switches and lights. Visual Field Test Apparatus 120 The system 100 comprises a visual field test apparatus 122. The visual field test apparatus 122 is configured to be used to conduct a visual field test. The visual field test apparatus 122 comprises at least one processor 124. In some embodiments, the visual field test apparatus 122 comprises a plurality of processors 124. The processor(s) 124 of the visual field test apparatus 122 may be referred to as visual field test apparatus processors. Therefore, the visual field test apparatus 122 may be said to comprise at least one visual field test apparatus processor. The visual field test apparatus 122 comprises memory 126. Memory 126 may be referred to as visual field test apparatus memory. The visual field test apparatus 122 may therefore be said to comprise visual field test apparatus memory. Memory 126 may comprise or be in the form of one or more non-transitory computer readable storage medium. Memory 126 stores program instructions 128. The program instructions 128 may be referred to as visual field test apparatus program instructions. The memory 126 may therefore be said to store visual field test apparatus program instructions. The visual field test apparatus 122 comprises a network interface 130. The network interface 130 may be referred to as a visual field test apparatus network interface. The program instructions 128 are accessible by the at least one processor 124. The at least one processor 124 is configured to execute the program instructions 128. That is, the at least one visual field test apparatus processor is configured to execute the visual field test apparatus program instructions. In particular, the at least one processor 124 is configured to execute the program instructions 128 to cause the visual field test apparatus 122 to function as described herein. That is, when executed, the program instructions 128 cause the at least one processor 124 to function as described herein. In some embodiments, the program instructions 128 are in the form of instruction program code. The at least one processor 124 comprises one or more microprocessors, central processing units (CPUs), application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs), tensor processing units (TPUs) or other processors capable of reading and executing program instructions. It will be appreciated that the at least one processor 124 may be a distributed processor. That is, one or more processor of the at least one processor 124 may be physically separated from one or more other processor of the at least one processor 124. In some embodiments, the visual field test apparatus 122 may be said to comprise a plurality of processors 124. Where functionality is described herein as being performed by the at least one processor 124, it will be understood that the relevant functionality may be performed by one or more, or a plurality of processors 124 of the visual field test apparatus 122. Memory 126 may comprise one or more volatile or non-volatile memory types. For example, memory 126 may comprise at least one of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Memory 126 may comprise one or more computer-readable storage medium. A computer-readable storage medium can be any medium that can tangibly contain or store computer-executable instructions, such as the program instructions 128, for use by or in connection with the visual field test apparatus 122. In some examples, the storage medium is a transitory computer-readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and / or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Memory 126 is configured to store the program instructions 128. The program instructions 128 are accessible by the at least one processor 124. The program instructions 128 may be referred to as computer-executable instructions. Memory 126 stores executable program code modules. The program code modules are configured to be executable by the at least one processor 124. The executable program code modules, when executed by the at least one processor 124, cause the visual field test apparatus 122 to perform certain functionality, as described herein. The program instructions 128 may comprise the program code modules. The network interface 130 facilitates communication between the visual field test apparatus 122 and one or more other components of the system 100. For example, the network interface 130 facilitates communication between the visual field test apparatus 122 and the computing system 102. Similarly, the network interface 110 of the computing system 102 facilitates communication between the computing system 102 and the visual field test apparatus 122. The network interface 130 may comprise a combination of network interface hardware and network interface software suitable for establishing, maintaining and facilitating communication over a relevant communications network. Examples of a suitable communications network include a cloud server network, wired or wireless internet connection, Bluetooth™ or other near field radio communication, and / or a physical network such as a wired Universal Serial Bus (USB) network or an Ethernet network. The visual field test apparatus 122 comprises a user interface 132. The user interface 132 may be referred to as a visual field test apparatus user interface. The user interface 132 is configured to enable a user of the visual field test apparatus 122 to interact with the visual field test apparatus 122. It will be understood that in some cases, the user of the visual field test apparatus 122 is a patient who’s visual field is being tested. In some cases, the user of the visual field test apparatus 122 may be a clinician that runs visual field tests. In some cases, the user of the visual field test apparatus 122 may be a technician that services or repairs the visual field test apparatus 122. The user interface 132 is configured to enable a user of the visual field test apparatus 122 to provide an input to the visual field test apparatus 122. That is, one or more user(s) of the visual field test apparatus 122 can submit requests to the visual field test apparatus 122 via the user interface 132. The user interface 132 can provide outputs to the user. In other words, the user interface 132 is configured to enable the visual field test apparatus 122 to provide one or more outputs to a user. The user interface 132 may comprise one or more user interface components, such as one or more of a display device, a touch screen display, a keyboard, a mouse, a camera, a microphone, buttons, switches and lights. Referring to Figures 3 and 4, the user interface 132 comprises a display system 134. The display system 134 is configured to display visual outputs to the user of the visual field test apparatus 122. The display system 134 is configured to display information to the user of the visual field test apparatus 122. The display system 134 comprises at least one emission unit 135. The display system 134 may comprise a plurality of emission units 135. The emission unit(s) 135 are configured to be activated to provide optical outputs that can be observed by the user. Specifically, one or more of the emission unit(s) 135 emit an optical output. Each emission unit 135 may be configured to emit an optical output. The display system 134 may comprise a display 137. In some embodiments, the display 137 comprises one or more of the emission unit(s) 135. In some embodiments, one or more of the emission unit(s) 135 emit an optical output that is viewable on the display 137. Emission units 135, or the optical outputs emitted by the emission unit(s) 135 on the display 137 are represented by dots in Figure 4. Each emission unit 135 is associated with an emission unit position. The emission unit position of an emission unit 135 is a particular point in space where the optical output emitted by the emission unit 135 is visible by the user of the visual field test apparatus 122. That is, the emission unit position of an emission unit 135 is the position on the display 137 at which an optical emission of the emission unit 135 is emitted, directed and / or visible. In some embodiments, one or more of the emission units 135 is positioned at the associated emission unit position. For example, one or more of the emission units 135 may take the form of a light-emitting optical component (such as a light emitting diode (LED)) that is positioned at a corresponding emission unit position. In some embodiments, one or more of the emission units 135 are configured to generate an optical output that is directed at the relevant emission unit position. For example, one or more of the emission units 135 may be in the form of a laser that emits an optical output that is directed at the relevant emission unit position. The emission unit positions form an optical output array. Each emission unit position is associated with a visual field spatial reference ൫^^^^൯. The visual field spatial reference ൫^^^^൯ of an emission unit position may be considered to be an index that emission unit As each emission unit 135 is associated with an emission each emission unit 135 may be said to be associated with a visual field spatial reference ൫^^^^൯. In particular, the visual field spatial reference ൫^^^^൯ that is associated with a particular emission unit 135 is the visual field spatial reference൫^^^^൯that is with the emission unit position of the emission unit 135. That is, each emission be uniquely referred to by referring to the visual field spatial reference ൫^^^^൯ of that unit position. Similarly, the emission unit 135 at that emission unit position may uniquely referred to by referring to the visual field spatial reference ൫^^^^൯ of the emission unit The number of emission units 135 may be equal to the number of emission unit positions. The number of emission units 135 may be equal to the number of visual field spatial references ൫^^^^൯. The number of emission unit positions may be different to the number of emission units 135. example, in some cases, one emission unit 135 may be capable of selectively providing an optical a plurality of emission unit positions. In such a case, the number of emission units 135 may be less than the number of emission unit positions. Similarly, the number of visual field spatial references൫^^^^൯may be different to the number of emission units 135. For example, the number of emission units be less than the number of visual field spatial references ൫^^^^൯. The visual field test apparatus 122 comprises an input system 136. The input system 136 is configured to enable the user to provide an input to the visual field test apparatus 122. The input system 136 may be a subsystem of the user interface 132. In some embodiments, the input system 136 comprises a button. The visual field test apparatus 122 detects when the user presses the button. Thus, the user is able to provide an input to the visual field test apparatus 122 by pressing the button. The input system 136 may comprise another form of input system or input device. The input system 136 may comprise a keyboard and / or an audio input device, for example. The computing system 102 communicates with the visual field test apparatus 122 via a communications network 138. The system 100 may comprise the communications network 138. Alternatively, one or more components of the system 100 may utilise a third party communications network. That is, the communications network 138 may be a third party communications network. The communications network 138 may comprise, or be in the form of a wireless local area network (WLAN) such as Wi-Fi (IEEE 82.15.1) or Zigbee (IEE 802.15.4), a wireless wide area network (WWAN) such as cellular 4G LTE and 5G or another cellular network connection, a low power wide area network (LPWAN) such as SigFox and Lora, Bluetooth™ and / or other near field radio communication. The communications network 138 may involve a connection with the Internet. The communications network 138 may comprise, or be in the form of a wired network. The system 100 may be said broadly to comprise at least one system processor. The at least one system processor may comprise the at least one processor 104 of the computing system 102. The at least one system processor may comprise the at least one processor 124 of the visual field test apparatus 122. Method 200 Figure 5 shows a process flow diagram of a method 200, according to some embodiments of the present disclosure. The method 200 is performed by the system 100. In particular, the method 200 may be performed entirely, or in part, by one or more of the components of the system 100. For example, one or more steps of the method 200 may be performed by the computing system 102. Specifically, one or more steps of the method 200 may be performed by the at least one processor 104 of the computing system 102. One or more steps of the method 200 may be performed by the visual field test apparatus 122. Specifically, one or more steps of the method may be performed by the at least one processor 124 of the visual field test apparatus 122. In some embodiments, some steps of the method 200 are performed by the computing system 102 and some steps of the method 200 are performed by the visual field test apparatus 122. In some embodiments, the entire method 200 may be performed by the visual field test apparatus 122. The method 200 may be referred to as a computer-implemented method. For the purposes of this description, 202, 204, 206, 208 and 210 are described as being performed by the computing system 102. In particular, 202, 204, 206, 208 and 210 are described as being performed by the at least one processor 104 of the computing system 102. It will be appreciated that in some embodiments, one or more of these steps may be performed by the visual field test apparatus 122. In particular, one or more of 202, 204, 206, 208 and 210 may be performed by the at least one processor 124 of the visual field test apparatus 122. Determining an Optimised Visual Field Test Activation Sequence for a Visual Field TestAt 202, the system 100 receives visual field loss data (^^, ^^). In particular, at 202, the at least oneprocessor 104 receives visual field loss data (^^, ^^). The visual field loss data (^^, ^^) is associated withone or more patterns of visual field loss. The visual field loss data (^^, ^^) is associated with one or morepsychophysical condition. In particular, the visual field loss data (^^, ^^) is associated with one or morepatterns of visual field loss associated with a particular psychophysical condition or conditions. Thevisual field loss data (^^, ^^) indicates a spatial pattern of visual field loss characterised by the particularpsychophysical condition or conditions. It will be appreciated that the at least one processor 104 may beconsidered to receive the visual field loss data (^^, ^^) in the case where the visual field loss data (^^, ^^) isstored in memory 106 and the at least one processor 104 retrieves the visual field loss data (^^, ^^) frommemory 106. In this case, the at least one processor 104 can be said to receive the visual field loss data(^^, ^^) in the process of retrieving and processing the visual field loss data (^^, ^^).The visual field loss data (^^, ^^) comprises one or more visual field loss maps (^^^^). For the purposes ofthis disclosure, ^^ is an index of the visual field loss maps (^^^^), with 1 ≤ ^^ ≤ ^^, where ^^ is the number ofvisual field loss maps (^^^^) of the visual field loss data (^^, ^^). In the embodiment of the present disclosuredescribed with reference to Figure 5, the visual field loss data (^^, ^^) comprises a plurality of visual fieldloss maps (^^^^). The visual field loss data (^^, ^^) may comprise one, two, three, four, five, six, seven,eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen or more than sixteen visual field lossmaps (^^^^). The visual field loss data (^^, ^^) may be said to comprise a list (^^) of visual field lossmaps (^^^^). The list (^^) of visual field loss maps (^^^^) may be said to be a set (^^) of visual field loss maps (^^^^). The list (^^) of visual field loss maps (^^^^) comprises ^^ visual field loss maps (^^^^). Each visual field loss map(^^^^)is associated with a respective pattern of visual field loss. In other words, each visual field loss map (^^^^) is associated with a visual field loss pattern. That is, each visual field loss map (^^^^) indicates a particular visual field loss pattern. Each visual field loss map (^^^^) may be associated with a different visual field loss pattern to each other visual field loss map (^^^^). That is, each visual field loss map (^^^^) may indicate a unique visual field loss pattern. One or more of the visual field loss maps (^^^^) may indicate a visual field loss pattern of a particular psychophysical condition. A visual field loss map (^^^^) may be stored in memory 106 as a matrix. A visual field loss map (^^^^) may therefore be referred to as a visual field loss matrix. A visual field loss map(^^^^)may be referred to as visual field loss map data. A visual field loss map (^^^^) comprises a plurality of visual field loss values ൫^^^,^൯. The visual field loss values ൫^^^,^൯ are scalars. A visual field loss value ൫^^^,^൯ is indicative of an extent of visual field loss. As described herein, visual field loss may be to a plurality of visual field spatial references ൫^^^^൯. For the purposes of this ^^ an index of the visual field spatial references൫^^^^൯, with ^^ ≤ ^^, where ^^ is the number of visual field spatial references ൫^^^^൯. The number ^^ of visualspatial references ൫^^^^൯, and their relative orientations and / or be dictated by the particular visual field 122 that is being used. Each visual field loss value ൫^^^,^൯ is associated with a respective visual field spatial reference ൫^^^^൯, in a visual field loss . In particular, each visual field loss value ൫^^^,^൯ indicates of visual field loss at the corresponding visual field spatial reference ൫^^^^൯ of visual field loss map(^^^^). The visual field loss values ൫^^^,^൯ are therefore ^^ herein, with the ^^ index of a visual field loss value ൫^^^,^൯ indicating the visual field loss map (^^^^) that visual field loss value൫^^^,^൯is associated with. Similarly, each visual field loss value ൫^^^,^൯ is associated with a respective visual field spatial reference ൫^^^^൯. This visual field spatial reference ൫^^^^൯ is a visual field spatial reference ൫^^^^൯ of the visual field (^^^^) that that visual field loss value ൫^^^,^൯ is associated with. Therefore, the visual field loss values ൫^^^,^൯ are indexed with ^^ herein, with the ^^ index of a visual field loss value ൫^^^,^൯ indicating the visual field spatial reference ൫^^^^൯ that visual field loss value ൫^^^,^൯ is associated with. The spatial references ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯. The list(^^)of visual field loss(^^^^)may be said to comprise a plurality of pairs: ^^^^^^, ^^^,^^|1 ≤ ^^ ≤ ^^^ where: ^^ is an index into a particular visual field spatial references ൫^^^^൯; ^^^^is a vector that specifies the particular visual field of the associated visual field loss value ൫^^ ^,^൯; and ^^ is the number of visual field spatial references of the visual field loss maps(^^^^). The visual field loss values ൫^^^,^൯ may be expressed in appropriate units (e.g. dB or cd / m2). A visual field spatial reference ൫^^^^൯ is associated with a particular portion of a visual field of an eye. One or more of the visual field ൫^^^൯ is associated with a respective visual field spatial reference ,^൫^^^^൯. In the illustrated field loss value ൫^^^,^൯ is associated with a respective field spatial reference ൫^^^^൯. visual field loss value ൫^^^,^൯ is associated with a visual field ൫^^^^൯ of the set (^^) of visual references ൫^^^^൯. A visual field loss value ൫^^^,^൯ is indicative of of vision loss at a portion of an eye’s visual field that corresponds to visual field spatial reference ൫^^^^൯. A visual field loss map(^^^^)therefore indicates the extent of vision loss of an eye associated with the visual field loss map (^^^^). In some embodiments, a relatively high visual field loss value ൫^^^,^൯ indicates a greater extent of vision loss at the respective visual field spatial references൫^^^^൯, lower visual field loss value ൫^^^,^൯. In some embodiments, a relatively low visual field loss value ൫^^^,^൯ indicates a greater extent of loss at the respective visual field spatial references൫^^^^൯, than a relatively higher visual field loss value ൫^^^,^൯. It will be appreciated that the practical implementation of such a relationship is merely dependent on a positive or negative relationship between a larger visual field loss value ൫^^^,^൯ and vision loss. As described herein, a visual field loss map (^^^^) may be stored in memory 106 in the form of a matrix. A visual field loss map (^^^^) may comprise a plurality of vectors. A number of the vectors may comprise a respective visual field loss value൫^^^,^൯as a vector element. Those vectors may also comprise the visual field spatial reference ൫^^^^൯ as vector elements. It will be appreciated that the visual field loss maps (^^^^) may be stored in memory 106 in any one of a number of ways. A visual field loss map (^^^^) may be stored in memory 106 as a lookup table indexed by the visual field spatial references൫^^^^൯. Each visual field loss value ൫^^^,^൯ in such a case would be stored as a value corresponding to the associated visual field spatial reference ൫^^^^൯. In the embodiment of the present disclosure, the visual field loss data (^^, ^^) comprises a plurality ofvisual field loss maps (^^^^), each of which is associated with a particular pattern of vision loss. One or more visual field loss map (^^^^) may be associated with a visual loss pattern that is characteristic of a particular psychophysical condition. That is, one or more visual field loss map (^^^^) may represent a visual loss pattern that is characteristic of a particular psychophysical condition. Each visual field loss map (^^^^) may be associated with a visual loss pattern that is characteristic of a particular psychophysical condition. Each visual field loss map(^^^^)may represent a visual loss pattern that is characteristic of a particular psychophysical condition. One or more visual field loss map(^^^^)may be associated with a visual loss pattern that is characteristic of a particular neurological condition. One or more visual field loss map (^^^^) may represent a visual loss pattern that is characteristic of a particular neurological condition. Each visual field loss map (^^^^) may be associated with a visual loss pattern that is characteristic of a particular neurological condition. Each visual field loss map (^^^^) may represent a visual loss pattern that is characteristic of a particular neurological condition.The visual field loss data (^^, ^^) also comprises a plurality of weights (^^^). In particular, the visual fieldloss data (^^, ^^) comprises a list (^^) of weights (^^^). Each weight (^^^) is associated with a respectivevisual field loss map (^^^^). For this reason, the weights (^^^) are also indexed with the index ^^. That is, weight ^^^is associated with visual field loss map (^^^^). The list(^^)of weights(^^^)may be referred to as a set (^^) of weights(^^^). Each visual field loss map(^^^^)is associated with a respective weight(^^^). That is, each visual field loss map(^^^^)is associated with a respective weight (^^^) of the list (^^) of weights (^^^). The weight (^^^) associated with a particular visual field loss map (^^^^) is associated with the probability that a patient presenting with the psychophysical condition associated with that visual field loss map (^^^^) will present with a visual field loss pattern that corresponds to that visual field loss map (^^^^). That is, the weight (^^^) associated with a particular visual field loss map(^^^^)indicates the probability that a patient presenting with the psychophysical condition associated with that visual field loss map (^^^^) will present with a visual field loss pattern that corresponds to that visual field loss map (^^^^). In other words, the weight (^^^) associated with a particular visual field loss map (^^^^) is proportional to an expected probability that a patient presenting with the psychophysical condition associated with that visual field loss map (^^^^) will present with a visual field loss pattern that corresponds to that visual field loss map(^^^^). For example, where a visual field loss map (^^^^) reflects a visual field loss pattern expected for some proportion of glaucoma patients, the weight(^^^)associated with that visual field loss map(^^^^)will be a number between 0 and 1 that is indicative of the probability of a new patient with glaucoma presenting with a visual field loss pattern corresponding to that visual field loss map(^^^^). In some cases, the visual field loss map(^^^^)may comprise the associated weight(^^^). For example, the weight(^^^)may be a vector element of the respective visual field loss map (^^^^). Each weight (^^^) is a scalar. In some embodiments, the sum of the weights (^^^) in the list (^^) of weights (^^^) is 1. Expressed differently: ∑^^^ = 1It will be appreciated that in some embodiments, the sum of the weights (^^^) in the list (^^) of weights (^^^) may be a value other than 1. It will be appreciated that the at least one processor 104 may execute the sequence determiner 109 to perform 202. At 206, the system 100 receives initial psychophysical testing data (^^). In particular, at 206, the at least one processor 104 receives initial psychophysical testing data (^^). The initial psychophysical testing data (^^) comprises procedure data. The procedure data may be configured to be executed by the at least one processor 104, with the at least one processor 104 determining an activation intensity procedure for each visual field spatial reference ൫^^^^൯, in response to executing the procedure data. In executing the procedure data, the at least one processor 104 may perform an algorithm that generates an activation intensity procedure for one or more of the visual field spatial references ൫^^^^൯. The procedure data may represent testing logic, associated with successive activations of emission units 135. The initial psychophysical testing data (^^) may comprise an activation intensity hierarchical data structure ൫^^^^൯. The initial psychophysical testing data (^^) may comprise a plurality of activation intensity structures൫^^^^൯. Alternatively, the at least one processor 104 may generate the activation intensity hierarchical data ൫^^^^൯ based on the initial psychophysical testing data (^^). That is, the at least one processor 104 may generate the activation intensity hierarchical data structures൫^^^^൯based on the procedure data. For example, the at least one processor 104 may generate the activation intensity hierarchical data structures ൫^^^^൯ by executing the procedure data. Each activation intensity hierarchical data structure ൫^^^^൯ is associated with a respective visual field spatial reference ൫^^^^൯ of the set(^^)of visual field spatial references ൫^^^^൯. For this reason, throughout this description, the activation intensity hierarchical data structures ൫^^^^൯ are indexed using index ^^. The value of the ^^ index of an activation intensity hierarchical data structure ൫^^^^൯ is the same as that of the visual field spatial reference ൫^^^^൯ with which it is associated. An activation intensity hierarchical data structure ൫^^^^൯ defines the intensity with which the emission unit(s) 135 of a particular visual field spatial reference ൫^^^^൯ are activated over sequential activations. A value associated with a respective node of the activation intensity hierarchical data structure൫^^^^൯may indicate or define the intensity with which the emission unit(s) 135 of a particular visual field spatial reference ൫^^^^൯ is activated for an activation. That is, the value of a respective node of the intensity hierarchical data structure ൫^^^^൯ indicates or defines the intensity with which the emission unit(s) 135 of the associated visual field reference ൫^^^^൯ is activated for an activation. It will be appreciated that the at least one 104 may be considered to receive the initial psychophysical testing data (^^) in the case where the psychophysical testing data (^^) is stored in memory 106 and the at least one processor 104 retrieves the psychophysical testing data (^^) from memory 106. In this case, the at least one processor 104 can be said to receive the psychophysical testing data (^^) in the process of retrieving and processing the psychophysical testing data(^^). One or more of the activation intensity hierarchical data structures ൫^^^^൯ is a tree data structure. In particular, a plurality of the activation intensity hierarchical data structures ൫^^^^൯ are tree data structures. In the described embodiment, each activation intensity hierarchical data structure ൫^^^^൯ is a tree data structure. A tree data structure comprises a plurality of nodes. Each node in a tree data structure may be represented using a vector. Each of the activation intensity hierarchical data structures ൫^^^^൯ comprises a plurality of nodes. In some embodiments, each node in an activation intensity hierarchical data structure ൫^^^^൯ is atriple, ^^^, ^^^^, ^^௬^^^. ^^ is a value of light stimuli that could be shown to a patient using the emission unit(s)135 of a particular visual field spatial reference ൫^^^^൯ at a point in an activation sequence corresponding to the relevant node. ^^^^is a decision tree to activate if the patient does not provide an input indicating that they see the light stimuli. ^^௬^^is a decision tree to activate if the patient does provide an input indicating that they see the light stimuli. Each activation intensity hierarchical data structure൫^^^^൯comprises a root node. If ^^௬^^and ^^^^are ^^^^^^^^, the node is called a ^^^^^^^^ node. In this case, the of the visual field at the relevant visual field spatial reference ൫^^^^൯ is given, using the value ^^ of the leaf node of the activation intensity hierarchical data structure ൫^^^^൯. Two nodes of an activation intensity hierarchical data structure ൫^^^^൯ are called siblings if the parent of each node is the same. Each of the activation intensity hierarchical data structures ൫^^^^൯ has an associated depth. The depth of an activation intensity hierarchical data structure ൫^^^^൯ is related to the number of levels of the activation intensity hierarchical data structure ൫^^^^൯. The root node is a first level of the activation intensity hierarchical data structure ൫^^^^൯, and has depth zero. The child nodes of the root node are a second layer of the activation intensity hierarchical data structure൫^^^^൯, and have depth 1. A chain of nodes is formed between the root node and each of the child nodes of the root node. A maximum depth of the activation intensity hierarchical data structure ൫^^^^൯ is the number of levels of the longest chain of the activation intensity hierarchical data structure ൫^^^^൯. That is, the maximum depth of the activation intensity hierarchical data structure ൫^^^^൯ is associated with a sum of the number of nodes of the activation intensity hierarchical data structure ൫^^^^൯ along the longest chain. In some embodiments, the nodes of the activation intensity hierarchical data structures ൫^^^^൯ are associated with two or more values. One of these values is indicative of an intensity of a particular emission unit 135 activation associated with that node. The other value is associated with a current ‘nest estimate’ of light sensitivity at that time / location. This second value may be used during execution of the visual field test to indicate an optimised next visual field spatial reference ൫^^^^൯ to activate. It will be appreciated that the at least one processor 104 may execute the sequence determiner 109 to in performing 206. At 208, the system 100 generates modified psychophysical testing data(^^ᇱ). In particular, the at least one processor 104 generates modified psychophysical testing data (^^ᇱ). The modified psychophysical testing data (^^ᇱ) comprises modified procedure data. The modified procedure data may be a modified version of the procedure data of the psychophysical testing data (^^). The modified procedure data may be configured to be executed by the at least one processor 104, with the at least one processor 104 determining a modified activation intensity procedure for each visual field spatial reference ൫^^^^൯, in response to executing the modified procedure data. In executing the modified procedure data, the at least one processor 104 may perform an algorithm that generates a modified activation intensity procedure for one or more of the visual field spatial references ൫^^^^൯. The modified procedure data may represent testing logic, associated with successive activations of emission units 135. The at least one processor 104 generates the modified psychophysical testing data(^^ᇱ)using the initial psychophysical testing data (^^). In other words, the at least one processor 104 generates the modified psychophysical testing data (^^ᇱ) based at least in part on the initial psychophysical testing data (^^).The at least one processor 104 compares the values ^^ of sibling nodes (^^^, ^^ଶ) of the activation intensityhierarchical data structures൫^^^^൯. In this case, ^^^is a first sibling node and ^^ଶis a second sibling node. The at least one processor 104 the values ^^ of sibling nodes of the activation intensity hierarchical data to an intensity difference criterion. In some embodiments, the intensitydifference criterion is associated with a difference in the values ^^ of the sibling nodes (^^^, ^^ଶ). Theintensity difference be a value to which the difference in the values ^^ of the sibling nodes(^^^, ^^ଶ) is compared. For example, the intensity difference criterion may be:|^^^ − ^^ଶ| < ^^where ^^ is an intensity difference threshold. The intensity difference threshold ^^ is a scalar. Thus, at 104, the at least one processor 104 computes a magnitude of a difference between the value (^^^) of a first sibling node ^^^and the value ^^ଶof a second sibling node ^^ଶof an activation intensity hierarchical data structure ൫^^^^൯, and compares the difference to the intensity difference threshold ^^. The at least one may perform this for each pair of sibling nodes (^^^, ^^ଶ) of each activation intensitystructure൫^^^^൯. In the case where the between the value ^^^of a first sibling node ^^^and the value ^^ଶof a second sibling node ^^ଶis greater than, or equal to, the intensity difference threshold ^^, the first sibling node ^^^and the second sibling node ^^ଶmay be kept in the activation intensity hierarchical data structure ൫^^^^൯. The at least one processor 104 removes the first sibling node ^^^and the second sibling node ^^ଶthe activation intensity hierarchical data structure ൫^^^^൯ if the difference between the value ^^^of a first sibling node ^^^and the value ^^ଶof a second sibling node ^^ଶis less than the intensity difference threshold ^^. In other words, the at least one processor 104 removes the first sibling node ^^^and the second sibling node ^^ଶfrom the activation intensity hierarchical data structure ൫^^^^൯ if the first sibling node ^^^and the second sibling node ^^ଶsatisfy the intensity difference The at least oneprocessor 104 performs this computation for each pair of sibling nodes (^^^, each activationintensity hierarchical data structure ൫^^^^൯. The at least one processor iteratively performs thedescribed sibling node each pair of sibling nodes (^^^, ^^ଶ) of a respective activationintensity hierarchical data structure ൫^^^^൯. Where a pair of sibling nodes (^^^, ^^ଶ) is removed, a new pair ofsibling nodes may be formed include the parent node of the removed sibling nodes(^^^, ^^ଶ)). This new pair of sibling nodes may now also be leaf nodes of the activation intensityhierarchical data structure ൫^^^^൯. The at least one processor 104 would, in this case, iteratively perform the comparison with the criterion, for the newly created sibling nodes that form leaves of the activation intensity hierarchical data structure ൫^^^^൯. In pruning sibling nodes as described herein, the at least one processor 104 generates pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯. In particular, the at least one processor 104 generates a plurality of pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯. That is, the activation intensity hierarchical data structures ൫^^^^൯, subsequent to the described pruning operation, are considered pruned activation intensity hierarchical data structures൫^^ᇱ^^൯. The modified psychophysical testing data (^^ᇱ) comprises the pruned intensity hierarchical data structures ൫^^ᇱ^^൯. Each of the pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯ has an associated depth. The depth of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is related to the number of levels of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯. The root node is a first level of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯, and has depth 0. The child nodes of the root node are a second layer of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯, and have a depth 1. A chain of nodes is formed between the root node and each of the child nodes of the root node. A maximum depth of the pruned activation intensity hierarchical data structure൫^^ᇱ^^൯is the number of levels of the longest chain of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯. That is, the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is associated with a sum of the number of nodes of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ along the longest chain. The maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ may be less than the maximum depth of the corresponding activation intensity hierarchical data structures ൫^^^^൯. Each pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is associated with a field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯. In particular, each pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is associated with the same visual field spatial reference൫^^^^൯as that which was associated with the activation intensity hierarchical data structure൫^^^^൯from which it was derived. For this reason, throughout this description, the pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯ are indexed using index ^^. As described herein, a challenge in performing visual field tests is ensuring the patient performs as much of the test as possible, and preferably the entire test. The method 200 involves pruning the activation intensity hierarchical data structures ൫^^^^൯ to remove nodes with activation intensities below a relevant threshold. As the difference in activation intensities between these nodes is relatively low (necessarily being below the threshold difference), the amount of information gained in the test if these activations were performed may not be sufficient to justify the additional risk introduced by lengthening the test to accommodate these activations. The threshold difference can be tailored, between specific tests, to accommodate patients of different capabilities. Figure 8 shows an example of a pruned activation intensity hierarchical data structure 800. The pruned activation intensity hierarchical data structure 800 comprises a plurality of nodes 802. Each node 802 is associated with a node value ^^. A node value ^^ may be referred to as an intensity value. The node values ^^ are represented numerically in Figure 8. The pruned activation intensity hierarchical data structure 800 comprises a root node. The root node has a node value ^^ of 20. The pruned activation intensity hierarchical data structure 800 comprises a plurality of leaf nodes. One or more of the nodes 802 are associated with respective child nodes. For example, the root node is associated with two child nodes. Thenode value ^^ of one of the child nodes is 17 (i.e. ^^ = 17). The node value ^^ of the other of the childnodes is 32. It will be appreciated that at least one of the tree data structures described herein could, in an alternative embodiment, take the form of activation procedural data that describes a hierarchal procedure. For example, where an activation intensity hierarchical data structure ൫^^^^൯ is described or referred to herein, it will be appreciated that the activation intensity hierarchical data structure ൫^^^^൯ could be substituted for another form of activation procedural data that encodes the procedure relevant activation intensity hierarchical data structure ൫^^^^൯. Therefore, at 206, the at least one processor 104 may receive activation procedural data that intensity with which the emission unit(s) 135 of a particular visual field spatial reference൫^^^^൯are over sequential activations of an emission sequence. Similarly, at 208, the at least one 104 may modify this activation procedural data, thereby generating modified activation data. The modified procedural data may define the intensity with which the emission unit(s) 135 of a particular visual field spatial reference ൫^^^^൯ are activated over sequential activations of an emission sequence. It will be appreciated that the at least one processor 104 may execute the sequence determiner 109 to perform 208.At 204, the at least one processor 104 generates modified visual field loss data (^^′, ^^). The at least oneprocessor 104 generates the modified visual field loss data (^^′, ^^) using the visual field loss data (^^, ^^).In other words, the at least one processor 104 generates the modified visual field loss data (^^′, ^^) based atleast in part on the visual field loss data (^^, ^^).Generating the modified visual field loss data (^^′, ^^) comprises applying a thresholding function ^^ to thevisual field loss data (^^, ^^). At 204, the at least one processor 104 applies the thresholding function ^^ toeach visual field loss map (^^^^). That is, the at least one processor 104 applies the thresholding function ^^to the visual field loss data (^^, ^^) to generate the modified visual field loss data (^^′, ^^). Generating themodified visual field loss data (^^′, ^^) comprises applying the thresholding function ^^ to each visual fieldloss maps (^^^^). In particular, generating the modified visual field loss data (^^′, ^^) comprises applying thethresholding function ^^ to the visual field loss values ൫^^^,^൯ of the visual field loss data (^^, ^^). The atleast one processor 104 applies the thresholding function ^^ to the visual field loss values ൫^^^,^൯ of thevisual field loss data (^^, ^^) to generate the field loss data (^^′, ^^).The thresholding function ^^ is associated with a number of thresholds. The thresholding function ^^ may act on scalars to classify the scalars based on the threshold(s). The thresholding function ^^ may act on scalars to transform the scalars to other scalars, based on the threshold(s). In some embodiments, the thresholding function ^^ is associated with one threshold. In some embodiments, the thresholding function ^^ is associated with a plurality of thresholds. The thresholding function ^^ may be associated with one threshold. If the visual field loss value ൫^^^,^൯ under consideration (i.e. that is an input of the thresholding function ^^) is above the output of the thresholding function ^^ will be a first output. The first output is a scalar. For example, output may be 0. If the visual field loss value ൫^^^,^൯ under consideration is equal to or below the threshold, the output of the thresholding function ^^ will be a second output. The second output is a scalar. For example, the second output may be 1. It will be appreciated that the thresholding function ^^ may be associated with any number of thresholds. The thresholding function ^^ may be represented as: ì^^^ ᇱ,^,^ , ^^^,^ ≤ ^^^^^௧where: ^^ is the thresholding ^^^,^is the ^^௧^visual field loss value of a visual field loss map ^^^^; ^^^ ᇱ,^,^ ^ ᇱ( ),^^,^,ଶ , … ^^^ᇱ,^,ோare possible output modified visual field loss values; the number of thresholds of the threshold function ^^; and ^^^, ^^ଶ … ^^௧ are respective thresholds.By way of example, if ^^^,^is between ^^^and ^^ଶ, the output of the above thresholding function ^^, will be ^^^ᇱ,^,ଶ. This output may be referred to as a modified visual field loss value. It may therefore be said that applying the thresholding function ^^ to one of the visual field loss maps (^^^^) comprises comparing each of the visual field loss values൫^^^,^൯of the respective visual field loss map (^^^^) to one or more visual loss thresholds. That is, the at least one processor 104 compares each of the visual field loss values ൫^^^,^൯ of the respective visual map (^^^^) to one or more visual loss thresholds. Further, the thresholding function ^^ to one of the visual field loss maps (^^^^) comprises generating visual field loss value൫^^^ᇱ,^ ൯based on the comparison. In other words, the at least one processor 104 generates the modified visual field loss value ൫^^^ᇱ,^൯ based on the comparison. The modified visual field loss value ൫^^^ᇱ,^൯ is an output of the threshold function ^^. In particular, the modified visual field loss value ൫^^^ᇱ,^൯ is an output of the threshold function ^^ when a visual field loss value ൫^^^,^൯ is the input of the threshold function ^^. The threshold function ^^ defines a set ൫^^ᇱ^^,^^,^^൯ of field loss values ൫^^^ᇱ,^൯. For the purposes of this disclosure, ^^ is an index of the visual values ൫^^^ᇱ,^ ൯ that may be outputs of the threshold function ^^, with 1 ≤ ^^ ≤^^, ^^ is associated with the number of thresholds of the threshold function ^^. ^^ may be the number of thresholds of the threshold function ^^.The modified visual field loss data (^^′, ^^) comprises a plurality of modified visual field loss maps (^^ᇱ^^). Each modified visual field loss map (^^ᇱ^^) is generated from a respective visual field loss map (^^^^). In particular, each modified visual field loss map(^^ᇱ^^)is generated by applying the thresholding function ^^ to the visual field loss values ൫^^^,^൯ of the respective visual field loss map (^^^^), and storing the outputs of the thresholding function ^^ part of a new field loss map, being the respective modified visual (ᇱ field loss map ^^^^). The the thresholding function ^^ are referred to as modified visual field loss values ൫^^^ᇱ,^൯. Each visual field loss map(^^ᇱ^^)may be said to comprise a plurality of modified visual field loss values ൫^^^ᇱ,^൯. The plurality of modified visual field loss values ൫^^^ᇱ,^൯ are determined from the respective visual field loss map(^^^^). In particular, each modified visual field loss value ൫^^^ᇱ,^൯ is determined by applying the thresholding function ^^ to a corresponding one of the visual field loss values൫^^^,^൯. As described herein, each visual field loss value ൫^^^,^൯ is associated with a respective visual field loss map (^^^^) and visual field spatial reference modified visual field loss value ൫^^ᇱ^,^൯ is also associated with a respective visual field spatial reference ൫^^^^൯. In particular, each modified visual field loss value ൫^^^ᇱ,^൯ is associated with a respective visual field spatial reference ൫^^^^൯, in a modified visual field loss map (^^ᇱ^^). The modified visual field loss values ൫^^ᇱ^,^൯ are therefore indexed with ^^ herein, with the ^^ index of a modified visual field loss value ൫^^^ᇱ,^൯ indicating the modified visual field loss map (^^ᇱ^^) that that modified visual field loss value ൫^^^ᇱ,^൯ is associated with. Similarly, each modified visual field loss value ൫^^^ᇱ,^൯ is associated with a respective visual field spatial reference ൫^^^^൯. This visual field spatial reference ൫^^^^൯ is a particular visual field spatial reference ൫^^^^൯ of the modified visual field loss map (^^ᇱ^^) that that modified visual field loss value ൫^^^ᇱ,^൯ is associated with. A modified visual field loss value ൫^^^ᇱ,^൯ may be associated with the same visual field spatial reference ൫^^^^൯ as that of the visual field loss value ൫^^^,^൯ from which the modified visual field loss value ൫^^^ᇱ,^൯ is generated. Therefore, the modified field loss values ൫^^^ᇱ,^൯ are indexed with ^^ herein, with the ^^ index of a modified visual field loss൯indicating the visual field spatial reference൫^^^^൯that modified visual field loss value ൫^^^ᇱ,^൯ is associated with. Each modified visual field loss map (^^ᇱ^^) may be said to be derived from a respective visual field loss map (^^^^).The modified visual field loss data (^^′, ^^) also comprises the list (^^) of weights (^^^). Each modifiedvisual field loss map (^^ᇱ^^) is associated with a respective weight (^^^) of the list (^^) of weights (^^^). In particular, each modified visual field loss map (^^ᇱ^^) is associated with the same weight (^^^) that was also associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) was derived. Thus, the weights(^^^)remain indexed by ^^ in the context of the modified visual field loss maps(^^ᇱ^^), herein. Each modified visual field loss value ൫^^^ᇱ,^൯ is associated with the same weight(^^^)that was also associated with the visual field loss map(^^^^)from which the modified visual field loss map(^^ᇱ^^)was derived. Figure 6 shows an example of a graphical representation of a modified visual field loss map 600. The modified visual field loss map 600 of Figure 6 shows a ring loss of visual field. The modified visual field loss map 600 of Figure 6 will be understood to be a graphical representation of the modified visual field loss map 600, which may be stored in memory 106 as modified visual field loss data (e.g. as a matrix). The modified visual field loss map 600 comprises a plurality of shaded portions 602. Each shaded portion 602 is associated with a modified visual field loss value ൫^^^ᇱ,^൯ and a visual field spatial reference ൫^^^^൯. The modified visual field loss values൫^^^ᇱ,^ ൯associated with the shaded portions 602 are shown as numbers in the centres of the shaded portions 602. Lower numbers indicate reduced vision at the portion of the visual field of the relevant eye that is associated with particular visual field spatial reference ൫^^^^൯. The depth of the shading at a particular visual field spatial reference ൫^^^^൯ is also proportional to respective modified visual field loss value ൫^^^ᇱ,^൯. Figure 7 shows an example of a plurality of modified visual field loss maps 702-740, that may form atleast part of an example of modified visual field loss data (^^ᇱ, ^^). In particular, Figure 7 shows 16modified visual field loss maps 702-740. A number of the modified visual field loss maps 702-740 are associated with respective glaucoma visual field loss patterns or neurological conditions. It will be appreciated that the at least one processor 104 may execute the sequence determiner 109 to perform 204. At 210, the at least one processor 104 determines an optimised visual field test activation sequence (^^). The optimised visual field test activation sequence(^^)defines a sequence of emission unit 135 activations. One or more activation of the optimised visual field test activation sequence (^^) may comprise an activation of one emission unit 135. Each activation of the optimised visual field test activation sequence (^^) may comprise an activation of one emission unit 135. One or more activation of the optimised visual field test activation sequence (^^) may comprise an activation of a plurality of emission units 135. The plurality of emission units 135 may be activated contemporaneously in this case. In some embodiments, a plurality of activations of the optimised visual field test activation sequence (^^) may comprise an activation of a plurality of emission units 135. The at least one processor 104 determines the optimised visual field test activation sequence (^^) based atleast in part on the modified visual field loss data (^^′, ^^). The optimised visual field test activationsequence (^^) comprises an ordered set of the visual field spatial references ൫^^^^൯. The optimised visual field test activation sequence(^^)defines an order in which the emission unit(s) 135 are to be activated in the visual field test. In other words, the order of the visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence(^^)defines the sequence in which the emission units 135 of the visual field test apparatus 122 are to be activated. As described herein, one or more of the emission unit(s) 135 are associated with one or more respective visual field spatial reference൫^^^^൯. The optimised visual field test activation sequence (^^) may therefore be said to define the order in which the emission unit(s) 135 associated with respective visual field spatial references ൫^^^^൯ are to be activated in the visual field test. The at least one processor 104 determines the order based on values of an information metric ^^. In particular, the at least one processor 104 determines the value of one or more information metric ^^ foreach visual field spatial reference ൫^^^^൯, using the modified visual field loss data (^^′, ^^). In someembodiments, the at least one processor 104 determines the value of a plurality of information metrics ^^,for each visual field spatial reference ൫^^^^൯, using the modified visual field loss data (^^′, ^^).The at least one processor 104 defines, for each visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯, a plurality of groups ൫^^^^,^^൯. The groups ൫^^^^,^^൯ are groups of modified visual field loss maps (^^ᇱ^^). Therefore, the at least one processor 104 defines, for each visual field spatial reference ൫^^^^൯ of the set(^^)of visual field spatial references ൫^^^^൯, a plurality of groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^). Each group൫^^^^,^^൯is associated with a particular visual field spatial reference൫^^^^൯. Each group൫^^^^,^^൯is associated with modified visual field loss value ൫^^^ᇱ,^൯. In particular, each group ൫^^^^,^^൯ comprises visual field loss maps (^^ᇱ^^) that have the particular modified visual field loss value ൫^^^ᇱ,^൯ at the visual field spatial reference ൫^^^^൯ associated with the group ൫^^^^,^^൯. In other words, the groups ൫^^^^,^^൯ are groups of modified visual field loss maps (^^ᇱ^^) that have the same modified visual field loss value൫^^^ᇱ,^ ൯at a particular reference visual field spatial reference൫^^^^൯for which the groups൫^^^^,^^൯were determined. The groups ൫^^^^,^^൯ may be referred to as subsets. Therefore, the at least one processor 104 may be said to define, for field spatial reference൫^^^^൯of the set (^^) of visual field spatial references൫^^^^൯, a plurality of ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^). One or more of൫^^^^,^^൯may comprise one or more modified visual field loss maps (^^ᇱ^^). Some groups ൫^^^^,^^൯ may comprise a plurality of modified visual field loss maps (^^ᇱ^^). Some groups ൫^^^^,^^൯ may comprise no modified visual field loss maps (^^ᇱ^^). These groups൫^^^^,^^൯may be referred to as ^^^^^^^^ groups. For the purposes of this disclosure, ^^ is an index of the groups with 1 ≤ ^^ ≤ ^^, where ^^ is thenumber of groups (^^^^) of modified visual field loss maps (^^ᇱ^^) that can be associated with a particular visual field spatial reference ൫^^^^൯. The number ^^ of groups(^^^^)of the plurality of groups(^^^^), for a particular visual field spatial reference ൫^^^^൯, is one greater than the number of thresholds of the thresholding function ^^. That is, the number of groups (^^^^) of the plurality of groups (^^^^), for a particular visual field spatial reference ൫^^^^൯, is related to the number of thresholds of the thresholding function ^^. The groups൫^^^^,^^൯are also indexed with index ^^, as each group൫^^^^,^^൯is associated with a respective visual field reference ൫^^^^൯, with the ^^ index of a group indicating the visual field spatial reference൫^^^^൯that that group൫^^^^,^^൯is associated with. By way of example, a set of modified visual field loss maps (^^ᇱ^^) may have modified visual field loss values ൫^^^ᇱ,^൯ that can take a value of 0 or a value of 1. In this case, the at least one processor 104 would define a plurality of groups for each visual field spatial reference ൫^^^^൯ of the modified visual field loss maps(^^ᇱ^^). For a visual field spatial reference ൫^^^^൯ with an index of 1 (^^. ^^. ^^^), the at least one processor 104 would define a first group ൫^^^^,^^൯, which would include all modified visual field loss maps (^^ᇱ^^) with a modified visual field loss value ൫^^^ᇱ,^൯ of 0 at the visual field spatial reference ൫^^^^൯ with an index of 1(^^. ^^. ^^^). The at least one processor 104 would define a second group ൫^^^^,^^൯, which would include allmodified visual field loss maps (^^ᇱ^^) with a modified visual field loss value ൫^^^ᇱ,^൯ of 1 at the visual fieldspatial reference ൫^^^^൯ with an index of 1 (^^. ^^. ^^^). In this case, both of the first group ൫^^^^,^^൯ and thesecond group൫^^^^,^^൯are associated with the visual field spatial reference൫^^^^൯with an index of 1, i.e. ^^^. The first group ൫^^^^,^^൯ is associated with the modified visual field loss value ൫^^^ᇱ,^൯ of 0. That is, the first group ൫^^^^,^^൯ modified visual field loss maps(^^ᇱ^^)for which the modified visual field loss value ൫^^^ᇱ ,^spatial reference (^^^^) is 0. The second group ൫^^^^,^^൯ is associated with the field loss value ൫^^^ᇱ,^൯ of 1. That is, the second group ൫^^^^,^^൯ comprises modified visual field loss maps (^^ᇱ^^) for which the modified visual field loss value൫^^^ᇱ,^ ൯of the visual field spatial reference (^^^^) is 1. This process would then be repeated for the visual field spatial reference ൫^^^^൯ with anindex of 2, (^^. ^^. ^^ଶ), to the visual field spatial reference ൫^^^^൯ with an index of ^^, with ^^ groups beingdetermined for each visual field spatial reference ൫^^^^൯ (if ^^^^^^^^ groups are still considered a group). The at least one processor 104 may apply a group assignment function to the modified visual field loss maps (^^ᇱ^^), when defining the plurality of groups ൫^^^^,^^൯. In other words, defining the plurality of groups ൫^^^^,^^൯ may comprise applying a group assignment function to the modified visual field loss maps (^^ᇱ^^). The group assignment function may be configured to assign a modified visual field loss map (^^ᇱ^^) to a respective group ൫^^^^,^^൯, for each visual field spatial reference ൫^^^^൯. The inputs to the group assignment function may the modified visual field loss maps (^^ᇱ^^) and one or more of the visual field spatial references ൫^^^^൯. The at least one processor 104 defines a weight dataset ൫^^^^൯. In particular, the at least one processor 104 defines a weight dataset ൫^^^^൯ comprising the list (^^^). Therefore, determining the optimised visual field sequence (^^) comprises defining a weight dataset ൫^^^^൯ comprising the list (^^) of weights (^^^). The weight dataset൫^^^^൯comprising the list (^^) of (^^^) may be referred to as an initial weight list. The weight ൫^^^^൯ comprises zero or more lists (^^^) of weights (^^^), at any particular point of the method 200. ^^ is of the lists (^^^) of weights (^^^) of theweight dataset ൫^^^^൯, with 1 ≤ ^^ ≤ ^^, where ^^ is a number of lists (^^^) of weights (^^^) in the weightdataset൫^^^^൯ point of the method. The number of lists (^^^) of weights (^^^) in the weight dataset may change throughout performance of the method 200. Determining the optimised visual field test activation sequence (^^) comprises the iterative performance of a number of steps. In other words, the at least one processor 104 iteratively performs a plurality of operations to determine the optimised visual field test activation sequence (^^). The operations are computing operations. Therefore, the at least one processor 104 iteratively performs a plurality of computing operations to determine the optimised visual field test activation sequence (^^). In particular, determining the optimised visual field test activation sequence (^^) comprises the iterative performance of one or more of the following steps.As described herein, the modified visual field loss data (^^′, ^^) comprises a list (^^) of weights (^^^). Alsoas described herein, the modified visual field loss data (^^′, ^^) comprises a plurality of modified visualfield loss maps (^^ᇱ^^), with each modified visual field loss map (^^ᇱ^^) being associated with a respective weight (^^^) of the list (^^) of weights (^^^). Each modified visual field loss map (^^ᇱ^^) comprises a plurality of modified visual field loss values ൫^^^ᇱ,^൯, each modified visual field loss value ൫^^^ᇱ,^൯ being associated with a respective visual field spatial reference൫^^^^൯of the set (^^) of visual references ൫^^^^൯. The at least one processor 104 removes a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯. Where this operation is part of the first iteration of the present steps, the at least one processor the ial weight list from the weight dataset൫ init ^^^^൯. The initial weight list may be a list of the weights (^^^). The weight dataset ൫^^^^൯ may be (i.e. ^^^^^^^^) following the removal of the initial weight list from the weight dataset Where this operation is part of a second or subsequent iteration of the present steps, the at processor 104 removes a first list from the weight dataset ൫^^^^൯. The first list is the list (^^^) of weights (^^^) of the weight dataset൫^^^^൯associated with a ^^ of 1. Therefore, the at least one processor 104 removes the of weights (^^^) associated with a ^^ index value of 1 from the weight dataset ൫^^^^൯. The at least one processor 104 updates the index value ^^ of each remaining list (^^^) of weights (^^^) in the weight dataset ൫^^^^൯. The at least one processor 104 may subtract one from the index value of each remaining list (^^^) in the weight dataset ൫^^^^൯. Therefore, the list of weights previously associated with an index value of 2, i.e. ^^ଶ, is updated with an index value of 1, i.e. ^^^. The at least one processor 104 updates the index value ^^ of each remaining list (^^^) of weights (^^^) in the weight dataset ൫^^^^൯ after the removal of the relevant list (^^^). The at least one processor 104 determines a plurality of modified visual field loss value weighted sums൫^^^,^൯. Each modified visual field loss value weighted sum൫^^^,^൯is associated with a respective visual field spatial reference ൫^^^^൯. Therefore, the modified visual field loss value weighted sums ൫^^^,^൯ are indexed herein with index ^^, with the ^^ index of a modified visual field loss value weighted sum ൫^^^,^൯ being the same as the ^^ index of the associated visual field spatial reference ൫^^^^൯. Each modified visual field loss value weighted sum ൫^^^,^൯ is associated with a respective group ൫^^^^,^^൯ of the plurality of groups ൫^^^^,^^൯. Therefore, the modified visual field loss value weighted sums ൫^^^,^൯ are indexed herein with index with the ^^ index of a modified visual field loss value weighted sum being the same as the ^^ of the associated group ൫^^^^,^^൯ of the plurality of groups ൫^^^^,^^൯. Each of the modified visual value weighted sums determined based on the modified visual field loss maps (^^ᇱ^^) of one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^). This is the group൫^^^^,^^൯that may be considered associated with the respective modified visual field loss value weighted sum ൫^^^,^൯. Therefore, each of the modified visual field loss value weighted sums ൫^^^,^൯ is determined based on the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯ of modified visual field loss maps(^^ᇱ^^). Each of the modified visual field loss value weighted is a sum of the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) of the group ൫^^^^,^^൯. In other words, a modified visual field loss value weighted sum ൫^^^,^൯ is a sum of the weights associated with the modified visual field loss maps (^^ᇱ^^) in one of the groups ൫^^^^,^^൯. The determination of the modified visual field loss value weighted sum may be expressed as: ^^^,^ = ∑^^^^ห^^ᇱ^ ∈ ^^^^,^^^ for each visual field spatial reference ൫^^^^൯, group ൫^^^^,^^൯. The at least one processor 104 computes a value of an metric ^^^. In particular, the at least one processor 104 computes the value of an information metric ^^^, for each visual field spatial reference ൫^^^^൯. The value of the information metric ^^^, for a visual field spatial reference ൫^^^^൯, may be determined using an information metric function. An example information metric function, for a visual field spatial reference ൫^^^^൯, is: ^ −^^^^,^^^^,^^ The at least one processor 104 determines the value of the information metric ^^^, for a particular visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field spatial reference൫^^^^൯. The at least one processor 104 determines an optimised information metric value ^^^. The at least one processor 104 determines the optimised information metric value ^^^from the plurality of information metric values ^^^that have been determined. The optimised information metric value ^^^may be the information metric value ^^^that is greatest. The optimised information metric value ^^^may be the value of the information metric ^^^that is smallest. The optimised information metric value ^^^may be the value of the information metric ^^^that is closest to 0. The optimised information metric value ^^^may be the value of the information metric ^^^that is closest to an information metric target value. For example, the optimised information metric value ^^^may be the value of the information metric ^^^that is closest to 0.5. In the illustrated case, the optimised information metric value ^^^is the value of the information metric ^^^that is greatest. Therefore: ^^^ = max൫^^^൯The optimised information metric value ^^^may be said to be associated with the visual field spatial reference ൫^^^^൯ of the corresponding information metric value ^^^. In other words, the optimised information metric value ^^^may be associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined. The at least one processor 104 adds the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence(^^). The visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^is added to the optimised visual field test activation sequence (^^) with a sequence index ^^. The sequence index ^^ is used to record an order of visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence (^^). Each visual field spatial reference ൫^^^^൯ listed in the optimised visual field test activation sequence (^^) is therefore associated with a corresponding sequence index ^^ indicating that instance of the visual field spatial reference’s ൫^^^^൯ position within the optimised visual field test activation sequence(^^). Where the optimised visual field test activation sequence (^^) comprises no entries (i.e. on the first iteration of this part of the method 200), the at least one processor 104 adds the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^as the first visual field spatial reference൫^^^^൯of the optimised visual field test activation sequence (^^). In this case, the sequence index ^^ may take the value of 0. Alternatively, the sequence index ^^ may take the value of 1. For later iterations, the at least one processor 104 adds the visual field spatial reference൫^^^^൯associated with the optimised information metric value ^^^of the relevant later iteration to the end of the optimised visual field test activation sequence (^^). The value of the sequence index ^^ associated with a subsequently added visual field spatial reference൫^^^^൯is one higher than the value of the sequence index ^^ of the immediately preceding visual field spatial reference ൫^^^^൯. In other words, the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^is added to the end of the optimised visual field test activation sequence(^^). The at least one processor 104 maintains a count ^^^for each visual field spatial reference ൫^^^^൯. The counts ^^^may be stored in memory 106. The count ^^^for each visual field spatial reference ൫^^^^൯ indicates the number of times that that visual field spatial reference ൫^^^^൯ has been included in the optimised visual field test activation sequence (^^). In other words, the count ^^^for each visual field spatial reference ൫^^^^൯ is associated with the number of instances of the visual field spatial reference ൫^^^^൯ in the optimised visual field test activation sequence (^^), at one or more points in the optimised visual field test activation sequence (^^). The at least one processor 104 adds one to the count ^^^associated with a visual field spatial reference ൫^^^^൯, each time that visual field spatial reference ൫^^^^൯ is added to the optimised visual field test activation sequence (^^). In particular, the at least one processor 104 adds one to the count ^^^associated with a visual field spatial reference ൫^^^^൯, each time that visual field spatial reference ൫^^^^൯ is added to the end of the optimised visual field test activation sequence (^^). The at least one processor 104 adds one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^). The at least one processor 104 compares the count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence(^^)to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with that visual field spatial reference ൫^^^^൯. If the count ^^^is less than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯, the at least one processor 104 generates, for each group ൫^^^^,^^൯, a modified list ൫^^^^,^^൯ of weights (^^^). Each modified list൫^^^^,^^൯of weights (^^^) is generated from the list൫^^^^൯of removed from weight dataset ൫^^^^൯. In particular, each modified list of weights(^^^)is generated by applying a down- function ^^(^^) to the list ൫^^^^൯ of weights (^^^) removed from weight dataset ൫^^^^൯. The output generated by applying the down-weighting function ^^(^^) is a respective modified list ൫^^^^,^^൯ of weights (^^^). The output generated by applying the down-weighting function ^^(^^) for each group ൫^^^^,^^൯. As described herein, each group ൫^^^^,^^൯ comprises a plurality of modified visual field loss maps (^^ᇱ^^). The effect of the down-weighting ^^(^^) is to generate a modified list ൫^^^^,^^൯ of weights (^^^), for each group൫^^^^,^^൯, with the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) respective group ൫^^^^,^^൯ being reduced in the modified list ൫^^^^,^^൯ of weights (^^^) generated for൫^^^^,^^൯. In the at least one processor 104, for each group൫^^^^,^^൯, generates a modified list of by applying the down-weighting function ^^(^^) to the weights (^^^) of the list are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group new instance of the list ൫^^^^൯ is used for each down-weighting operation performed for a ൫^^^^,^^൯. Thus, the modified lists൫^^^^,^^൯of weights (^^^) generated by the at least one is equal to the of groups ൫^^^^,^^൯ for the chosen visual field spatial reference൫^^^^൯. An example down-weighting function ^^(^^) may be:ᇱ^^(^^) = ^0.05 ^^^^^^^ ^^^ ∈ ^^^^^^^^^ℎ^^^^^^^^^^^^ where ^^ is the group൫^^^^,^^൯for which the modified list൫^^^^,^^൯of weights (^^^) is being generated. It will be the down-weighting function used to generate one modified list ൫^^^^,^^൯ of weights(^^^)may be different to the down-weighting function used to generate another list൫^^^^,^^൯of weights (^^^). That is, the down-weighting function used to generate a modified list൫^^^^,^^൯of (^^^) associated with one group ൫^^^^,^^൯ may be different to the down-weighting function used to generate a modified list ൫^^^^,^^൯ of associated with another group ൫^^^^,^^൯. The down-weighting function associated with a particular visual field spatial ൫^^^^൯ may be different to the down-weighting function associated with another visual field spatial ൫^^^^൯. That is, the down-weighting function used to generate a modified list ൫^^^^,^^൯ of weights (^^^) group ൫^^^^,^^൯ associated with one visual field spatial reference ൫^^^^൯ may be different to the down- function used to generate a modified list (^^^) for a group ൫^^^^,^^൯ associated with another visual field spatial reference ൫^^^^൯. Further, the at least one processor 104 adds the modified lists ൫^^^^,^^൯ of weights (^^^) to an end of the weight dataset ൫^^^^൯. That is, if the count ^^^is less than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference least one 104 adds the modified lists ൫^^^^,^^൯ of weights (^^^) to an end of the weight dataset ൫^^^^൯. The at processor 104 may add the modified lists ൫^^^^,^^൯ of weights (^^^) to an end of dataset ൫^^^^൯ in an addition order. The addition order may be any order. The addition order addition order may be in accordance with the ^^ indexes of the modified lists ൫^^^^,^^൯ of weights . example, the addition order may be in order of increasing ^^ index. The addition order may be in order of decreasing ^^ index. If the count ^^^is equal to or greater than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯, the at least one processor discards the list ൫^^^^൯ of weights(^^^removed from the weight dataset )൫^^^^൯. The at least one processor 104 terminates the iterations of 210 when the weight dataset൫^^^^൯of an iteration, other than the first iteration, is empty. In other words, the at least one 104 terminates the iterations of 210 when the weight dataset ൫^^^^൯ of an iteration is empty. This may when the weight dataset ൫^^^^൯ is empty after an been performed. At this point, the at least one processor 104 be said to have optimised visual field test activation sequence(^^). The optimised test activation sequence (^^) may be stored in memory 106 as an ordered list of visual field spatial references ൫^^^^൯. The at least one processor 104 may store the optimised visual field test activation sequence(^^)in memory 106 as the ordered list of visual field spatial references ൫^^^^൯. The computing system 102 may communicate the optimised visual field test activation sequence (^^) to the visual field test system 122. For example, the computing system 102 may transmit the optimised visual field test activation sequence (^^) to the visual field test system 122, using the communicationsnetwork 138. The computing system 102 may transmit one or more of: the visual field loss data (^^, ^^),the modified visual field loss data (^^ᇱ, ^^), the initial psychophysical testing data (^^), the modifiedpsychophysical testing data (^^ᇱ), the groups൫^^^^,^^൯, the weight dataset൫^^^^൯and the modified visual field loss value weighted sums ൫^^^,^൯ to the visual field test system 122, using the communications network 138. It will be appreciated that the at least one processor 104 may execute the sequence determiner 109 to in performing 210. Performing a Visual Field Test At 212, the visual field test system 122 performs a visual field test. In particular, the at least one processor 124 of the visual field test system 122 controls the visual field test system 122 such that the visual field test system 122 performs the visual field test. Performing the visual field test comprises activating the emission units 135 of the visual field test system 122. Therefore, at 212, the at least one processor 124 activates the emission units 135 of the visual field test system 122. Performing the visual field test comprises activating the emission units 135 of the visual field test system in accordance with the optimised visual field test activation sequence (^^). Therefore, the at least one processor 124 activates the emission units 135 of the visual field test system 122 in accordance with the optimised visual field test activation sequence (^^). One emission unit 135 may be activated at a given time, during the visual field test. Alternatively, a plurality of the emission units 135 may be activated for a particular activation, during the visual field test. As described herein, each emission unit 135 is associated with a respective visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references൫^^^^൯. Further, the optimised visual field test activation (^^) comprises an ordered list of the spatial references ൫^^^൯. Thus, the emission unit ^135 associated with a particular visual field൫^^^^൯is activated, by the at least one processor 104, when that visual field spatial reference ൫^^^^൯ occurs in the optimised visual field test activation sequence(^^), and the at least one processor 124 progresses through the optimised visual field test activation sequence (^^). The intensities of the activations performed as part of the visual field test are defined by the values of the nodes of the pruned activation intensity hierarchical data structures൫^^ᇱ^^൯. The at least one processor 124 refers to a reference node of a pruned activation intensity data structure൫^^ᇱ^^൯when determining the intensity to activate an emission unit 135 during an emission. As described herein, each visual field spatial reference ൫^^^^൯ is associated with a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯. The pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ dictates the intensity of each activation, at the respective visual field spatial reference ൫^^^^൯, as part of the emission sequence. The intensity of the first activation of an emission unit 135 at a respective visual field spatial reference ൫^^^^൯ is associated with the value of the root node of the associated pruned activation intensity hierarchical data structure൫^^ᇱ^^൯. The intensity of subsequent activations of the emission unit 135 at that visual field spatial reference൫^^^^൯is dependent on whether or not a user input is received in response to the previous activation of that emission unit 135. A position on the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is updated, based on a response, or lack thereof, from the user, following an activation of the emission unit 135 at the relevant visual field spatial reference ൫^^^^൯. In other words, the reference node for a future emission is updated based on the response, or lack thereof, from the user, following the activation of the emission unit at the relevant visual field spatial reference൫^^^^൯. An updated reference node of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ is determined, based on the user input, or lack thereof. Where a user input is received, the position on the pruned activation intensity hierarchical data structure൫^^ᇱ^^൯(which corresponds to the relevant reference node) is updated to a first child node of the reference node of the activation. The first child node may be associated with a lower intensity future activation. That is, the intensity value of the first child node may be lower than the intensity value of the reference node. Where a user input is not received, the position on the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ (i.e. the reference node for the next activation at the visual field spatial reference ൫^^^^൯) is updated to a second child node of the reference node of the activation. The second child node may be associated with a higher intensity future activation. That is, the intensity value of the second child node may be higher than the intensity value of the reference node. Activations of an emission unit 135 at a particular visual field spatial reference ൫^^^^൯ terminate when the node referenced for an intensity of a particular activation has no child nodes. In some embodiments, the optimised visual field test activation sequence (^^) may be updated if a node referenced for an intensity of a particular activation has no child nodes, to remove subsequent instances of the associated visual field spatial reference൫^^^^൯from the optimised visual field test activation sequence (^^). The at least one processor 124 stores inputs received from the user during the visual field test as visual field test data. The at least one processor 124 stores the visual field test data in memory 126. The at least one processor 124 generates an output for each visual field spatial reference൫^^^^൯, based at least in part on the visual field test data. These outputs may be referred to as visual field test outputs. An output is associated with an extent of vision loss of the user, at a position in their field of view that corresponds to the respective visual field spatial reference ൫^^^^൯. The outputs may be scalars. A graphical representation of the visual field test output may be rendered based on the visual field test outputs. The visual field test data may comprise the visual field test outputs. In other words, the visual field test data may comprise a visual field test output, for one or more of the visual field spatial references ൫^^^^൯, the visual field test output being indicative of an extent of vision loss of the patient at a point in their visual field that corresponds to the respective visual field spatial reference ൫^^^^൯. At 214, a graphical output is displayed. In some embodiments, a plurality of graphical outputs are displayed. For example, one graphical output for each eye of the patient may be displayed. In some embodiments, the visual field test system 122 displays the graphical output. The graphical output may be a graphical representation of at least part of the visual field test data. The graphical output may be determined based on the visual field test data. That is, the at least one processor 124 may determine the graphical output based on the visual field test data. In particular, the at least one processor 124 may determine the graphical output based on the visual field test outputs. In some embodiments, the visual field test system 122 displays the graphical output. The visual field test system 122 may display the graphical output on a display of the visual field test system 122. In some embodiments, the visual field test system 122 may transmit the graphical output to one or more other computing devices for display. In some embodiments, the visual field test system 122 may transmit the visual field test data to one or more other computing devices, which may generate the appropriate graphical output based on the visual field test data. That is, in some embodiments, the visual field test system 122 may transmit the visual field test data to one or more other computing devices for display as a graphical output on that device. For example, the visual field test system 122 may transmit the visual field test data to the computing device 102. The visual field test system 122 may transmit the visual field test data to the computing device 102 via the communications network 138. Transmitting the visual field test data may comprise transmitting the visual field test outputs. The at least one processor 104 of the computing device 102 may receive the visual field test data. The at least one processor 104 of the computing device 102 may store the visual field test data in memory 106. The at least one processor 104 of the computing device 102 may render a graphical output on a display of the computing system 102, based on the visual field test data. Alternative Implementations While the steps of the method 200 are numerically ordered (e.g.202, 204, 206 …), it will be appreciated that the number associated with a particular step does not necessarily indicate the order of that step with respect to other steps of the method 200. For example, in some embodiments, one or both of steps 202 and 204 may be performed prior to one or both of steps 206 and 208. However, in some embodiments, one or both of steps 206 and 208 may be performed before one or both of steps 202 and 204. Alternatively, steps 202, 204, 206 and 208 may be performed at the same time. That is, one or more of steps 202, 204, 206 and 208 may be performed, by the at least one processor 104, as one or more other step of steps 202, 204, 206 and 208 is performed. It will also be appreciated that some steps of the method 200 may be considered optional. For example, the at least one processor 104 may receive visual field loss maps that have been pre-processed to be suitable to be used for generating the optimised visual field test activation sequence (^^) without the performance of step 202. Similarly, the at least one processor 104 may receive activation intensity hierarchical data structures that have been pre-processed to be suitable to be used in the performance of the visual field test. In such a case, the method may be performed without the performance of step 206. Therefore, one or more steps of the method 200 may be considered optional. The method 200 may be considered to be a combination of a plurality of methods. For example, steps 202, 204, 206, 208 and 210 of the method 200 may be considered a method of determining an optimised visual field test activation sequence. These steps can be performed independently of steps 212 and 214. The output of step 210, namely, the optimized visual field test activation sequence, can be provided to a plurality of visual field test apparatuses like that described herein, to enable an optimised visual field test to be performed. Similarly, the visual field test apparatus 122 can receive an optimised visual field test activation sequence like that described herein, and perform a visual field test in accordance with this activation sequence. Further, the output of the visual field test may be provided to another computing device for rendering into a graphical output. In other words, one or both of steps 212 and 214 of the method 200 can be performed independently. Advantages As described herein, the method 200 of the present application enables the determination of an optimised visual field test activation sequence. The optimised visual field test activation sequence is such that higher priority areas of the patient’s visual field are tested earlier, in a systematic order that enables useful information to be derived from the results of the test, even in the case where the patient stops providing useful input prior to the end of the activation sequence. In this way, a detailed map of the patient’s visual field can be generated in the case where the patient completes the full visual field test; however, in addition to this, a useful, whilst not necessarily complete map of the patient’s vision can be determined in cases where the test is only partially complete. The determination of, and performance of, the optimised visual field test activation sequence described herein can allow for visual field maps to be determined for patients that would otherwise be unable or unwilling to complete a full visual field test. Figures 9 to 12 show example graphical outputs of visual field test data, according to some embodiments. The graphical outputs may be referred to as graphical representations. Figure 9 shows a left eye graphical output 902 and a right eye graphical output 904. The left eye graphical output 902 is generated using visual field test data from 11 emission unit activations that were performed in accordance with the method 200. The right eye graphical output 904 is generated using visual field test data from 9 emission unit activations that were performed in accordance with the method 200. Figure 10 shows a left eye graphical output 1002 and a right eye graphical output 1004. The left eye graphical output 1002 is generated using visual field test data from 15 emission unit activations that were performed in accordance with the method 200. The right eye graphical output 1004 is generated using visual field test data from 15 emission unit activations that were performed in accordance with the method 200. As can be seen, the graphical outputs 1002, 1004 of Figure 10 provide a higher resolution, useful insight to the patient’s visual field than those of Figure 9, even though a complete visual field test has not been completed. Figure 11 shows a left eye graphical output 1102 and a right eye graphical output 1104. The left eye graphical output 1102 is generated using visual field test data from 27 emission unit activations that were performed in accordance with the method 200. The right eye graphical output 1104 is generated using visual field test data from 23 emission unit activations that were performed in accordance with the method 200. As can be seen, the graphical outputs 1102, 1104 of Figure 11 provide a higher resolution, useful insight to the patient’s visual field than those of Figures 9 and 10, even though a complete visual field test has not been completed. Figure 12 shows a left eye graphical output 1202 and a right eye graphical output 1204. The left eye graphical output 1202 is generated using visual field test data from 69 emission unit activations that were performed in accordance with the method 200. The right eye graphical output 1204 is generated using visual field test data from 67 emission unit activations that were performed in accordance with the method 200. The graphical outputs 1202, 1204 may be associated with a complete visual field test. As can be seen, the graphical outputs 1202, 1204 of Figure 12 provide a higher resolution, useful insight to the patient’s visual field than those of Figures 9, 10 and 11, but the graphical output of the earlier figures still provide useful insights in the case where it is difficult or impossible to complete a full visual field test. As the sequence of emission unit 135 activations is optimised in accordance with the method 200, even the left eye graphical output 902 and a right eye graphical output 904 of Figure 9 provide useful information in relation to possible psychophysical conditions the patient may have, such as an ophthalmological condition, with only 11 and 9 emission unit activations for the respective eyes. By prioritising higher priority visual field regions with earlier emissions, if a patient is unable to complete a visual field test, even a relatively small number of emissions can provide useful psychophysical information. The optimised visual field test activation sequence determined and performed in accordance with the method 200 also enables the visual field test to be interruptible. The visual field test can be paused at a particular point of the optimised visual field test activation sequence, and recommenced, from the same point of the sequence, at a later point in time. In this way, a preliminary output of the patient’s visual field can be established from part of the sequence, and if a higher resolution map of the patient’s visual field is desired, the test can be recommenced from where it was terminated in the previous instance. This functionality is particularly beneficial in cases where patients are unable to perform a complete test in one sitting. The method 200 of the present disclosure enables the identification of visual field loss that presents with any particular pattern. This is a significant advantage over certain methods, which may only be capable of identifying visual field loss that has presented in a similar or the same pattern previously (e.g. a pattern that exists in a training data set). Throughout this description, where a particular feature is a vector, the reference label for that feature is typically bolded. It will be appreciated that in some embodiments, a feature that is described as a vector, with its reference label therefore bolded, could be a scalar. In some embodiments, that particular feature may be a matrix. In some embodiments, that particular feature may be an array. In the claims which follow, and in the preceding description, except where the context requires otherwise due to express language or necessary implication, the word “comprise” and variations such as “comprises” or “comprising” are used in an inclusive sense, i.e., to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the system and method as disclosed herein.

Claims

CLAIMS 1. A method of determining an optimised visual field test activation sequence (^^), the method comprising: receiving visual field loss data (^^,^^) that is associated with one or more patterns of visual field loss; generating modified visual field loss data (^^′,^^) using the visual field loss data (^^,^^), the modified visual field loss data (^^′,^^) comprising a plurality of modified visual field loss values ൫^^^ᇱ,^൯, each modified visual field loss value ൫^^ᇱ^,^൯ being associated with a respective visual fieldreference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯; and determining an optimised visual field test (^^), based at least in part on the modified visual field loss data(^^′,^^), wherein:the optimised visual field test activation sequence(^^)defines an order in which emission units associated with respective visual field spatial references ൫^^^^൯ are to be activated in a visual field test; and the order is determined based on a value of at least one information metric determined for each visual field spatial reference ൫^^^^൯, using the modified visual field loss data (^^′,^^).

2. The method of claim 1, the visual field loss data (^^,^^) comprises: a plurality of visual field loss maps(^^^^); and a list (^^) of weights (^^^), each visual field loss map (^^^^) being associated with a respective weight (^^^) of the list (^^) of weights (^^^).

3. The method of claim 2, wherein each visual field loss map (^^^^) comprises a plurality of visual field loss values ൫^^^,^൯, each visual field loss value ൫^^^,^൯ being associated with a respective visual field spatial of the set (^^) of visual field spatial references൫^^^^൯.

4. The method of any one of claims 1 to 3, wherein: generating the modified visual field loss data(^^′,^^)comprises applying a thresholding function ^^ to the visual field loss data (^^,^^); and the modified visual field loss data (^^′, ^^) comprises:a plurality of modified visual field loss maps (^^ᇱ^^); and the list (^^) of weights(^^^), each modified visual field loss map(^^ᇱ^^)being associated with a respective weight (^^^) of the list (^^) of weights (^^^).

5. The method of claim 4, wherein each modified visual field loss map (^^ᇱ^^): is generated from a respective visual field loss map (^^^^); and comprises the plurality of modified visual field loss values ൫^^^ᇱ,^൯, each modified visual field loss value ൫^^^ᇱ,^൯ being associated with a respective visual field spatial reference ൫^^^^൯ of the set(^^)of visual field spatial references൫^^^^൯.

6. The method of5, wherein each modified visual field loss value ൫^^^ᇱ,^൯ is: generated using a respective visual field loss value൫^^^,^൯of the visual field loss map (^^^^) from which the respective modified visual field loss map (^^ᇱ^^) is generated; and associated with the same visual field spatial reference ൫^^^^൯ as that of the visual field loss value ൫^^^,^൯ from which the modified visual field loss value ൫^^^ᇱ,^൯ is generated.

7. The method of any one of claims 4 to 6, wherein each modified visual field loss map (^^ᇱ^^) is associated with the weight (^^^) that is associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) is generated.

8. The method of any one of claims 4 to 7, wherein applying the thresholding function ^^ to the visual field loss data(^^,^^)comprises applying the thresholding function ^^ to each visual field loss map(^^^^).

9. The method of claim 8, wherein applying the thresholding function to one of the visual field loss maps (^^^^) comprises: comparing each of the visual field loss values൫^^^,^൯of the respective visual field loss map (^^^^) to one or more visual loss thresholds; and generating the modified visual field loss value ൫^^^ᇱ,^൯ for each of the visual field loss values ൫^^^,^൯ based on the comparison.

10. The method of any one of claims 1 to 9, further comprising receiving initial psychophysical testing data (^^), the initial psychophysical testing data (^^) comprising a plurality of activation intensity hierarchical data structures ൫^^^^൯.

11. The method of claim 10, wherein each activation intensity hierarchical data structure൫^^^^൯is associated with a respective visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references൫^^^^൯.

12. The method of claim 10 or claim 11, further comprising generating modified psychophysical testing data (^^ᇱ), based at least in part on the initial psychophysical testing data (^^).

13. The method of claim 12, wherein generating the modified psychophysical testing data (^^ᇱ) comprises iteratively: comparing differences in values of sibling nodes of the activation intensity hierarchical data structures ൫^^^^൯ to an intensity difference criterion; and pairs of sibling nodes that satisfy the intensity difference criterion from the respective hierarchal data structures ൫^^^^൯, thereby generating a plurality of pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯, psychophysical testing data (^^ᇱ) comprising the pruned activation intensity hierarchical data൫^^ᇱ^^൯.

14. The method of any one of claims 1 to 13, wherein: the optimised visual field test activation sequence (^^) comprises an ordered set of the visual field spatial references ൫^^^^൯; and an order of the visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence(^^)defines a sequence in which the units are to be activated.

15. The method of claim 4, or any one of claims 5 to 14, when dependent on claim 4, further comprising defining, for each visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references ൫^^^^൯, a plurality of groups ൫^^^^,^^൯, each group ൫^^^^,^^൯ comprising modified visual field loss maps (^^ᇱ^^) that have the same modifiedloss൯associated with the visual field spatial reference ൫^^^^൯ associated with that group ൫^^^^,^^൯.

16. The method of claim 15, wherein defining the plurality of groups ൫^^^^,^^൯ comprises applying a group assignment function to each modified visual field loss map (^^ᇱ^^).

17. The method of claim 15 or claim 16, wherein determining the optimised visual field test activation sequence (^^) comprises defining a weight dataset ൫^^^^൯, the weight dataset ൫^^^^൯ comprising the list (^^) of weights(^^^).

18. The method of claim 17, wherein determining the optimised visual field test activation sequence(^^)comprises iteratively performing a plurality of operations.

19. The method of claim 18, wherein the plurality of operations that are iteratively performed comprise removing a list (^^^) of weights (^^^) from the weight dataset൫^^^^൯.

20. The method of claim 18 or claim 19, wherein the plurality of operations that are iteratively performed comprise determining a plurality of modified visual field loss value weighted sums ൫^^^,^൯.

21. The method of claim 20, wherein each modified visual field loss value weighted sum ൫^^^,^൯ is associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^).

22. The method of claim 20 or claim 21, wherein each modified visual field loss value weighted sum൫^^^,^൯is determined based on the modified visual field loss maps (^^ᇱ^^) of the respective group൫^^^^,^^൯of visual field loss maps (^^ᇱ^^).

23. The method of any one of claims 20 to 22, wherein each modified visual field loss value weighted sum ൫^^^,^൯ is a sum of the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯.

24. Theone of claims 18 to 23, wherein the plurality of operations that are iteratively performed comprise computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field൫^^^^൯.

25. The method of any one of claims 18 to 24, wherein the plurality of operations that are iteratively performed comprise determining an optimised information metric value ^^^.

26. The method of claim 25, wherein the optimised information metric value ^^^is associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined.

27. The method of any one of claims 18 to 26, wherein the plurality of operations that are iteratively performed comprise adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence (^^).

28. The method of any one of claims 18 to 27, wherein the plurality of operations that are iteratively performed comprise adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^).

29. The method of claim 28, wherein the plurality of operations that are iteratively performed comprise comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯.

30. The method of claim 29, wherein the plurality of operations that are iteratively performed comprise: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weighting function to the weights(^^^)of the list ൫^^^^൯ that are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯; adding the modified lists൫^^^^,^^൯of weightsend of the weight dataset൫^^^^൯; if the count ^^^is less than the maximum depth of the pruned activation data structure൫^^ᇱ൯that is assoc^^iated with the respective visual field spatial reference 31. The method of claim 29 or claim 30, wherein the plurality of operations that are iteratively performed comprise discarding the list ൫^^^^൯ of weights (^^^) removed from the weight dataset ൫^^^^൯, if the count ^^^is equal to or greater depth of the pruned activation൫ᇱdata structure ^^^^൯ that is associated respective visual field spatial reference ൫^^^^൯.

32. The method of claim 17, wherein determining the optimised visual field test activation sequence(^^)comprises iteratively: removing a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯; determining a plurality of modified visual field loss valuesums ൫^^^,^൯, wherein each modified visual field loss value weighted sum൫^^^,^൯is: associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); determined based on the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); and a sum of the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯;of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field spatial reference൫^^^^൯; determining an optimised information metric value ^^^, the optimised information metric value ^^^being associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined;adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence (^^); adding one to a count ^^^associated visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence(^^); and comparing the count ^^^to a maximum depth of the pruned activationhierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯; if count ^^^is less than the maximum depth of the pruned activation hierarchical data structure that is associated with the respective visual field spatialfor each group൫^^^^,^^൯, generating a modified list൫^^^^,^^൯of weights (^^^) by applying a down- function (^^^) of the list ൫^^^^൯ that are associated with the modified visual field loss maps (^^ᇱ^^)of the group൫^^^^,^^൯;adding the modified lists to an end of the weight dataset ൫^^^^൯; andif the count ^^^is equal to or greater maximum depth of the prunedᇱhierarchical data structure ൫^^^^൯ that is associated with the respective visual field spatial ൫^^^^൯, discarding the list ൫^^^^൯ (^^^) removed from the weight dataset ൫^^^^൯.

33. The one of claims 18 to 32, further comprising the iterations when the weight dataset ൫^^^^൯ is empty at an end of an iteration.

34. Aa visual field test; and generating a visual field test output based at least in part on visual field test data stored during the visual field test; wherein: performing the visual field test comprises iteratively: activating one or more emission unit of a visual field test apparatus in accordance with an optimised visual field test activation sequence (^^); each emission unit being associated with a respective visual field spatial reference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯; and an intensity of a respective activation being defined by a value of a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is also associated with the visual field spatial reference൫^^^^൯of the activated emission unit; and storing visual field test data associated with the activation.

35. The method of claim 34, wherein performing the visual field test comprises, for at least one iteration, determining an updated reference node of a pruned activation intensity hierarchical datastructure ൫^^ᇱ^^൯, based on the stored visual field test data associated with the activation, if a reference node of the activation is not a leaf node.

36. The method of claim 34, wherein performing the visual field test comprises, for at least one iteration, updating the optimised visual field test activation sequence (^^) if a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ associated with the activation is a leaf node, to remove subsequent activations of the respective emission unit from the optimised visual field test activation sequence (^^).

37. The method of any one of claims 34 to 36, wherein generating the visual field test output comprises determining an output value for each visual field spatial reference൫^^^^൯, based on the stored visual field test data.

38. The method of any one of claims 34 to 37, further comprising rendering a graphical output determined based on the visual field test data on a display.

39. The method of any one of claims 34 to 37, wherein the optimised visual field test activation sequence (^^) is determined in accordance with the method of any one of claims 1 to 33.

40. A computing system comprising: at least one processor; and memory storing program instructions accessible by the at least one processor, the program instructions being configured to cause the at least one processor to: receive visual field loss data (^^,^^) that is associated with one or more patterns of visual field loss; generate modified visual field loss data (^^′,^^) using the visual field loss data (^^,^^), the modified visual field loss data(^^′,^^)comprising a plurality of modified visual field loss values ൫^^^ᇱ,^൯, each modified visual field loss value൫^^ᇱ^,^ ൯being associated with a respective visual field spatial reference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯; and determine an optimised visual field testsequence(^^), based at least in part on the modified visual field loss data (^^′,^^), wherein: the optimised visual field test activation sequence (^^) defines an order in which emission units associated with respective visual field spatial references ൫^^^^൯ are to be activated in a visual field test; and the order is determined based on a value of at least one information metric determinedfor each visual field spatial reference ൫^^^^൯, using the modified visual field loss data (^^′, ^^).

41. The computing system of claim 40, wherein the visual field loss data (^^,^^) comprises: a plurality of visual field loss maps(^^^^); and a list (^^) of weights(^^^), each visual field loss map(^^^^)being associated with a respective weight (^^^) of the list (^^) of weights (^^^).

42. The computing system of claim 40 or claim 41, wherein each visual field loss map (^^^^) comprises a plurality of visual field loss values ൫^^^,^൯, each visual field loss value ൫^^^,^൯ being associated with a respective visual field spatial of the set (^^) of visual field spatial references൫^^^^൯.

43. The computing system of any one of claims 40 to 42, wherein generating the modified visual fieldloss data (^^′, ^^) comprises applying a thresholding function ^^ to the visual field loss data (^^,^^); andthe modified visual field loss data (^^′, ^^) comprises:a plurality of modified visual field loss maps (^^ᇱ^^); and the list (^^) of weights (^^^), each modified visual field loss map (^^ᇱ^^) being associated with a respective weight (^^^) of the list (^^) of weights (^^^).

44. The computing system of claim 43, wherein each modified visual field loss map (^^ᇱ^^): is generated from a respective visual field loss map (^^^^); and comprises the plurality of modified visual field loss values൫^^^ᇱ,^ ൯, each modified visual field loss value ൫^^^ᇱ,^൯ being associated with a respective visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯.

45. The computing system of claim 44, wherein each modified visual field loss value ൫^^^ᇱ,^൯ is: generated using a respective visual field loss value ൫^^^,^൯ of the visual field loss map (^^^^) from which the respective modified visual field loss mapand associated with the same visual field spatial reference ൫^^^^൯ as that of the visual field loss value൫^^^,^൯from which the modified visual field loss value൫^^^ᇱ,^ ൯is generated.

46. The computing system of any one of claims 43 to 45, wherein each modified visual field loss map (^^ᇱ^^) is associated with the weight (^^^) that is associated with the visual field loss map (^^^^) from which the modified visual field loss map (^^ᇱ^^) is generated.

47. The computing system of any one of claims 43 to 46, wherein applying the thresholding function ^^ to the visual field loss data (^^,^^) comprises applying the thresholding function ^^ to each visual field loss map (^^^^).

48. The computing system of claim 47, wherein applying the thresholding function to one of the visual field loss maps (^^^^) comprises: comparing each of the visual field loss values൫^^^,^൯of the respective visual field loss map (^^^^) to one or more visual loss thresholds; and generating the modified visual field loss൯for each of the visual field loss values൫^^^,^൯based on the comparison.

49. The computing system of any one of claims 40 to 48, wherein the program instructions are further configured to cause the at least one processor to receive initial psychophysical testing data (^^), the initial psychophysical testing data (^^) comprising a plurality of activation intensity hierarchical data structures ൫^^^^൯.

50. The computing system of claim 49, wherein each activation intensity hierarchical data structure൫^^^^൯is associated with a respective visual field spatial reference൫^^^^൯of the set (^^) of visual field spatial references ൫^^^^൯.

51. The computingof claim 49 or claim 50, wherein the program instructions are further configured to cause the at least one processor to generate modified psychophysical testing data (^^ᇱ), based at least in part on the initial psychophysical testing data (^^).

52. The computing system of claim 51, wherein generating the modified psychophysical testing data (^^ᇱ) comprises iteratively: comparing differences in values of sibling nodes of the activation intensity hierarchical data structures ൫^^^^൯ to an intensity difference criterion; and pairs of sibling nodes that satisfy the intensity difference criterion from the respective activation intensity hierarchal data structures൫^^^^൯, thereby generating a plurality of pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯,psychophysical testing data (^^ᇱ) comprising the pruned activation intensity hierarchical data structures ൫^^ᇱ^^൯.

53. The computing system of any one of claims 40 to 52, wherein: the optimised visual field test activation sequence (^^) comprises an ordered set of the visual field spatial references ൫^^^^൯; and an order of the visual field spatial references ൫^^^^൯ in the optimised visual field test activation sequence(^^)defines a sequence in which theunits are to be activated.

54. The computing system of claim 43, or any one of claims 44 to 53, when dependent on claim 43, wherein the program instructions are further configured to cause the at least one processor to define, for each visual field spatial reference ൫^^^^൯ of the set (^^) of visual field spatial references ൫^^^^൯, a plurality of groups൫^^^^,^^൯, each group൫^^^^,^^൯comprising modified visual field loss maps (^^ᇱ^^) that have the same modified visual field loss value ൫^^^ᇱ,^൯ associated with the visual field spatial reference ൫^^^^൯ associated with that group ൫^^^^,^^൯.

55. Thesystem of claim 54, wherein defining the plurality of groups ൫^^^^,^^൯ comprises applying a group assignment function to each modified visual field loss map .

56. The computing system of claim 54 or claim 55, wherein determining the optimised visual field test activation sequence(^^)comprises defining a weight dataset ൫^^^^൯, the weight dataset ൫^^^^൯ comprising the list (^^) of weights (^^^).

57. The computing system of claim 56, wherein determining the optimised visual field test activation sequence (^^) comprises iteratively performing a plurality of operations.

58. The computing system of claim 57, wherein the plurality of operations that are iteratively performed comprise removing a list (^^^) of weights (^^^) from the weight dataset ൫^^^^൯.

59. The computing system of claim 57 or claim 58, wherein the plurality of operations that are iteratively performed comprise determining a plurality of modified visual field loss value weighted sums ൫^^^,^൯. The computing system of claim 59, wherein each modified visual field loss value weighted sum ൫^^^,^൯ is associated with a visual field spatial reference ൫^^^^൯ and one of the groups ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^).

61. The computing system of claim 59 or claim 60, wherein each modified visual field loss value weighted sum൫^^^,^൯is determined based on the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^).

62. The computing system of any one of claims 59 to 61, wherein each modified visual field loss value weighted sum ൫^^^,^൯ is a sum of the weights (^^^) associated with the modified visual field loss maps (^^ᇱ^^) of the respective group൫^^^^,^^൯.

63. The computing system of any one of claims 57 to 62, wherein the plurality of operations that are iteratively performed comprise computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field spatial reference ൫^^^^൯.

64. The computingone of claims 57 to 63, wherein the plurality of operations that are iteratively performed comprise determining an optimised information metric value ^^^.

65. The computing system of claim 64, wherein the optimised information metric value ^^^is associated with the visual field spatial reference൫^^^^൯of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined.

66. computing system of any one of claims 57 to 65, wherein the plurality of operations that are iteratively performed comprise adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence(^^).

67. The computing system of any one of claims 57 to 66, wherein the plurality of operations that are iteratively performed comprise adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^). The computing system of claim 67, wherein the plurality of operations that are iteratively performed comprise comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯.

69. The computing of claim 68, wherein the plurality of operations that are iteratively performed comprise: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weightingto the weights (^^^) of the list൫^^^^൯that are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯;adding the modified lists൫^^^^,^^൯of weights (^^^) to an end of the weight dataset൫^^^^൯; if the count ^^^is less than the maximum depth of the pruned activationdata structure൫^^ᇱ^^൯that is associated with the respective visual field spatial reference൫^^^^൯.

70. The computing system of claim 68 or claim 69, wherein the plurality of operations that are iteratively performed comprise discarding the list ൫^^^^൯ of weights (^^^) removed from the weight dataset൫^^^^൯, if the count ^^^is equal to or greater than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯.

71. The computingof claim 56, wherein determining the optimised visual field test activation sequence (^^) comprises iteratively: removing a list (^^^) of weights (^^^) from the weight dataset൫^^^^൯; determining a plurality of modified visual field loss value sums ൫^^^,^൯, wherein each modified visual field loss value weighted sum൫^^^,^൯is:associated with a visual field spatial reference ൫^^^^൯ and one of ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); determined based on the modified visual field loss maps(^^ᇱ^^)of the group ൫^^^^,^^൯ of modified visual field loss maps (^^ᇱ^^); and a sum of the weights(^^^)associated with the modified visual field loss maps(^^ᇱ^^)of the respective group ൫^^^^,^^൯; computing a value of the information metric ^^^, for each visual field spatial reference ൫^^^^൯, using the modified visual field loss value weighted sums ൫^^^,^൯ associated with that visual field spatial reference൫^^^^൯; determining an optimised information metric value ^^^, the optimised information metric value ^^^associated with the visual field spatial reference ൫^^^^൯ of the modified visual field loss value weighted sums ൫^^^,^൯ from which it is determined; adding the visual field spatial reference ൫^^^^൯ associated with the optimised information metric value ^^^to the optimised visual field test activation sequence (^^); adding one to a count ^^^associated with the visual field spatial reference ൫^^^^൯ that is added to the optimised visual field test activation sequence (^^); and comparing the count ^^^to a maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯; if count ^^^is less than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯: for each group ൫^^^^,^^൯, generating a modified list ൫^^^^,^^൯ of weights (^^^) by applying a down-weighting function to the weights(^^^)of the list ൫^^^^൯ that are associated with the modified visual field loss maps (^^ᇱ^^) of the respective group ൫^^^^,^^൯;adding the modified lists ൫^^^^,^^൯ of weights(^^^)to an end of the weight dataset ൫^^^^൯; andif the count ^^^is equal to or greater than the maximum depth of the pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is associated with the respective visual field spatial reference ൫^^^^൯, discarding the list ൫^^^^൯ of(^^^)removed from the weight dataset ൫^^^^൯.

72. Theof any one of claims 57 to 71, wherein theinstructions are further configured to cause the at least one processor to terminate the iterations when the weight dataset ൫^^^^൯ is empty at an end of an iteration.

73. A visual field test apparatus comprising: one or more emission units; at least one visual field test apparatus processor; and visual field test apparatus memory storing visual field test apparatus program instructions accessible by the at least one visual field test apparatus processor, the visual field test apparatus program instructions being configured to cause the at least one visual field test apparatus processor to: perform a visual field test; and generate a visual field test output based at least in part on visual field test data stored during the visual field test; wherein: performing the visual field test comprises iteratively: activating the one or more emission units in accordance with an optimised visual field test activation sequence (^^); each emission unit being associated with a respective visual field spatial reference ൫^^^^൯ of a set (^^) of visual field spatial references ൫^^^^൯; and an intensity of a respectivebeing defined by a value of a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ that is also associated with the visual field spatial reference ൫^^^^൯ of the activated emission unit; and storing visual field test data associated with the activation.

74. The visual field test apparatus of claim 73, wherein performing the visual field test comprises, for at least one iteration, determining an updated reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯, based on the stored visual field test data associated with the activation, if a reference node of the activation is not a leaf node.

75. The visual field test apparatus of claim 73, wherein performing the visual field test comprises, for at least one iteration, updating the optimised visual field test activation sequence (^^) if a reference node of a pruned activation intensity hierarchical data structure ൫^^ᇱ^^൯ associated with the activation is a leafnode, to remove subsequent activations of the respective emission unit from the optimised visual field test activation sequence (^^).

76. The visual field test apparatus of any one of claims 73 to 75, wherein generating the visual field test output comprises determining an output value for each visual field spatial reference ൫^^^^൯, based on the stored visual field test data.

77. The visual field test apparatus of any one of claims 73 to 76, wherein the visual field test apparatus program instructions are further configured to cause the at least one visual field test apparatus processor to render a graphical output determined based on the visual field test data on a display.

78. The visual field test apparatus of any one of claims 73 to 77, wherein the optimised visual field test activation sequence (^^) is determined in accordance with method of any one of claims 1 to 33.

79. A system comprising: the computing system of any one of claims 40 to 72; and the visual field test apparatus of any one of claims 73 to 78.

80. A method comprising performing a visual field test, wherein performing the visual field test comprises activating one or more emission units of a visual field test apparatus in the order defined in the optimised visual field test activation sequence (^^) determined in any one of claims 1 to 33.

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