Bayesian Adaptive Visual Sensitivity Measurement
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Solution Overview
Problem
Current methods for assessing visual contrast sensitivity are either subjective and prone to biases or objective but time-consuming, lacking efficiency in measuring visual sensitivity across various spatial frequencies and illumination conditions.
Innovation Solution
A method using Bayesian adaptive inference to determine visual sensitivity parameters by iteratively presenting visual stimuli and updating probabilities based on responses, allowing for rapid measurement of contrast sensitivity functions across different spatial frequencies and illumination conditions using mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive testing methods with computationally intense algorithms are used to estimate contrast sensitivity functions, then measurement precision is improved, but productivity deteriorates due to increased testing time
Solution Approach 1:
The system performs preliminary action by using a pre-test to obtain rough estimates of contrast sensitivity parameters before the main adaptive testing. These preliminary parameter estimates serve as informed priors for the Bayesian inference algorithm, allowing the main test to converge faster to precise measurements while maintaining accuracy.
Solution Approach 2:
The system implements feedback through iterative Bayesian inference where each subject response updates the probability distribution of sensitivity parameters. The algorithm uses this feedback to adaptively select subsequent stimuli that maximize information gain, efficiently converging on precise parameter estimates with fewer trials than traditional methods.
2Measurement precision
If narrow-band grating stimuli are used to isolate frequency-specific channels, then measurement precision for specific spatial frequencies is improved, but adaptability deteriorates due to limited coverage across different spatial frequencies
Solution Approach 1:
The system applies universality by using a single adaptive testing framework that can estimate contrast sensitivity across the entire spatial frequency spectrum. The Bayesian inference algorithm universally handles different spatial frequencies through parameter estimation, eliminating the need for separate narrow-band tests for each frequency while maintaining frequency-specific precision.
Solution Approach 2:
The system transitions from one-dimensional frequency-specific testing to two-dimensional parameter space estimation. Instead of measuring sensitivity at isolated frequencies, the algorithm estimates continuous sensitivity functions across frequency and contrast dimensions, providing both frequency-specific precision and broad spectral coverage simultaneously.
3Adaptability or versatility
If multiple visual sensitivity tests are conducted across different spatial frequencies and illumination conditions, then adaptability is improved, but loss of time increases due to extended testing duration
Solution Approach 1:
The system merges multiple testing objectives into a single integrated adaptive testing procedure. The Bayesian inference simultaneously estimates contrast sensitivity parameters across different spatial frequencies and illumination conditions by combining information from all trials, achieving comprehensive condition coverage without requiring separate test sessions for each condition.
Solution Approach 2:
The system maintains continuity of useful action through iterative Bayesian updating where each trial contributes information to the parameter estimates. The testing continuously refines sensitivity measurements across all conditions without interruption, maximizing information gain per unit time and eliminating the time loss associated with transitioning between separate tests.
Data Source
AI summary
Data is received characterizing a result of a first visual sensitivity test assessing capacity to detect spatial form across one or more different target sizes, and different contrasts. Using the received data, one or more first parameters defining a first estimated visual sensitivity for a first range of contrasts and a second range of spatial frequencies is determined. One or more second parameters defining a second estimated visual sensitivity for a third range of contrasts and a fourth range of spatial frequencies is determined using the one or more first parameters and a statistical inference by at least presenting a first visual stimulus, receiving a response, and determining a second visual stimulus based at least on the response and at least a rule. The one or more second parameters is provided. Related apparatus, systems, techniques and articles are also described.