Cognitive Load Index via Gaze and Pupillometry
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Solution Overview
Problem
Conventional methods for assessing cognitive functions and mental impairments through random sequence generation are limited by the short-term memory span and lack of precise trial-wise or response-wise analysis, making it difficult to accurately evaluate cognitive dysfunctions across various medical conditions.
Innovation Solution
A processor-implemented method that assigns a primary executive task of random number generation, digitizes human-generated random numbers, and imposes secondary cognitive load tasks while receiving gaze and pupillometry data to compute a load index indicative of the effect of secondary cognitive load on the primary task, using a velocity-based method to extract fixation patterns and metrics for randomness deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 'call out' or 'write down' procedures are used to generate random number sequences, then the task can be performed with simple equipment, but the measurement precision and analysis capability are limited due to short-term memory constraints and inability to capture precise trial-wise data
Solution Approach 1:
The patent replaces manual 'call out' or 'write down' procedures with a computer-based system that uses a graphical user interface and eye tracker. The system automatically captures gaze data, pupillometry data, and random number generation responses, eliminating the limitations of human memory and manual recording while enabling precise trial-wise analysis of cognitive performance.
Solution Approach 2:
The patent introduces an eye tracker and computer system as intermediaries between the subject and the assessment process. These intermediaries capture physiological signals (gaze position, pupil diameter) and behavioral data (response times, accuracy) that provide objective measures of cognitive load and attention, thereby improving measurement precision without requiring complex experimental procedures from the subject.
2Difficulty of detecting and measuring
If traditional random sequence generation tasks are used, then the assessment method is simple and easy to operation, but the ability to detect and measure cognitive dysfunctions is insufficient due to lack of physiological markers
Solution Approach 1:
The patent utilizes physiological signal changes (pupil diameter variations, gaze pattern changes) as indicators of cognitive state. These natural physiological responses serve as automatic markers of cognitive load and attention, enabling detection of cognitive dysfunctions without requiring the subject to perform complex tasks or the examiner to conduct complex analyses.
Solution Approach 2:
The system automatically captures and analyzes physiological data (gaze position, pupil diameter, response times) without requiring manual intervention or complex subject cooperation. The eye tracker and computer system self-service the data collection and preliminary analysis, making the assessment both sensitive to cognitive dysfunction and easy to administer.
3Loss of information
If secondary cognitive load tasks are imposed to assess cognitive interference, then the effect on primary task can be measured, but the device complexity and data processing requirements increase significantly
Solution Approach 1:
The patent employs a multi-functional assessment system where a single integrated platform performs multiple functions: presenting the primary random number generation task, superimposing secondary cognitive load tasks, tracking eye movements, measuring pupil diameter, and analyzing cognitive interference effects. This universal system reduces overall complexity compared to separate systems for each function while capturing comprehensive information about cognitive interference.
Solution Approach 2:
The patent merges the primary task assessment, secondary task administration, and physiological data collection into a single integrated experimental paradigm. The eye tracker simultaneously records gaze and pupil data during both primary and secondary tasks, allowing the system to capture cognitive interference effects without requiring separate assessment sessions or complex coordination between multiple independent systems.
Data Source
AI summary
This disclosure relates to analyzing effect of a secondary cognitive load task on a primary executive task. Human random sequence generation is a marker to study cognitive functions and inability to generate random sequences (RS) can reveal underlying impairments. Traditionally, ‘call out’ or ‘write down’ procedures are used to obtain human generated numbers, wherein short term memory and number of previously generated entities visible to a subject plays a major role. Also precise trial-wise or response-wise analysis may not be possible. In the present disclosure, the human generated random numbers are digitized into RS and a cognitive load (CL) inducing task is imposed on the executive task. The CL demanding task disrupts randomization performance. Deviation from randomness, load index based on gaze data and deviation from pupillometry data of healthy subjects are provided as indicators of an interference effect imposed by the CL and thereby indicative of underlying impairments.