Behavior Model for Perceptual Sensitivity Measurement
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Solution Overview
Problem
Current methodologies for measuring perceptual sensitivity changes are inefficient and prone to bias, as they typically require numerous trials and only provide an average sensitivity measurement over time intervals, failing to capture sensitivity changes within each interval.
Innovation Solution
A computer-implemented method and system that generate a behavior model based on prior behavior data, determine stimulus parameters for performance tests, control the application of these tests, receive response data, and update the model to accurately characterize perceptual sensitivity changes using Bayesian inference and adaptive psychophysical frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical psychophysical methodologies (method of constant stimuli) are used to measure perceptual sensitivity, then measurement coverage across multiple stimulus dimensions is achieved, but the number of trials required increases to hundreds or thousands of trials taking hours
Solution Approach 1:
The patent segments the measurement process into multiple independent intervals, with each interval measuring sensitivity at a specific time point. This allows parallel processing of multiple measurements and reduces the sequential time required while maintaining comprehensive coverage across stimulus dimensions and time courses.
Solution Approach 2:
The patent implements dynamic measurement intervals that adapt to the subject's performance and the specific sensory system being tested. The intervals are optimized to capture sensitivity changes at different time scales (seconds to years) without requiring fixed, excessive numbers of trials, thus reducing measurement time while preserving precision.
2Productivity
If average sensitivity measurement over time intervals is used, then measurement efficiency is improved, but the ability to capture sensitivity changes within each interval is lost
Solution Approach 1:
The patent divides the measurement into discrete time intervals, with each interval providing a separate sensitivity measurement. This segmentation enables both efficient processing (by treating each interval independently) and precise time-course analysis (by preserving within-interval variation patterns across multiple intervals).
Solution Approach 2:
The patent employs periodic measurement intervals to sample sensitivity changes over time. This periodic structure maintains measurement efficiency through regular sampling while capturing sensitivity dynamics within and between intervals, allowing reconstruction of time-course changes without continuous measurement.
3Reliability
If numerous trials are conducted to ensure measurement accuracy, then measurement reliability is improved, but the complexity of the measurement protocol increases
Solution Approach 1:
The patent segments the protocol into standardized, repeatable intervals that can be independently administered and analyzed. This segmentation reduces overall protocol complexity by breaking down complex measurements into manageable units while maintaining reliability through consistent application across multiple intervals.
Solution Approach 2:
The patent varies specific parameters (such as stimulus characteristics and interval durations) across different measurement intervals to capture sensitivity changes under different conditions. This parameter variation maintains measurement reliability by sampling across multiple states while avoiding the need for excessively complex fixed protocols.
Data Source
AI summary
The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.