Visual Stimulus Response Scoring for Cognitive Complexity Measurement
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Solution Overview
Problem
Existing technologies struggle to quantitatively measure the psychological complexity elicited by visual stimuli, such as images, videos, and physical artifacts, which is essential for understanding human understanding, preferences, and decision-making processes.
Innovation Solution
A machine learning-based approach using a system and method to generate different dataset types of reference text descriptions, collect input response text descriptions, score differences, and generate a complexity score representative of cognitive complexity, employing a neural network and natural language processing to analyze human responses to visual stimuli.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to measure psychological complexity, then the measurement process is simple, but the measurement precision is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/survey-based measurement methods with a machine learning-based computational system. The system uses trained models to generate reference text descriptions and automatically score participant responses, substituting manual psychological assessment with automated AI-driven analysis to achieve more precise and scalable measurements
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the visual stimuli and the complexity measurement. The models generate reference text descriptions that serve as a bridge, allowing indirect measurement of psychological complexity through linguistic analysis rather than direct observation of cognitive processes
2Measurement precision
If machine learning models are used to generate reference text descriptions and score responses, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent segments the complexity measurement task into distinct functional modules: a reference description generation model that creates benchmark descriptions, an input response collection module that gathers participant responses, and a scoring module that calculates complexity scores by comparing responses to references. This segmentation allows each component to be optimized independently while maintaining overall system precision
Solution Approach 2:
The patent performs preliminary action by pre-training machine learning models to generate accurate reference text descriptions before the actual measurement process. These pre-generated references serve as standardized benchmarks that enable consistent and precise scoring of participant responses without requiring complex real-time analysis
Data Source
AI summary
A method for quantitatively measuring a psychological complexity of a response to visual stimuli is described. The method includes generating, by a trained machine learning model, different dataset types of reference text descriptions and/or representations of the visual stimuli. The method also includes collecting input response text descriptions from an individual after perceiving the visual stimuli. The method further includes scoring a difference between the input response text descriptions and the different dataset types of reference text descriptions and/or representations of the visual stimuli, in which a score is generated based on the different dataset types of reference text descriptions. The method also includes generating a complexity score representative of a cognitive complexity of the input response text description from the individual based on the score generated based on the different dataset types of reference text descriptions.