Brain Activity Signal Extraction for Decision Uncertainty Testing
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Solution Overview
Problem
Decision uncertainty, a form of metacognition reflecting the degree of certainty in decision-making results, is difficult to measure objectively, posing a challenge in understanding and quantifying cognitive processes.
Innovation Solution
A testing method and device that acquire brain imaging data from specific brain regions, such as the anterior cingulate cortex, lateral frontopolar cortex, and ventral striatum, to extract brain activity signals and generate a test result indicating the subject's certainty in their judgments, using a pre-constructed test model trained on trial sets with varying levels of certainty and uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain imaging data is acquired and processed to obtain objective decision uncertainty measurements, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using brain imaging data collected during test experiment phases. The models are trained to recognize patterns in brain activity signals that correlate with decision uncertainty, allowing the system to make rapid, accurate measurements during actual testing without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The patent uses machine learning models as intermediaries between raw brain imaging data and decision uncertainty measurements. These models process the complex neural signals and translate them into interpretable uncertainty scores, bridging the gap between sophisticated imaging technology and practical measurement applications.
2Measurement precision
If brain imaging data acquisition and processing is implemented, then objective measurement capability is improved, but ease of operation deteriorates
Solution Approach 1:
The system applies self-service by automatically processing brain imaging data through pre-trained machine learning models without requiring manual analysis or interpretation. The models autonomously generate decision uncertainty measurements from raw neural signals, eliminating the need for expert operators to manually analyze complex brain imaging data.
3Measurement precision
If multiple brain regions are monitored to improve measurement accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the brain into specific regions of interest (ROI) such as the anterior cingulate cortex, lateral prefrontal cortex, and ventral striatum. Each region is monitored separately for its specific neural signatures, and the results are integrated to provide comprehensive decision uncertainty measurements. This approach focuses computational resources on key brain areas rather than analyzing the entire brain uniformly.
Data Source
AI summary
Embodiments of the present disclosure provide a testing method and device on decision uncertainty, the method including: acquiring brain imaging data of a subject in a period from its receiving a target problem to its making a judgment on the target problem; extracting brain activity signals from a region of interest (ROI) in the brain imaging data, the ROI including at least one region among the anterior cingulate cortex, the lateral frontopolar cortex, and the ventral striatum; and obtaining, based on the brain activity signals, a first test result reflecting a degree of certainty of the subject on correctness of the judgment.