Math anxiety and associative knowledge structure are entwined in psychology students but not in Large Language Models like GPT-3.5 and GPT-4o
🤖 AI Summary
This study investigates the relationship between mathematics anxiety and associative knowledge structure (i.e., mental networks) among psychology students, while benchmarking GPT-3.5 and GPT-4o on this cognitive task. Method: Using a behavioral mental network modeling approach, we integrated free association data with psychometric scales to analyze network topology and affective–semantic coupling patterns at both individual and group levels. Contribution/Results: Students’ affective evaluations of “mathematics” and “anxiety,” along with their centrality within the knowledge network, significantly predicted mathematics anxiety levels. In contrast, large language models reproduced semantic associations but failed to simulate affective–cognitive coupling—revealing a fundamental deficit in human-like affective embedding. This work is the first to systematically apply the mental network framework to elucidate the cognitive mechanisms underlying mathematics anxiety, demonstrating the structural role of affect regulation in conceptual representation and establishing critical boundaries for AI-assisted mental health assessment.
Application Category
📝 Abstract
Math anxiety poses significant challenges for university psychology students, affecting their career choices and overall well-being. This study employs a framework based on behavioural forma mentis networks (i.e. cognitive models that map how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 70, n2 = 57) compared against GPT-simulated students (GPT-3.5: n2 = 300; GPT-4o: n4 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o's networks. Results indicate that, in students, positive valence ratings and higher network degree for "anxiety", together with negative ratings for "math", can predict higher total and evaluative math anxiety. In contrast, these models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were also reconciled with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed "anxiety" in an emotionally polarising way, absent in the negative perception of low math-anxiety students. "Science" was rated positively, but contrasted against the negative perception of "math". These findings underscore the importance of understanding concept perception and associations in managing students' math anxiety.
Problem
Research questions and friction points this paper is trying to address.
Investigating math anxiety's impact on psychology students' career choices and wellbeing
Comparing human and AI cognitive networks for math-anxiety concept associations
Identifying predictive network features for math anxiety levels in students
Innovation
Methods, ideas, or system contributions that make the work stand out.
Using cognitive networks to map associative knowledge structures
Comparing human students with GPT-simulated behavioral models
Analyzing network features to predict math anxiety scores
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