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.

We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulations testing structure-function relationships in cognitive processes. By comparing simulation results with grounded theories in knowledge modelling, SpreadPy enables systematic investigations of how activation dynamics reflect cognitive, psychological and clinical phenomena. We demonstrate the library's utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Simulations of a creativity task show that activation trajectories vary with task difficulty, exposing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy's flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

Prime Minister Rishi Sunak caused a stir earlier this week when he announced plans to force every student in England to study maths until the age of 18. In his speech, he said this was to ensure young people are better equipped for the'jobs of the future' by combating high rates of innumeracy in the UK. While many have criticised this agenda, including actor Simon Pegg, some scientific studies do support it. For example, a 2021 study from the University of Oxford found that quitting mathematical studies at the age of 16 may have an adverse effect on brain development. MailOnline takes a look at some of the studies which support or contradict Mr Sunak's controversial new plan to extend compulsory maths education (stock image) Pupils will be forced to take'some form' of maths delivered through new courses or existing qualifications Another study suggested that those who took maths at A-level had a salary 11 per cent higher than those who did not by age 33.

Although Machine Learning and AI is becoming more and more accessible, knowing the math behind the algorithms makes you a better practitioner. Lots of people have math anxiety, which deters them from the mathematical background of the field. In this post, we collected books that help you overcome math anxiety and pick up enough math to understand and appreciate the mathematical background of Machine Learning and Artificial Intelligence. If you are looking for a one-stop-shop for learning the math behind Machine Learning, Mathematics for Machine Learning by Deisenroth et al. is the ideal book for you! However, this book assumes that its readers are familiar with integrals, derivatives, and geometric vectors.

Matthew Peterson is a pretty inspirational guy. As a dyslexic child he found math class difficult, so as an adult he resolved to totally change the way math is taught. After completing his studies in biology, electrical engineering, and Chinese language and literature at the University of California, Irvine, Peterson co-founded the nonprofit MIND Research Institute and set about developing "Spatial Temporal (ST) Math," a computer game-based method of teaching that doesn't rely on language as a medium. Instead it uses spatial-temporal reasoning--the ability to move stuff around in your mind and work out how it fits together. Proponents point to recent findings in neuroscience and education research--showing that early music training can enhance spatial-temporal reasoning, for example--as justification for this shift.