Abstract representations of events arise from mental errors in learning and memory

• Published in: Nature communications Vol. 11; no. 1; pp. 2313 - 12
• Main Authors: Lynn, Christopher W., Kahn, Ari E., Nyema, Nathaniel, Bassett, Danielle S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.05.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477/2811; 639/766/530/2801; 639/766/530/2804; Accuracy; Bayes Theorem; Bayesian analysis; Entropy (Information theory); Free energy; Human subjects; Humanities and Social Sciences; Humans; Hypotheses; Information sources; Information theory; Learning; Learning - physiology; Maximum entropy; Memory; Memory - physiology; multidisciplinary; Neurosciences; Reaction Time - physiology; Representations; Science; Science (multidisciplinary); Statistical analysis; Statistical inference
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-15146-7

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Using the free energy principle, which bridges information theory and Bayesian inference, we derive a maximum entropy model of people’s internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources. Humans can easily uncover abstract associations. Here, the authors propose that higher-order associations arise from natural errors in learning and memory. They suggest that mental errors influence the humans’ representation of the world in significant and predictable ways.