Humans are adept at uncovering complex associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve sophisticated mental processes, expending valuable computational resources . Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events . Importantly, our model analytically accounts for previously unexplained network effects on human expectations and quantitatively describes human reaction times in probabilistic sequential motor tasks. Additionally, our model asserts that human expectations should depend critically on the different topological scales in a transition network, a prediction that we subsequently test and validate in a novel experiment. Generally, our results highlight the important role of mental errors in shaping abstract representations , and directly inspire new physically-motivated models of human behavior .
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 . Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) 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 .
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complex
abstract
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sophisticated mental processes, expending valuable computational resources
complex mental processes
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a competing
an alternative
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actually
instead
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and memory
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novel maximum entropy
maximum entropy (or minimum complexity)
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expectations about the transition structures underlying sequences of ordered events
representations of the transitions between stimuli
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analytically accounts for previously unexplained network effects
(i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure
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and quantitatively describes
, and (iii) quantitatively predicts
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Additionally, our model asserts that human expectations should depend critically on the different topological scales in a transition network, a prediction that we subsequently test and validate in a novel experiment. Generally, our results highlight the important role of mental errors in shaping abstract representations , and directly inspire new physically-motivated models of human behavior
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