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Experiment 1. Human participants use discriminative attention when generalizing to novel examples a, A model using distance to define state membership can fail to generalize if a new observation differs in a highly regular, but non-informative feature (left). A model using discriminative boundaries easily generalizes in this scenario (right). b, Participants learn the latent rules defining action-reward associations. During the tutorial, participants are instructed on stochastic rewards, that a single action can garnish rewards for multiple states, and that a single state can be rewarded for multiple actions. On a given trial, participants encounter an ‘alien artefact’ activated with one of four actions. The main task is composed of two blocks. During block 1, they learn the latent states with an initial set of examples. The transition to the second block occurs without notice to the participant. During block 2, new examples are introduced that differ in a previously non-discriminative feature (left). c, Top: initially learned examples (present block 1 and 2). Bottom: the novel generalization examples (introduced in block 2). Action D is never rewarded. d, Top: without discriminative attention, novel examples are separated from the initially learned examples by their texture. Bottom: with attention, novel examples are projected into the same feature space as the initially learned examples. e, Left: during the generalization block, differences in performance on the first appearance of initially learned versus novel examples measure generalization. Middle: the difference in error rates over the course of a session for discriminatively identical paired examples also quantifies generalization. Right: when first encountering a novel example, the choice of action D for novel example indicates a weak prior over state membership. f, Left: the First-Generalization-Appearance metric reveals that most participants generalize to new examples, while a tail in the distribution indicates individual variation. Middle: Paired-Generalization difference shows similar results. Right: exploration errors are below chance for most participants. First gen, First Generalization. g, Learning curves during initial state formation are similar across participants with different First-Generalization-Appearance scores, but diverge during generalization. The shaded areas represent the s.e.m. and the dotted lines denote chance levels. Source data
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The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that d...
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... The existence of spatial "cognitive maps" which efficiently organize knowledge is well-documented in the literature [45][46][47][48][49] . These maps guide attention and learning across different domains 45,46,50 . While two-dimensional representations are often emphasized in cognitive tasks, research suggests that cognitive representations are multi-dimensional and can be compressed or unfolded based on task demands 17,51 . ...
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... Hybrid Concept Learning Using Bayesian Principles. Today, the most prolific theories of concept learning are hybrids that have a duality of both rule-and similarity-based interpretations (Pettine et al. 2023). One influential example is Bayesian concept learning (Figure 1d;Tenenbaum & Griffiths 2001), which uses a distribution over hypothesized category boundaries (rectangles in Figure 1d) to categorize novel stimuli (Sidebar 2.1). ...
Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.
... Perception in neural circuits is fundamentally belief in a description of the world -we see trees and leaves, not green and brown pixels (Gibson 1977;Rao and Ballard 1999;Friston 2005;Adams et al. 2013;Sterzer et al. 2018). Perception entails a process of categorization and generalization through parallel distributed processing that depends on experience and expertise (Grossberg 1976;Hertz et al. 1991;McClelland and Rogers 2003;Pettine et al. 2023). Motivation and evaluation are computational memory processes of their own (Balleine and Dickinson 1991;Andermann and Lowell 2017;Sharpe et al. 2021). ...
Current theories of decision making suggest that the neural circuits in mammalian brains (including humans) computationally combine representations of the past (memory), present (perception), and future (agentic goals) to take actions that achieve the needs of the agent. How information is represented within those neural circuits changes what computations are available to that system which changes how agents interact with their world to take those actions. We argue that the computational neuroscience of decision making provides a new microeconomic framework (neuroeconomics) that offers new opportunities to construct policies that interact with those decision-making systems to improve outcomes. After laying out the computational processes underlying decision making in mammalian brains, we present four applications of this logic with policy consequences: (1) contingency management as a treatment for addiction, (2) precommitment and the sensitivity to sunk costs, (3) media consequences for changes in housing prices after a disaster, and (4) how social interactions underlie the success (and failure) of microfinance institutions.
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