Biology subjects

Gershman, S.

Publications and source records attributed to Gershman, S..

5 recordsLinked to original sources

bioRxiv2018

Communicating compositional patterns

How do people perceive and communicate structure? We investigate this question by letting participants play a communication game, where one player describes a pattern, and another player redraws it based on the description alone. We use this paradigm to compare two models of pattern description, one compositional (complex structures built out of simpler ones) and one non-compositional. We find that compositional patterns are communicated more effectively than non-compositional patterns, that a compositional model of pattern description predicts which patterns are harder to describe, and that this model can be used to evaluate participants drawings, producing human-like quality ratings. Our results suggest that natural language can tap into a compositionally structured pattern description language.

animal behavior and cognition↗

bioRxiv2018

Dopaminergic genes are associated with both directed and random exploration

In order to maximize long-term rewards, agents must balance exploitation (choosing the option with the highest payoff) and exploration (gathering information about options that might have higher payoffs). Although the optimal solution to this trade-off is intractable, humans make use of two effective strategies: selectively exploring options with high uncertainty (directed exploration), and increasing the randomness of their choices when they are more uncertain (random exploration). Using a task that independently manipulates these two forms of exploration, we show that single nucleotide polymorphisms related to dopamine are associated with individual differences in exploration strategies. Variation in a gene linked to prefrontal dopamine (COMT) predicted the degree of directed exploration, as well as the overall randomness of responding. Variation in a gene linked to striatal dopamine (DARPP-32) predicted the degree of both directed and random exploration. These findings suggest that dopamine makes multiple contributions to exploration, depending on its afferent target.

neuroscience↗

bioRxiv2018

Reference-dependent preferences arise from structure learning

Modern theories of decision making emphasize the reference-dependency of decision making under risk. In particular, people tend to be risk-averse for outcomes greater than their reference point, and risk-seeking for outcomes less than their reference point. A key question is where reference points come from. A common assumption is that reference points correspond to expectations about outcomes, but it is unclear whether people rely on a single global expectation, or multiple local expectations. If the latter, how do people determine which expectation to apply in a particular situation? We argue that people discover reference points using a form of Bayesian structure learning, which partitions outcomes into distinct contexts, each with its own reference point corresponding to the expected outcome in that context. Consistent with this theory, we show experimentally that dramatic change in the distribution of outcomes can induce the discovery of a new reference point, with systematic effects on risk preferences. By contrast, when changes are gradual, a single reference point is continuously updated.

animal behavior and cognition↗

bioRxiv2017

Neural Computations Underlying Causal Structure Learning

Behavioral evidence suggests that beliefs about causal structure constrain associative learning, determining which stimuli can enter into association, as well as the functional form of that association. Bayesian learning theory provides one mechanism by which structural beliefs can be acquired from experience, but the neural basis of this mechanism is unknown. A recent study (Gershman, 2017) proposed a unified account of the elusive role of \"context\" in animal learning based on Bayesian updating of beliefs about the structure of causal relationships between contexts and cues in the environment. The model predicts that the computations which arbitrate between these abstract causal structures are distinct from the computations which learn the associations between particular stimuli under a given structure. In this study, we used fMRI with male and female human subjects to interrogate the neural correlates of these two distinct forms of learning. We show that structure learning signals are encoded in rostrolateral prefrontal cortex and the angular gyrus, anatomically distinct from correlates of associative learning. Within-subject variability in the encoding of these learning signals predicted variability in behavioral performance. Moreover, representational similarity analysis suggests that some regions involved in both forms of learning, such as parts of the inferior frontal gyrus, may also encode the full probability distribution over causal structures. These results provide evidence for a neural architecture in which structure learning guides the formation of associations.\n\nSignificance StatementAnimals are able to infer the hidden structure behind causal relations between stimuli in the environment, allowing them to generalize this knowledge to stimuli they have never experienced before. A recently published computational model based on this idea provided a parsimonious account of a wide range of phenomena reported in the animal learning literature, suggesting that the neural mechanisms dedicated to learning this hidden structure are distinct from those dedicated to acquiring particular associations between stimuli. Here we validate this model by measuring brain activity during a task which dissociates structure learning from associative learning. We show that different brain networks underlie the two forms of learning and that the neural signal corresponding to structure learning predicts future behavioral performance.

neuroscience↗

bioRxiv2017

Amortized Hypothesis Generation

Bayesian models of cognition posit that people compute probability distributions over hypotheses, possibly by constructing a sample-based approximation. Since people encounter many closely related distributions, a computationally efficient strategy is to selectively reuse computations - either the samples themselves or some summary statistic. We refer to these reuse strategies as amortized inference. In two experiments, we present evidence consistent with amortization. When sequentially answering two related queries about natural scenes, we show that answers to the second query vary systematically depending on the structure of the first query. Using a cognitive load manipulation, we find evidence that people cache summary statistics rather than raw sample sets. These results enrich our notions of how the brain approximates probabilistic inference.

animal behavior and cognition↗