Neural Computations Underlying Causal Structure Learning

Abstract

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.