The pervasive presence of relational information in concepts, and its indirect presence in sensory input, raises the question of how it is extracted from experience. We operationalized experience as a stream of events in which reliable predictive relationships exist among random ones, and in which learners are naïve as to what they will learn (i.e., a statistical learning paradigm). First, we asked whether predictive event pairs would spontaneously be seen as causing each other, given no instructions to evaluate causality. We found that predictive information indeed informed later causal judgments but did not lead to a spontaneous sense of causality. Thus, event contingencies are relevant to causal inference, but such interpretations may not occur fully bottom-up. A second question was how such experience might be used to learn about novel objects. Because events occurred either around or involving a continually present object, we were able to distinguish objects from events. We found that objects can be attributed causal properties by virtue of a higher-order structure, in which the objects identity is linked not to the increased likelihood of its effect, but rather, to the predictive structure among events, given its presence. This is an important demonstration that objects causal properties can be highly abstract: They need not refer to an occurrence of a sensory event per se, or its link to an object, but rather to whether or not a predictive relationship holds among events in its presence. These learning mechanisms may be important for acquiring abstract knowledge from experience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

When learning about events through visual experience, one must not only identify which events are visually similar but also retrieve those events associates-which may be visually dissimilar-and recognize when different events have similar predictive relations. How are these demands balanced? To address this question, we taught participants the predictive structures among four events, which appeared in four different sequences, each cued by a distinct object. In each, one event (cause) was predictably followed by another (effect). Sequences in the same relational category had similar predictive structure, while across categories, the effect and cause events were reversed. Using functional magnetic resonance imaging data, we measured associative coding, indicated by correlated responses between effect and cause events; perceptual coding, indicated by correlated responses to visually similar events; and relational category coding, indicated by correlated responses to sequences in the same relational category. All three models characterized responses within the right middle temporal gyrus (MTG), but in different ways: Perceptual and associative coding diverged along the posterior to anterior axis, while relational categories emerged anteriorly in tandem with associative coding. Thus, along the posterior-anterior axis of MTG, the representation of the visual attributes of events is transformed to a representation of both specific and generalizable relational attributes.

How does the similarity structure of memory influence credit assignment in reinforcement learning? Memory spaces vary in how integrated versus separable their constituent dimensions are, and how clustered versus distributed items are across dimensions. Greater integration may cause people to overattribute value to multiple dimensions, potentially leading to generalization errors. Greater clustering may bias people to attribute value to discrete category-centers (category-based learning) rather than map value continuously across the space (function learning). In this study, subjects complete a value-learning task in which stimuli are sampled from low-dimensional perceptual spaces, and reward is mapped to one dimension. Each space is intended to engender a different degree of integration and clustering in memory, such that effects of memory organization on learning can be probed. Additionally, we investigate how credit assignment on each of these artificial perceptual spaces differs to credit assignment on more complex spaces that define real-world semantic concepts.

Values drive our behavioral choices. Ample research has explored the cognitive and neural underpinnings of value- based computations related to decision-making. However, behaviorally relevant values that we associate with real-world objects are often not monetary. For instance, social values associated with specific people are crucial for social behaviors and interactions. Moreover, understanding and attributing social values allows for proper evaluations of potential interactions with others, and can lead to more beneficial social behaviors and relationships. Learning social values has been shown to recruit the same systems as reward values, however how they become associated with specific people remains to be established. The present study examined social value learning of other people using naturalistic face images. We found that before learning, distances between the faces in conceptual similarity spaces were organized corresponding to their perceptual similarity. However, after learning, faces were shifted in a manner that reflected similarity of their associated social values (generosity). Furthermore, distances were positively correlated with a post- learning index of preference to interact with a person in a future cooperative game. In other words, learned social values of the faces seemed to influence their representations in conceptual space, and such representational changes were related to propensities in future behavior.

Semantic knowledge can facilitate or distort new memories,depending on their alignment. We aimed to quantifydistortions in memory by examining how categorymembership biases new encoding. Across two experiments,participants encoded and retrieved image-locationassociations on a 2D grid. The locations of images weremanipulated so that most members of a category (e.g. birds)were clustered near each other, but some were in randomlocations. Memory for an item’s location was more precisewhen it was near members of the same category.Furthermore, typical category members’ retrieved locationswere more biased towards their semantic neighbors, relativeto atypical members. This demonstrates that the organizationof semantic knowledge can explain bias in new memories.

Cognitive scientists have discovered much about the acquisition, consolidation, and retrieval of episodic memories; how-ever, much less is known about how memories of our daily experiences are organized, nor how this organization maychange as memories become consolidated. Here, we apply computational network science methodologies to quantify theorganization of recent (within the past year) and remote (5 10 years ago) autobiographical memories and quantitativelyexamine how these networks change over time. We found that remote memories exhibited higher global connectivityrelative to recent memories, and that this increased connectivity is coupled with lower subjective ratings of vividness. Ourresults demonstrate how such cognitive features of episodic memory can be quantitatively examined and shed novel lighton the organization and reconfiguration of episodic memories over time.

Past work has made conflicting proposals about the mechanisms underlying instructed reinforcement learning (RL)specifically,that prefrontal cortex, representing instruction, either biases, attenuates, or overrides learning signals in the brain. Weleverage the sensitivity of pattern-similarity analysis of fMRI data to distinguish between the qualitative features of theseaccounts. Participants learn the value of six novel stimuli after receiving false information that one is of high value. Wetrack markers of value learning in visual cortex during a value-independent perceptual judgement task presented betweenintervals of RL. We predict that with learning, the correlation between activation patterns for similarly valued stimuli willincrease. To characterize influences on learning, we examine how the rate at and direction in which these patterns changein similarity will be influenced by explicit instruction about stimulus value. This work will help us identify the principlecognitive and neural mechanisms underlying instructed RL.

The mind adeptly registers statistical regularities inexperience, often incidentally. We use a visual statisticallearning paradigm to study incidental learning of predictiverelations among animated events. We ask what kinds ofstatistics participants automatically compute, even whentracking such statistics is task-irrelevant and largely implicit.We find that participants are sensitive to a quantity governingassociative learning, DP, independently of conditionalprobabilities and chunk frequencies, as previously considered.DP specifically reflects the uniqueness, as well as strength, ofconditional probabilities; we find that uniqueness is equallyaffected by a single strong alternative predictor as by severalweak predictors. Performance is well captured with anadapted version of the Rescorla-Wagner delta learning rule(Rescorla & Wagner, 1972). We conclude that incidentalpredictive learning is governed by considerations ofuniqueness, and that this is computed by normalizingconditional probabilities by events’ base-rates. This opens thepossibility of common mechanisms between statisticallearning, associative learning, and causal inference.