In everyday learning, people often receive conflicting information from different sources. What factors determine whichsources influence learning? In this study, we consider whether social characteristics of a source, such as attractivenessand gender, affect belief-updating in a simple category learning task. Participants sorted novel stimuli into two categories.After establishing an initial category boundary, two companions were introduced from whom categorization advice wasreceived. These sources did not always agree, and participants were never told which of them was correct. Acrossparticipants, the gender and attractiveness of the companions was varied. After 300 trials receiving this feedback for arange of stimuli, participants category boundaries were again measuredallowing a determination of belief-shifts. For bothmale and female participants, attractiveness had a significant impact, and female sources were afforded more weight thanmales. Our results suggest that category learning can be influenced by social factors like gender and attractiveness.

Human knowledge is generative: from everyday learning people extract latent features that can recombine to producenew imagined forms. This ability is critical to cognition, but its computational bases remain elusive. Recent researchwith -regularized Variational Autoencoders (-VAE) suggests that generativity in visual cognition may depend on learningdisentangled (localist) feature representations. We tested this proposal by training -VAEs and standard autoencoders toreconstruct bitmaps showing a single object varying in shape, size, location, and color, and manipulating hyperparame-ters to produce differentially-entangled feature representations. These models showed variable generativity, with somestandard autoencoders capable of near-perfect reconstruction of 43 trillion images after training on just 2000. However,constrained -VAEs were unable to reconstruct images reflecting feature combinations which were systematically withheldduring training (e.g. all blue circles). Thus, deep auto-encoders may provide a promising tool for understanding visualgenerativity and potentially other aspects of visual cognition.

Cognitive science often views human learning as rational. Why then do false beliefs arise, and why are they resistantto change? False beliefs might arise when people (1) lack knowledge in some domain, (2) adopt beliefs aligning withimplicit causal theories, or (3) encounter, through media or social networks, sets of beliefs that strongly covary. To testthese hypotheses we composed a survey assessing beliefs about matters of fact across a wide range of knowledge domainsand collected responses from 500 MTurkers. We then conducted a factor analysis to determine which false beliefs co-vary together, clustered respondents to find groups that adopt comparable false belief sets, and used regression to identifysociodemographic and media-consumption features that predict susceptibility to different kinds of false beliefs. The resultssuggest that some kinds of false belief may arise and persist merely from covariance in the opinions learners encounter insocial life.

While people are faster to categorize objects at an intermediate or basic level of specificity (e.g. “bird”), severalrecent studies have shown them to have much earlier access to more general category information (e.g. “animal”). Ultra-rapidsuperordinate classification has been taken as evidence that recognition processes are largely feed-forward. In simulations witha deep neural network model, we show that this conclusion does not follow: even a model that is fully recurrent and interactiveshows ultra-rapid superordinate classification patterns when tested with analogs of behavioral tasks such as rapid serial visualpresentation or deadline classification. Moreover, this recurrent model explains recently-observed similarities and differencesin the time-course of classification as estimated by electro-encephlography (EEG) versus human electro-corticography (ECoG),and also account for the well-known basic-level advantage in non-speeded classification. These results provide evidence thatultra-rapid and unconstrained visual object recognition is supported by interactive processes in the brain.

Cognitive models of learning often propose that belief formation is rational: new experience is combined with prior belief to support statistically optimal inferences. Why then are false beliefs so ubiquitous and difficult to correct? Prior work using a novel category-learning paradigm suggests that false beliefs may arise from the way that learners, when presented with contradictory information, choose to weight different information sources. When at least one source provides evidence highly consistent with the learner’s current belief, other sources are strongly discounted and little belief change is observed; but when evidence from the most agreeable source is more discrepant with the learner, other sources receive proportionally more weight and effect larger belief change. The current work assesses whether a similar model explains patterns of belief change, not in a novel lab-based task, but for propositional real-world beliefs held by participants prior to coming into the lab. In a paradigm analogous to the previous category-learning study, participants known to hold a polarized opinion on a controversial topic read arguments varying in agreement and/or opposition to that opinion. The discrepancy between the participant’s existing belief and that expressed by the arguments was experimentally varied. The pattern of belief change was remarkably similar to that observed in the novel category-learning task, suggesting that a similar underlying model can explain belief elasticity even for existing real-world beliefs.

How are our conceptual representations organized, and how does this semantic structure differ in individuals or groups? We included 36 faces from the Chicago Face Database (Ma et al., 2015) varying in race, gender, and facial expression in a triplet judgement task, where participants were showed a target face and asked which of two other faces is most similar to the target face expression. Each of our 71 participants completed 1,012 triplet judgements. Using their judgements, we fit individualized embeddings capturing distances between faces in semantic space, resulting in 71 embeddings. We calculated the Procrustes similarity between all pairs of embeddings and clustered similarities, revealing four clusters of respondents. Each cluster organized faces differently, indicating group-wide differences in semantic organization of expression. At the individual level, we find that each participant’s responses for held-out items are better predicted by their own embedding than by other participants’ embeddings, p < .00001.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm---suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

Executive control processes allow task-appropriate behaviour across cognitive domains, yet, children have a long devel-opmental period with little or no control. Traditionally, this is viewed as a negative but necessary consequence of the timetaken for prefrontal development and learning control processes. Here we examine a recent model of controlled semanticcognition (https://biorxiv.org/cgi/content/short/860528v1) as a test case to present evidence for an alternative (yet perhaps,complementary) view; that a developmental period without control has a positive functional role in learning. Varying thelength of a developmental period without control, we identify an optimal period (around one third of the learning time)which allows conceptual learning to happen much faster, without loss of conceptual abstraction ability. This speeding ismediated by the way control interacts with representation regions (deeper multimodal ¿ shallower input areas). This hasimplications for our understanding of controlled semantic cognition and the development of control more generally.