Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning behavior and computational modeling to estimate belief updating across individuals with and without mental illness, online participants, and rats chronically exposed to methamphetamine, an elicitor of paranoia in humans. Paranoia is associated with a stronger prior on volatility, accompanied by elevated sensitivity to perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further assessment of the interplay between uncertainty and belief-updating across individuals and species.

The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour. ArticlesNATure HumAN BeHAVIOur (BF 10 = 0.163, strong evidence for the null hypothesis) or reversals achieved (BF 10 = 0.210, strong evidence for the null hypothesis) between social and non-social tasks. Computational modelling. Probabilistic reversal learning involves decision-making under uncertainty. The reasons for decisions may not be manifest in simple counts of choices or errors. By modelling Reward probability (%) 10

Significance We quickly and irresistibly form impressions of what other people are like based solely on how their faces look. These impressions have real-life consequences ranging from hiring decisions to sentencing decisions. We model and visualize the perceptual bases of facial impressions in the most comprehensive fashion to date, producing photorealistic models of 34 perceived social and physical attributes (e.g., trustworthiness and age). These models leverage and demonstrate the utility of deep learning in face evaluation, allowing for 1) generation of an infinite number of faces that vary along these perceived attribute dimensions, 2) manipulation of any face photograph along these dimensions, and 3) prediction of the impressions any face image may evoke in the general (mostly White, North American) population.

Visual processing recovers not only simple features, such as color and shape, but also seemingly higherlevel properties, such as animacy. Indeed, even abstract geometric shapes are readily perceived as intentional agents when they move in certain ways, and such percepts can dramatically influence behavior. In the wolfpack effect, for example, subjects maneuver a disc around a display in order to avoid several randomly moving darts. When the darts point toward the disc, subjects (falsely) perceive that the darts are chasing them, and this impairs several types of visuomotor performance. Are such effects reflexive, automatic features of visual processing? Or might they instead arise only as contingent strategies in tasks in which subjects must interact with (and thus focus on the features of) such objects? We explored these questions in an especially direct way-by embedding such displays into the background of a completely independent "foraging" task. Subjects now moved their disc to collect small "food" dots (which appeared sequentially in random locations) as quickly as possible. The darts were task-irrelevant, and subjects were encouraged to ignore them. Nevertheless, foraging was impaired when the randomly moving darts pointed at the subjects' disc, as compared to control conditions in which they were either oriented orthogonally to the subjects' disc or pointed at another moving shape-thereby controlling for nonsocial factors. The perception of animacy thus influences downstream visuomotor behavior in an automatic manner, such that subjects cannot completely override the influences of seemingly animate shapes even while attempting to ignore them.

Research shows that positive mood can serve to broaden the scope of attention at both the perceptual and conceptual level (e.g., increasing the size of spatial attentional focus and semantic access to remote associates). We investigated whether this relaxation of attentional filters by positive affect reduces their selectivity for basic visual features. We induced positive, neutral, or negative affect and asked observers to identify a target motion direction in a series of rapid random moving dot displays. Using a reverse correlation method, we examined the differential effects of emotion on observers' perceptual tuning curves for motion direction. Here we find that positive affect reduces selectivity for motion direction by broadening observers' perceptual tuning relative to both neutral and negative affect conditions. These findings provide the first behavioral evidence that positive emotion influences selectivity for basic visual features through modulation of tuning properties.