Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence.

When we look at someone's face, we rapidly and automatically form robust impressions of how trustworthy they appear. Yet while people's impressions of trustworthiness show a high degree of reliability and agreement with one another, evidence for the accuracy of these impressions is weak. How do such appearance-based biases survive in the face of weak evidence? We explored this question using an iterated learning paradigm, in which memories relating (perceived) facial and behavioral trustworthiness were passed through many generations of participants.

Generative models for visualizing idiosyncratic impressions.

In their comprehensive review of research on impressions from faces, Sutherland and Young (this issue) highlight both the remarkable progress and the many challenges facing the field. We focus on two of the challenges: the need for generative, powerful models of impressions and the idiosyncratic nature of complex impressions.

A data-driven, hyper-realistic method for visualizing individual mental representations of faces.

Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., "X type of face looks trustworthy").

Deep models of superficial face judgments.

The diversity of human faces and the contexts in which they appear gives rise to an expansive stimulus space over which people infer psychological traits (e.g., trustworthiness or alertness) and other attributes (e.

The Efficiency of Demography in Face Perception.

When we look at a face, we cannot help but "read" it: Beyond simply processing its identity, we also form robust impressions of both transient psychological states (e.g., surprise) and stable character traits (e.

Paranoia and belief updating during the COVID-19 crisis.

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.

Paranoia and belief updating during a crisis.

The 2019 coronavirus (COVID-19) pandemic has made the world seem unpredictable. During such crises we can experience concerns that others might be against us, culminating perhaps in paranoid conspiracy theories. Here, we investigate paranoia and belief updating in an online sample (N=1,010) in the United States of America (U.

Paranoia as a deficit in non-social belief updating.

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.

Teleface: Serial reproduction of faces reveals a whiteward bias in race memory.

How is race encoded into memory when viewing faces? Here we demonstrate a novel systematic bias in which our memories of faces converge on certain prioritized regions in our underlying "face space," as they relate to perceived race. This convergence was made especially salient using a new visual variant of the method of serial reproduction: "TeleFace." A single face was briefly presented, with its race selected from a smooth continuum between White and Black (matched for mean luminance).

Statistical learning of movement.

The environment is dynamic, but objects move in predictable and characteristic ways, whether they are a dancer in motion, or a bee buzzing around in flight. Sequences of movement are comprised of simpler motion trajectory elements chained together. But how do we know where one trajectory element ends and another begins, much like we parse words from continuous streams of speech? As a novel test of statistical learning, we explored the ability to parse continuous movement sequences into simpler element trajectories.

The automaticity of perceiving animacy: Goal-directed motion in simple shapes influences visuomotor behavior even when task-irrelevant.

Visual processing recovers not only simple features, such as color and shape, but also seemingly higher-level 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.

Seeing the world through target-tinted glasses: Positive mood broadens perceptual tuning.

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).