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.g., age or adiposity). Machine learning methods, in particular deep neural networks, provide expressive feature representations of face stimuli, but the correspondence between these representations and various human attribute inferences is difficult to determine because the former are high-dimensional vectors produced via black-box optimization algorithms. Here we combine deep generative image models with over 1 million judgments to model inferences of more than 30 attributes over a comprehensive latent face space. The predictive accuracy of our model approaches human interrater reliability, which simulations suggest would not have been possible with fewer faces, fewer judgments, or lower-dimensional feature representations. Our model can be used to predict and manipulate inferences with respect to arbitrary face photographs or to generate synthetic photorealistic face stimuli that evoke impressions tuned along the modeled attributes.

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.

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. Stimuli consisted of pairs of computer-generated people's faces and exact dollar amounts that those fictional people shared with partners in a trust game. Importantly, the faces were designed to vary considerably along a dimension of perceived facial trustworthiness. Each participant learned (and then reproduced from memory) some mapping between the faces and the dollar amounts shared (i.e., between perceived facial and behavioral trustworthiness). Much like in the game of 'telephone', their reproductions then became the training stimuli initially presented to the next participant, and so on for each transmission chain. Critically, the first participant in each chain observed some mapping between perceived facial and behavioral trustworthiness, including positive linear, negative linear, nonlinear, and completely random relationships. Strikingly, participants' reproductions of these relationships showed a pattern of convergence in which more trustworthy looks were associated with more trustworthy behavior - even when there was no relationship between looks and behavior at the start of the chain. These results demonstrate the power of facial stereotypes, and the ease with which they can be propagated to others, even in the absence of any reliable origin of these stereotypes.

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"). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method.

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.g., trustworthiness). But perhaps the most fundamental traits we extract from faces are their social demographics, for example, race, age, and gender. How much exposure is required to extract such properties? Curiously, despite extensive work on the temporal efficiency of extracting both higher-level social properties (such as competence and dominance) and more basic characteristics (such as identity and familiarity), this question remains largely unexplored for demography. We correlated observers' percepts of the race/age/gender of unfamiliar faces viewed at several brief durations (and then masked) with their judgments after unlimited exposure. Performance reached asymptote by 100 ms, was above chance by only 33.33 ms, and had a similar temporal profile to detecting faces in the first place. This was true even when the property to be reported wasn't revealed until after the face had disappeared, and when the faces were matched for several lower-level visual properties. Collectively, these results demonstrate that the extraction of demographic features from faces is highly efficient, and can truly be done at a glance.

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.S.A). We demonstrate the pandemic increased individuals' self-rated paranoia and rendered their task-based belief updating more erratic. Local lockdown and reopening policies, as well as culture more broadly, markedly influenced participants' belief-updating: an early and sustained lockdown rendered people's belief updating less capricious. Masks are clearly an effective public health measure against COVID-19. However, state-mandated mask wearing increased paranoia and induced more erratic behaviour. Remarkably, this was most evident in those states where adherence to mask wearing rules was poor but where rule following is typically more common. This paranoia may explain the lack of compliance with this simple and effective countermeasure. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable, but at the same time predicted more rewards. In a follow-up study we found people who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines - again, mask attitude and conspiratorial beliefs were associated with erratic task behaviour and changed priors. Future public health responses to the pandemic might leverage these observations, mollifying paranoia and increasing adherence by tempering people's expectations of other's behaviour, and the environment more broadly, and reinforcing compliance.

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. 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.

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. 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.

Everyone has had fleeting concerns that others might be against them at some point in their lives. Sometimes these concerns can escalate into paranoia and become debilitating. Paranoia is a common symptom in serious mental illnesses like schizophrenia. It can cause extreme distress and is linked with an increased risk of violence towards oneself or others. Understanding what happens in the brains of people experiencing paranoia might lead to better ways to treat or manage it. Some experts argue that paranoia is caused by errors in the way people assess social situations. An alternative idea is that paranoia stems from the way the brain forms and updates beliefs about the world. Now, Reed et al. show that both people with paranoia and rats exposed to a paranoia-inducing substance expect the world will change frequently, change their minds often, and have a harder time learning in response to changing circumstances. In the experiments, human volunteers with and without psychiatric disorders played a game where the best choices change. Then, the participants completed a survey to assess their level of paranoia. People with higher levels of paranoia predicted more changes would occur and made less predictable choices. In a second set of experiments, rats were put in a cage with three holes where they sometimes received sugar rewards. Some of the rats received methamphetamine, a drug that causes paranoia in humans. Rats given the drug also expected the location of the sugar reward would change often. The drugged animals had harder time learning and adapting to changing circumstances. The experiments suggest that brain processes found in both rats, which are less social than humans, and humans contribute to paranoia. This suggests paranoia may make it harder to update beliefs. This may help scientists understand what causes paranoia and develop therapies or drugs that can reduce paranoia. This information may also help scientists understand why during societal crises like wars or natural disasters humans are prone to believing conspiracies. This is particularly important now as the world grapples with climate change and a global pandemic. Reed et al. note paranoia may impede the coordination of collaborative solutions to these challenging situations.

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). The observer then reproduced that face, using a slider to morph a test face along this continuum. Their response was then used as the face initially presented to the next observer, and so on down the line in each reproduction chain. White observers' chains consistently and steadily converged onto faces significantly Whiter than they had initially encountered-Whiter than both the original face in the chain and the continuum's midpoint-regardless of where chains began. Indeed, even chains beginning near the Black end of the continuum inevitably ended up well into White space. Very different patterns resulted when the same method was applied to other arbitrary face stimuli. These results highlight a systematic bias in memory for race in White observers, perhaps contributing to the more general notion in social cognition research of a 'White default.' (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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. 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.

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. Across four experiments, we showed that people can robustly parse such sequences from a continuous stream of trajectories under increasingly stringent tests of segmentation ability and statistical learning. Observers viewed a single dot as it moved along simple sequences of paths, and were later able to discriminate these sequences from novel and partial ones shown at test. Observers demonstrated this ability when there were potentially helpful trajectory-segmentation cues such as a common origin for all movements (Experiment 1); when the dot's motions were entirely continuous and unconstrained (Experiment 2); when sequences were tested against partial sequences as a more stringent test of statistical learning (Experiment 3); and finally, even when the element trajectories were in fact pairs of trajectories, so that abrupt directional changes in the dot's motion could no longer signal inter-trajectory boundaries (Experiment 4). These results suggest that observers can automatically extract regularities in movement - an ability that may underpin our capacity to learn more complex biological motions, as in sport or dance.

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). 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.