Choosing face : The curse of self in profile image selection

This research was supported by Australian Research Council grants to DW (LP130100702) and CS (DP170104602), postdoctoral research support from the Australian Research Council Centre of Excellence in Cognition and its Disorders, University of Western Australia (CE110001021) and an ESRC Overseas Institutional Visit award (ES/1900748/1) to CS. The authors thank Manuela Tan and undergraduate volunteers at the UNSW School of Psychology for assisting with the pilot work that led to this research.Peer reviewedPublisher PD

Facial image manipulation : A tool for investigating social perception

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (CE110001021), an ARC Discovery Outstanding Researcher Award to Rhodes (DP130102300), and an ARC Discovery grant to Rhodes, Sutherland, and Young (DP170104602).Peer reviewedPostprin

AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones

Recent evidence shows that AI-generated faces are now indistinguishable from human faces. However, algorithms are trained disproportionately on White faces, and thus White AI faces may appear especially realistic. In Experiment 1 (N = 124 adults), alongside our reanalysis of previously published data, we showed that White AI faces are judged as human more often than actual human faces-a phenomenon we term AI hyperrealism. Paradoxically, people who made the most errors in this task were the most confident (a Dunning-Kruger effect). In Experiment 2 (N = 610 adults), we used face-space theory and participant qualitative reports to identify key facial attributes that distinguish AI from human faces but were misinterpreted by participants, leading to AI hyperrealism. However, the attributes permitted high accuracy using machine learning. These findings illustrate how psychological theory can inform understanding of AI outputs and provide direction for debiasing AI algorithms, thereby promoting the ethical use of AI

The unique contributions of perceiver and target characteristics in person perception

This research was partially supported by a SSHRC Institutional Grant and SSHRC Insight Development Grant (430-2016-00094) to EH and postdoctoral research support from the Australian Research Council Centre of Excellence in Cognition and its Disorders, University of Western Australia (CE110001021) and an Australian Research Council Discovery Project Grant (DP170104602) to CS.Peer reviewedPostprin

Modeling first impressions from highly variable facial images

The research was funded in part by an Economic and Social Research Council Studentship ES/I900748/1 (to C.A.M.S.).Peer reviewedPublisher PD

Sexual unfaithfulness can be judged with some accuracy from men's but not women's faces

Peer reviewedPublisher PD

Personality judgments from everyday images of faces

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: an ESRC studentship [ES/I900748/1] and postdoctoral research support from the Australian Research Council Centre of Excellence in Cognition and its Disorders, University of Western Australia (CE110001021), to the first author. The work was completed while the first author was at the University of York, UK. We thank Richard Vernon for calculating the attributes used in Study 2.Peer reviewedPublisher PD

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Hearing brighter: changing in-depth visual perception through looming sounds.

Rapidly approaching (looming) sounds are ecologically salient stimuli that are perceived as nearer than they are due to overestimation of their loudness change and underestimation of their distance (Neuhoff, 1998; Seifritz et al., 2002). Despite evidence for crossmodal influence by looming sounds onto visual areas (Romei, Murray, Cappe, & Thut, 2009, 2013; Tyll et al., 2013), it is unknown whether such sounds bias visual percepts in similar ways. Nearer objects appear to be larger and brighter than distant objects. If looming sounds impact visual processing, then visual stimuli paired with looming sounds should be perceived as brighter and larger, even when the visual stimuli do not provide motion cues, i.e. are static. In Experiment 1 we found that static visual objects paired with looming tones (but not static or receding tones) were perceived as larger and brighter than their actual physical properties, as if they appear closer to the observer. In a second experiment, we replicate and extend the findings of Experiment 1. Crucially, we did not find evidence of such bias by looming sounds when visual processing was disrupted via masking or when catch trials were presented, ruling out simple response bias. Finally, in a third experiment we found that looming tones do not bias visual stimulus characteristics that do not carry visual depth information such as shape, providing further evidence that they specifically impact in-depth visual processing. We conclude that looming sounds impact visual perception through a mechanism transferring in-depth sound motion information onto the relevant in-depth visual dimensions (such as size and luminance but not shape) in a crossmodal remapping of information for a genuine, evolutionary advantage in stimulus detection