First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Toward a social psychophysics of face communication

As a highly social species, humans are equipped with a powerful tool for social communication—the face, which can elicit multiple social perceptions in others due to the rich and complex variations of its movements, morphology, and complexion. Consequently, identifying precisely what face information elicits different social perceptions is a complex empirical challenge that has largely remained beyond the reach of traditional research methods. More recently, the emerging field of social psychophysics has developed new methods designed to address this challenge. Here, we introduce and review the foundational methodological developments of social psychophysics, present recent work that has advanced our understanding of the face as a tool for social communication, and discuss the main challenges that lie ahead

How Troublesome are Stereotypes in International Business?

Substantial concern has been raised in international business writing that national stereotypes bias perception of employees, customers, and others. That concern is certainly supported by findings in person perception research. But some constraints of that research, such as the provision of incomplete information and uninteresting stimuli may well have caused an overestimation of the impact of stereotypes in business situations. This research shows that the impact of stereotypes is likely less than previously thought. When current diagnostic information is available, that information is used, leading to unbiased assessments. Only when information is limited are stereotype-biased judgments generated. A second experiment further shows that people feel more confident in assessments based on current information than in those where information is limited. These relatively optimistic findings suggest methods that managers can use to overcome national stereotype bias in international business situations

Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

The influence of film music on moral judgments of movie scenes and felt emotions

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Music can modulate perceptions, actions, and judgments in everyday situations. The aim of this study was to investigate a potential influence of music on moral judgments in the context of film reception. In the course of an online experiment, 252 participants were assigned to three different experimental conditions (no, positive, or negative music). Participants were requested to assess actions shown in two 2–3-minute audio-visual film excerpts with regard to their perceived moral rightness and to report induced emotions after watching the film clips. Afterwards, they were asked to complete the MFQ-30 questionnaire measuring the foundations of their moral judgments. Results revealed that in one of four cases (i.e. happiness in film excerpt 1), music had a significant effect on recipients’ emotions and also indirectly influenced their moral judgment. In three of four cases, however, the intended emotion induction through film music did not succeed, and thus a significant indirect influence of music on moral judgment was not found. Furthermore, associations between moral foundations, perceived rightness of action, and induced emotions were observed. Future lab studies are indicated to investigate potential moderating influences of the experimental environment on emotion induction through film music

A general model of fluency effects in judgment and decision making

Processing or cognitive fluency is the experienced ease of ongoing mental processes. This experience infl uences a wide range of judgments and decisions. We present a general model for these fluency effects. Based on Brunswik’s lens-model, we conceptualize fluency as a meta-cognitive cue. For the cue to impact judgments, we propose three process steps: people must experience fluency; the experience must be attributed to a judgment-relevant source; and it must be interpreted within the judgment context. This interpretation is either based on available theories about the experience’s meaning or on the learned validity of the cue in the given context. With these steps the model explains most fl uency effects and allows for new and testable predictions

Reach and speed of judgment propagation in the laboratory

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce a novel experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor's judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people's errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory

Judgments of effort exerted by others are influenced by received rewards

Estimating invested effort is a core dimension for evaluating own and others’ actions, and views on the relationship between effort and rewards are deeply ingrained in various societal attitudes. Internal representations of effort, however, are inherently noisy, e.g. due to the variability of sensorimotor and visceral responses to physical exertion. The uncertainty in effort judgments is further aggravated when there is no direct access to the internal representations of exertion – such as when estimating the effort of another person. Bayesian cue integration suggests that this uncertainty can be resolved by incorporating additional cues that are predictive of effort, e.g. received rewards. We hypothesized that judgments about the effort spent on a task will be influenced by the magnitude of received rewards. Additionally, we surmised that such influence might further depend on individual beliefs regarding the relationship between hard work and prosperity, as exemplified by a conservative work ethic. To test these predictions, participants performed an effortful task interleaved with a partner and were informed about the obtained reward before rating either their own or the partner’s effort. We show that higher rewards led to higher estimations of exerted effort in self-judgments, and this effect was even more pronounced for other-judgments. In both types of judgment, computational modelling revealed that reward information and sensorimotor markers of exertion were combined in a Bayes-optimal manner in order to reduce uncertainty. Remarkably, the extent to which rewards influenced effort judgments was associated with conservative world-views, indicating links between this phenomenon and general beliefs about the relationship between effort and earnings in society

The simultaneous extraction of multiple social categories from unfamiliar faces

The research was supported by an award from the Experimental Psychology Society's Small Grant scheme.Peer reviewedPostprin

Getting to know you: Accuracy and error in judgments of character

Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate