Detection of arousal and valence from facial expressions and physiological responses evoked by different types of stressors.

Automatically detecting mental state such as stress from video images of the face could support evaluating stress responses in applicants for high risk jobs or contribute to timely stress detection in challenging operational settings (e.g., aircrew, command center operators). Challenges in automatically estimating mental state include the generalization of models across contexts and across participants. We here aim to create robust models by training them using data from different contexts and including physiological features. Fifty-one participants were exposed to different types of stressors (cognitive, social evaluative and startle) and baseline variants of the stressors. Video, electrocardiogram (ECG), electrodermal activity (EDA) and self-reports (arousal and valence) were recorded. Logistic regression models aimed to classify between high and low arousal and valence across participants, where "high" and "low" were defined relative to the center of the rating scale. Accuracy scores of different models were evaluated: models trained and tested within a specific context (either a baseline or stressor variant of a task), intermediate context (baseline and stressor variant of a task), or general context (all conditions together). Furthermore, for these different model variants, only the video data was included, only the physiological data, or both video and physiological data. We found that all (video, physiological and video-physio) models could successfully distinguish between high- and low-rated arousal and valence, though performance tended to be better for (1) arousal than valence, (2) specific context than intermediate and general contexts, (3) video-physio data than video or physiological data alone. Automatic feature selection resulted in inclusion of 3-20 features, where the models based on video-physio data usually included features from video, ECG and EDA. Still, performance of video-only models approached the performance of video-physio models. Arousal and valence ratings by three experienced human observers scores based on part of the video data did not match with self-reports. In sum, we showed that it is possible to automatically monitor arousal and valence even in relatively general contexts and better than humans can (in the given circumstances), and that non-contact video images of faces capture an important part of the information, which has practical advantages.

Four studies yield limited evidence for prepared (disgust) learning via evaluative conditioning.

Prepared learning accounts suggest that specialized learning mechanisms increase the retention of associations linked to ancestrally-prevalent threats. Few studies have investigated specialized aversion learning for pathogen threats. In four pre-registered studies (N's = 515, 495, 164, 175), we employed an evaluative conditioning procedure to test whether foods (versus non-foods) are more readily associated with negative content associated with pathogens than negative content not associated with pathogens. Participants saw negatively valenced (either pathogen-relevant or -irrelevant), neutral or positively-valenced stimuli paired with meats and plants (in Studies 1 and 2) and with meats and abstract shapes (in Studies 3 and 4). They then evaluated each stimulus explicitly via self-reports (Studies 1-4) and implicitly via an Affect Misattribution Procedure (Studies 3 and 4). Linear mixed models revealed general evaluative conditioning effects, but inconsistent evidence for specialized (implicit or explicit) learning for a food-pathogen association. However, results from a mega-analysis across studies revealed stronger conditioning effects for meats paired with pathogen-relevant negative stimuli than pathogen-irrelevant negative stimuli.

WITHDRAWN: Four studies Yield limited evidence for prepared (disgust) learning via evaluative conditioning.

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.

To monitor adherence to diets and to design and evaluate nutritional interventions, it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries) and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using nonobtrusive technology in daily life would overcome this. Here, we provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. A total of 1328 studies were screened and, after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.

Visual disgust elicitors produce an attentional blink independent of contextual and trait-level pathogen avoidance.

Multiple studies report that disgust-eliciting stimuli are perceived as salient and subsequently capture selective attention. In the current study, we aimed to better understand the nature of temporal attentional biases toward disgust-eliciting stimuli and to investigate the extent to which these biases are sensitive to contextual and trait-level pathogen avoidance motives. Participants (N = 116) performed in an emotional attentional blink task in which task-irrelevant disgust-eliciting, fear-eliciting, or neutral images preceded a target by 200, 500, or 800 ms (i.e., lag 2, 5 and 8, respectively). They did so twice-once while not exposed to an odor and once while exposed to either an odor that elicited disgust or an odor that did not-and completed a measure of disgust sensitivity. Results indicate that disgust-eliciting visual stimuli produced a greater attentional blink than neutral visual stimuli at lag 2 and a greater attentional blink than fear-eliciting visual stimuli at both lag 2 and at lag 5. Neither the odor manipulations nor individual differences measures moderated this effect. We propose that visual attention is engaged for a longer period of time following disgust-eliciting stimuli because covert processes automatically initiate the evaluation of pathogen threats. The fact that state and trait pathogen avoidance do not influence this temporal attentional bias suggests that early attentional processing of pathogen cues is initiated independent from the context in which such cues are perceived. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Why do people vary in disgust?

People vary in the degree to which they experience disgust toward-and, consequently, avoid-cues to pathogens. Prodigious work has measured this variation and observed that it relates to, among other things, personality, psychopathological tendencies, and moral and political sentiments. Less work has sought to generate hypotheses aimed at explaining why this variation exists in the first place, and even less work has evaluated how well data support these hypotheses. In this paper, we present and review the evidence supporting three such proposals. First, researchers have suggested that variability reflects a general tendency to experience anxiety or emotional distress. Second, researchers have suggested that variability arises from parental modelling, with offspring calibrating their pathogen avoidance based on their parents' reactions to pathogen cues. Third, researchers have suggested that individuals calibrate their disgust sensitivity to the parasite stress of the ecology in which they develop. We conclude that none of these hypotheses is supported by existing data, and we propose directions for future research aimed at better understanding this variation.This article is part of the Theo Murphy meeting issue 'Evolution of pathogen and parasite avoidance behaviours'.