The Role of Trait Inferences in Evaluative Conditioning

Evaluative Conditioning (EC) effect is a change in evaluative responding to a neutral stimulus (CS) due to its pairing with a valenced stimulus (US). Traditionally, EC effects are viewed as fundamentally different from per-suasion effects. Inspired by a propositional perspective to EC, four studies (N = 1,284) tested if, like persuasion effects, EC effects can also be driven by trait inferences. Experiments 1-2 found that promoting trait inferences (by pairing people with trait words rather than nouns) increased EC effects. Experiments 3-4 found that under-mining trait inferences (by questioning the validity of those inferences) decreased EC effects. In all experiments, however, EC effects were still significant when trait inferences were invalid. Taken together, our findings (a) suggest that trait inferences can play an important role in EC effects, (b) constrain theoretical models of EC, and (c) have important implications for applied EC interventions.

Facial Expressions and Emotion Labels Are Separate Initiators of Trait Inferences From the Face

Facial inferencing research began with an inadvertent confound. The initial work by Paul Ekman and Wallace Friesen identified the six now-classic facial expressions by the emotion labels chosen by most participants: anger, disgust, fear, happiness, sadness, and surprise. These labels have been used by most of the published facial inference research studies over the last 50 years. However, not all participants in these studies labeled the expressions with the same emotions. For example, that some participants labeled scowling faces as disgusted rather than angry was seen in very early research by Silvan Tomkins and Robert McCarty. Given that the same facial expressions can be paired with different emotions, our research focused on the following questions: Do participants make different personality, temperament, and social trait inferences when assigning different emotion labels to the same facial expression? And what is the stronger cause of trait inferences, the facial expressions themselves, or the emotion labels given to the expressions? Using an online survey format participants were presented with older and younger female and male smiling or scowling faces selected from a validated facial database. Participants responded to questions regarding the social traits of attractiveness, facial maturity, honesty, and threat potential, the temperament traits of positiveness, dominance, excitability, and the Saucier Mini-marker Big Five personality trait adjective scale, while viewing each face. Participants made positive inferences to smiling faces and negative inferences to scowling faces on all dependent variables. Data from participants labeling the scowling faces as angry were compared to those who labeled the faces as disgusted. Results indicate that those labeling the scowling faces as angry perceived the faces significantly more negatively on 11 of the 12 dependent variables than those who labeled the same faces as disgusted. The inferences made by the “disgust” labelers were not positive; just less negative. The results indicate that different emotion labels made to scowling faces can either intensify or reduce negativity in inferences, but the facial expressions themselves determine negativity or positivity.

Emotional authenticity modulates affective and social trait inferences from voices

The human voice is a primary tool for verbal and nonverbal communication. Studies on laughter emphasize a distinction between spontaneous laughter, which reflects a genuinely felt emotion, and volitional laughter, associated with more intentional communicative acts. Listeners can reliably differentiate the two. It remains unclear, however, if they can detect authenticity in other vocalizations, and whether authenticity determines the affective and social impressions that we form about others. Here, 137 participants listened to laughs and cries that could be spontaneous or volitional and rated them on authenticity, valence, arousal, trustworthiness and dominance. Bayesian mixed models indicated that listeners detect authenticity similarly well in laughter and crying. Speakers were also perceived to be more trustworthy, and in a higher arousal state, when their laughs and cries were spontaneous. Moreover, spontaneous laughs were evaluated as more positive than volitional ones, and we found that the same acoustic features predicted perceived authenticity and trustworthiness in laughter: high pitch, spectral variability and less voicing. For crying, associations between acoustic features and ratings were less reliable. These findings indicate that emotional authenticity shapes affective and social trait inferences from voices, and that the ability to detect authenticity in vocalizations is not limited to laughter. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.

Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.

I Need a Doctor, Call Me a Doctor: Attachment and the Evaluation of General Practitioners before and during the COVID-19 Pandemic

Attachment is a system of threat regulation, and insecure (anxious and avoidant) attachment orientations are important individual difference antecedents to the cognitive and affective attributions of trait inferences. However, little is known about how threat-related contexts, such as the current COVID-19 pandemic, influence attachment-related socio-cognitive schemas. Using an experimental research design across two independent samples of 330 (pre-onset of COVID-19) and 233 (post-onset of COVID-19) participants, we tested whether attachment orientations influenced general practitioner (GP) ratings and selection differently pre- and post-onset of the COVID-19 pandemic. We found that during the COVID-19 pandemic, when presented with only negative information signals, avoidant individuals attributed positive ratings to GPs, with differing ratings as the number of positive signals increased. Differences between pre- and post-onset of the COVID-19 pandemic were less pronounced with regards to positive signals. We discuss these results in line with signal detection theory (SDT) and provide practical implications in response to our findings.

Emotional authenticity modulates affective and social trait inferences from voices

The human voice is a primary tool for verbal and nonverbal communication. Studies on laughter emphasize a distinction between spontaneous laughter, which reflects a genuinely felt emotion, and volitional laughter, associated with more intentional communicative acts. Listeners can reliably differentiate the two. It remains unclear, however, if they can detect authenticity in other vocalizations, and whether authenticity determines the affective and social impressions that we form about others. Here, 137 participants listened to laughs and cries that could be spontaneous or volitional, and rated them on authenticity, valence, arousal, trustworthiness, and dominance. Bayesian mixed models indicated that listeners detect authenticity similarly well in laughter and crying. Speakers were also perceived to be more trustworthy, and in a higher arousal state, when their laughs and cries were spontaneous. Moreover, spontaneous laughs were evaluated as more positive than volitional ones, and we found that the same acoustic features predicted perceived authenticity and trustworthiness in laughter: high pitch, spectral variability, and less voicing. For crying, associations between acoustic features and ratings were less reliable. These findings indicate that emotional authenticity shapes affective and social trait inferences from voices, and that the ability to detect authenticity in vocalizations is not limited to laughter.

Easy to Make, Hard to Revise: Updating Spontaneous Trait Inferences in the Presence of Trait-Inconsistent Information

Previous research has shown that perceivers spontaneously form trait inferences from others' behaviors received at a single point in time. The present work examined the persistence of spontaneous trait inferences (STIs) in the presence of trait-inconsistent information about others. We hypothesized that STIs should be resistant to change over time and in the presence of new trait-inconsistent information due to perceivers forming and storing multiple STIs independently in memory. Consistently, Experiments 1a and 1b showed that initial STIs were not affected by new trait-inconsistent information. Experiments 2 and 3 revealed that STIs were persistent over 48 hours. Two experiments also tested memory reconsolidation as a possible mechanism of updating first impressions. While STIs were not substantially affected, spontaneous goal inferences (SGIs) were elevated among those with a better explicit memory of behaviors after learning trait-inconsistent information following a memory reactivation procedure. Implications of these findings on impression formation and updating processes are discussed.

Psychological trait inferences from women’s clothing: human and machine prediction

People use clothing to make personality inferences about others, and these inferences steer social behaviors. The current work makes four contributions to the measurement and prediction of clothing-based person perception: first, we integrate published research and open-ended responses to identify common psychological inferences made from clothes (Study 1). We find that people use clothes to make inferences about happiness, sexual interest, intelligence, trustworthiness, and confidence. Second, we examine consensus (i.e., interrater agreement) for clothing-based inferences (Study 2). We observe that characteristics of the inferring observer contribute more to the drawn inferences than the observed clothes, which entails low to medium levels of interrater agreement. Third, the current work examines whether a computer vision model can use image properties (i.e., pixels alone) to replicate human inferences (Study 3). While our best model outperforms a single human rater, its absolute performance falls short of reliability conventions in psychological research. Finally, we introduce a large database of clothing images with psychological labels and demonstrate its use for exploration and replication of psychological research. The database consists of 5000 images of (western) women’s clothing items with psychological inferences annotated by 25 participants per clothing item.