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.

The effect of gender stereotypes on cross-modal spatial attention

Using a spatial-cueing paradigm in which trait words were set as visual cues and gender words were set as auditory targets, we examined whether cross-modal spatial attention was influenced by gender stereotypes. Results of an experiment conducted with 24 participants indicate that they tended to focus on targets in the valid-cue condition (i.e., the cues located at the same position as targets), regardless of the modality of cues and targets, which is consistent with the cross-modal attention effect found in previous studies. Participants tended to focus on targets that were stereotype-consistent with cues only when the cues were valid, which shows that stereotype-consistent information facilitated visual–auditory cross-modal spatial attention. These results suggest that cognitive schema, such as gender stereotypes, have an effect on cross-modal spatial attention.

Four dimensions characterize attributions from faces using a representative set of English trait words

People readily (but often inaccurately) attribute traits to others based on faces. While the details of attributions depend on the language available to describe social traits, psychological theories argue that two or three dimensions (such as valence and dominance) summarize social trait attributions from faces. However, prior work has used only a small number of trait words (12 to 18), limiting conclusions to date. In two large-scale, preregistered studies we ask participants to rate 100 faces (obtained from existing face stimuli sets), using a list of 100 English trait words that we derived using deep neural network analysis of words that have been used by other participants in prior studies to describe faces. In study 1 we find that these attributions are best described by four psychological dimensions, which we interpret as “warmth”, “competence”, “femininity”, and “youth”. In study 2 we partially reproduce these four dimensions using the same stimuli among additional participant raters from multiple regions around the world, in both aggregated and individual-level data. These results provide a comprehensive characterization of trait attributions from faces, although we note our conclusions are limited by the scope of our study (in particular we note only white faces and English trait words were included).

The Big Five Personality Trait Factors

The field of dispositional traits of personality is best summarized in terms of five fundamental dimensions: the Big Five personality trait factors, namely Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Intellect. The Big Five find their origin in psycho-lexical work in which the lexicon of a language is scanned for all words that can inform about personality traits. The Big Five factors have emerged most articulately in Indo-European languages in Europe and the United States, and weaker versions have appeared in non-Indo-European languages. The model is most functional and detailed in a format that integrates simple structure and circular representations. Such a format gives the Big Five system great accommodative potential, meaning that many or most of the concepts developed in approaches other than the Big Five can be located in that system, thus enhancing the communication about personality traits in the field. The Big Five model has been applied in virtually all disciplines of psychology, including clinical, social, organizational, and developmental psychology. In particular, the Big Five have been found useful in the field of learning and education where the factor Conscientiousness has been identified as a strong predictor of academic performance, but where other factors of the Big Five also have been demonstrated to play important roles, often in moderating or mediating sense. The Big Five model has faced a number of critical issues, one of which concerns the criteria of inclusion of trait-descriptive words from the lexicon. With relaxed criteria, allowing more than just dispositional trait words (e.g., trait words that are predominantly evaluative in nature), additional dimensions may emerge beyond the Big Five, mostly conveying features of morality. An important issue regards the cross-cultural applicability of trait-descriptive dimensions. With a cross-cultural emphasis, possibly no more than three factors, expressive of traits of Extraversion, Agreeableness, and Conscientiousness, make the best chance for claims of universality. For a good understanding of traits representing the remaining Big Five dimensions, and also dimensions that have sometimes been identified beyond the Big Five, it is not only important to specify their regional applicability, but also to articulate differences in research methodology.

Four dimensions characterizing comprehensive trait judgments of faces

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies across social and developmental psychology, social neuroscience, and engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments.

Exploring Positive Classical Conditioning Procedure Effects on Evaluations of Children, Thoughts About Children, and Behaviors Toward Children: Two Experiments

Exposing parents to a positive classical conditioning (+CC) procedure can (a) prompt positive evaluations of children, (b) alter judgments made about children from their behavior, and (c) reduce harsh behaviors enacted toward children. Two studies explored possible limits of these effects. Results from Study 1 showed that only some +CC effects evinced in prior research emerged when the positive trait words used as the unconditioned stimuli in prior research were replaced with positive emojis. Results from Study 2 showed with positive trait word stimuli that a backward +CC procedure produced many of the same effects produced by the forward +CC procedure. These results collectively support the idea that +CC procedures may simultaneously prompt several different kinds of learning. From a practical perspective, consideration of these various kinds of learning is important to an understanding of when the use of the +CC procedure might reduce child abuse risk.

Classification of Participants Based on Increase–Decrease Rate Model of Reaction Time to Personality Trait Words

In this experiment, we tried to measure personality by reaction time (RT) to stimuli of personality trait words. There were interindividual and intraindividual differences in the factors that caused the reaction time to fluctuate. The intraindividual differences for personality trait words were caused by changes due to circumstances for the same participant. The increased stimulus reaction time (sRT) model for simple reaction time was used as an index to indicate personality traits. As a result, participants could be classified into two major hierarchical clusters. The participants in Cluster 1 showed innovative dominance. The participants in Cluster 2 were obedient and conservative. The independent variable was measured by the physiological index using sRT for classify the participants. Participants in Cluster 2 had a reduced stress response to the experiment and showed a tendency to be compliant. Moreover, immediately after the RT measurement session with a laptop computer started, participants in Cluster 1 showed decreased HEG and increased amylase values and had a somewhat negative attitude. The physiological dependent variable were measured by using salivary amylase and hemoencephalography (HEG). And, the psychological dependent variable was the Big Five personality inventory. All of them ware using to verify the participant’s classification. Participants in Cluster 2 had significantly higher conscientiousness than those in Cluster 1. Therefore, we suggest that it is possible to classify personality traits from RT by using sRT based on intraindividual differences.

Four dimensions characterize comprehensive trait judgments of faces

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive1. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing2,3. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance4, a highly influential framework that has been the basis for numerous studies in social and developmental psychology5–10, social neuroscience11,12, and in engineering applications13,14. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition15 and personality psychology16.