Humans readily form social impressions, such as attractiveness and trustworthiness, from a stranger’s facial features. Understanding the provenance of these impressions has clear scientific importance and societal implications. Motivated by the efficient coding hypothesis of brain representation, as well as Claude Shannon’s theoretical result that maximally efficient representational systems assign shorter codes to statistically more typical data (quantified as log likelihood), we suggest that social “liking” of faces increases with statistical typicality. Combining human behavioral data and computational modeling, we show that perceived attractiveness, trustworthiness, dominance, and valence of a face image linearly increase with its statistical typicality (log likelihood). We also show that statistical typicality can at least partially explain the role of symmetry in attractiveness perception. Additionally, by assuming that the brain focuses on a task-relevant subset of facial features and assessing log likelihood of a face using those features, our model can explain the “ugliness-in-averageness” effect found in social psychology, whereby otherwise attractive, intercategory faces diminish in attractiveness during a categorization task.

Reverse correlation is an influential psychophysical paradigm that uses participant’s responses to randomly varying images to build a Classification Image (CI), which is commonly interpreted as a visualization of a participant’s mental representation. It is unclear, however, how to statistically quantify the amount of signal present in CIs, which limits the interpretability of these images. In this paper, we propose a novel metric, infoVal, which assess informational value relative to a resampled random distribution, and can be interpreted as a z-score. In the first part, we define the infoVal metric and show, through simulations, that it adheres to typical Type I error-rates rated under various task conditions (internal validity). In the second part, we show that the metric correlates with markers of data quality in empirical RC data, such as the subjective recognizability, objective discriminability and test-retest reliability of CIs (convergent validity). In the final part, we demonstrate how the infoVal metric can be used to compare the informational value of reverse correlation datasets, comparing data acquired online versus data acquired in a controlled lab environment. We recommend a new standard of good practice where researchers assess infoVal scores of reverse correlation data to ensure that they do not read signal in CIs, where no signal is present. The infoVal metric is implemented in the open source rcicr R-package to facilitate its adoption. This work has now been published in Behavior Research Methods, and can be found here: https://link.springer.com/article/10.3758%2Fs13428-019-01232-2

Reverse correlation is an influential psychophysical paradigm that uses a participant’s responses to randomly varying images to build a classification image (CI), which is commonly interpreted as a visualization of the participant’s mental representation. It is unclear, however, how to statistically quantify the amount of signal present in CIs, which limits the interpretability of these images. In this article, we propose a novel metric, infoVal, which assesses informational value relative to a resampled random distribution and can be interpreted like a z score. In the first part, we define the infoVal metric and show, through simulations, that it adheres to typical Type I error rates under various task conditions (internal validity). In the second part, we show that the metric correlates with markers of data quality in empirical reverse-correlation data, such as the subjective recognizability, objective discriminability, and test–retest reliability of the CIs (convergent validity). In the final part, we demonstrate how the infoVal metric can be used to compare the informational value of reverse-correlation datasets, by comparing data acquired online with data acquired in a controlled lab environment. We recommend a new standard of good practice in which researchers assess the infoVal scores of reverse-correlation data in order to ensure that they do not read signal in CIs where no signal is present. The infoVal metric is implemented in the open-source rcicr R package, to facilitate its adoption.Electronic supplementary materialThe online version of this article (10.3758/s13428-019-01232-2) contains supplementary material, which is available to authorized users.

Summary Objective The goal was to conduct exploratory analysis to determine if executive functions (EFs) and food responsiveness/satiety responsiveness (appetitive behaviours that describe one's tendency to eat in the presence of food or food cues) interact to influence weight status among preschool children participating in a trial promoting self‐regulation around energy‐dense foods. Methods At baseline, parents completed the Behaviour Rating Inventory of Executive Function‐Preschool and the Child Eating Behaviour Questionnaire. Children completed anthropometric measurements at the preschool. Spearman's correlation, linear regression, and tests of interaction were conducted. The relationship between weight status and EFs among those who were high vs low in food responsiveness and satiety responsiveness was examined. Results Children (n = 92) had a mean age of 5.1 years and body mass index (BMI) percentile of 57.6; half (54%) were male. There were significant correlations between food responsiveness and several EFs (emotional control, inhibitory control, working memory, and plan/organize). In the stratified analysis, children with high food responsiveness or low satiety responsiveness had higher BMI percentiles as emotional control skills worsened. BMI percentiles were not elevated among children with low food responsiveness and poor emotional control. Conclusion These results suggest that EFs may be more relevant to weight status if preschool children had high levels of food responsiveness or low levels of satiety responsiveness (ie, increased tendency to be influenced by environmental food cues). This analysis should be replicated with direct measures of executive function and appetitive behaviours in larger samples of young children to examine longitudinal impact on weight status.