Seth Pollak, PhD – Slide of the Week

Seth Pollak Slide of the Week

Title: Probabilistic Learning of Emotion Categories

Legend: Experiment 1 testing phase: Model predictions and participant-level data. Lines are point estimates from logistic mixed-effects models with the three-way interaction between Age Group, Condition (dummy coded for the sake of graphing), and Percent Anger, and all lower-order effects. Error bands represent standard errors of the point estimates. Points are individual participants’ proportion of “upset” responses at a given morph value. Vertical lines indicate the implied category boundary for each condition. Horizontal bars at the top indicate the range of morphed images to which participants in each of the three conditions were exposed. See the online article for the color version of this figure.

Citation: Plate RC, Wood A, Woodard K, Pollak SD. (2018). Probabilistic learning of emotion categories. Journal of Experimental Psychology General. 2018 Dec 20. doi: 10.1037/xge0000529.

Abstract: Although the configurations of facial muscles that humans perceive vary continuously, we often represent emotions as categories. This suggests that, as in other domains of categorical perception such as speech and color perception, humans become attuned to features of emotion cues that map onto meaningful thresholds for these signals given their environments. However, little is known about the learning processes underlying the representation of these salient social signals. In Experiment 1 we test the role of statistical distributions of facial cues in the maintenance of an emotion category in both children (6–8 years old) and adults (18–22 years old). Children and adults learned the boundary between neutral and angry when provided with explicit feedback (supervised learning). However, after we exposed participants to different statistical distributions of facial cues, they rapidly shifted their category boundaries for each emotion during a testing phase. In Experiments 2 and 3, we replicated this finding and also tested the extent to which learners are able to track statistical distributions for multiple actors. Not only did participants form actor-specific categories, but the distributions of facial cues also influenced participants’ trait judgments about the actors. Taken together, these data are consistent with the view that the way humans construe emotion (in this case, anger) is not only flexible, but reflects complex learning about the distributions of the myriad cues individuals experience in their social environments.

About the Lab: 

Research projects in The Child Emotion lab are focused upon children’s emotional development and the relationship between early emotional experience and child psychopathology. We are particularly interested in understanding two related aspects of emotional development:

What are the mechanisms of normal emotional development?
To what extent are emotions shaped by nature and nurture?
Does it make sense to try and separate biology and experience?
How are emotions related to the development of psychopathology in children?
Might the development of emotional processes help explain the link between people’s early experiences and later development of psychological difficulties?

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