Automated facial expression measurement in a longitudinal sample of 4- and 8-month-olds: Baby FaceReader 9 and manual coding of affective expressions.

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9's global emotional valence formula yielded reasonable classification accuracy (AUC = .81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC = .58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC = .86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC = .79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas.

From movement to action: An EEG study into the emerging sense of agency in early infancy.

Research into the developing sense of agency has traditionally focused on sensitivity to sensorimotor contingencies, but whether this implies the presence of a causal action-effect model has recently been called into question. Here, we investigated whether 3- to 4.5-month-old infants build causal action-effect models by focusing on behavioral and neural measures of violation of expectation. Infants had time to explore the causal link between their movements and audiovisual effects before the action-effect contingency was discontinued. We tested their ability to predict the consequences of their movements and recorded neural (EEG) and movement measures. If infants built a causal action-effect model, we expected to observe their violation of expectation in the form of a mismatch negativity (MMN) in the EEG and an extinction burst in their movement behavior after discontinuing the action-effect contingency. Our findings show that the group of infants who showed an MMN upon cessation of the contingent effect demonstrated a more pronounced limb-specific behavioral extinction burst, indicating a causal action-effect model, compared to the group of infants who did not show an MMN. These findings reveal that, in contrast to previous claims, the sense of agency is only beginning to emerge at this age.

Cognitive strategies revealed by clustering eye movement transitions.

In cognitive tasks, solvers can adopt different strategies to process information which may lead to different response behavior. These strategies might elicit different eye movement patterns which can thus provide substantial information about the strategy a person uses. However, these strategies are usually hidden and need to be inferred from the data. After an overview of existing techniques which use eye movement data for the identification of latent cognitive strategies, we present a relatively easy to apply unsuper-vised method to cluster eye movement recordings to detect groups of different solution processes that are applied in solving the task. We test the method's performance using simulations and demonstrate its use on two examples of empirical data. Our analyses are in line with presence of different solving strategies in a Mastermind game, and suggest new insights to strategic patterns in solving Progressive matrices tasks.