Expertise-dependent individualization of faces of one’s own and another visual phenotype and resulting generalization of threat associations: An ERP-Study

Reduced individuation of other-ethnicity faces (outgroup homogeneity effect) results from overlapping neural representations due to low perceptual expertise. While established expertise for own-ethnicity faces leads to distinct neural representations and therefore lower repetition suppression between consecutive perceptions of different faces than of the same face, this distinction was shown to be absent with other-ethnicity faces. Here we attempt to replicate this result for the N170 repetition suppression. Additionally, to study psychological consequences, we ask if reduced individuation on a perceptual and neural level leads to the generalization of associations over members of a different ethnicity. Specifically, we investigate if the threat association of a single other-ethnicity identity results in pronounced LPP amplitudes elicited by different other-ethnicity faces not associated with threat

Revisiting the electrophysiological correlates of valence and expectancy in reward processing – A Multi-lab replication

Two event-related brain potential (ERP) components elicited during feedback processing are the frontocentral feedback-related negativity (FRN), followed by the posterior P300. According to the Error-Related Reinforcement Learning Theory (Holroyd & Coles, 2002), the FRN amplitude is largest when the outcome is negative and unexpected. Complementing this, studies on the subsequent P300 have often reported larger amplitudes for positive than negative outcomes. In an influential ERP study, Hajcak et al., (2005) manipulated outcome valence and expectancy in a guessing task. However, they found that the FRN component was larger for negative (no-reward) than positive (reward) outcomes, irrespective of expectancy. Conversely, the P300 component was larger for unexpected than expected outcomes, irrespective of valence. These results were at odds with prominent theories and extant literature. Here, we aim to replicate these results within the #EEGManyLabs project (Pavlov et al., 2021). Across thirteen labs we will not only undertake a close replication, but test the robustness of these effects to analytical choices (e.g. quantification of ERPs) and supplement the findings with Bayesian multilevel linear models to test for the reported absence of the effects