Error processing mechanisms in evaluative concerns perfectionists: Effects of feedback presentation
Tue—Casino_1.811—Poster2—5803
Presented by: André Mattes
A key dimension of perfectionism is evaluative concerns perfectionism (ECP), i.e. the tendency to strive for flawlessness out of fear of being judged negatively by others if performance is flawed. Previous studies have shown that despite their preoccupation with errors, high-EC perfectionists show less error-specific neural activity. Two accounts have been proposed to explain this finding. The avoidance account posits a motivational lack of error processing and postulates that high-EC perfectionists avoid processing their errors in an attempt to avoid the negative consequences associated with errors. The capacity account posits a structural lack of error processing, claiming that high-EC perfectionists spend many of their cognitive resources on error-related content, such as worrying about errors, leaving fewer resources for error processing. Distinguishing between these accounts is challenging, as they predict similar outcomes. To address this, we developed an experimental design that allowed us to disentangle the two accounts. Participants completed an Eriksen flanker task. In one part of the experiment, they received no feedback on their performance. In the other part of the experiment, participants were continuously confronted with their current performance throughout the entire experimental block. The avoidance hypothesis predicts increased error processing in the feedback condition for high-EC perfectionists, as avoiding error processing is no longer advantageous. Conversely, the capacity hypothesis predicts unaffected or reduced error processing due to increased allocation of cognitive resources to performance feedback. We will present initial results from this ongoing study to elucidate the underlying mechanisms of impaired error processing in high-EC perfectionists.
Keywords: error processing, cognitive control, perfectionism, error-related negativity, drift diffusion model, computational modelling, cognitive neuroscience