Investigating the Emotion-Cognition Interaction: Effects of Affective Distractors on Working Memory Load

Abstract

In the past decade, theoretical models of modular architectures with cold cognitive and hot affective-emotional systems have been progressively revised [1-3]. Nowadays, these mechanisms are suggested to be interwoven [4-5] and even processed in shared underlying neurocircuitry (e.g., [1,3]). Particularly in naturalistic environments, we are confronted with complex, (socio-)emotional stimuli claiming attentional and working memory resources (e.g., a crying baby during home office or laughter in open-plan offices). However, the precise nature of emotion-cognition interactions is still subject to research [5-8]. Previous studies revealed detrimental effects of emotional distraction on cognitive processes [9-11] with strongest interference when cognitive load is low and distractors’ valence deviates from neutral [1,12]. Electroencephalography (EEG) is a technique that provides separable brain correlates for emotional and cognitive states. EEG research suggested the frontal alpha asymmetry (FAA) as a suitable correlate indicating emotional states [13-15] and the ratio of frontal theta (4 – 7 Hz) and parietal alpha (8 – 12 Hz) power to index cognitive load (workload (WL); [16-17]). Here, we investigate whether these correlates can capture interactions between cognitive control and affective-emotional distraction processes. More precisely, we are interested in how auditory distractors and their affective valence influence neurophysiological indices associated with valence and cognitive load (here working memory load, WML). We assume stronger detrimental effects (i) under low WML because of sufficient available resources to process emotional distractors fully, and (ii) for (potentially harming) stimuli with low valence due to a higher salience and relevance (cf., [1,18]).

M.Sc. Katharina Lingelbach

PhD student, Neuroscientist, and Psychologist (she/her/hers)

My research focuses on interacting neuronal dynamics of emotional and cognitive processes, decoding approaches of neuronal dynamics using machine learning using electrophysiology and neuroimaging methods