Conditioned task-set competition: Neural mechanisms of emotional interference in depression

Aleks Stolicyn, J. Douglas Steele, Peggy Seriès (Lead / Corresponding author)

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    192 Downloads (Pure)

    Abstract

    Depression has been associated with increased response times at the incongruent, neutral, and negative-word trials of the classical and emotional Stroop tasks (Epp et al., 2012). Response time slow-down effects at incongruent and negative-word trials of the Stroop tasks were reported to correlate with depressive severity, indicating strong relevance of the effects to the symptomatology. The current study proposes a novel integrative computational model of neural mechanisms of both the classical and the emotional Stroop effects, drawing on the previous prominent theoretical explanations of performance at the classical Stroop task (Cohen et al., 1990; Herd et al., 2006), and in addition suggesting that negative emotional words represent conditioned stimuli for future negative outcomes. The model is shown to explain the classical Stroop effect and the slow (between-trial) emotional Stroop effect with biologically-plausible mechanisms, providing an advantage over the previous theoretical accounts (Matthews and Harley, 1996; Wyble et al., 2008). Simulation results suggested a candidate mechanism responsible for the pattern of depressive performance at the classical and the emotional Stroop tasks. Hyperactivity of the amygdala, together with increased inhibitory influence of the amygdala over dopaminergic neurotransmission, could be at the origin of the performance deficits.
    Original languageEnglish
    Pages (from-to)269-289
    Number of pages21
    JournalCognitive, Affective, and Behavioral Neuroscience
    Volume17
    Issue number2
    Early online date9 Dec 2016
    DOIs
    Publication statusPublished - Apr 2017

    Keywords

    • Depression
    • Amygdala
    • Computational model
    • Emotion
    • Dopamine
    • Neural network

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