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Individual differences and modulation of conflict monitoring intensity as a determinant of avoidance learning: Evidence for an integrative ACC theory?

Subject Area Personality Psychology, Clinical and Medical Psychology, Methodology
Term from 2008 to 2019
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 98772683
 
Final Report Year 2018

Final Report Abstract

As an extension of prior findings illustrating the modulation of the conflict monitoring intensity by means of required effort and aversive reinforcement we investigated conflict monitoring and passive avoidance learning in the context of recent theories on anterior cingulate cortex (ACC) activity. Till date, the neural processes of conflict monitoring and passive avoidance learning as one form of reinforcement learning have been investigated rather independently. However, as long as it has not yet been investigated whether, as postulated by Botvinick, an intensified conflict monitoring results in an intensified passive avoidance learning during task performance, we cannot conclude that an intensified conflict monitoring serves as an aversive teaching signal in decision-making processes and during passive avoidance learning. Those findings would, however, further the most recent and extensive theoretical debate on the functional relevance of the ACC. In order to enhance the conflict monitoring intensity and subsequent passive avoidance learning we manipulated motivational factors in Study 1. We expected that an increased conflict monitoring intensity induced by means of an individuals effort motivation intensifies passive avoidance learning. A total of 133 individuals were recruited at the University of Bonn and took part in Study 1 voluntarily. Participants with less than seven artefact free epochs in one of the analyzed categories were dropped, leaving a final sample of 120 subjects. The results reveal that learning and numerical reasoning are related to conflict monitoring whereas an enhancement of conflict monitoring by means of motivational cues was not found. Moreover, higher numerical reasoning scores were associated with a more intense conflict monitoring (i.e., more negative nogo N2) for motivating words than for neutral words. If an intensified conflict monitoring and subsequently an intensified passive avoidance learning are of importance for every-day contexts the effect of intensified conflict monitoring on avoidance learning should generalize to discrimination tasks that are embedded into everyday contexts. Accordingly, in Study 2, participants performed a go/nogo learning task that was based on a vignette describing a more natural (working-related) context. Study 2 investigated individual differences of conflict monitoring, feedback processing, and behavioral adaptation in a go/nogo learning task based on faces. The induction of conflict monitoring and reinforcement learning by means of a vignette activating a specific context and a subsequent go/nogo learning task based on faces is a new approach. Moreover, the presence of peers was manipulated in order to investigate the intensification of conflict monitoring and thereby reinforcement learning. We observed the expected more intense conflict monitoring (i.e., more negative N2 amplitude) for non-collaborative faces than for collaborative faces especially at fronto-central sites and learning effects especially in the first task block. Our data support prior predictions arguing that teaching signals (embedded go vs. nogo stimuli) trigger the intensification of conflict monitoring. More anxious individuals revealed a more intense conflict monitoring following collaborative faces whereas individuals with higher reasoning scores showed a less intense conflict monitoring. Our data suggest different strategies of cognitive adjustment. In Study 3 we adopted the go/nogo learning task that applied collaborative vs. non-collaborative faces to a forensic setting. We informed participants to learn by trial-anderror who of the individuals presented might have been involved in a capital crime and who of the individuals shown was involved (suspect) and who was not involved (no suspect). The results show a similar pattern of an intensified conflict monitoring during the reinforcement learning process (block 1). Higher anxiety-related scores were associated with more intense conflict monitoring throughout the task. Overall, our data show that across contexts an intensified conflict monitoring triggers reinforcement learning. Individual differences of anxietyrelated traits and reasoning modulate different strategies of cognitive adjustment.

Publications

  • (2019) The N2 component in a go-nogo learning task: Motivation, behavioral activation, and reasoning. International Journal of Psychophysiology 137 1-11
    Scheuble, Vera; Nieden, Katharina; Leue, Anja; Beauducel, André
    (See online at https://doi.org/10.1016/j.ijpsycho.2018.12.012)
  • (2020) Individual differences of conflict monitoring and feedback processing during reinforcement learning in a mock forensic context. Cognitive, Affective, & Behavioral Neuroscience 20( 2) 408-426
    Leue, Anja; Nieden, Katharina; Scheuble, Vera; Beauducel, André
    (See online at https://doi.org/10.3758/s13415-020-00776-7)
 
 

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