Trial-by-trial adaptation of decision making performance: a model-based EEG analysis


Theoretical Background Whenever we engage in a task, it is crucial we monitor our performance to make sure that it does not decline. When it does decline, the performance monitoring system takes action to remedy that, e.g., by slowing down responding (Laming, 1979; Rabbitt, 1966). Botvinick, Braver, Barch, Carter, and Cohen (2001) proposed that this slowing of response times (RTs) reflected conflict that stirred a performance monitoring system to action. There exist various decision systems that adapt performance (Daw, Niv, & Dayan, 2005), each of which have been associated with specific neural correlates. Some of the behavioral adjustment is thought to be implemented by the medial frontal cortex (MFC), thought to implement reinforcement learning mechanisms for behavioral adjustment (Cohen & Ranganath, 2007). This is contrasted to the striatum that is thought to implement rule-based behavioral adjustments. Activity of the MFC in EEG (electroencephalography) is typically associated with two components: the error-related negativity (ERN) in the first 100 ms after a response, and the feedback-related negativity (FRN) about 200–400 ms post-response. Both components are more negative after errors when compared to correct trials, and opinions differ about what cognitive processes they reflect. The ERN covaries with individual differences in personality traits, e.g., with a participants' tendency to learn more from negative than from positive feedback (Frank, D'Lauro, & Curran, 2007). The FRN covaries with valence of the feedback but not so much with its magnitude (Hajcak, Moser, Holroyd, & Simons, 2006), although this has been disputed (Bellebaum & Daum, 2008). The ERN and FRN have both been related to reinforcement learning (RL) models of learning in decision making, and are thought to reflect prediction errors. Although good at describing across-trial dynamics, these RL models do not describe within-trial dynamics. Trial-by-Trial Adaptation of Decision Making Performance -A Model-Based EEG Analysis (PDF Download Available). Available from: [accessed Nov 07 2017].


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