Learning Context-Sensitive Control

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2021

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Abstract

Cognitive control refers to the use of internal goals to guide how we process stimuli and select responses, and control can be applied proactively (in anticipation of a stimulus) or reactively (once that stimulus has been presented). The application of control can be guided by memory (“control-learning”); for instance, people typically learn to adjust their level of attentional selectivity to changing task statistics, such as different frequencies of hard and easy trials on attention-demanding tasks. This type of control-learning is highly adaptive, but its boundary conditions are not well understood. The aim of this dissertation, therefore, is to examine three core principles thought to underlie control-learning: its context-sensitivity, reward-sensitivity, and implicit nature. Two chapters use standard control-learning paradigms that manipulate the proportion of (easy) Stroop congruent trials within blocks and for specific stimuli. Here, I show that people can learn to generalize learned control across related contexts and that reinforcement may selectively enhance the recruitment of control in some specific contexts, if at all. The last chapter deploys a precuing paradigm to test whether conscious cue perception and knowledge impacts the recruitment of control, complementing the previous chapters with a causal manipulation of explicit awareness and showing that people make control adjustments most easily when they can consciously perceive and are aware of upcoming control-demand. These results have important implications for experimental designs, potential psychiatric treatment, and theoretical accounts of the mechanisms underlying control-learning. My findings will broaden our understanding of the relationship between attention and memory and add insight into how people can learn to flexibly adapt their processing strategies to changing demands in a context-dependent manner.

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Bejjani, Christina (2021). Learning Context-Sensitive Control. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/22974.

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