Learning when effort matters: neural dynamics underlying updating and adaptation to changes in performance efficacy

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Decision Science

Background:

• Cognitive control allocation depends on perceived task reward efficacy.
• Understanding how individuals learn performance efficacy in dynamic environments is crucial.

Purpose of the Study:

• To elucidate the neurocomputational mechanisms underlying the learning and updating of performance efficacy expectations.
• To investigate how efficacy expectations proactively influence cognitive control allocation.

Main Methods:

• Combined computational modeling with behavioral (task performance) and neurophysiological (EEG) measures.
• Employed an incentivized cognitive control task with dynamically varying performance efficacy across two studies.

Main Results:

• Individuals update efficacy beliefs based on prediction errors, utilizing neural and computational substrates similar to reward learning.
• Cognitive control allocation is adjusted in accordance with learned efficacy expectations.
• Computational modeling indicated that control adjustments stem from altered information processing, not speed-accuracy trade-offs.

Conclusions:

• Demonstrates a neurocomputational mechanism for learning the value of cognitive control in performance-based reward scenarios.
• Highlights the role of prediction errors in updating efficacy beliefs and guiding adaptive control allocation.
• Provides mechanistic insight into proactive adjustments in cognitive control based on environmental feedback.