PROJECT SUMMARY / ABSTRACT
This proposal investigates in the nonhuman primate how attentional load changes the behavioral and neural
strategies for flexibly learning object relevance. High attentional load characterizes real-world learning scenarios
with multiple, multidimensional objects. Evidence suggests that the neural mechanisms underlying learning
during high attentional load fundamentally differs from neural mechanisms used to learn under low load. Our
proposal elucidates how learning at increasing attentional load (1) changes the cognitive subcomponent
processes used to succeed learning, (2) changes which brain areas are used to flexibly learn, and (3) recruits
additional neural circuit mechanisms to realize fast adjustments.
First, we will address the specific behavioral subcomponent processes used for learning the relevance of objects
in environments with increasing number of visual feature dimensions reflecting increasing attentional load.
Simple learning can be achieved efficiently with a hybrid mechanism that uses working memory (WM) of recently
rewarded objects to guide future choices together with slower reinforcement learning (RL) for updating longer-
term value expectations. When attentional load increases working memory breaks down, and efficient learners
flexibly adjust their exploration rates and attentional prioritization to speed up reinforcement learning. Our
proposal quantifies these changing learning strategies with multi-component WM-RL modeling.
Second, while subjects learn with varying strategies which features to use for making a decision, we will test the
causal role of three brain regions implicated to realize the respective learning mechanisms. We use transcranial
focused ultrasound stimulation to induce transient, fully reversible lesions allowing to functionally disrupt confined
neuronal ensembles. With this tool we elucidate the hypothesized contributions of ventrolateral prefrontal cortex
to learning using fast working memory of rewarded objects, the contribution of the anterior cingulate cortex in
adjusting exploration strategies and the contribution of the anterior striatum for attentional biasing of slower
reinforcement learning of the highest reward-value object within a complex, multidimensional feature space.
Third, our project elucidates how the local circuits in each of the three brain areas contribute to successful
learning with varying strategies. We use massively parallel recordings of single neuron activity in ventrolateral
prefrontal cortex, anterior cingulate cortex, and anterior striatum to extract those cell classes whose firing
encodes the key learning variables. We expect that subclasses of interneurons maximally correlate their firing
only during those periods when the area specific learning strategy is realized. This approach pinpoints the cell
classes that maximally correlate with choice probabilities, prediction errors, working memory, and exploration
rates when subjects adjust their learning strategies to successfully learn the relevance of objects with real-world