Cognition is context-sensitive, as the same sensory information is processed differently depending on its context (e.g., on its probabilistic association with goal-directed actions and their outcomes). Despite this, the concept of context in studies of higher-order cognitive processes, like cognitive control, is often simplified to nominal stimulus categories (like a target vs. distractor). Here we propose that quantifying contextual information to model cognitive demands is (1) fruitful as it c…
Read moreCognition is context-sensitive, as the same sensory information is processed differently depending on its context (e.g., on its probabilistic association with goal-directed actions and their outcomes). Despite this, the concept of context in studies of higher-order cognitive processes, like cognitive control, is often simplified to nominal stimulus categories (like a target vs. distractor). Here we propose that quantifying contextual information to model cognitive demands is (1) fruitful as it can provide novel insight into the nature of cognitive control, (2) accessible as simple probability models offer a tool for quantifying context and (3) analytically useful as a fine-grained quantification of context can be entered into more complex models as regressors that allow a direct way of appropriately exploring many of the defining tenants of cognitive control (e.g., rapidly changing trial-by-trial neural dynamics). Here, we briefly describe the logic of using context-sensitive measures of cognitive control and highlight recent studies from this approach using simple information theory metrics.
