Hierarchical control of procedural and declarative category-learning systems

Abstract

Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum—in coordination with the DMN—serves a critical role in passing control between procedural and declarative memory systems.

Unique motor plans facilitate learning during task switching, but at the expense of greater switch costs

“…To the degree that predictions can be derived from them, they would seem to predict that switching occurs often, and without cost to accuracy or RTs attributable to the switching mechanism itself. The first prediction is at odds with the observation here and in previous reports that task switching in category learning is very difficult, even after substantial practice (Crossley et al, 2018;Erickson, 2008;Turner et al, 2017), and the second prediction is at odds with an enormous literature reliably indicating switch costs are due to task switching (Kiesel et al, 2010;Monsell, 2003). Therefore, our results seem to suggest that the trial-by-trial switching assumptions made by current category-learning theories might need some revision.…”

“…These studies reveal that task switching is costly -for example, switch trials reliably increase response times (RTs) and often decrease accuracy relative to stay trials. Many factors are known to influence switch costs, including the number and identity of response options Philipp et al, 2013), the complexity of the stimuli (Witt & Stevens, 2013), the abstractness of the rules (Stelzel et al, 2011), the perceptual and attentional demands of the component tasks (Arrington et al, 2003;Chiu & Yantis, 2009;Nagahama et al, 2001;Ravizza & Carter, 2008;Rushworth et al, 2002), and the underlying memory systems supporting performance of each task (Crossley et al, 2018;Turner et al, 2017). The task-switching literature, however, has mostly focused on switching between tasks that are well-learned and can be performed with high accuracy when in a single-task context.…”

Developmental differences in brain functional connectivity during social interaction in middle childhood

“…We generated individual beta estimates for each trial following the estimation approach recommended by Mumford et al (2012) to gain trial-specific activation estimates, similar to previous studies (e.g., Cisler et al, 2014 ; Ray et al, 2017 ; Turner et al, 2017 ). First, we used AFNI’s 3dDeconvolve to construct the design matrix.…”

Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning

“…For example, a well‐known variable that increasingly favors engagement of procedural memory over declarative memory is the amount of repeated exposure to stimuli (e.g., Henke, 2010; Packard & Goodman, 2013). Increasing reliance on procedural processing in these conditions has been associated with a corresponding weaker engagement of the declarative learning route (possibly due to neural inhibitory mechanisms; see, e.g., Poldrack et al., 2001; Turner, Crossley, & Ashby, 2017; Ullman, 2015). In a related study, Morgan‐Short et al.…”