Stimulus-Response Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Nole M. Hiebert ◽  
Marc R. Lawrence ◽  
Hooman Ganjavi ◽  
Mark Watling ◽  
Adrian M. Owen ◽  
...  

2020 ◽  
Vol 117 (49) ◽  
pp. 31427-31437
Author(s):  
Jesse P. Geerts ◽  
Fabian Chersi ◽  
Kimberly L. Stachenfeld ◽  
Neil Burgess

Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus–response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In “response learning,” associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In “place learning,” associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a “successor representation” of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to “blocking” by prior state–reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal–striatal system as a general system for decision making via adaptive combination of stimulus–response learning and the use of a cognitive map.


2007 ◽  
Vol 14 (1-2) ◽  
pp. 109-116 ◽  
Author(s):  
L. Schwabe ◽  
M. S. Oitzl ◽  
C. Philippsen ◽  
S. Richter ◽  
A. Bohringer ◽  
...  

Neuroscience ◽  
2019 ◽  
Vol 415 ◽  
pp. 161-172 ◽  
Author(s):  
Terrell A. Jenrette ◽  
Jordan B. Logue ◽  
Kristen Ashley Horner

2010 ◽  
Vol 22 (4) ◽  
pp. 790-805 ◽  
Author(s):  
Valorie N. Salimpoor ◽  
Catie Chang ◽  
Vinod Menon

We investigated the neural basis of repetition priming (RP) during mathematical cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning as an alternate model for behavioral facilitation. We examined the neural basis of RP during mathematical problem solving in the context of these two models of learning. Brain imaging and behavioral data were acquired from 39 adults during novel and repeated presentation of three-operand mathematical equations. Despite wide-spread decreases in activation during repeat, compared with novel trials, there was no direct relation between behavioral facilitation and the degree of repetition suppression in any brain region. Rather, RT improvements were directly correlated with repetition enhancement in the hippocampus and the posteromedial cortex [posterior cingulate cortex, precuneus, and retrosplenial cortex; Brodmann's areas (BAs) 23, 7, and 30, respectively], regions known to support memory formation and retrieval, and in the SMA (BA 6) and the dorsal midcingulate (“motor cingulate”) cortex (BA 24d), regions known to be important for motor learning. Furthermore, improvements in RT were also correlated with increased functional connectivity of the hippocampus with both the SMA and the dorsal midcingulate cortex. Our findings provide novel support for the hypothesis that repetition enhancement and associated stimulus-response learning may facilitate behavioral performance during problem solving.


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