Possible Neural Correlates for the Mechanism of Stimulus-Response Association in the Monkey

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data on a subject-by-subject basis confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

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Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets

10.1101/713156 ◽

2019 ◽

Cited By ~ 1

Author(s):

Flora Bouchacourt ◽

Stefano Palminteri ◽

Etienne Koechlin ◽

Srdjan Ostojic

Keyword(s):

Reaction Times ◽

Neural Correlates ◽

Behavioral Performance ◽

Bold Signal ◽

Improve Performance ◽

Task Set ◽

Neural Data ◽

Stimulus Response ◽

Task Sets ◽

And Task

AbstractDepending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

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Neural correlates of stimulus response and reinforcement

Brain Research ◽

10.1016/0006-8993(70)90247-7 ◽

1970 ◽

Vol 17 (3) ◽

pp. 377-385 ◽

Cited By ~ 13

Author(s):

D.N. Spinelli ◽

Karl H. Pribram

Keyword(s):

Neural Correlates ◽

Stimulus Response

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The Effect of Real-Time Feedback Using a Smartwatch on the Clinical Behavior of Novice Student Clinicians

Perspectives of the ASHA Special Interest Groups ◽

10.1044/persp2.sig11.79 ◽

2017 ◽

Vol 2 (11) ◽

pp. 79-90

Author(s):

Courtney G. Scott ◽

Trina M. Becker ◽

Kenneth O. Simpson

Keyword(s):

Real Time ◽

Rating Scales ◽

Qualitative Interviews ◽

Communication Disorders ◽

Preliminary Evidence ◽

Single Subject ◽

Clinical Behavior ◽

Stimulus Response ◽

Treatment Data ◽

Computer Monitors

The use of computer monitors to provide technology-based written feedback during clinical sessions, referred to as “bug-in-the-eye” (BITi) feedback, recently emerged in the literature with preliminary evidence to support its effectiveness (Carmel, Villatte, Rosenthal, Chalker & Comtois, 2015; Weck et al., 2016). This investigation employed a single-subject, sequential A-B design with two participants to observe the effects of implementing BITi feedback using a smartwatch on the clinical behavior of student clinicians (SCs). Baseline and treatment data on the stimulus-response-consequence (S-R-C) contingency completion rates of SCs were collected using 10 minute segments of recorded therapy sessions. All participants were students enrolled in a clinical practicum experience in a communication disorders and sciences (CDS) program. A celeration line, descriptive statistics, and stability band were used to analyze the data by slope, trend, and variability. Results demonstrated a significant correlative relationship between BITi feedback with a smartwatch and an increase in positive clinical behaviors. Based on qualitative interviews and exit rating scales, SCs reported BITi feedback was noninvasive and minimally distracting. Preliminary evidence suggests BITi feedback with a smartwatch may be an effective tool for providing real-time clinical feedback.

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