Classical and instrumental conditioning experiments.

BACKGROUND: Dietary restraint represents an individual’s intent to limit their food intake and has been associated with impaired passive food reinforcement learning. However, the impact of dietary restraint on an active, response dependent learning is poorly understood. In this study, we tested the relationship between dietary restraint and food reinforcement learning using an active, instrumental conditioning task. METHODS: A sample of ninety adults completed a response-dependent instrumental conditioning task with reward and punishment using sweet and bitter tastes. Brain response via functional MRI was measured during the task. Participants also completed anthropometric measures, reward/motivation related questionnaires, and a working memory task. Dietary restraint was assessed via the Dutch Restrained Eating Scale. RESULTS: Two groups were selected from the sample: high restraint (n=29, score >2.5) and low restraint (n=30; score <1.85). High restraint was associated with significantly higher BMI (p=0.003) and lower N-back accuracy (p=0.045). The high restraint group also was marginally better at the instrumental conditioning task (p=0.066, r=0.37). High restraint was also associated with significantly greater brain response in the intracalcarine cortex (MNI: 15, -69, 12; k=35, pfwe< 0.05) to bitter taste, compared to neutral taste.CONCLUSIONS: High restraint was associated with improved performance on an instrumental task testing how individuals learn from reward and punishment. This may be mediated by greater brain response in the primary visual cortex, which has been associated with mental representation. Results suggest that dietary restraint does not impair response-dependent reinforcement learning.

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The temporal triangle: Response substitution in instrumental conditioning.

Psychological Review ◽

10.1037/0033-295x.85.1.22 ◽

1978 ◽

Vol 85 (1) ◽

pp. 22-47 ◽

Cited By ~ 112

Author(s):

Howard Rachlin ◽

Barbara Burkhard

Keyword(s):

Instrumental Conditioning

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Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning

Science ◽

10.1126/science.1094285 ◽

2004 ◽

Vol 304 (5669) ◽

pp. 452-454 ◽

Cited By ~ 1223

Author(s):

J. O'Doherty

Keyword(s):

Instrumental Conditioning ◽

Dorsal Striatum

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A global framework for a systemic view of brain modeling

Brain Informatics ◽

10.1186/s40708-021-00126-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Frederic Alexandre

Keyword(s):

Instrumental Conditioning ◽

The Body ◽

Posterior Cortex ◽

Reciprocal Relationships ◽

Brain Modeling ◽

Brain Theory ◽

Habitual Behavior ◽

Definition Of ◽

The Brain ◽

Specific Contribution

AbstractThe brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

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