The Neuroscience of Learning: Beyond the Hebbian Synapse

Annual Review of Psychology

Vol. 64:169-200 (Volume publication date January 2013)
First published online as a Review in Advance on July 12, 2012
https://doi.org/10.1146/annurev-psych-113011-143807

C.R. Gallistel1 and Louis D. Matzel2

1Rutgers Center for Cognitive Science and

2Department of Psychology, Rutgers University, Piscataway, New Jersey 08854-8020; email:

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Abstract

From the traditional perspective of associative learning theory, the hypothesis linking modifications of synaptic transmission to learning and memory is plausible. It is less so from an information-processing perspective, in which learning is mediated by computations that make implicit commitments to physical and mathematical principles governing the domains where domain-specific cognitive mechanisms operate. We compare the properties of associative learning and memory to the properties of long-term potentiation, concluding that the properties of the latter do not explain the fundamental properties of the former. We briefly review the neuroscience of reinforcement learning, emphasizing the representational implications of the neuroscientific findings. We then review more extensively findings that confirm the existence of complex computations in three information-processing domains: probabilistic inference, the representation of uncertainty, and the representation of space. We argue for a change in the conceptual framework within which neuroscientists approach the study of learning mechanisms in the brain.

Keywords

LTP, spatial learning, Bayesian inference, information theory, cognitive map, geometric module

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Supplemental Materials

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Figure 1: Number of scholarly publications from 1970 to 2013 that refer to “emotion(s)/affect/mood and decision making” (green bars) and proportion of all scholarly publications referring to “decision...

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Figure 2: Toward a general model of affective influences on decision making: the emotion-imbued choice model.

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Executive functions (EFs) make possible mentally playing with ideas; taking the time to think before acting; meeting novel, unanticipated challenges; resisting temptations; and staying focused. Core EFs are inhibition [response inhibition (self-control—...Read More

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Figure 1: Passive-dissipation model showing how delay can improve performance on inhibitory tasks (from Simpson et al. 2011).

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Figure 2: At every age studied, children were slower and less accurate on the congruent block than on the incongruent block. That effect is completely absent in adults, who are as fast and as accurate...

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Figure 3: Comparison of the mixed conditions of the Dots (now called Hearts and Flowers) and Simon tasks in percentage of correct responses (based on Davidson et al. 2006).

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Figure 4: Executive functions and related terms.

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Figure 1: The original Baddeley & Hitch (1974) working memory model.

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Figure 2: A modification of the original model to take account of the evidence of links between working memory and long-term memory (LTM).

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Figure 3: The model following the introduction of a fourth component, the episodic buffer, a system for integrating information from a range of sources into a multidimensional code (Baddeley 2000).

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Figure 4: My current view of the complex and multiple links between working memory (WM) and long-term memory (LTM).

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Figure 5: A speculative view of the flow of information from perception to working memory. VSSP, visuo-spatial sketchpad.

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Figure 1: Proportion of correct responses following error-generating and error-free study. Figure adapted with permission from Kornell et al. (2009).

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Figure 2: Event-related potentials (ERPs) synchronized to the onset of corrective feedback given to high- and low-confidence errors made by young adults and older adults. The ERP tracings show a promi...

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Figure 3: Contrast map of high-confidence errors versus low-confidence errors. Red areas were more active for high-confidence errors, and green areas were more active for low-confidence errors; both a...

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