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Related Experiment Video

Updated: May 24, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

The successor representation and temporal context.

Samuel J Gershman¹, Christopher D Moore, Michael T Todd

¹Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA. sjgershm@princeton.edu

Neural Computation

|February 28, 2012

Summary

This summary is machine-generated.

The successor representation (SR) in reinforcement learning can model episodic memory. A variant of the temporal context model (TCM) estimates the SR using temporal difference learning, unifying learning systems.

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Area of Science:

Cognitive Science
Neuroscience
Artificial Intelligence

Background:

The successor representation (SR) facilitates generalization in reinforcement learning (RL).
RL has been widely applied to model psychological and neural processes.
The psychological validity and application of SR beyond RL, particularly in episodic memory, remain underexplored.

Purpose of the Study:

To explore the psychological validity of the successor representation (SR).
To investigate the potential of SR in episodic learning.
To demonstrate a connection between the temporal context model (TCM) and SR.

Main Methods:

Utilized a variant of the temporal context model (TCM).
Applied temporal difference (TD) learning algorithm.
Analyzed the estimation of SR within the TCM framework.

Main Results:

Showed that a variant of TCM directly estimates the successor representation (SR).
Demonstrated that temporal difference (TD) learning underlies this estimation.
Established a formal equivalence between TCM and SR estimation.

Conclusions:

The temporal context model (TCM) can be interpreted as estimating the successor representation (SR).
This provides a generalized framework for TCM and generates new experimental predictions.
Highlights a novel connection between reinforcement learning and episodic memory systems.