Necessary and Sufficient Behavioral Evidence for Episodic Memory Traces Computational Neuroscience


Some of the most controversial topics in cognitive science are the question whether nonhuman animals possess a certain cognitive capacity or not. This is the case for episodic memory. Cheng et al. (2016) suggested that episodic memory can be dissociated into different components. Among them the episodic memory trace, semantic information and scenario generation. We argue that nonhuman animals probably share some, but perhaps not all, of the components of human episodic memory. In particular, we suggest that nonhuman animals can form and retrieve episodic memory traces, and thus remember the sequential order of events in an episode (Cheng & Werning, 2015). The goal of this project is to identify what behavioral evidence would be necessary and sufficient to claim that a nonhuman species possesses episodic memory traces. The project requires background knowledge of memory and experience in developing philosophical analyses.


Cheng S, Werning M, Suddendorf T (2016) Dissociating memory traces and scenario construction in mental time travel. Neurosci Biobehav Rev 60:82–89,

Cheng S, Werning M (2016) What is Episodic Memory if it is a Natural Kind? Synthese, 193(5), 1345-1385,


Prof. Dr. Sen Cheng, Institute for Neural Computation and Prof. Markus Werning, Institute for Philosophy II

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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