What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior.

By Timothy Edward John Behrens, Timothy H. Muller, James C. R. Whittington, Shirley Mark, Alon Baram, Kimberly L. Stachenfeld, Zeb Kurth-Nelson. Neuron, 2018

941 citations recorded by the source corpus.

It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behavior, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviors has rekindled the notion of a systematic organization of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalizable knowledge and suggest that map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organizing knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behavior and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalizations, abstractions, and inferences that characterize human cognition.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/9de3347a33f8881840fb6e614aa61da6e50849f8

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Citations in the corpus

On the link between conscious function and general intelligence in humans and machines (2022, background): “In the brain, the hippocampus has been linked with the generation of spatially and temporally coherent sequential trajectories (Hassabis & Maguire, 2009; Behrens et al., 2018).”
Learning Structures: Predictive Representations, Replay, and Generalization (2020, background): “Thus, prefrontal and entorhinal representations may compute and represent generalization and basis sets of structures, task sets, and schema [3,23,42].”
If deep learning is the answer, what is the question? (2020, background): “In parallel, this difference provides an incentive for neuroscientists to study how biological brains encode, compose and generalize abstract knowledge 82–84 .”
Reinforcement Learning, Fast and Slow. (2019, positive): “Much work has been devoted to understanding how the brain learns and uses models [46, 101, 102, 103].”
Human Replay Spontaneously Reorganizes Experience (2019, background): “It has been theorized that such inferences rely on internal models of the world, which are supported by the same neural mechanisms underpinning relational reasoning in space (Behrens et al., 2018; Buzsáki and Moser, 2013; Constantinescu et al., 2016; Eichenbaum, 2017; Tolman, 1948).”
The geometry of abstraction in hippocampus and prefrontal cortex (2020, methodology): “The SD is higher in this interval than in the earlier time epoch: HPC 0.88, DLPFC 0.89, and ACC 0.88.”
Mental graphs structure the storage and retrieval of visuomotor associations (2025, unknown): “Our study thus makes new connections between research on cognitive maps and graphs in organizing behaviour and knowledge 2,60,61 and the study of sensorimotor learning and control, perhaps offering a new avenue for understanding the format of mental representations in complex, naturalistic…”
Intracranial Recordings Demonstrate Both Cortical and Medial Temporal Lobe Engagement in Visual Search in Humans (2021, background): “Moreover, future research may benefit from the inclusion of additional tasks to delineate towhat extent these task-active effects are specific to visual search versus general tomultiple cognitive domains that engage the MTL system (Behrens et al., 2018; Piai et al., 2016; Stachenfeld, Botvinick, & G”