A map of abstract relational knowledge in the human hippocampal–entorhinal cortex

By Mona M. Garvert, R. J. Dolan, Timothy Edward John Behrens. eLife, 2017

339 citations recorded by the source corpus.

The hippocampal–entorhinal system encodes a map of space that guides spatial navigation. Goal-directed behaviour outside of spatial navigation similarly requires a representation of abstract forms of relational knowledge. This information relies on the same neural system, but it is not known whether the organisational principles governing continuous maps may extend to the implicit encoding of discrete, non-spatial graphs. Here, we show that the human hippocampal–entorhinal system can represent relationships between objects using a metric that depends on associative strength. We reconstruct a map-like knowledge structure directly from a hippocampal–entorhinal functional magnetic resonance imaging adaptation signal in a situation where relationships are non-spatial rather than spatial, discrete rather than continuous, and unavailable to conscious awareness. Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted sum of future states, akin to the successor representation that has been proposed to account for place and grid-cell firing patterns. DOI: http://dx.doi.org/10.7554/eLife.17086.001

View Paper: https://www.semanticscholar.org/paper/b0346300efc7ba8f5d3a8ec2b7bfe1e8d7e58376

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Citations in the corpus

Navigating cognition: Spatial codes for human thinking (2018, background): “Beyond navigable space, the hippocampalentorhinal system extracts statistical regularities of nonspatial sequences (122, 123) and forms relational maps of the underlying structures from which the sequences were derived (104, 124), potentially drawn upon to plan trajectories through task spaces.”
If deep learning is the answer, what is the question? (2020, background): “Indeed, fMRI studies of statistical learning have revealed that neural similarities (such as multivoxel pattern overlap) in the MTL recapitulate association strengths for pairs, lines, maps or hierarchies of stimuli 94,98–104 .”
The successor representation in human reinforcement learning (2016, background): “…is explicitly represented in the brain: the SR defined over space can capture many properties of rodent hippocampal place cells 11 , whereas in tasks with more abstract sequential stimulus structure, the SR captures properties of fMRI pattern similarity in hippocampal and prefrontal areas 12,13 .”