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

Mona M Garvert,Raymond J Dolan,Timothy Ej Behrens

doi:10.7554/elife.17086

Abstract

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.

Highlights

Animals efficiently extract abstract relationships between landmarks, events, and other types of conceptual information, often from limited experience
To test whether this exposure to object sequences induced implicit knowledge about the graph, we scanned the subjects on a subsequent day using functional magnetic resonance imaging (fMRI) while exposing them to a subset of the same objects presented in a random order
We find that neural activity bilaterally in the hippocampal–entorhinal system scales with communicability

Summary

Introduction

Animals efficiently extract abstract relationships between landmarks, events, and other types of conceptual information, often from limited experience Knowing such regularities can help us act in an environment, because the relationships between items that have never been experienced together can be computed and exploited in order to make novel inference. It is likely that the particular form of these representations enables rapid computations of spatial relationships such as distances and vector paths (Bush et al, 2015; Stemmler et al, 2015) The potential for such rapid online computations embedded into neuronal representations may explain how animals can find novel paths through space (McNaughton et al, 2006; Mittelstaedt and Mittelstaedt, 1980) or rapidly reroute when obstacles are introduced (Alvernhe et al, 2011) or removed (Alvernhe et al, 2008). In humans, signals that encode distance metrics between landmarks (Howard et al, 2014; Morgan et al, 2011) and directions to goals (Chadwick et al, 2015) can be read out directly from functional magnetic resonance imaging (fMRI) data in the entorhinal cortex

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