Article

Generalization in Sensorimotor Networks Configured with Natural Language Instructions

2022-02-24

Abstract excerpt

<h4> A bstract </h4> One of humans’ most fundamental cognitive feats is the ability to interpret linguistic instructions in order to perform novel tasks without any explicit experience with the task. Yet, the computations that the brain might use to accomplish such a feat remains poorly understood. Here we use the latest advances in Natural Language Processing to create a neural model of generalization based on...

Topics

Open a Topic to create a Post that cites this publication.

Uncategorized literature

Identifiers and source

Literature Corpus work: 5abb58bc-e502-5d46-a1e2-9966e8f75c6b
DOI: 10.1101/2022.02.22.481293

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generalization in Sensorimotor Networks Configured with Natural Language InstructionsDOI 10.1101/2022.02.22.481293Read

Abstract representations emerge naturally in neural networks trained to perform multiple tasksSemantic proximity Read

The neural architecture of language: Integrative modeling converges on predictive processingSemantic proximity Read

The Generalization Artist - Or: How Can We Characterize Human Generalization?Semantic proximity Read

Top-down effects in motor generalizationSemantic proximity Read

A neural mechanism for compositional structure transfer in humansSemantic proximity Read

Decomposing dynamical subprocesses for compositional generalizationSemantic proximity Read

Learning generalizable visuomotor mappings fo <i>r de novo</i> skillsSemantic proximity Read

Using top-down modulation to optimally balance shared versus separated task representationsSemantic proximity Read

Novel verbal instructions recruit abstract neural patterns of time-variable information dimensionalitySemantic proximity Read

Generalizing to generalize: when (and when not) to be compositional in task structure learningSemantic proximity Read

Multiple-Demand Network encoding geometry balances generalization and dimensionality during novel task assemblySemantic proximity Read

Motor abstraction training generalizes to the refinement of specific movement patternsSemantic proximity Read