An abstract language of thought for spatial sequences in humans

Marie Amalric, Liping Wang, Pierre Pica, Santiago Figueira, Mariano Sigman, Stanislas Dehaene

Overview

Human memory compresses spatial sequences to their minimal description length in a recursive language of thought.

Summary

We ask whether a "geometrical language" with recursive embedding underlies the human ability to encode sequences of spatial locations

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Dr. Pierre Pica, PhD
Dr. Pierre Pica, PhD
Centre National de la Recherche Scientifique
Linguistics
Paris | France
Is there a dedicated language of thought for geometry ?Dr. Pierre Pica, PhD

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Supporting information
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005273#sec038

The language of geometry: Fast comprehension of geometrical primitives and rules in human adults and preschoolers.

Authors:
Dr. Pierre Pica, PhD
Dr. Pierre Pica, PhD
Centre National de la Recherche Scientifique
Linguistics
Paris | France

PLoS Comput Biol 2017 01 26;13(1):e1005273. Epub 2017 Jan 26.

Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif/Yvette, France.

During language processing, humans form complex embedded representations from sequential inputs. Here, we ask whether a "geometrical language" with recursive embedding also underlies the human ability to encode sequences of spatial locations. We introduce a novel paradigm in which subjects are exposed to a sequence of spatial locations on an octagon, and are asked to predict future locations. The sequences vary in complexity according to a well-defined language comprising elementary primitives and recursive rules. A detailed analysis of error patterns indicates that primitives of symmetry and rotation are spontaneously detected and used by adults, preschoolers, and adult members of an indigene group in the Amazon, the Munduruku, who have a restricted numerical and geometrical lexicon and limited access to schooling. Furthermore, subjects readily combine these geometrical primitives into hierarchically organized expressions. By evaluating a large set of such combinations, we obtained a first view of the language needed to account for the representation of visuospatial sequences in humans, and conclude that they encode visuospatial sequences by minimizing the complexity of the structured expressions that capture them.

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http://dx.doi.org/10.1371/journal.pcbi.1005273DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5305265PMC
January 2017
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