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From known to unknown: moving to unvisited locations in a novel sensorimotor map
Author(s) -
Vugt Floris T.,
Ostry David J.
Publication year - 2018
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.13608
Subject(s) - workspace , interpolation (computer graphics) , computer science , set (abstract data type) , movement (music) , artificial intelligence , space (punctuation) , computer vision , motion (physics) , physics , acoustics , robot , programming language , operating system
Abstract Sensorimotor learning requires knowledge of the relationship between movements and sensory effects: a sensorimotor map. Generally, these mappings are not innate but have to be learned. During learning, the challenge is to build a continuous map from a set of discrete observations, that is, predict locations of novel targets. One hypothesis is that the learner linearly interpolates among discrete observations that are already in the map. Here, this hypothesis is tested by exposing human subjects to a novel mapping between arm movements and sounds. Participants were passively moved to the edges of the workspace receiving the corresponding sounds and then were presented intermediate sounds and asked to make movements to locations they thought corresponded to those sounds. It is observed that average movements roughly match linear interpolation of the space. However, the actual distribution of participants' movements is best described by a bimodal reaching strategy in which they move to one of two locations near the workspace edge where they had prior exposure to the sound–movement pairing. These results suggest that interpolation happens to a limited extent only and that the acquisition of sensorimotor maps may not be driven by interpolation but instead relies on a flexible recombination of instance‐based learning.