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One Dimensional Turing-Like Handshake Test for Motor Intelligence
In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MyJove Corporation
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537195/ https://www.ncbi.nlm.nih.gov/pubmed/21206462 http://dx.doi.org/10.3791/2492 |
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author | Karniel, Amir Avraham, Guy Peles, Bat-Chen Levy-Tzedek, Shelly Nisky, Ilana |
author_facet | Karniel, Amir Avraham, Guy Peles, Bat-Chen Levy-Tzedek, Shelly Nisky, Ilana |
author_sort | Karniel, Amir |
collection | PubMed |
description | In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake. |
format | Online Article Text |
id | pubmed-3537195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | MyJove Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-35371952013-01-09 One Dimensional Turing-Like Handshake Test for Motor Intelligence Karniel, Amir Avraham, Guy Peles, Bat-Chen Levy-Tzedek, Shelly Nisky, Ilana J Vis Exp Neuroscience In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake. MyJove Corporation 2010-12-15 /pmc/articles/PMC3537195/ /pubmed/21206462 http://dx.doi.org/10.3791/2492 Text en Copyright © 2010, Journal of Visualized Experiments http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visithttp://creativecommons.org/licenses/by-nc-nd/3.0/ |
spellingShingle | Neuroscience Karniel, Amir Avraham, Guy Peles, Bat-Chen Levy-Tzedek, Shelly Nisky, Ilana One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title | One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title_full | One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title_fullStr | One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title_full_unstemmed | One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title_short | One Dimensional Turing-Like Handshake Test for Motor Intelligence |
title_sort | one dimensional turing-like handshake test for motor intelligence |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537195/ https://www.ncbi.nlm.nih.gov/pubmed/21206462 http://dx.doi.org/10.3791/2492 |
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