Intentional Communication: Computationally Easy or Difficult?
Human intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that...
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2011-06-01
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Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00052/full |
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doaj-c8eb88ae2f0c49d0970101030ada2d7c2020-11-25T02:14:45ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612011-06-01510.3389/fnhum.2011.000522207Intentional Communication: Computationally Easy or Difficult?Iris eVan Rooij0Johan eKwisthout1Mark eBlokpoel2Jakub eSzymanik3Todd eWareham4Ivan eToni5Radboud University NijmegenRadboud University NijmegenRadboud University NijmegenUniversity of GroningenMemorial University of NewfoundlandRadboud University NijmegenHuman intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that capacity. It is thus of importance for cognitive neuroscience to know how computationally complex intentional communication actually is. Though the subject of considerable debate, the computational complexity of communication remains so far unknown. In this paper we defend the position that the computational complexity of communication is not a constant, as some views of communication seem to hold, but rather a function of situational factors. We present a methodology for studying and characterizing the computational complexity of communication under different situational constraints. We illustrate our methodology for a model of the problems solved by receivers and senders during a communicative exchange. This approach opens the way to a principled identification of putative model parameters that control cognitive processes supporting intentional communication.http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00052/fullCommunicationcomputational modelingBayesian modelingcomputational complexityintractabilitygoal inference |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Iris eVan Rooij Johan eKwisthout Mark eBlokpoel Jakub eSzymanik Todd eWareham Ivan eToni |
spellingShingle |
Iris eVan Rooij Johan eKwisthout Mark eBlokpoel Jakub eSzymanik Todd eWareham Ivan eToni Intentional Communication: Computationally Easy or Difficult? Frontiers in Human Neuroscience Communication computational modeling Bayesian modeling computational complexity intractability goal inference |
author_facet |
Iris eVan Rooij Johan eKwisthout Mark eBlokpoel Jakub eSzymanik Todd eWareham Ivan eToni |
author_sort |
Iris eVan Rooij |
title |
Intentional Communication: Computationally Easy or Difficult? |
title_short |
Intentional Communication: Computationally Easy or Difficult? |
title_full |
Intentional Communication: Computationally Easy or Difficult? |
title_fullStr |
Intentional Communication: Computationally Easy or Difficult? |
title_full_unstemmed |
Intentional Communication: Computationally Easy or Difficult? |
title_sort |
intentional communication: computationally easy or difficult? |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2011-06-01 |
description |
Human intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that capacity. It is thus of importance for cognitive neuroscience to know how computationally complex intentional communication actually is. Though the subject of considerable debate, the computational complexity of communication remains so far unknown. In this paper we defend the position that the computational complexity of communication is not a constant, as some views of communication seem to hold, but rather a function of situational factors. We present a methodology for studying and characterizing the computational complexity of communication under different situational constraints. We illustrate our methodology for a model of the problems solved by receivers and senders during a communicative exchange. This approach opens the way to a principled identification of putative model parameters that control cognitive processes supporting intentional communication. |
topic |
Communication computational modeling Bayesian modeling computational complexity intractability goal inference |
url |
http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00052/full |
work_keys_str_mv |
AT irisevanrooij intentionalcommunicationcomputationallyeasyordifficult AT johanekwisthout intentionalcommunicationcomputationallyeasyordifficult AT markeblokpoel intentionalcommunicationcomputationallyeasyordifficult AT jakubeszymanik intentionalcommunicationcomputationallyeasyordifficult AT toddewareham intentionalcommunicationcomputationallyeasyordifficult AT ivanetoni intentionalcommunicationcomputationallyeasyordifficult |
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1724899891805683712 |