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...

Full description

Bibliographic Details
Main Authors: Iris eVan Rooij, Johan eKwisthout, Mark eBlokpoel, Jakub eSzymanik, Todd eWareham, Ivan eToni
Format: Article
Language:English
Published: Frontiers Media S.A. 2011-06-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00052/full
id doaj-c8eb88ae2f0c49d0970101030ada2d7c
record_format Article
spelling 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
_version_ 1724899891805683712