Innateness of colour categories is a red herring: insights from computational modelling Tony Belpaeme Artificial Intelligence Lab Vrije Universiteit Brussel.

Slides:



Advertisements
Similar presentations
Cognitive Systems, ICANN panel, Q1 What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence,
Advertisements

Cross Cultural Research
Chapter Thirteen Conclusion: Where We Go From Here.
Christian Devalez (Sunday, 19 April 2015) Mobility and Progressive Application Streaming.
The cultural origins of colour categories Tony Belpaeme Artificial Intelligence Lab Vrije Universiteit Brussel.
Artificial Intelligence and Lisp TDDC65 Course leader: Erik Sandewall Lab assistants: Henrik Lundberg, John Olsson Administrator: Anna Grabska Eklund Webpage:
Rational Decision Making As A Unifying Paradigm In Cognitive Science, or Why Animal Are Rationals, And Why It's No Big Deal Benoit Hardy-Vallée, EHESS,
An Introduction to Artificial Intelligence Presented by : M. Eftekhari.
The dynamics of iterated learning Tom Griffiths UC Berkeley with Mike Kalish, Steve Lewandowsky, Simon Kirby, and Mike Dowman.
Organizational Notes no study guide no review session not sufficient to just read book and glance at lecture material midterm/final is considered hard.
Assessment Centre Procedures: Reducing Cognitive Load During the Observation Phase Nanja J. Kolk & Juliette M. Olman Department of Work and Organizational.
Introduction to Cognitive Science Lecture #1 : INTRODUCTION Joe Lau Philosophy HKU.
Printed by The question how the human brain works can be approached from different angles. Scientists from disciplines like biology,
What is Cognitive Science? … is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience,
Overview and History of Cognitive Science. How do minds work? What would an answer to this question look like? What is a mind? What is intelligence? How.
Physical Symbol System Hypothesis
What is Cognitive Science? … is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience,
Contemporary Perspectives. What is a “perspective”? What do you think???
Chapter 12: Simulation and Modeling Invitation to Computer Science, Java Version, Third Edition.
The History and Methods of Cognitive Psychology. What is Cognitive Psychology? The branch of psychology that studies how we perceive, attend, recognize,
Vedrana Vidulin Jožef Stefan Institute, Ljubljana, Slovenia
CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang.
Chapter 12: Simulation and Modeling
Does Language Affect Colour Perception? Miscal Avano-Nesgaard Thursday October 27, 2005.
C. 2008, Pearson Allyn & Bacon Introduction to Cognition Chapter 1.
Intro to the Social Sciences
An Architecture for Empathic Agents. Abstract Architecture Planning + Coping Deliberated Actions Agent in the World Body Speech Facial expressions Effectors.
Language evolution and robotics Paul Vogt Universiteit van Tilburg Nederland.
Cognitive Psychology Spring Discussion Section-
A Gibsonian analysis of linguistic information Finding Common Ground: UConn 2014 Sabrina Golonka Leeds Metropolitan University Centre for Applied Social.
Lecture 2b Readings: Kandell Schwartz et al Ch 27 Wolfe et al Chs 3 and 4.
Colour Language 2: Explaining Typology Mike Dowman Language and Cognition 5 October, 2005.
Multi-Agent Modeling of Societal Development and Cultural Evolution Yidan Chen, 2006 Computer Systems Research Lab.
Kavita Singh CS-A What is Swarm Intelligence (SI)? “The emergent collective intelligence of groups of simple agents.”
Chapter 9 Psycholinguistics
Artificial Intelligence By Michelle Witcofsky And Evan Flanagan.
Modelling Language Evolution Lecture 1: Introduction to Learning Simon Kirby University of Edinburgh Language Evolution & Computation Research Unit.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Extracting meaningful labels for WEBSOM text archives Advisor.
Graph Evolution: A Computational Approach Olaf Sporns, Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405
Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme Project funded by the Future and Emerging Technologies.
By – Neha Gupta Ajay Rajaram.  How are multi-linguistic skills developed in humans?
Comparing the Two Theories
Animal Behavior Section 1: Evolution of Behavior
Introduction to Cognitive Science Lecture #2 : Mental Representations Joe Lau Philosophy HKU.
Neural Networks and Machine Learning Applications CSC 563 Prof. Mohamed Batouche Computer Science Department CCIS – King Saud University Riyadh, Saudi.
Modelling the evolution of language for modellers and non-modellers IJCAI Hands on demonstration Nature of colour categories.
Changing the Rules of the Game Dr. Marco A. Janssen Department of Spatial Economics.
Toward a universalism Paul Kay 3308 Psycholinguistics.
Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-like Systems, Sendhoff et al. Course: Robots Learning from Humans Baek, Da Som.
Cultural Anthropology What is it?. Anthropology  Comparative study of human societies and cultures.
Grounded cognition. Barsalou, L. W. (2008). Annual Review of Psychology, 59, Grounded theories versus amodal representations. – Recapitulation.
Artificial Intelligence: Research and Collaborative Possibilities a presentation by: Dr. Ernest L. McDuffie, Assistant Professor Department of Computer.
Cognitive Science Overview Introduction, Syllabus
Organic Evolution and Problem Solving Je-Gun Joung.
1 ARTIFICIAL INTELLIGENCE Gilles BÉZARD Version 3.16.
Chapter 4. Analysis of Brain-Like Structures and Dynamics (2/2) Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans 09/25.
The highly intelligent virtual agents for modeling financial markets G. Yang 1, Y. Chen 2 and J. P. Huang 1 1 Department of Physics, Fudan University.
A Self-organizing Semantic Map for Information Retrieval Xia Lin, Dagobert Soergel, Gary Marchionini presented by Yi-Ting.
Evolving robot brains using vision Lisa Meeden Computer Science Department Swarthmore College.
Cognitive Modeling Cogs 4961, Cogs 6967 Psyc 4510 CSCI 4960 Mike Schoelles
Language and Brain Summer, 2017.
Chapter 12: Simulation and Modeling
Outline Of Today’s Discussion
Chapter 1 Introduction.
Macrolinguistics Linguistics is not the only field concerned with language. Other disciplines such as psychology, sociology, ethnography, the science of.
Cognitive Brain Dynamics Lab
The MIT Artificial Intelligence Lab
Information Overload and National Culture: Assessing Hofstede’s Model Based on Data from Two Countries Ned Kock, Ph.D. Dept. of MIS and Decision Science.
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
Presentation transcript:

Innateness of colour categories is a red herring: insights from computational modelling Tony Belpaeme Artificial Intelligence Lab Vrije Universiteit Brussel

Colour categories The colour spectrum is continuous Still, we divide it into colour categories What are the origins of colour categories? (Insights might be applicable to other perceptual categories as well)

Importance for language “… this may at first appear to be a comparatively trivial example of some minor aspect of language, but the implications for other aspects of language evolution are truly staggering.” (Deacon, 1997)

Universalism Berlin and Kay (1969) used naming experiments to extract colour categories

Universalism This universal character has been hailed by many and has been reconfirmed by some. (among others Rosch-Heider, 1972; Kay and McDaniel, 1978; Durham, 1991; Shepard, 1992; Kay and Regier, 2003)

Three positions Supposing we accept a certain universality of colour categorisation, what mechanisms could underlie this? –Nativism: genetic makeup. –Empiricism: interaction with the environment. –Culturalism: cultural interaction with others.

Nativism Colour categories are directly or indirectly genetically specified. –Regularities in human early visual perception, especially the opponent character of colour vision. (Kay and McDaniel, 1978) –Regularities in the neural coding of the brain. (Durham, 1991) –Genetic coding of colour categories. (Shepard, 1992)

Empiricism Our ecology contains a certain chromatic structure which is reflected in our colour categories. We extract colour categories by interacting with our environment. (e.g. Elman et al., 1996; Shepard, 1992; Yendrikhovskij, 2001) This all happens without the influence of culture or language.

Culturalism Colour categories are culture-specific. They are learned with a strong causal influence of language and propagate in a cultural process. (e.g. Whorf, 1954; Davidoff et al., 2001; Roberson, 2005; Belpaeme and Steels)

Nativism, empiricism or culturalism? The discussion has been held on many different fronts –Neurology. –Psychology. –Anthropology. –Linguistics. –Ophthalmology. –Philosophy. We will tackle the discussion from artificial intelligence and computer modelling.

How can Artificial Intelligence help? Artificial Intelligence allows us to create models of natural phenomena, of which we then observe their behaviour. Different premises can be implemented in the models, allowing us to get an insight into the validity of the premises. –E.g. traffic modelling.

Studying empiricism Procedure –Collect chromatic data. –Extract colour categories. For this we use a clustering algorithm. –Compare extracted categories with each other and with human colour categories. If empiricism holds, we would expect a high correlation between the extracted categories and human categories.

Chromatic data Three data sets: natural, urban and random

Extracting categories Categories from natural data: Categories from urban data:

Quantitative comparison 11 categories extracted from natural and urban data Correlation with human colour categories

Reflections on empiricism The claim that human colour categories are specified by the distribution of chromatic stimuli in the world is not supported by our data. However, there does seem to be a twofold influence by –The structure of the perceptual colour space. –The properties of perceptual categories.

Studying culturalism Procedure –Take a population of simulated individuals that learn colour categories and communicate about colour. If culturalism holds, we expect linguistic interactions to cause sharing of colour categories.

The simulations Agent-based simulations –An agent is a simulated individual, with perception, categorisation, lexicalisation and communication. –Perception maps spectral power distribution onto an internal colour space. –Categorisation maps percepts onto categories, categories have prototypical behaviour. –Lexicalisation connects categories to words. –Communication takes care of uttering word forms. –The agents have no way to access the internal state of other agents: there is no telepathy!

Results Colour categories of two agents Agents arrive at colour categories that are “shared”.

Results (2) Influence of linguistic interactions on categories. But as language is culture-specific, cultural evolution cannot explain universalism.

Summary Empiricism is not a good candidate to explain universalism –There is not enough ecological pressure. Culturalism can explain the sharing of categories in a culture, but not universalism. Nativism can explain universalism, but is to slow to follow ecological changes. –Also, recent neurophysiological and molecular studies point out many differences in colour perception between individuals.

Conclusion A blend of all three positions is needed to explain universalism. But language and culture play a crucial role as the catalysts which binds the perceptual categories of individuals. Read the full story at Steels & Belpaeme (2005) Coordinating Perceptually Grounded Categories through Language: A Case Study for Colour. Behavioral and Brain Sciences. To appear.