What is Cognitive Science?

… is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology

(Stanford Encyclopedia of Philosophy)http://plato.stanford.edu/entries/cognitive-science/

Most cognitive scientists are cognitive psychologists or computer scientists…

(from: Schunn et al. 2005)

Cognitive Science

Computer Science/ Artificial Intelligence

Neuroscience

Philosophy

ExperimentalCognitive Psychology Linguistics

Understanding Computation

To understand limits of theories

To understand structure of language

To understand howthe brain works

For human data in various tasks

We will focus mostly on insights from Cognitive Psychology

Interdisciplinarystudy of intelligentbehavior

Areas of Study

• Cognition is about internal processes that are often unobservable, e.g.:

Perception, Attention, Memory, Visual Imagery, Language, Concept Learning, Reasoning

• Need converging evidence from different perspectives to really understand cognitive processes

?

Information Processing

• Information processing models resemble processing in computers – made cognitive psychology popular

• Information made available by the environment is processed by a series of processing systems

• Processing systems transform or alter the information in various systematic ways

• The major goal of research is to specify these processes and structures

Types of Processing

• Bottom-up processing• Top-down processing• Parallel processing• Cascade processing

An early version of the information-processing

approach

purely bottom up or stimulus-driven

A Demonstration of Top-Down Processing

Top-down processing

Later stages of processing affect

earlier stages

can explain effects of Knowledge, memory,

expectations and context

(Kleffner & Ramachandran, ’92)

Why do we seem to have a fairly robust interpretation of which shapes are concave and convex when the perceptual information is perfectly ambiguous? -> perception affected by knowledge

Top-down processing: perception affected by knowledge of world

Top down processing: perception affected by memory

• First time, sine wave speech sounds incomprehensible (to most)

• After hearing the natural utterance, perception of sine-wave speech seems to be quite different

"The steady drip is worse than a drenching rain."

(for more info: http://www.haskins.yale.edu/haskins/MISC/SWS/SWS.html)

http://psiexp.ss.uci.edu/research/teachingP140C/demos/sinewavespeech.aif

http://psiexp.ss.uci.edu/research/teachingP140C/demos/naturalutterance.aif

Sound Induced Illusory Flashes

• Example of parallel and interactive processing: perception of visual event affected by perception of auditory events

• http://www.cns.atr.jp/~kmtn/soundInducedIllusoryFlash2/

McGurk EffectPerception of auditory event affected by visual processing

Harry McGurk and John MacDonald in "Hearing lips and seeing voices", Nature 264, 746-748 (1976).

AVI: http://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.aviMOV: http://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.mov

McGurk Effect

• Demonstrates parallel & interactive processing: speech perception is based on multiple sources of information, e.g. lip movements, auditory information.

• McGurk effect in video: – lip movements = “ga” – speech sound = “ba”– speech perception = “da” (for 98% of adults)

• Brain makes reasonable assumption that both sources are informative and “fuses” the information.

Four Main Approaches

• Experimental cognitive psychology

• Cognitive neuropsychology

• Computational cognitive science

• Cognitive neuroscience

COMPUTATIONAL COGNITIVE SCIENCE

Computer Models

• Computational modeling– Programming computers to model or mimic

some aspects of human cognitive functioning. Modeling natural intelligence.

• Artificial intelligence– Constructing computer systems that produce

intelligent outcomes

Simulations of behavior

Why do we need computational models?

• Provides precision need to specify complex theories. Makes vague verbal terms specific

• Provides explanations

• Can lead to predictions – just as meteorologists use computer models to predict

tomorrow’s weather, the goal of modeling human behavior is to predict performance in novel settings

Production Systems

Connectionist Networks

• Also known as: – PDP: parallel distributed processing approach– Artificial Neural Networks

• Alternative to traditional information processing models

• Connectionist models are networks of simple processors that operate simultaneously

• Some biological plausibility

idealized neurons (units)

Output

Processor

Inputs

Abstract, simplified description of a neuron

Connectionist Networks

• Inspired by real neurons and brain organization but are highly idealized

• Can spontaneously generalize beyond information explicitly given to network

• Retrieve information even when network is damaged (graceful degradation)

• Networks can be taught: learning is possible by changing weighted connections between nodes

• Diagram showing how the inputs from a number of units are combined to determine the overall input to unit-i. Unit-i has a threshold of 1; so if its net input exceeds 1 then it will respond with 1, but if the net input is less than 1 then it will respond with –1

Different ways to represent information with connectionist networks: localist representation

concept 1

concept 2

concept 3

Each unit represents just one item “grandmother” cells

1 0 0 0 0 0

0 0 0 1 0 0

0 1 0 0 0 0

Unit 1

Unit 2Unit 3

Unit 4Unit 5

(activations of units; 0=off 1=on)

Unit 6

Coarse Coding/ Distributed Representations

concept 1

concept 2

concept 3

1 1 1 0 0 0

1 0 1 1 0 1

0 1 0 1 0 1

(activations of units; 0=off 1=on)

Each unit is involved in the representation of multiple items

Unit 1

Unit 2Unit 3

Unit 4Unit 5 Unit 6

Advantage of Distributed Representations

• Efficiency – Solve the combinatorial explosion problem:

With n binary units, 2n different representations possible. (e.g.) How many English words from a combination of 26 alphabet letters?

• Damage resistance– Even if some units do not work, information is still

preserved – because information is distributed across a network, performance degrades gradually as function of damage

– (aka: robustness, fault-tolerance, graceful degradation)

Suppose we lost unit 5

concept 1

concept 2

concept 3

1 1 1 0 0 0

1 0 1 1 0 1

0 1 0 1 0 1

(activations of units; 0=off 1=on)

Can the three concepts still be discriminated?

Unit 1

Unit 2Unit 3

Unit 4Unit 5 Unit 6

Multi-layered Connectionist Networks

• Activation flows from a layer of input units through a set of hidden units to output units

• Weights determine how input patterns are mapped to output patterns

• Network can learn to associate output patterns with input patterns by adjusting weights

• Hidden units tend to develop internal representations of the input-output associations

• Backpropagation is a common weight-adjustment algorithm

hidden units

input units

output units

Example: NETtalk

7 groups of 29 input units

26 output units

80 hidden units

_ a _ c a t _ 7 letters of text input

(after Hinton, 1989)

target letter

teacher

/k/

target output

Connectionist network learns to pronounce English words: i.e., learns spelling to sound relationships. Listen to this audio demo.

Other demos of neural networks

Hopfield network

http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html

Backpropagation algorithm and competitive learning:

http://www.psychology.mcmaster.ca/4i03/demos/demos.html

Competitive learning:

http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html

Various networks:

http://diwww.epfl.ch/mantra/tutorial/english/

Optical character recognition:

http://sund.de/netze/applets/BPN/bpn2/ochre.html

Brain-wave simulator

http://www.itee.uq.edu.au/%7Ecogs2010/cmc/home.html

Limitations of Modeling Approach

• Computational models are rarely used to make new predictions

• Connectionist models do not resemble the human brain

• Numerous models can generally be found to “explain” the same set of findings

• Computational models often fail to capture the scope of cognitive phenomena

• Computational cognitive science may fail to deliver a general unified theory of cognition