Computational cognitive modeling

IntroductionCognitive models

Bayesian approachCoin flipping

Concepts and categoriesMFT

Hierarchical Bayes

Computational cognitive modelingBayesian approach

Karla Stepanova

CVUT v PrazeFakulta Elektrotechnicka

Katedra kybernetikyVyzkumna skupina BioDathttp://bio.felk.cvut.cz

Vyzkumna skupina Incognitehttp://incognite.felk.cvut.cz

April 13, 2015

Karla Stepanova Computational cognitive modeling

Hierarchical Bayes

1 Introduction

2 Cognitive models

3 Bayesian approach

4 Coin flipping

5 Concepts and categories

7 Hierarchical Bayes

Hierarchical Bayes

Computational cognitive modeling

= simulations of complex mental processes in different areas of cognition, the goal - to

understand, describe, model and predict observed human behavior

Cognition

=mental process of knowing, including aspects such as awareness, perception, reasoning and

judgement

Latin word cognitio: -co (intensive) + nosecere (to learn)

Modeling

Data never speak for themselves, require a model to be understood and explained

Several alternative models − > compare – quantitative evaluation and intellectual judgement

Hierarchical Bayes

Motivation

Figure: Encephalisation quotient

Hierarchical Bayes

Motivation

Figure: Brain mass: Chart by Nick Matzke

Language

Technology

Art, culture, high tech

Henneberg and de Miguel (2004): Variation in hominid brain size. How much is due to method?Karla Stepanova Computational cognitive modeling

Hierarchical Bayes

Motivation

Figure: Brain learns

Hierarchical Bayes

Motivation

Figure: Brain learns

Hierarchical Bayes

Motivation

Movement is essential for perceptual learning (visually-guided behavior - depth

perception, paw placement, visual cliff, blink to an approaching object etc.)- brain

doesn’t consist of separated neurons

Held, Hein (1963): Movement-produced stimulation in the visually guided behavior

Hierarchical Bayes

Motivation

John Langford:

“A human brain has about 1015 synapses which operate at about 102 per second implying

about 1017 bit ops per second”

So.. A transcription of 1 second of brain activity at the neural spike level would fill up about 40,000 ordinary 300Gb hard drives

...and consumes 20% of body’s oxygen (approx 1.3 kg)

Is it worth?

Kandel (1995)

Hierarchical Bayes

Processing information

Hierarchical Bayes

Internalized representations of world

Hierarchical Bayes

Multimodal association - creating internal representations

Hierarchical Bayes

Cognitive architectures - Marr’s levels of abstraction

Marr’s levels of abstraction

Computational: What are the abstract inference problems that the mind needs to solve,

and what are the solutions? Bayesian parametric modeling

Algorithmic: What information and processing steps are followed to arrive at the solutions?

Connectionism

Implementation: How does the brain carry out these operations?

Sun’s levels

Sociological level - inter-agent processes, collective behavior of agents

Psychological level - individual behavior of agents

Componential level - intra-agent processes, modular construction of agents

Physiological level - biological implementation

Marr, D (1982). Vision. A Computational Investigation into the Human Representation and Processing of Visual

Information.Karla Stepanova Computational cognitive modeling

Hierarchical Bayes

Cognitive architectures - Marr’s levels of abstraction

Marr’s levels of abstraction

Computational: What are the abstract inference problems that the mind needs to solve,

and what are the solutions? Bayesian parametric modeling

Algorithmic: What information and processing steps are followed to arrive at the solutions?

Connectionism

Implementation: How does the brain carry out these operations?

Sun’s levels

Sociological level - inter-agent processes, collective behavior of agents

Psychological level - individual behavior of agents

Componential level - intra-agent processes, modular construction of agents

Physiological level - biological implementation

Marr, D (1982). Vision. A Computational Investigation into the Human Representation and Processing of Visual

Information.Karla Stepanova Computational cognitive modeling

Hierarchical Bayes

Disiderata - Cognitive architectures

Newell (1990). Unified theories of

cognition

Flexibility

Adaptivity

Autonomy

Self-awarness

Operation in real-time and in complex environment

Usage of symbol and abstractions

Usage of language

Learning from environment

Acquiring capabilities through development,

Be realizable as a neural system

Be constructable by an embryological growth process

Arise through evolution

Hierarchical Bayes

Disiderata - Computational cognitive neuroscience model

The neuroscience ideal

A CCN model should not make any assumptions that are known to contradict the current neuroscience literature.

The simplicity heuristic

No extra neuroscientific detail should be added to the model unless there are data to test this component of the model or the

model cannot function without this detail.

The Set-in-Stone Ideal

Once set, the architecture of the network and the models of each individual unit should remain fixed throughout all applications.

The Goodness-of-Fit Ideal

A CCN model should provide good accounts of behavioral and at least some neuroscience data.

G. F. Ashby and S. Helie(2011). A tutorial on computational cognitive neuroscience: Modeling the neurodynamics of

cognition

Hierarchical Bayes

Functionalism

Hierarchical Bayes

Connectionism

Hierarchical Bayes

Similarities and differences

Hierarchical Bayes

The challange

How do we generalize successfully from very limited data?

Hierarchical Bayes

Bayes rule

Hierarchical Bayes

Why Bayes?

Hierarchical Bayes

Examples - Learning word meanings

Hierarchical Bayes

Examples - Vision as probabilistic parsing

Hierarchical Bayes

Examples - Vision as probabilistic parsing

Hierarchical Bayes

Examples - Grammar

Hierarchical Bayes

Examples - Causal learning and reasoning

Hierarchical Bayes

Examples - Motor control

Hierarchical Bayes

Bayes rule

Hierarchical Bayes

Bayes rule - Priors

Prior knowledge about the world − > interpret data in the case of the uncertainity

Prediction - the more uncertain the data, the more the prior should influence the

interpretation

Priors should reflect the statistics of the sensory world

Hierarchical Bayes

Coin flipping

What process produced these sequences?

Hierarchical Bayes

Coin flipping

Contrast simple hypotheses:

h1: “fair coin”, P(H) = 0.5

h2:“always heads”, P(H) = 1.0

Bayes’ rule:

P(h|d) = P(h)P(d |h)∑hiP(hi )P(d |hi )

With two hypotheses, use odds form

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Coin flipping

Hierarchical Bayes

Concepts and categories

Hierarchical Bayes

Concepts and categories

Hierarchical Bayes

Concepts - necessity and sufficiency

Hierarchical Bayes

Concepts - necessity and sufficiency

Hierarchical Bayes

Concepts - graded membership

Hierarchical Bayes

Computational cognitive modeling

Documents

Transcript of Computational cognitive modeling

Computational Cognitive Neuroscience (2012)

Neural Modeling and Computational Neurosciencedidawiki.cli.di.unipi.it/.../computational-neuroscience/neuralmodeling_3.pdf · computational brain, modeling neuron-astrocyte interaction,

Cognitive Modeling of Behavioral Experiments in … Modeling of Behavioral Experiments in Network Science 243 4 Cognitive Modeling In the following, the computational cognitive mechanisms

Computational Explorations in Cognitive Neuroscience

Computational Cognitive Modelling

computational modeling human cognitionmozer/Teaching/syllabi/... · 2004. 11. 23. · Computational Modeling Computer simulation of neural and/or cognitive processes that underlie

Computational modeling for addiction medicine: From ... · Computational modeling for addiction medicine: From cognitive models to clinical applications Woo Young Ahn*,†, Junyi

Computational Cognitive Morphosemantics: Modeling ...nschneid/bls36.pdfComputational Cognitive Morphosemantics: Modeling Morphological Compositionality in Hebrew Verbs ... 2 About

Artificial Intelligence and Cognitive Modeling Laboratory for Cognitive Modeling 4.11.2011 lkm.fri.uni-lj.si.

6 Computational Cognitive Neuroscience Approaches to ...ski.clps.brown.edu/papers/Frank_Strungmann.pdf · 6 Computational Cognitive Neuroscience Approaches to Deconstructing Mental

Toward a computational framework for cognitive biology: …€¦ · Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative

Cognitive Modeling - Introduction

Anatomy and computational modeling of networks underlying ... · Anatomy and computational modeling of networks underlying cognitive-emotional interaction Yohan J. John1, Daniel Bullock2,

Computational Modeling of Cognitive Processes in Plan ... · Computational Modeling of Cognitive Processes in Plan Authoring 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Cognitive Aspects of Computational Modeling and Simulation ... · PDF fileCognitive Aspects of Computational Modeling and Simulation in Teaching and Learning . Osman Ya. şar The College

Computational Cell Modeling

Computational Modeling Module: Electronic Band …coen.boisestate.edu/bknowlton/files/2011/09/Computational-Modeling... · Computational Modeling Module: ... • I will grade your

Explanation and Simulation in Cognitive Science Simulation and computational modeling Symbolic models Connectionist models Comparing symbolism and connectionism.

Handbook on Computational Cognitive Modeling

Introduction to Computational Cognitive Modeling