neurocognitive

7/25/2019 Neurocognitive

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Neurocognitive science:Neurocognitive science:

mind from brainmind from brain??

Wodzisaw DuchWodzisaw Duch

Department of Informatics,

Nicolaus Copernicus niversit!, "oru#, $%

Dept& of Comp& 'cience, 'chool of Comp&(ngineering, Nan!ang "echnological niversit!,

'ingapore

)oogle: Duch


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Cognitive 'cienceCognitive 'cience

Cognitive 'cienceCognitive 'cience

"he Central $arado. of Cognition: how can the structure andmeaning, e.pressed in s!mbols and ideas at the mentallevel, result from numerical processing at the brain level?

/er! few general laws in ps!cholog! 0mostl!

ps!choph!sical1&$s!cho2log! lost the soul 0ps!che1?

Cognitive science: mi.ture 0s!ntop!?1 of cognitiveps!cholog!, neurosciences, 3I, linguistics, philosoph! of

mind, ps!choph!sics, anthropolog! &&& but &&&

"here is no central model of mind in cognitive science4 foundations of cognitive sciences are full ofphilosophical problems 0'earle, Chalmers, Nagel, *acson &&&1


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5ind the )ap5ind the )ap)ap between neuroscience and ps!cholog!: cognitivescience is at best incoherent mi.ture of various branches&

Is a satisfactor! understanding of the mind possible ?

6oger 'hepard, Toward a universal law of generalizationfor psychological science0'cience, 'ept& 7891:;What is reerent conception of the problem&

@ 5ind is what the brain does, a potentiall! conscioussubset of brain processes&

Aow to appro.imate the d!namics of the brainto get satisfactor! 0geometric?1 picture of the mind?


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From molecules ...From molecules ...

From molecules ...From molecules ...

10-10 m, molecular level: ion channels, synapses, membraneproperties, neurochemistry, biophysics, psychopharmacology,

mind from molecular perspective (Ira Blac!"

10-# m, single neurons: biophysics, computational neuroscience ($%!,

compartmental models, spies, &', &'), neurochemistry *

neurophysiology.

10-+ m, neural assemblies: cortical columns, multielectrode * large

electrode recordings, microcircuits, neurodynamics,

neuroscience, $%.

10- m, small netors: neurodynamics, recurrence, spiing neurons,

synchroniation, neural code (li/uid"!, memory effects,

multielectrode recordings, neurophysiology, $%.


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to behavior.to behavior.

to behavior.to behavior.

10-

m, mesoscopic netors: self-organiation, sensory and motormaps, population coding, continuous activity models,

mean field theories, brain imaging, 223, 423, f45I.

10-1 m, transcortical netors, large brain structures: simplified

models of corte6, limbic structures, subcortical nuclei, integration of

functions, concept formation, sensorimotor integration,neuropsychology, computational psychiatry ...

7nd then a miracle happens

1m, $8%, brain level: intentional behavior, psychology,

thining, reasoning, language, problem solving, symbolicprocessing, goal oriented noledge-based systems, 7I.

9here is psyche, the inner perspective"

&ost in translation: netors ; finite state automata ; behavior

7lternative: latonic model ; mental events.


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Brain-lie computingBrain-lie computing

Brain-lie computingBrain-lie computing

Brain states are physical, spatio-temporal states of neural tissue.

< I can see, hear and feel only my brain states= 26: change blindness.< $ognitive processes operate on highly processed sensory data.< 5edness, seetness, itching, pain ... are all physical states of brain

tissue.

In contrast to computer registers,brain states are dynamical, and

thus contain in themselves many

associations, relations.

Inner orld is real= 4ind is basedon relations of brain>s states.

$omputers and robots do not

have an e/uivalent of such 94.


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%tatic latonic model: motivation%tatic latonic model: motivation%tatic latonic model: motivation%tatic latonic model: motivation

lato believed in reality of mind, ideal formsrecognied by intellect.

7 useful metaphor: perceivedmind content is lie a shado ofideal, real orld of ob?ectspro?ected on the all of a cave.

(draing: 4arc $ohen!

5eal mind ob?ects: shados of neurodynamics"

8eurocognitive science: sho ho to do it=


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hysics and psychologyhysics and psychologyhysics and psychologyhysics and psychology

5. %hepard (BB%, 001!:psychological las should be formulated

in appropriate psychological abstract spaces.

hysics - macroscopic properties results from microscopic interactions.

)escription of movement - invariant in appropriate spaces:


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-spaces-spacessychological spaces:

. &ein, 'he conceptual representation and themeasurement of psychological forces (1C+D!, cognitivedynamic movement in phenomenological space.

3eorge elly (1CEE!, personalconstruct psychology ($!,geometry of psychologicalspaces as alternative to logic.

7 complete theory of cognition,action, learning and intention.

$ netor, society, ?ournal,softare


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-space definition-space definition

-space: region in hich e may place and classifyelements of our e6perience, constructed and evolving,a space ithout distanceG, divided by dichotomies.

-spaces should have (%hepard 1CEH-001!:


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&as of generaliation&as of generaliation&as of generaliation&as of generaliation%hepard (1CDH!, niversal la of generaliation.

'enenbaum, 3riffith (001!, Bayesian frameor unifying set-theoretic approach (introduced by 'versy 1CHH! ith %hepard ideas.

3eneraliation gradients tend to fall off appro6imately e6ponentially

ith distance in an appropriately scaled psychological space.

)istance - from 4)% maps of perceived similarity of stimuli.

3()! probability of response learned to stimulus for )0, for

many visualJauditory tass, falls e6ponentially ith the distance.


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4inds or in lo )=4inds or in lo )=4inds or in lo )=4inds or in lo )=4ind uses only those features that are useful to actJdecide.

'he structure of the orld is internalied in the brain.

+ e6amples of elegant lo-) mental principles in vision:

< In a +-) vector space, in hich each variation in naturalillumination is cancelled by application of its inverse from the

three-dimensional linear group of terrestrial transformations ofthe invariant solar source, color constancy is achieved.

< ositions and motions of ob?ects represented as points andconnecting geodesic paths in the #-) manifold (+-) 2uclidean

group of positions and +-) rotation of each ob?ect! conservetheir shapes in the geometrically fullest and simplest ay.

< inds of ob?ects support optimal generaliationJcategoriationhen represented as connected regions ith shapes

determined by Bayesian revision of ma6imum-entropy priors.


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Kb?ect recognitionKb?ect recognitionKb?ect recognitionKb?ect recognition8atural ob?ect recognition (%. 2delman, 1CCH!%econd-order similarity in lo-dimensional (L+00! space is sufficient.

opulation of columns as ea classifiers oring in chorus - stacing.


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%tatic latonic model%tatic latonic model%tatic latonic model%tatic latonic model

8eton introduced space-time, arena for physical events.4ind events need psychological spaces.

Goal: integrate neural and behavioral information in one model,

create model of mental processes at intermediate level beteen

psychology and neuroscience.

Static version: short-term response properties of the brain,

behavioral (sensomotoric! or memory-based (cognitive!.

Approach:


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Mo to mae static model"Mo to mae static model"Mo to mae static model"Mo to mae static model"

From neural responses to stimulus spaces.Bayesian analysis of multielectrode responses (FNldia!.

(riOs!, i1..N computed from multi-electrode measurements

'he posterior probability (sOr! (stimulus O response!

Bayes la:

( ) ( )( )

( )

11 2

' 1

( ) |

| | , ..

( ') | '

N

i

iN N

i

s i

P s P r s

P s r P s r r r

P s P r s

=

=

= =

opulation analysis: visual ob?ect represented

as population of column activities.

%ame for ords and abstract ob?ects

(evidence from brain imaging!.


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%emantic memory%emantic memory

7utoassociative netor, developing internalrepresentations (4c$lleland-8aughton-K>5eilly, 1CCE!.

7fter training distance relations beteen differentcategories are displayed in a dendrogram, shoingnatural similaritiesJ clusters.

4)% mappings: min (Rij rij!

from internal neural activations@

from original data in the -space - hypercube,dimensions

for predicates, e6. robin(x! P0, 1Q@

from psychological e6periments, similarity matrices@

sho similar configurations.


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8eural distances8eural distances

7ctivations of groups of neurons presented in activation spacedefine similarity relations in geometrical model.


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%imilarity beteen concepts%imilarity beteen concepts

&eft: 4)% on vectors from neural netor.5ight: 4)% on data from psychological e6periments ith perceivedsimilarity beteen animals.


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From neurodynamics to -spacesFrom neurodynamics to -spacesFrom neurodynamics to -spacesFrom neurodynamics to -spaces

4odeling inputJoutput relations ith some internal parameters.

9alter Freeman: model of olfaction in rabbits, E types of odors, Etypes of behavior, very comple6 model in beteen.%implified models: M. &il?estrNm.

7ttractors of dynamics in high-dimensional space ; via fuy symbolic

dynamics allo to define probability densities ()F! in feature spaces.

4ind ob?ects - created from fuy prototypesJe6emplars.


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4ore neurodynamics4ore neurodynamics

7mit group, 1CCH-001,

simplified spiing neuronmodels of column activityduring learning.

Formation of ne attractors

;formation of mind ob?ects.

)F: p(activity of columnsO

given presented features!

%tage 1: single columns

respond to some feature.%tage : several columns

respond to different features.

%tage +: correlated activity

of many columns appears.


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$ategory learning$ategory learning&arge field, many models.

$lassical e6periments: %hepard, Movland and Renins (1C#1!,

replicated by 8osofsy et al. (1CC!

roblems of increasing comple6ity@ results determined by logical rules.

+ binary-valued dimensions:

shape (s/uareJtriangle!, color (blacJhite!, sie (largeJsmall!. ob?ects in each of the to categories presented during learning.

'ype I - categoriation using one dimension only.

'ype II - to dim. are relevant (SK5 problem!.

'ypes III, IT, and T - intermediate comple6ity beteen 'ype II - TI.7ll + dimensions relevant, Usingle dimension plus e6ceptionU type.

'ype TI - most comple6, + dimensions relevant,

logic enumerate stimuli in each of the categories.

)ifficulty (number of errors made!: 'ype I L II L III V IT V T L TI


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$anonical neurodynamics.$anonical neurodynamics.9hat happens in the brain during category learning"

$omple6 neurodynamics L; simplest, canonical dynamics.

For all logical functions one may rite corresponding e/uations.

For SK5 (type II problems! e/uations are:

( ) ( )

( )

( )

( )

22 2 2

2 2 2

2 2 2

2 2 2

1, , 34

3

3

3

V x y z xyz x y z

Vx yz x y z x

x

Vy xz x y z y

yV

z xy x y z zz

= + + +

= = + +

= = + +

= = + +

4

4

4

$orresponding feature space for relevant

dimensions 7, B


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Inverse based ratesInverse based rates5elative fre/uencies (base rates! of categories are used for classification:

if on a list of disease and symptoms disease $ associated ith ($, I!symptoms is + times more common as 5,then symptoms $ ; $, I ; $ (base rate effect!.

redictions contrary to the base:inverse base rate effects (4edin, 2delson 1CDD!.

7lthough $ A I A 5 ; $ (#0W ansers!$ A 5 ; 5 (#0W ansers!

9hy such ansers"sychological e6planations are not convincing.

2ffects due to the neurodynamics of learning"

I am not aare of any dynamical models of such effects.


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IB5 neurocognitive e6planationIB5 neurocognitive e6planation

sychological e6planation:

R. rusche, Base 5ates in $ategory &earning (1CC#!.

5 is attended to because it is a distinct symptom, although $ is morecommon.

Basins of attractors - neurodynamics@

)Fs in -space P$, 5, I, $, 5Q.

5 A $ activation leads more fre/uently

to 5 because the basin of attractor for 5 is

deeper.

$onstruct neurodynamics, get )Fs.nfortunately these processes are in E).

rediction: ea effects due to order and timing of presentation($, 5! and (5, $!, due to trapping of the mind state by differentattractors.


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&earning&earning

8eurocognitive sychology

IA$ more fre/uent ; strongersynaptic connections, larger and

deeper basins of attractors.

%ymptoms I, $ are typical for $because they appear more often.

'o avoid attractor around IA$leading to $, deeper, morelocalied attractor around IA5is created.

5are disease 5 - symptom I ismisleading, attention shifted to5 associated ith 5.

oint of vie


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robingrobing

8eurocognitive sychology

oint of vie

7ctivation by I leads to $ becauselonger training on IA$ createslarger common basin than IA5.

I ; $, in agreement ith baserates, more fre/uent stimuli IA$are recalled more often.

7ctivation by IA$A5 leadsfre/uently to $, because IA$puts the system in the middle ofthe large $ basin and even for 5geadients still lead to $.

IA$A5 ; $ because allsymptoms are present and $ ismore fre/uent (base rates again!.

7ctivation by 5A$ leads morefre/uently to 5 because the basinof attractor for 5 is deeper, andthe gradient at (5,$! leads to5.

$A5 ; 5 because 5 is distinctsymptom, although $ is morecommon.


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7utomatiation of actions7utomatiation of actions

&earning: initially conscious involvement (large

brain areas active! in the end becomes automatic,subconscious, intuitive (ell-localied activity!.

Formation of ne resonant states - attractors inbrain dynamics during learning ; neural models.

5einforcement learning re/uires observing and evaluating hosuccessful are the actions that the brain has planned and is e6ecuting.

5elating current performance to memoried episodes of performancere/uires evaluation A comparison (3ray X subiculum!, folloed by

emotional reactions that provide reinforcement via dopamine release,facilitating rapid learning of specialied neural modules.9oring memory is essential to perform such comple6 tas.2rrors are painfully conscious, and should be remembered.

$onscious e6periences provide reinforcement (is this main function ofconsciousness"!@ there is no transfer from conscious to subconscious.


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Feature %pace 4appingFeature %pace 4appinglatonic 4odel: inspiration for F%4 ()uch 1CC! - neurofuy system for

modeling )Fs using separable transfer (fuy membership! functions.$lassification, e6traction of logical rules, decision support.

%et up (fuy! facts e6plicitly as dense regions in the feature space@Initialie by clusteriation - creates rough )F landscape.'rain by tuning adaptive parameters @

novelty criteria allo for creation of ne nodes as re/uired.%elf-organiation of G(X@P! prototypes of ob?ects in the feature space.

5ecognition: find local ma6imum

of the F(X@P! function.

( )

( )

,

1

1

( ; ) ;

( ; ) ;

Np p

p p i i i

in

p

p p

p

g g x P

F W g

=

=

=

=

X P

X P X P


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Intuitive thiningIntuitive thining

3eometric representation of facts:

A increasing, 0 constant, - decreasing.

Khm>s la V=IR; irhoff>sV=V1+V.

'rue (I-,V-,R0!, (I+,V+,R0!,false (I+,V-,R0!.

E las: + Khm>s * irhoff>s las.7ll las 7BA$, 7B$ , 7-1B-1A$-1,

have identical geometric interpretation=

1+ true, 1 false facts@ simple -space,

comple6 neurodynamics.

Yuestion in /ualitative physics:

if Rincreases, R1and Vtare constant,

hat ill happen ith current and V1, V "


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Intuitive reasoningIntuitive reasoning

E las are simultaneously fulfilled, all have the same representation:

Yuestion: If R2=+, R10and V 0, hat can be said about I, V1, V "

Find missing value giving F(V0, R, I,V1, V, R10, RA! ;0%uppose that variableX A, is it possible"

8ot, if F(V0, R, I,V1, V, R10, RA! 0, i.e. one la is not fulfilled.

If nothing is non 111 consistent combinations out of 1DH (EW! e6ist.

5

1 2 1 2

1

( , , , , , , ) ( , , )t i i i ii

F V R I V V R R F A B C=

=

Intuitive reasoning, no manipulationof symbols@ heuristics: selectvariable giving uni/ue anser.%oft constraints or semi-/uantitative; small OF%4(S!O values.


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latonic mind modellatonic mind modelFeature detectorsJeffectors: topographic maps.Kb?ects in long-term memory (parietal, temporal, frontal!: local -spaces.

4ind space (oring memory, prefrontal, parietal!: construction of mindspace featuresJob?ects using attention mechanisms.Feelings: gradients in the global space.


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&anguage for psychology&anguage for psychologyrecise language, replacing fol psychology,

reducible to neurodynamics.4ind state dynamics modeled by gradientdynamics in mind space, sticingG to )Fma6ima, for e6ample:

here g(x) controls the sticingG and

(t! is a noise A e6ternal forces term.

4ind state has inertia and momentum@transition probabilities beteen mind ob?ects

should be fitted to transition prob. beteen

corresponding attractors of neurodynamics

(Y4-lie formalism!.

rimary mind ob?ects - from sensory data.

%econdary mind ob?ects - abstract categories.

( )( )( )

(0) ;

( ) ( ; ) 1 ; ( )

inp

S

S X

S t M S t g M S t t

=

= + +4


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%ome connections%ome connections3eometricJdynamical ideas related to mind may be found in many fields:

Neuroscience:

). 4arr (1CH0! Zprobabilistic landscapeG.

$.M. 7nderson, ).$. van 2ssen (1CC!: %uperior $olliculus )F maps

%. 2delman: Zneural spacesG, ob?ect recognition, global representation space

appro6imates the $artesian product of spaces that code ob?ect fragments,representation of similarities is sufficient.

Psychology:

. &evin, psychological forces.

3. elly, ersonal $onstruct sychology.5. %hepard, universal invariant las.

. Rohnson-&aird, mind models.

Folk psychology: to put in mind, to have in mind, to eep in mind

(mindmap!, to mae up one[s mind, be of one mind ... (space!.


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4ore connections4ore connectionsAI: problem spaces - reasoning, problem solving, %K75, 7$'-5,

little or on continuous mappings (4ac&ennan! instead of symbols.

Engineering: system identification, internal models inferred from inputJoutputobservations X this may be done ithout any parametric assumptions if anumber of identical neural modules are used=

Philosophy:. 3\rdenfors, conceptual spaces

5.F. ort, '.van 3elder, ed. 4ind as motion (4I' ress 1CCE!

Linguistics:

3. Fauconnier, 4ental %paces ($ambridge .. 1CC!.

4ental spaces and non-classical feature spaces.

R. 2lman, &anguage as a dynamical system@ R. Feldman neural basis@

%tream of thoughts, sentence as a tra?ectory in -space.

Psycholinguistics: '. &andauer, %. )umais, &atent %emantic 7nalysis,

sych. 5ev. (1CCH! %emantic for #0 ords corpus re/uires about +00 dim.


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$onclusions$onclusions$omple6 neurodynamics ; dynamics in -spaces.

&o-dimensional representation of mental events.

Is this a good bridge beteen mind and brain"

sychological interpretations may be illusory=

seful applications of the static latonic model.

Kpen /uestions:

Migh-dimensional -spaces ith Finsler geometry needed for visualiation

of mind events - ill such model be still understandable"

4athematical characteriation of mind space" 4any choices.

$hallenges: simulations of brains may lead to mind functions, but ithout

conceptual understanding@

neurodynamical models ; -spaces for money categoriation.

7t the end of the road: physics-lie theory of events in mental spaces"


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$apers 0)oogle: Duch1$apers 0)oogle: Duch1

@ W& Duch, )eometr!czn! model um!su&Bognit!wist!a i 5edia w (duaci - 0++1 7882+E

@ Fiz!a um!su& $ostGp! Fiz!i HED 0++1, 8+27E@ rain2inspired conscious computing architecture&

*ournal of 5ind and ehavior +- 0+H1 72++

@ $latonic model of mind as an appro.imation toneurod!namics& In: rain2lie computing and intelligentinformation s!stems 0'pringer, 'ingapore 7881, chap& +,pp& J872H7+

@ Computational ph!sics of the mind&Computer $h!sics Communication 8 0788-1 7E-27HE

@ From cognitive models to neurofuzz! s!stems 2 the mindspace approach& '!stems 3nal!sis25odeling2'imulation +J0788-1 HE2-H

@ From brain to mind to consciousness without hard problems,'!mpozum Bognit!wne KwiadomoLM a $ercepca& 35 788-

@ 5ind space approach to neurofuzz! s!stems& In: $roc& of the

* N l N t ' 788J " b *7E 7J

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