cognitive neuroscience of emotions

7/28/2019 Cognitive Neuroscience of Emotions

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Possible Solutions from the

Cognitive Neuroscience ofEmotion

David Sander

Geneva Emotion Research Group

University of Geneva


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A role for CN in designing

emotion-oriented systems?

Levels of analyses in CN

Problems, and CN directions

Artificial emotions

Recognition of facial expression


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What is CN?

The emergence of a disciplineCognitive Neuroscience Institute (Dartmouth): 1982

Journal of Cognitive Neuroscience: 1988

Cognitive Neuroscience Society: 1993

Institute of Cognitive Neuroscience (London): 1996

the task of cognitive neuroscienceis to mapthe information-processing structure of the human mind

and to discover how this computational organization isimplemented in the physical organization of the brain

Tooby & Cosmides (2000)


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B

E

H

A

V

I

O

R

Many Psychologicalmodels are sitting only

on a behavioral account


The perils of sitting on a one-legged stool(Kosslyn & Intrilligator, 1992)

Information-

processing modelOnly one

paradigmatic

leg

a stabilityperil for the

model


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B

R

A

I

N

Many Neurobiological

and some

neuropsychological

models are sitting only

on a brain account



Information-


paradigmatic

leg

a stability

peril for the

model


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Many Artificial

Intelligence models are

sitting only on a

computational account

C

O

M

P

U

T

A

T

I

O

N



Information-


paradigmatic

leg

a stability

peril for the

model


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Information-

processing model

B

E

H

A

V

I

O

R

BR

A

I

N

COMPUTATI

ON

The advantage of sitting on a three-legged stool

Information-

processing modelThree

paradigmatic

legs

more stability

for the model

Ideal CN

models are

sitting onbehavioral,

brain, and

computational

accounts


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Cognitive Neuroscience Triangle

Behavior

Computation Brain

Analyses Models Neural Activity Areas & Connections

(Neurophysiology) (Neuroanatomy)


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Emotion-oriented system, but......oriented towards which level?

BehavioralComputational

(or representational)

Neural

Other (?)

An artificialbehaviorally believable

output response given

a natural input,

whatever the

plausibility of thearchitecture

Problems, and CN directions: Problem 1

1


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Natural Processes versus Artificilal EfficiencyIs it important to know how the human brain computes emotion in

order to develop a humaine emotion-oriented system?

Problem 1

Appraisal of a threat,Autonomic activity,

Withdrawing,Expression, andFeeling of being afraid

humaine

emotion-oriented

system

Behavioral plausible output:Autonomic activity,Withdrawing,Expression of fear.

P bl 1


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Emotion-oriented system, but...

...oriented towards which level?

Behavioral



a natural input,

whatever the

plausibility of thearchitecture

Computational


An artificial system that

is constrained by the

functional architecturedesigned by CN results

Problem 1

CN is useless


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Selecting the functional architecture to be

implemented in an artificial emotion system

Problem 2

i. Dissociation of emotional processes

ii. Implementation of emotional

processes in the brain

iii. Time course of emotional processes


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Three main approaches:

Basic Emotions Approach

Dimensional Approach

Systems-level Approach

Problem 2: selecting the functional architecture

2 i f i i


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Most of the past Cognitive Neuroscience researches

on emotion focused on the attempt to find specific

brain regions implementing discrete basic emotions:

The various classes of emotion are mediated by

separate neural systems (...) (LeDoux, 1996)

CN and Basic Emotions


P bl 2 l i h f i l hi


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CN and Basic Emotions


hman & Mineka (2001):The amygdala is a fear module

Basica!y, the fear module is a device for activatingdefensive behaviour and associated psychophysiological

responses and emotional feelings to threatening stimuli.


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237-239

Panksepp (2003)

P bl 2 l ti th f ti l hit t


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Some recent Cognitive Neuroscience researches were

interested in dissociating the dimensions ofValence

andIntensity (Anderson et al., 2003; Small et al.,

2003).

(!! IntensityActivation !!)

CN and the Dimensional ApproachProblem 2: selecting the functional architecture


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Valence versus Intensity

Anderson et al. (2003),Nature Neuroscience

P bl 2 l ti th f ti l hit t


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Some CN researchers take into consideration the

complexity of emotion by parsing its subcomponents

at the systems-level and, sometimes, by attempting to

model the interactions between the proposed

processes:

Action tendencies (e.g., Davidson)

Somatic signals (e.g., Damasio)

Feeling (e.g., Lane)

CN at the systems-level



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Action tendencies (e.g., Davidson, 1995)

Perception/Production

distinction between perception ofthe emotional value of a stimulusversus the production ofexpressive behavior

Anterior activation

asymmetry model

Left anterior region

associated with approach-

related emotions

Right anterior region

associated with withdrawal-

related emotions

S i i l ( D i 1998)


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A critical function of somatic-related signals and their

integration with the otherbrain signals.

Somatic signals (e.g., Damasio, 1998)


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Feeling

Feeling as an integration of some emotional signals

The conscious experience is integratedvia a

convergence zone that could be the Anterior Cingulate

and/or the Medial Prefrontal Cortex (Reiman. 1997;

Lane, 2000).

The subjective feeling is integratedvia the

synchronization of other components (Scherer, 2003).

Binding through synchronization was proposed for the

visual system for example.

A i l Th


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Action Tendencies Withdrawal

Subjective Feeling

I am a$aid

Appraisal Processes

Relevant(e.g., unpleasant, goal obstructive),Difficult to cope with

Event

Emotional Expression

Autonomic activation

Appraisal Theory


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Event

Amy

Coarse

exteroceptive

processing

Relevance

detection

Sensory

Thalamus

Somatosensory-

related corticaland subcortical

structures

Body state

Emotional

expression

Somatic maps

Neuroendocrine/Autonomic/Somatic NS

DLPFC

ACC

Goalrepresentation

Regulation,coping

Action

tendency

Normative

Significance

MPFCHippo OFC

Conte

xt

depende

nce

Sensory

cortices

Integrative

cortices

High level

exteroceptive

processing

Implication

Intrinsic

pleasant-

nessVentral

Striatum

Motivational

bases (reward)

Cognitive Neuroscience of Appraisal Processes

Sander & Scherer, in prep.

Problem 3


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Recognition of facial expressionProblem 3

(from Haxby et al., 2000)


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Colliculus-pulvinar-amygdala

Pathway

LGB: Lateral Geniculate Body

SC: Superior Colliculus

V1: Primary Visual Cortex

Pulvinar

SC

Amygdala

Visual Cortex

LGB

Retina

V1


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Stimulus120 ms:

Fast early processing of highly

relevant events

From Adolphs (2002).Current Opinion in Neurobiology

Recognition of a facial

expression of fear

A, amygdala; FFA, fusiform face area;

INS, insula; O, orbitofrontalcortex;SC, superior colliculus; SCx, striate

cortex; SS, somatosensorycortex; STG,

superior temporal gyrus; T, thalamus.


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170 ms:

- detailed perception;

- emotional reaction involving the

body

> 300ms:

Conceptual knowledge of the

emotion signaled by the face


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Emotion-oriented system, but...

...oriented towards which level?

Behavioral



a natural input,

whatever the

plausibility of the

architecture

Computational



is constrained by the

functional architecture

designed by CN results

CN is useless CN can help

Neural


is constrained by thefunctional architecture

andnatural neural

networks properties

CN can help

Problem 4


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Multimodal integration

Timing: Results suggest that audio-visual emotional binding is

early in time (110 ms post-stimulus)

Integrative structure

->Test of multimodal emotion display in ECA using brain-imaging

Amygdala response

to congruent fearfulvoices and faces

Dolan et al. (2001)

Problem 4

Problem 5


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Influence of dynamism in the facial

expression on perceived emotion

Emotion morphs depicted expression changes of gettingscared or getting angry in real-time.

Brain regions implicated in processing facial affect, including

the amygdala and fusiform gyrus, showed greater responses to

dynamic versus static emotional expressions.

Labar et al. (2003)

Problem 5


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ConclusionCognitive Neuroscience can help to find solutions for emotion-

oriented systems mainly if they are focused on thecomputational, and/or the neural levels.

Artificial emotions: A decisive choice between:

as many systems as emotions

different systems for approach-related versus withdrawal-related emotions

a system for intensity, a system for valence (but, onlyfeeling)

a system for each emotional component

Recognition of emotional expression: Modeling two pathways(one for coarse and fast processing, and one for detailed proc.).

A computational model of emotional processes would benefit

from modeling other closely related cognitive processes, such as

attention.

cognitive neuroscience of emotions

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