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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
Levels of analyses in CN
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
Levels of analyses in CN
The perils of sitting on a one-legged stool(Kosslyn & Intrilligator, 1992)
Information-
processing modelOnly one
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
Levels of analyses in CN
The perils of sitting on a one-legged stool(Kosslyn & Intrilligator, 1992)
Information-
processing modelOnly one
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
An artificialbehaviorally believable
output response given
a natural input,
whatever the
plausibility of thearchitecture
Computational
(or representational)
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
Problem 2: selecting the functional architecture
P bl 2 l i h f i l hi
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CN and Basic Emotions
Problem 2: selecting the functional architecture
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
Problem 2: selecting the functional architecture
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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
An artificialbehaviorally believable
output response given
a natural input,
whatever the
plausibility of the
architecture
Computational
(or representational)
An artificial system that
is constrained by the
functional architecture
designed by CN results
CN is useless CN can help
Neural
An artificial system that
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.