Transcript Emotional Consciousness - University of Waterloo

Sketch of a Neurocomputational
Explanation of
Emotional Consciousness
Paul Thagard
University of Waterloo
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Mechanistic Explanation of
Emotional Consciousness
1.
2.
3.
4.
5.
6.
Consciousness
Explanation
Brains
GAGE
Objections
Conclusions
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Origins of Consciousness
• Creation: God’s gift.
• None: consciousness is mythical, like
demons and caloric.
• By-product of evolution of cognitive
complexity.
• Evolution by natural selection: increases
ability to survive and reproduce.
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What is the Function of
Consciousness?
•
•
•
•
•
Emergency interrupt?
Improve perception, sensation, inference?
Improve problem solving?
Improve teaching of skills?
All of these seem like minor improvements.
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Humphrey’s Social Theory
• Humphrey:
The function of
consciousness is social,
improving the ability to
understand, predict, and
manipulate the behavior
of others.
• Implication: Emotional
consciousness is central.
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What are Emotions?
• Cognitive theory: Emotions are appraisals
of situations.
• Physiological theory: Emotions are
physiological reactions to situations.
• Integration: Emotions are mental (brain)
states caused by interplay of physiological
reactions and cognitive appraisals.
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Explanation Targets
• What is it like to be happy? Highly
misleading question. Alive.
• Why does someone become happy?
• Why does someone go from being happy to
being sad?
• How does happiness affect behavior?
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How Consciousness Helps
• There are unconscious emotions.
• If you become conscious of your emotions, then
you make approximate generalizations:
– If <situation> then <emotion>
– If <emotion> then <behavior>
• Linguistic representation of emotional states
requires conscious awareness of them.
• Explanation target: How do brains become aware
of emotional states in order to reason about them?
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Mechanistic Explanation
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•
•
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How does a bicycle move?
Parts: frame, wheels, gears, chain, pedals, etc.
Relations: e.g. pedal connected to gear.
Behaviors: e.g. pedal moves when pressed.
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Brains: Neurons
• Parts of brains: neurons, glia, neural populations,
brain areas.
• 100 billion neurons, with thousands of
connections.
• Main behavior: spike as result of chemical inputs.
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Brains: Neural Populations
• Computational Functions of Neural
Populations (Eliasmith & Anderson, 2003).
– Encode information, e.g. perceptual input
encoded by spiking patterns of a population.
– Decode information, taking inputs from other
neural populations.
– Transform information, changing the internal
representation of information. EXAMPLES?
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Brains: Areas
• Areas are anatomically identifiable
collections of neural populations that are
highly interconnected with each other.
• For emotion, some important areas are:
amygdala (fear), nucleus accumbens
(reward), insula, ventromedial and
dorsolateral prefrontal cortex.
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Neural Mechanism
• GAGE model: Wagar & Thagard,
Psychological Review, 2004.
VMPFC
Amg
Somatic state
HC
VTA
To Action/
Overt
NAc
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Key Brain Areas
• Prefrontal cortex: responsible for
reasoning.
• Ventromedial PFC: connects input from
sensory cortices with amygdala etc.
• Amygdala: processes emotional signals,
especially fear. Somatic input.
• Nucleus accumbens: processes emotional
signals, especially reward.
• Hippocampus: crucial for memory
formation.
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How GAGE Explains Phineas
• Damasio: Effective decision making
depends on integration of cognitive
information with somatic markers.
• Damage to VMPFC prevents this
integration.
• GAGE shows a plausible mechanism for
integration that is disrupted by VMPFC
damage.
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GAGE II - under development
1. Incorporate additional brain areas: insula,
anterior cingulate cortex, dorsolateral
prefrontal cortex.
2. Incorporate higher level representations of
relational information, to describe
situation-emotion-behavior connections.
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Components of Emotional
Consciousness
1. Spiking neurons are organized into neural
populations.
2. Some neural populations encode perceptual and
somatic inputs.
3. Some neural populations decode, encode, and
transform inputs from (2) plus cognitive inputs.
4. Feedback loops are common.
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Higher Order Representations
Hypothesis 1: There are neural populations,
possibly distributed across brain areas,
that encode emotions.
Hypothesis 2: There are neural populations
that encode generalizations of the form
<situation>
<emotion>
<emotion>
<behavior>
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Agenda
• Design system of brain areas that conducts
neural transformations of transformations of
sensory, somatosensory, and memory
inputs.
• Apply this system to explaining emotional
phenomena.
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Explanation Targets
• Onset and end of
grief
positive and negative
emotions.
anger
• Increase and decrease
- sadness
in intensity.
Intense
elation
joy
happiness
bored
anxious
+
amused
Weak
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Mechanisms
• Onset of positive emotions results from
perceptual or memory input that activates
reward areas.
• Intensity is a function of degree of cognitive
evaluation and physiological inputs.
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Scientific Objections
• Need for more detail about how the
encodings work.
• Need application to specific aspects of
emotional consciousness.
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Philosophical Objections
• Zombies: We can imagine creatures just like us
but lacking emotional consciousness. Response:
imagination is a poor guide to reality.
• What Mary knows: Mary (without emotional
consciousness) could know everything about the
neuroscience of happiness, but not know what
happiness is. Response: Mary would never have
made it through kindergarten.
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Philosophical Objections
• The mechanistic theory of emotional
consciousness doesn’t tell us what it is like to be
emotional.
• Response: it also doesn’t tell us how many hours
there are in a kilogram.
• Better response: it should be able to explain why
we feel positive/negative, weak/intense,
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Conclusion
Mechanistic explanations
of emotional
consciousness are
feasible.
They will require further
understanding of the
functions of
different brain areas.
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Web sites
• http://cogsci.uwaterloo.ca/
• http://faculty.washington.edu/chudler/neurok.html
• http://www.thebrain.mcgill.ca/flash/index_i.html
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