Transcript Hot Thought: Mechanisms of Emotional Cognition

Discovery and Neural
Computation
Paul Thagard
University of Waterloo
1
Outline
1. Discovery
2. Neural Computation
3. Multimodal
representation
4. Abduction
5. Conclusions
2
Creative Scientific Discoveries
•
•
•
•
New hypotheses, e.g. sound is a wave
New concepts, e. g. sound wave
New instruments
New methods
3
Why Neural Computation?
• Scientists have brains
• Brains have powerful computational capacities
• Multimodal representations: sensory, motor, emotion
• Understanding of causality
• Parallel constraint satisfaction
• Cognitive science studies mechanisms at multiple
levels: social, psychological, neural, and
molecular.
4
Theoretical Neuroscience
•
•
•
•
•
Beyond connectionism, PDP
Spiking neurons
Large populations
Multiple, organized brain regions
Representations tied to sensory, motor,
emotional regions
5
Representation
• Neural populations represent the world by
encoding inputs from external sensors.
Eliasmith: causal correlations.
• Neural populations represent the body by
encoding inputs from internal sensors.
• Neural populations represent neural
populations by encoding inputs from neural
populations.
6
Neural Representation
world
external
sensors
neural
population
(a)
representation
body
internal
sensors
neural
population
(b)
representation
neural
population
representation
neural
population
neural
population
(c)
7
Multimodal Representations
• Sensory: concepts are patterns of firing
activity in multiple brain regions, e.g.
visual, auditory, tactile
• Causality: sensory-motor-sensory patterns
• Emotions: patterns include ones for bodily
input and cognitive appraisal in regions
such as the nucleus accumbens
• Emotional consciousness: Google Thagard
8
Emotions in Scientific Thinking
interest
curiosity
wonder
Generate
questions
avoid
boredom
happiness
hope
Try to answer
questions
fear
anger
frustration
happiness
surprise
beauty
happiness
Generate
answers
Evaluate
answers
worry
disappointment
9
Hypothesis Generation
•
•
•
Causal
Creative
Simplest form, abductive:
•
•
•
Why effect?
If cause then effect.
So maybe cause.
10
Neurocomputing Problems
• How to represent causal if-then?
• How to connect with emotions?
• How to make inference of cause from
effect?
• First attempt: Thagard & Litt, in press.
11
Representation
• Represent if-then relations by holographic
reduced representations (Plate)
• Relations are vectors built out of vectors for
concepts and roles
• Translate vectors into neural populations
(Eliasmith). 6000 neurons
• Simplify emotions as vectors (Litt)
12
Processes
• Representation of B marked as
emotionally surprising.
• Retrieve A -> B from memory of rules.
• Extract A by decomposing holographic
representation of A -> B.
• Mark A as emotionally satisfying.
13
Current Work
• Understand causality as intervention
• Not just statistical or universal
• Causes make things happen
• Babies and monkeys understand causality
• Sensory-motor-sensory schemas
• Now developing model using Neural Engineering
Framework
14
Open Problems
• Generation of new concepts
• Not just learning from examples
• Need new nodes based on new experiences
• Theoretical concepts combine previous
concepts, but how does this work neurally?
• More complex hypothesis formation
• Integration with analogy
15
Conclusions
1. Creative discoveries are
made by human brains.
2. Brains have
representational and
computational resources
not present in current AI
models, e.g. emotion.
3. Neurocomputational
models of discovery can
be developed.
16