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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
