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J e a n n a N i k o l o v - R a m i r e z . M E i : C o g S c i 2 0 1 5 S u p e r v i s o r : T. S c h ö b e r l . J u n e 2 0 1 5
CONNECTIONIST AND DYNAMICAL SYSTEMS APPROACHES TO COGNITION
Based on: “Letting structure emerge: Connectionist and dynamical systems approaches to cognition” (McClelland, et al., 2010)
https://lh3.googleusercontent.com/-Cbr3OJ-Wqq0/U8vLJB5r4XI/AAAAAAAAAGU/4-lrLwF_aG4/s150-c/photo.jpg
OUTLINE
• Authors • Definitions • Emergence • Connectionism • Semantic Cognition • Universal Grammar • A-not-B error
• Structured Probabilistic Approaches
• Arguments against Structured Probabilistic Models
• Pending Questions
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AUTHORS
• James L McClelland, Psychology, Stanford
• Matthew M. Botvinick, Psychology, Princeton/Cornell etc.
• David C. Noelle, Associate Professor, School of Social Sciences, Humanities, and Arts, Merced
• David C. Plout, Computational Psycholinguist
• Timothy T. Rogers, Psychologist • Mark S. Seidenberg, Psychologist • Linda B. Smith, Distinguished
Professor and Chancellor's Professor of Psychological and Brain Sciences
EMERGENT ANT HILLS
“Ants are sensitive to certain gasses within their nests; when these gasses build up they move grains of dirt to the outside. This activity lets the gasses escape and has the byproduct of creating the elaborate structure of the nest.”
EMERGENCE
“Human thoughts, language and behavior have a rich and complex structure that is the emergent consequence of a large number of simpler processes.”
CONNECTIONISM
• Neurons basic information processing structures in the brain
• Every sort of information the brain processes occurs in networks of interconnected neurons.
• Models knowledge and knowledge acquisition as adjusting strengths on connections of networks of “neuron-like processing units”
Neural Networks http://www.iep.utm.edu/wp-content/media/Figure-4-Backprop-Net.gif
SEMANTIC COGNITION
UNIVERSAL GRAMMAR
http://www.uark.edu/misc/lampinen/sm.html
A-NOT-B ERROR
STRUCTURED PROBABILISTIC APPROACHES TO COGNITION
MARR’S TRI-LEVEL HYPOTHESIS
• Computational Level WHAT?
• Algorithmic Level HOW?
• Implementational Level PHYSICALLY REALIZED?
http://academic.reed.edu/biology/courses/BIO342/2012_syllabus/2012_WEBSITES/Erin_A_and_Ryan_S%2011-20final/images/Seed%20at%20al%202009%20fig%202%20morphpsychmodel.jpeg
ARGUMENTS AGAINST PROBABILISTIC MODELS
• No real basis – not representative of the actual processes – use of probabilistic models is unnecessary and dangerous
• Don’t account for (explain) the development of cognitive abilities • What if the high-level models are wrong? • NOT about probabilities – both approaches emphasize statistics • NOT about bottom-up over top-down – both are important • IS about cognition being a choice of statistical models
COGNITIVE MODEL: DEVELOPMENT
• Probabilistic: • Stages of development (Piaget) • Object permanence
• A-not-B • Objects exist independent of one’s own action • Not an explicit focus of research in probabilistic modeling
• Connectionist: • Dynamic Field Theory – integrates multiple sources of relevant
information • Situation (events, past reaches, object positions) • Motor planning (direction of next reach)
CONCLUSIONS
• Cognition depends fundamentally on underlying mechanism – abstract models will miss important aspects
• Connectionist modeling efforts have led to advances in cognitive theories
• Authors advocate an integrated approach where high-level models are informed by knowledge about underlying neural mechanisms
PENDING QUESTIONS
1. Why is there a need to model cognition? 2. Are there other types of symbolic models
(other than probabilistic) that may be appropriate for cognitive modeling?
3. Is there always a need to choose one modeling approach over the other?
4. Did the authors convincingly demonstrate that higher cognitive abilities can be modeled at the connectionist level?
5. “Understanding how each and every neuron functions still tells us absolutely nothing about how the brain manufactures a mental state.” (Gazzaniga, 2010)
6. Problem of Consciousness
