Cogs 202 (SP12): Cognitive Science Foundations. Computational Modeling of Cognition. Prof. Angela Yu. Department of Cognitive Science, UCSD

Transcription

1 Cogs 202 (SP12): Cognitive Science Foundations Computational Modeling of Cognition Prof. Angela Yu Department of Cognitive Science, UCSD

2 Today Self-introductions Introduction to cognitive modeling Syllabus Assignments/grading

3 What is cognitive modeling and why do it? Actually, why do we study cognitive science at all? To understand how the mind works How we process information and act on it How we learn and generalize, and create new ideas How we think, reason, and make decisions To make predictions of how people & animals behave in new situations To treat pathology in cognition To build intelligent artificial systems and agents

4 What is cognitive modeling and why do it? It s possible to study the mind without modeling But discovering facts is only the beginning

5 What is cognitive modeling and why do it? Principles of Neural Science (Kandel, Schwartz, & Jessel) No. pages Year of publishing Facts understanding, description understanding Our goal is to make the book shorter!

6 What is cognitive modeling and why do it? The description is long because the system is complex Understanding physics is child s play compared to understanding child s play -- Albert Einstein A theory makes it possible to Explain why we (scientists) observe what we observe Predict what would happen in a new situation A model is just a very explicit theory Forces explicitness in assumptions, logic, and predictions Implications often defy expectations Aids communication among scientists Support cumulative progress

7 What is cognitive modeling and why do it? Verbally expressed statements are sometimes flawed by internal inconsistencies, logical contradictions, theoretical weaknesses and gaps. A running computational model, on the other hand, can be considered as a sufficiency proof of the internal coherence and completeness of the ideas it is based upon... (Fum, Del Misser, Stocco, 2007)

8 Analogy from image compression The goal is to have as concise a description of the image as possible Doing so requires modeling the (statistical) relationship among components of the image (information theory) Minimum description length = Bayesian inference A concise representation not only saves storage space, but makes it possible to create new images

9 Analogy from image compression Seam Carving for Image Resizing (Re-targeting)

10 Having established that modeling is useful... How does it fit into the scientific study of cognition?

11 Environment Stimuli that are perceived by the body and nervous system Behavior 39

12 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior 40

13 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Theory Behavior

14 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior predicts Theory

15 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior describes predicts Theory

16 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior describes predicts Model

17 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior implements generates Model

18 Environment Stimuli that are perceived by the body and nervous system Cognitive Mechanism (representations, processes) Behavior implements generates Model manipulates observes Experiment refines/tests

19 Model Taxonomy: Levels of Analysis David Marr (1969): Brain = Information Processor computational goals of computation why things work the way they do algorithmic representation of input/output how one is transformed into the other V := sup (τ,µ) [ ] E 1 m {τ+t0<θ} j=1 r j1 {µ=j,m=j} E [(τ + T 0 ) Θ] implementational how is the system physically realized in hardware (architecture, dynamics)

20 Model taxonomy: core assumptions Representation symbolic or distributed Domain-specificity and modularity distinct or shared mechanism/architecture across cognitive domains Nature vs. nurture what and how much is innate? what are learned? Embodiment Studying/comparing different models sheds light on the Big Questions in cognitive science to what extent are cognitive abilities determined by the body and environment?

21 Model taxonomy: approach Different modeling approaches make different core assumptions, aim at different levels of analysis, and are applied to different aspects of cognition connectionist/neural network Bayesian/ideal-observer symbolic/rule-based dynamical systems cognitive architectures

22 Modeling approaches Connectionist emphasizes distributed representations and general-purpose, experience-dependent learning mechanisms typically implemented as artificial neural networks (ANN) Figure:

23 Modeling approaches Bayesian/ideal observer emphasizes computational-level explanations using probability theory, optimal behavior under uncertainty and noise shares techniques with statistical machine learning methods Figure: Steyvers and Griffiths, 2007

24 Modeling approaches Symbolic/rule-based emphasizes procedural steps involved in processing information usually in a specific Figure: Perruchet and Vinter, 1998

25 Modeling approaches Dynamical systems: emphasizes the dynamic interaction between agent and environment, as well as among computational components within the agent connections to robotics and philosophy of embodied cognition

26 Modeling approaches Cognitive architectures: emphasizes information flow and modularity, as well as timing. Rulebased or hybrid (rules + activation levels) Also has a more applied bend than other approaches, e.g. how will adding a new display to a control panel affect a pilot s reaction time? Figure: ACT-R, from

27 Course schedule 04/02: Introduction 04/09: Foundational issues in cognitive modeling 04/16: Neural network and connectionist models 04/23: Information theory and ideal observer models 04/30: Bayesian/probabilistic models 05/07: Dynamical systems models 05/14: Hybrid models (Bayesian + NN, Bayesian + dynamical systems) 05/21: Cognitive architectures 05/28: (no class) 06/04: Decision theoretic and reinforcement learning models

28 Class format 45 min: background (conceptual, technical) 5 min: break 30 min: paper 1 5 min: break 30 min: paper 2 5 min: break 45 min: discussion

29 04/02: Introduction Presenter schedule 04/09: Foundational issues in cognitive modeling (RT, AA) 04/16: Neural network and connectionist models (DFry, MB, LE) 04/23: Information theory and ideal observer models (MR, CF) 04/30: Bayesian/probabilistic models (MB, MR, LE) 05/07: Dynamical systems models (DFry, RT, DF) 05/14: Hybrid models (Bayesian + NN, Bayesian + dynamical systems) (CF, EJK, DF) 05/21: Cognitive architectures (EJK, SI) 05/28: (no class) Every student presents twice 06/04: Decision theoretic and reinforcement learning models (SI, AA)

30 Grading 50% participation reading (please read assigned papers before class) in-class discussion wiki (required if cannot be in class) 30% discussion leading 20% final project (wiki page) No laptop, tablet, or cell phone in class, unless you need it for presentation (get a notebook to take notes!)

31 Course wiki Accessible from course website Forum for discussion, feedback, and extra references Testing ground for final wiki page cogs202:cogs202pd