Emre Ugur

Ph.D.

Introduction to Cognitive Science
Tuesday 9:00-11:00 BM A3,
Wednesday 9:00-10:00 BM A3


Aim: Introduction of basic concepts, approaches and issues in the field of cognitive science to increase the awareness of the students to the questions raised in the disciplines of computer science, linguistics, philosophy and psychology; focus on the interaction of these disciplines in approaching the study of the mind; specialization on topics central to cognitive science such as the nature of mental representation, reasoning, perception, language use, learning as well as other cognitive processes of humans and other intelligent systems.
Textbook: The course will have no main textbook, but the following topics and readings week by week.
Lecture slides: Available at the course page.
Instructor: Emre Ugur (contact)
Office hours: Anytime. Drop an email before coming.
Mailing-list: Please send email to instructor if you are not registered!
Grades

Schedule (subject to change):

26.09.2017 Introduction: Introduction to the study of cognitive sciences. A brief history of cognitive science. Methodological concerns in philosophy, artificial intelligence and psychology.
  • Gardner, The Mind's New Science, chapters 2,3,4
  • Kihlstrom and Park, "Cognitive Psychology, Overview", Encyclopedia of the Human Brain, 2002 (optional)
  • One Hundred Year Study on Artificial Intelligence (AI100), Stanford University, accessed August 1, 2016
  • Nilsson, The Quest for Artificial Intelligence. (optional)
Slides
27.09.2017 Guest Lecture: Cem Say - Artificial Intelligence
03.10.2017
04.10.2017 		The Brain as a System: Structure and constituents of the brain, brief history of neuroscience, looking a brain signals.
  • Atkinson et al., Intro. To Psychology, chapters 1 and 2
  • Bermúdez, chapters 1 and 2
  • O'Shea, The Brain: A very short introduction, chapter 2,3 (optional)
Slides
10.10.2017 Brain and sensorymotor information: looking at brain signals, Mirror Neuron System
  • O'Shea, The Brain: A very short introduction, shapter 5
  • Sajda, Neural Networks
  • Stein et al., Multisensory integration

Guest Lecture: Erhan Oztop - Mirror Neurons 		Slides
11.10.2017 Mathematical models
17.10.2017 Representation of sensory information: Neural Network Models; Processing of sensory information in the brain; motor and sensory areas; visual pathways; overview of senses; perceptual fusion in different modalities.
  • Sajda, Neural Networks
  • Marr, David, "A computational investigation into the human representation and processing of visual information." WH San Francisco: Freeman and Company 1.2 (1982).
  • Tim Van Gelder, What Might Cognition Be, If Not Computation?, The Journal of Philosophy, Vol. 92, No. 7 (Jul., 1995), pp. 345-381
Slides
18.10.2017 Guest lecture: Lucas Thorpe
24.10.2017 From Sensation to Cognition; Roots of Cognitive Science: Multisensory integration in cortex; information fusion; from sensation to cognition;
Slides
25.10.2017 Guest Lecture: Yagmur Denizhan - Cybernetics
31.10.2017 Language: What is language?; linguistic knowledge: syntax, semantics, (and pragmatics); generative linguistics; brain and language; language disorders; lateralization; the great past tense debate
  • Fromkin, Rodman, and Hyams. An Introduction to Language, Boston, MA: Thomson Wadsworth, 9th edition, 2011. Chapter 1 (other chapters optional)
  • Bermudez, Chapter 1, 1.3: Linguistics and the formal analysis of language
  • Introduction to Psychology, Chapter 2, Asymmetries in the brain
  • For details of aphasia categories: "Language and the Brain", https://web.stanford.edu/~zwicky/language-and-the-brain-ch4-8.pdf
  • Caplan, Neural Basis of Language - optional
  • Elman et al. Rethinking Innateness, chapter 3 - optional
Slides
01.11.2017 Guest Lecture: Mine Nakipoglu
04.11.2017 (Saturday) Embodiment cognitivist and emergent standpoints; a robotic perspective;
  • Vernon&Furlong, Philosophical Foundations of AI, in M. Lungarella et al. (eds.): 50 Years of AI, Festschrift, LNAI, 4850, pp. 53-62, 2007
  • Styles, Attention, Perception and Memory, chapter 9
  • Ziemke, What's that Thing Called Embodiment? 2003
  • Vernon et al., "A Survery of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents" IEEE Transactions on Evolutionay Computation, Vol. 11, No. 2, April 2007.
Slides
07.11.2017 Midterm
08.11.2017 Affordances
  • Sahin, E., Cakmak, M., Dogar, M. R., Ugur, E., and Ucoluk, G. (2007). To afford or not to afford: A new formalization of affordances toward affordance-based robot control. Adaptive Behavior, 15(4), 447-472.
  • Jamone, L., Ugur, E., Cangelosi, A., Fadiga, L., Bernardino, A., Piater, J. and Santos-Victor, J., 2016. Affordances in psychology, neuroscience and robotics: a survey. IEEE Transactions on Cognitive and Developmental Systems.
Slides
14.11.2017 Cognitive Development: Development, child, robotic development
Guest lecture: Junko Kanero		 Slides
15.11.2017 Guest Lecture: Gaye Soley
21.11.2017 Attention Attention and related concepts; human visual attention; computational models of attention; applications of computational models of attention
  • Vecera and Luck, Attention, Encyclopedia of the Human Brain, pp. 269-284.
  • Knudsen, Fundamental concepts of attention, Annual Review of Neuroscience, 30:57-78, 2007.
  • Itti, Koch, "Computational Modeling of Visual Attention", Nature Reviews Neuroscience, 2001.
Slides
22.11.2017 Guest Lecture: Inci Ayhan
28.11.2017 Learning: Categories and concepts, concept learning; logic; machine learning
  • Neisser, Ulric, and Paul Weene. "Hierarchies in concept attainment." Journal of Experimental Psychology 64.6 (1962): 640
  • E. Alpaydın, Intro. to Machine Learning, Chapter 1
  • Douglas Navarick, Learning and Memory
Slides
29.11.2017 Multi-layer perceptrons
05.12.2017 Memory: Constucting memories; explicit vs. implicit memory; information processing (three-boxes) model of memory; sensory memory; short-term/long-term/episodic memory
  • Kassin, chapter 6: memory
Slides
06.12.2017 Guest Lecture Esra Mungan Slides
12.12.2017 Reasoning Rationality; bounded rationality; prospect theory; heuristics and biases; reasoning in computers
  • + Atkinson&Hilgard's Introduction to Psychology Chapter 9,
Slides

Grading: TBA
  • Quizzes: 10
  • 10 homeworks: 30
  • midterm: 20
  • final: 20
  • project: 20

Project: Project or term paper.
Quizzes: One quiz in each lecture at a random time. Please bring your own paper.
Cheating: Any sharing or copying will be considered as cheating. Please do not cheat! See CMPE procedures for cheating behavior.