University Homepage School Homepage School Contacts School Search

Main | Descriptor | Staff | Assessment | Time and Place | Lectures | Tutorials | Labs | Assignments | Reading | Links | Non-Informatics students

Informatics 1 Cognitive Science

Lectures

This schedule is subject to change. Updated slides will generally be posted after class. See the reading list for links to papers.

The readings for the course are only accessible from within the ed.ac.uk domain.

Video recordings of the lectures are available from here.

No.	Week	Lecturer	Topic and slides	Reading
1	1	Shillcock	General Introduction [1up] [3up]	Jones N. (2014). The Learning Machines, Nature, 505, 146--148; Maguire et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. PNAS 97(8), 4398--4403; Frank et al. (2008). Number as a cognitive technology: Evidence from Piraha language and cognition. Cognition, 108(3), 819--24.
2	1	Lapata	Language: Introduction [1up] [4up]	Pinker ch. 1
3	1	Lapata	Language: Regular and Irregular Verbs 1 [1up] [4up]	Pinker ch. 2
4	2	Lapata	Language: Regular and Irregular Verbs 2 [1up] [4up]	Pinker ch. 3, 7
5	2	Lapata	Perceptrons [1up] [4up]	Pinker ch. 4, K. Gurney. An Introduction to Neural Networks. (1997). Routledge. Ch. 2 and 4
6	2	Lapata	Multilayer Perceptrons and Backpropagation[1up] [4up]	Pinker ch. 4, K. Gurney. An Introduction to Neural Networks. (1997). Routledge. Ch. 5-6.5
7	3	Lapata	Modeling the Past Tense [1up] [4up]	Rumelhart, D. E. & McClelland, J. L. On learning the past tenses of English verbs. Vol II, Ch 18.
8	3	Lapata	Speech Segmentation [1up] [4up]	M. R. Brent and T. A. Cartwright (1996). Distributional regularity and phonotactic constraints are useful for segmentation. Cognition 61, 93-125.
9	3	Lapata	Word Learning [1up] [4up]	Harley, ch. 4
10	4	Lapata	Learning Syntactic Categories [1up] [4up]	Redington et al. (1998). Distributional Information: A Powerful Cue for Acquiring Syntactic Categories. Cognitive Science 22, 425-469.
11	4	Lapata	Concepts and Categories [1up] [4up]	Harley, ch. 10
12	4	Lapata	Word Meaning [1up] [4up]	Harley, ch. 10
13	5	Lapata	Vector Space Model [1up] [4up]	Landauer et al., (1998). An Introduction to Latent Semantic Analysis. Discourse Processes, 25, 259--284; J.A. Bullinaria and J. P. Levy (2007). Extracting Semantic Representations from Word Co-occurrence Statistics: A Computational Study. Behavior Research Methods, 39, 510-526.
14	5	Lapata	Understanding Sentences [1up] [4up]	Harley, ch. 9
15	5	Lucas	Inductive reasoning: Introduction [1up] [6up]	Griffiths, T.L. (2010): Bayesian models as tools for exploring inductive biases. Generalization of knowledge: Multidisciplinary perspectives, 135-156.
---	---	---	Innovative learning week (no lectures)	---
16	6	Lucas	Inductive reasoning: Categories revisited [1up] [6up]	Osherson et al. (1990): Category-based induction. Griffiths et al. (2007): Unifying Rational Models of Categorization via the Hierarchical Dirichlet Process
17	6	Lucas	Cognitive development: Theory of mind [1up] [6up]	Wellman et al., (2001): Meta-Analysis of Theory-of-Mind Development: The Truth about False Belief
18	6	Lucas	Biases and [ir]rationality 1 [1up] [6up]	Tversky and Kahneman (1974): Judgment under Uncertainty: Heuristics and Biases
19	7	Lucas	Biases and [ir]rationality 2 [1up] [6up]	Oaksford, M., & Chater, N. (2009). Precis of Bayesian rationality: The probabilistic approach to human reasoning
20	7	Shillcock	Vision: the Beginnings [1up] [3up]	Land, M. F., & Hayhoe, M. (2001). In what ways do eye movements contribute to everyday activities. (2013). Vision Research, 41(25), 3559-3565
21	7	Shillcock	Vision: Computational Aspects [1up] [3up]	Hirsch, H. V., & Spinelli, D. N. (1971). Modification of the distribution of receptive field orientation in cats by selective visual exposure during development. Experimental Brain Research, 12(5), 509-527; Hubel, D. H., & Wiesel, T. N. (1963). Receptive fields of cells in striate cortex of very young, visually inexperienced kittens. Journal of Neurophysiology, 26(6), 994-1002.
22	8	Shillcock	Vision: the higher levels[1up] [3up]	Quiroga et al. Sparse but not grandmother-cell coding in the medial temporal lobe. (2008). Trends in Cognitive Sciences, 12(3), 87--91.
23	8	Shillcock	Memory: Impairments [1up] [3up]	Luria, A. R., & Solotaroff, L. T. (1987). The mind of a mnemonist: A little book about a vast memory. Harvard University Press
24	8	Shillcock	Memory: Computational Issues [1up] [3up]	Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396-408.
25	9	Shillcock	Memory: More Computational Issues [1up] [3up]	Lindsay, S., & Gaskell, M. G. (2013). Lexical integration of novel words without sleep. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 608-622.
26	9	Shillcock	Face recognition: experiments, computation and the phenomenology of prosopagnosia (Perdita Stevens) [1up] [3up]	Rezlescu et al. (2014). Normal acquisition of expertise with greebles in two cases of acquired prosopagnosia. PNAS, 2014; Kanwisher, N. (2000). Domain specificity in face perception. Nature Neuroscience, 3, 759-763.
27	9	Shillcock	Capgras Delusion [1up] [3up]	Hadyn D. Ellis and Michael B. Lewis (2001). Capgras delusion: a window on face recognition. TRENDS in Cognitive Sciences, 5(4), 149-156.
28	10	Shillcock	Neuroimaging [1up] [3up]	Page, M. (2006). What can't functional neuroimaging tell the cognitive psychologist? Cortex 42, 428-443; Coltheart, M. (2006). Perhaps functional neuroimaging has not told us anything about the mind (so far). Cortex, 42, 422-427.
29	10	Shillcock	Cognitive modelling: Some examples, some advantages, some limitations [1up] [3up]	McClellan, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11-38.
30	10	Shillcock	Some philosophical choices within cognitive modelling [1up] [3up]	Shillcock, R. (2013). The concrete universal and cognitive science. Axiomathes, 24, 63-80.