Topics in Cognitive Modelling
Reading List 2015
Note that this list is subject to minor changes.
Students will not be expected to read every paper on the
reading list; instead, they will choose papers they are more
interested in (with some constraints). Students may also propose
their own readings, to be approved by the instructor. We will give
more details on this during the first lecture but essentially each
student will be assigned a topic to present and, together with one or two
other students, will be responsible for a single class on that topic
in which the group will present two modelling papers. Some topics
have enough papers that we may use two classes (and two groups of
students) to cover that topic. For each presentation day, all students
who are not presenting will be expected to read and respond briefly to
one modelling paper listed under that day's topic.
Accessing readings using links on this page: Some links on this page require a username and password (this is to respect copyright laws, which allow distribution of certain materials for course use only, or because the articles are not yet in final versions). We will distribute the password information in class. You may also find that some links only work from computers inside the University or for some reason don't work; in these cases you should still be able to find the articles through the library (see below).
Accessing readings without links: These readings should all be available through the library's
e-journal services .
You will need to log in through Ease in order to access journals online. You can either look up the journal by name, and then find the volume and issue you need, or (in many cases) if you are inside the University's intranet, you can search for the article title in Google Scholar and will see a 'findit@:edinburgh' link. Clicking on this link should take you straight to a page with a link to the article.
Are humans born with strong domain-specific knowledge and learning abilities, or is most of what we know learned through experience, using powerful domain-general learning mechanisms? Are mental representations symbolic or distributed? How is cognition affected by the nature of our physical being and its interaction with the world?
- Elman, J., Bates, E., Johnson, M., Karmiloff-Smith, A., Parisi, D., and Plunkett, K. 1996.
Rethinking innateness: a connectionist perspective on development,
Chapter 1: New perspectives on development, pp. 18-46.
Cambridge, MA: MIT Press.
- Spelke, E. (1994).
Initial knowledge: six suggestions.
Cognition, 50, 431-445.
- Barsalou, L. W. (2008).
Annual Review of Psychology, 59:617-45.
- Clark, A. 1999.
An embodied cognitive science?
Trends in Cognitive Sciences 3, 345-351.
- Levels of analysis:
- Dynamical systems:
Bermudez, Jose Luis. 2010.
Cognitive Science. Chapter 13: New Horizons: Dynamical systems and situated cognition, Sections 1 and 2. Cambridge University Press.
- Bayesian/rational analysis:
- ACT-R (cognitive architecture):
Anderson, J.R. and Bothell, D. and Byrne, M.D. and Douglass, S. and Lebiere, C. and Qin, Y. 2004. An integrated theory of the mind.
Psychological review 111(4), 1036-1060.
None of these is required reading, but they may be useful for those with less background in these areas in order to better understand some of the required readings.
- Robert Jacobs'
Computational Cognition Cheat Sheets.
[A set of brief introductions
to various computational methods you may come across, including
backpropagation, Bayesian estimation, Hidden Markov Models, Principal
Components Analysis, etc.]
- Sharon Goldwater's notes on
Basic Probability and Distributions and
[from a previous semester's Computational Cognitive Science course.]
Manning, Christopher D. and Hinrich Schütze. 1999.
Foundations of Statistical Natural Language Processing.
Chapter 2: Mathematical Foundations. Cambridge, MA: MIT Press.
[An introduction to basic probability theory using the more standard set-theoretic approach (in contrast to Goldwater's notes above, which take a different approach).]
- Charniak, Eugene. 1991.
Bayesian networks without tears. AI Magazine 12(4): 50-63.
[An introduction to Bayes nets.]
Griffiths, Tom L. and Alan Yuille. 2006.
A primer on probabilistic inference.
Trends in Cognitive Sciences 10(7).
Supplement to special issue on Probabilistic Models of Cognition.
[An introduction to probabilistic modelling techniques and Bayesian techniques especially.]
- Jain, A.K., Jianchang Mao, and Mohiuddin, K.M. 1996.
Artificial neural networks: a tutorial.
Computer 29(3), 31-44.
[An introduction to various types of neural nets, activation functions, and learning rules.]
Papers are divided into topics, with 2 or more modelling papers per topic,
each of which is marked according to the modelling approach taken:
- [Pr] (probabilistic),
- [Comp] (computational-level but not explicitly probabilistic),
- [NN] (neural network),
- [Dyn] (dynamical systems),
- [Alg] (algorithmic/mechanistic),
- [Arch] (cognitive architecture).
In addition, there are [Emp] (empirical) and [Rev] (review)
articles given for many topics. These are not required reading but
may provide useful context, especially for students preparing
presentations on that topic or those who want to explore the topic
further out of interest or for their final paper.
[Emp] and [Rev]
papers may not be selected as your one required reading per topic.
Segmentation of sequences and scenes:
The work in this area often goes by the term "statistical learning" and much of it has been done using language-like stimuli, but there are several papers suggesting it is a more domain-general learning mechanism. We have included both linguistic and non-linguistic papers/models below.
- [Emp] Saffran, J.R., Aslin, R.N., and Newport, E.L. (1996).
Statistical learning by 8-month old infants.
Science, 274, 1926-1928.
- [Emp] Natasha Z. Kirkham, Jonathan A. Slemmer, Scott P. Johnson. 2002.
Visual statistical learning in infancy: evidence for a domain general learning mechanism
Cognition, 83, B35-B42.
- [Rev] Brent, M. R. (1999).
Speech segmentation and word discovery: a computational perspective.
Trends in Cognitive Sciences, 3(8), 294301.
- [Alg] Perruchet, P., and Vinter, A. (1998).
PARSER: A model for word segmentation.
Journal of Memory and Language, 39(2), 246-263.
- [NN] Christiansen, M.H. and Allen, J. and Seidenberg, M.S. (1998).
Learning to segment speech using multiple cues: A connectionist model.
Language and cognitive processes 13(2-3):221-268.
- [Pr] Orban G., Fiser J., Aslin R., and Lengyel M. (2008).
Bayesian learning of visual chunks by human observers.
Proceedings of the National Academy of Sciences of the USA, 105: 2745-50.
- [Pr] Sharon Goldwater, Thomas L. Griffiths, and Mark Johnson. 2007.
Distributional cues to word segmentation: Context is important.
Proceedings of the 31st Boston University Conference on Language Development, pp. 239-250. Somerville, MA: Cascadilla Press.
(If you want more details, a longer version of this paper is available here.)
Learning past tense verbs and the rules vs. analogies debate:
The past tense of English verbs has received a lot of attention due to the presence of both
regular (walk-walked) and irregular (run-ran) verbs. The regular-irregular distinction shows
up in lots of other areas of language but verbs have been used as a case example. The question
under debate is how these verbs are processed: is there one system (analogy) that works for both, or two systems (rules plus exceptions)? How could these be modeled?
- [Rev] Steven Pinker, Michael T. Ullman. (2002).
The past and future of the past tense
Trends in Cognitive Sciences, 6(11) 456-463.
- [NN] Plunkett, K. and Marchman, V. (1993).
From rote learning to system building: acquiring verb morphology in children and connectionist nets.
Cognition, 48, 21-69.
- [Alg] Charles Ling (1994).
Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models.
Journal of Artificial Intelligence Research, 1, 209-229.
- [Alg] Albright, A. and Hayes, B. (2002).
Rules vs. analogy in English past tenses: a computational/experimental Study.
Cognition, 90, 119-161.
Grammar (learning and knowledge):
Syntax appears to be highly structured and governed by rules, at least according to most linguistic theories. Is it possible to get this behavior using distributed representations or other non-rule-based models? Can we use computational models to help us understand what kinds of grammars children actually have?
- [Rev, Pr] Chater, N., & Manning, C. D. (2006).
Probabilistic models of language processing and acquisition.
Trends in cognitive sciences, 10(7), 335-344.
- [NN] Elman, J. L. (1990).
Finding structure in time.
Cognitive Science, 14, 179-211.
- [NN] Christiansen and Chater (1999).
Toward a connectionist model of recursion in human linguistic performance.
Cognitive Science, 23:2, 157-205.
- [Arch] Freudenthal, D., Pine, J. M., & Gobet, F. (2002).
Subject omission in children's language; The case for performance limitations in learning.
Proceedings of CogSci.
- [Pr] Bannard, C., Lieven, E., and Tomasello, M. (2009).
Modeling children's early grammatical knowledge.
Proceedings of the National Academy of Sciences, 106,17284-17289.
Categorization by adults:
When people encounter an object they have never seen before, they make generalizations about its behavior and talk about the category it belongs to (e.g. this furry thing I see is a cat). How do we do this, and what is our mental representation of categories?
- [Rev] Murphy, G. L. 2004.
The Big Book of Concepts,
Cambridge, MA: MIT Press.
- [Alg] Nosofsky, R.M., Palmeri, T.J., and McKinley, S.C. 1994.
Rule-plus-exception model of classification learning.
Psychological Review 101 (1), 53-79.
- [Comp] Nosofsky, R. M. (1998).
Optimal performance and exemplar models of classification.
In M. Oaksford and N. Chater (Eds.),
Rational models of cognition (pp. 218-247). Oxford: Oxford University Press.
- [NN] Love, B. C., Medin, D. L., and Gureckis, T. M. (2004).
SUSTAIN: A network model of category learning.
Psychological Review, 111, 309-332.
- [Pr] Griffiths, T. L., Sanborn, A. N., Canini, K. R., and Navarro, D. J. (2008).
Categorization as nonparametric Bayesian density estimation.
In M. Oaksford and N. Chater (Eds.).
The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press.
- [Comp] Goodwin, Geoffrey P. and Johnson-Laird, P.N. (2011).
Mental models of Boolean concepts.
Cognitive Psychology 63, 34-59.
Categorization and development:
How do infants learn to categorize objects? What role does perception play, and are there other factors at work? How is it the same/different than adult categorization?
- [NN] French, R. M., Mareschal, D., Mermillod, M., and Quinn, P. C. (2004).
The role of bottom-up processing in perceptual categorization by 3- to 4-month-old infants: Simulations and data.
Journal of Experimental Psychology: General, 133, 382-397.
- [NN] Gliozzi, V., Mayor, J., Hu, J.-F., and Plunkett, K. (2009).
Labels as features (not names) for infant categorisation: A neuro-computational approach.
Cognitive Science, 33(4), 709-738.
How do people learn and represent the meanings of words and concepts?
Development of object knowledge:
As adults, we know that objects are permanent (do not disappear unless under some outside force), move as one thing, and have many other physical properties. How does this knowledge develop in infants, and is it learned?
- [Rev] Mareschal, D. (2000).
Object knowledge in infancy: current controversies and approaches.
Trends in Cognitive Sciences 4, 408-416.
- [Alg] Prazdny, S. (1980).
A computational study of a period of infant object-concept development.
Perception, 9, 125-150.
- [NN] Mareschal, D., Plunkett, K., and Harris, P. (1999).
A computational and neuropsychological account of object-oriented behaviours in infancy.
Developmental Science, 2(3), 306-317.
- [Dyn] Thelen E, Schoner G, Scheier C, Smith LB. 2001.
The dynamics of embodiment: a field theory of infant perseverative reaching.
Behavioral and Brain Sciences. 24:1-86.
[You need only read pp. 1-34, i.e., not the Peer Commentary].
Learning about causal relationships:
How do we infer from observations that one thing causes another? Sometimes we do this based on only one or two observations. How and in what circumstances?
- [NN/Dyn] H. Chaput and L. Cohen. 2001.
A model of infant causal perception and its development.
Proceedings of the Twenty-third Annual Conference of the Cognitive Science Society, pp. 82-88.
- [Pr] Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Schulz, T., and Danks, D. (2004).
A theory of causal learning in children: Causal maps and Bayes nets.
Psychological Review, 111(1):1-31.
- [Pr] Bes, B., Sloman, S., Lucas, C. G., and Raufaste, E. (2012).
Non-Bayesian Inference: Causal Structure Trumps Correlation.
Abstraction, overhypotheses, and the shape bias:
An "overhypothesis" is a fancy word for a hypothesis about hypotheses, or (in Bayesian terminology) a hierarchical model. This concept has been used to explain how we might generalize correctly to a new situation when very little data is observed for that situation; for example, if we have seen several bags of marbles containing all green, all blue, or all red marbles, and we pull a single black marble from a new bag, we might predict with high certainty that the next marble will be black, because we've learned an overhypothesis that all bags contain only one color of marbles.
- [Rev] Johnson-Laird, P.N. (2010).
Mental models and human reasoning.
Proceedings of the National Academy of Sciences 107(43), 18243-18250.
- [Alg] Braine, Martin D. S.; O'Brien, David P.; Noveck, Ira A.; Samuels, Mark C.; Lea, R. Brooke; Fisch, Shalom M.; Yang, Yingrui (1995).
Predicting Intermediate and Multiple Conclusions in Propositional Logic Inference Problems: Further Evidence for a Mental Logic.
Journal of Experimental Psychology: General 124(3), 263-292.
- [Alg] Jahn, G., Knauff, M., & Johnson-Laird, P. N. (2007).
Preferred mental models in reasoning about spatial relations.
Memory and Cognition 35(8), 2075-87.
- [Arch] Ragni, M., Fangmeier, T., & Brüssow, S. (2010).
Deductive spatial reasoning: From neurological evidence to a cognitive model.
Proceedings of the 10th International Conference on Cognitive Modeling 193-198.
[For more background on ACT-R and its relationship to the brain, see the ACT-R paper in the Background Reading section, or
- [Rev] John M. Henderson. 2003.
Human gaze control during real-world scene perception.
Trends in Cognitive Sciences, 7:11, 498-504.
- [NN] L. Itti, C. Koch, E. Niebur. 1998.
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:11, 1254-1259.
- [Pr] A. Torralba, A. Oliva, M. Castelhano and J. M. Henderson. 2006.
Contextual Guidance of Attention in Natural scenes: The role of Global features on object search.
Psychological Review, 113:4, 766-786.
- [Pr] L. Itti, P. Baldi. 2009. Bayesian surprise attracts human attention. Vision research, 49:10: 1295-1306.
Deciding between options:
How do people decide which option to choose from a set of possibilities? When deciding repeatedly, how do people balancing exploring -- learning which options are good by trying them out -- and exploiting the option that currently seems to be the best?
How do we explain Hick's law, a systematic relationship between the number of options and the time it takes people to decide?
- [Pr] M. Steyvers, M. Lee, and E. J. Wagenmakers. (2009)
A Bayesian analysis of human decision-making on bandit problems.
Journal of Mathematical Psychology. 53(3), 168-179.
- [Pr] Hawkins, G., Brown, S. D., Steyvers, M., and Wagenmakers, E. J. (2012).
Context Effects in Multi-Alternative Decision Making: Empirical Data and a Bayesian Model.
- [Arch] Schneider, D. W., and Anderson, J. R. (2011).
A memory-based model of Hick's law.
Cognitive psychology, 62(3), 193-222.
Sensorimotor learning and control:
How do people integrate their perceptions of the world to formulate movements and motor plans?
- [Rev] Koerding, K. and Wolpert, D. (2006).
Bayesian decision theory in sensorimotor control.
Trends in Cognitive Sciences 10(7) 320-326.
- [Pr] Braun, D.A., Waldert, S., Aertsen, A., Wolpert, D.M., and Mehring, C. (2010).
Structure learning in a sensorimotor association task.
PLoS ONE 5(1): e8973
- [Pr] Trommershauser, J., Maloney, L., and Landy, M. (2003).
Statistical decision theory and the selection of rapid, goal-directed movements.
Journal of the Optical Society of America A, 20, 1419-1433.
- [Pr] Koerding, K. P. and Wolpert, D. (2004).
Bayesian Integration in Sensorimotor Learning.