What We Do
Research in our lab examines a range of questions in cognitive science, from language to concepts to decision-making. Most recently we’ve been interested in addressing these question from a social perspective: how does the fact that people learn from other people — and transmit ideas through others — shape what is learned? On a methodological front, we use a combination of experimentation and computational modelling, often but not always within the Bayesian framework. You can get a good sense of what we do by checking out the papers linked to in our publication list. But if you just want a quick overview, read on...
Language
Our research in language centres on questions of representation, evolution, and acquisition. What biases explain people's language learning, and to what extent are they domain general? What drives the difference in language acquisition abilities between adults and children? What structure do our mental linguistic representations have, and why? How is language, or any cultural product, shaped by the organisation of the world, the nature of communication, and the structure of the human mind? How is language and language learning shaped by the fact that it is a fundamentally social construct shared by social agents?
Current projects focus mostly on the dynamics of information transmission, social assumptions in language learning, statistical learning, the distributional structure of language, and semantic network structure. Previous work (to which we may return) centres on phoneme learning, grammatical representation and learning, and regularisation.
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Relevant publications
- S De Deyne, A Perfors and DJ Navarro (2017). Predicting human similarity judgments with distributional models: The value of word associations. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 4806:4810 (published version)
- DJ Navarro, A Perfors, A Kary, S Brown and C Donkin (2017). When extremists win: On the behavior of iterated learning chains when priors are heterogeneous. In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 847-852 (published version)
- K Ransom, W Voorspoels, A Perfors, and DJ Navarro (2017). A cognitive analysis of deception without lying. In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 992-997 (published version)
- K Smith, A Perfors, O Feher, A Samara, K Swoboda and E Wonnacott (2017). Language learning, language use, and the evolution of linguistic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (published version)
- S De Deyne, DJ Navarro, A Perfors and G Storms (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145(9), 1228-54 (published version)
- S De Deyne, A Perfors, and DJ Navarro (2016). Predicting human similarity judgments with distributional models: The value of word associations. [Best Paper Award] 26th International Conference on Computational Linguistics, Osaka, Japan: 1861–1870 (published version)
- A Perfors (2016). Adult regularization of inconsistent input depends on pragmatic factors. [Peter Jusczyk Best Paper Award Winner] Language Learning and Development, 12, 138-155 (published version)
- N Chater, A Clark, J Goldsmith and A Perfors (2015).Empiricism and language learnability. Oxford University Press. (published version)
- S De Deyne, S Verheyen, A Perfors and DJ Navarro (2015). Evidence for widespread thematic structure in the mental lexicon. In DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock, CD Jennings and PP Maglio (Ed.) Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 518-523) (supplementary materials, published version)
- A Perfors, K Ransom and DJ Navarro (2014). People ignore token frequency when deciding how widely to generalize. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 2759-2764 (supplementary materials, published version)
- A Perfors (2014). Representations, approximations, and limitations within a computational framework for cognitive science: Commentary on article by Tecumseh Fitch. Physics of Life Reviews, 11, 369-370 (published version)
- A Perfors and DJ Navarro (2014). Language evolution can be shaped by the structure of the world. Cognitive Science (published version)
- WK Vong, A Perfors and DJ Navarro (2014). The relevance of labels in semi-supervised learning depends on category structure. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 1718-1723 (published version)
- S De Deyne, DJ Navarro, A Perfors and G Storms (2012). Strong structure in weak semantic similarity: A graph based account. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 1464-1469 (published version)
- A Perfors (2012). When do memory limitations lead to regularization? An experimental and computational investigation. Journal of Memory and Language, 67, 486-506 (published version)
- A Perfors (2012). Probability matching vs over-regularization in language: Participant behavior depends on their interpretation of the task. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 845-850(published version)
- A Perfors and DJ Navarro (2012). What Bayesian modelling can tell us about statistical learning: What it requires and why it works. In P Rebuschat and J Williams (Ed.) Statistical learning and language acquisition: 383-408 (published version)
- A Perfors and JH Ong (2012). Musicians are better at learning non-native sound contrasts even in non-tonal languages. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 839-844 (published version)
- P Shafto, B Eaves, DJ Navarro and A Perfors (2012). Epistemic trust: Modeling children's reasoning about others' knowledge and intent. Developmental Science, 15, 436-447 (published version)
- A Perfors (2011). Simplicity and fit in grammatical theory. In E Bender and J Arnold (Ed.) Language from a cognitive perspective, 99-120
- A Perfors (2011). Memory limitations alone do not lead to over-regularization: An experimental and computational investigation. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 3274-3279 (published version)
- A Perfors and DJ Navarro (2011). Language evolution is shaped by the structure of the world: An iterated learning analysis. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 477-482 (published version)
- A Perfors, JB Tenenbaum and T Regier (2011). The learnability of abstract syntactic principles Cognition, 118, 306-338 (published version)
- S Yuan, A Perfors, J Tenenbaum and F Xu (2011). Learning individual words and learning about words simultaneously. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 3280-3285 (published version)
- A Perfors and E Wonnacott (2011). Bayesian modeling of sources of constraint in language acquisition. In I Arnon and E Clark (Ed.) Experience, Variation, and Generalization: Learning a first language (pp. 277-294)
- L Maurits, A Perfors and DJ Navarro (2010). Why are some word orders more common than others? A uniform information density account. In J Lafferty, CKI Williams, J Shawe-Taylor, RS Zemel and A Culotta (Ed.) Advances in Neural Information Processing Systems: 1585-1593 (supplementary materials, published version)
- A Perfors and N Burns (2010). Adult language learners under cognitive load do not over-regularize like children. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society: 2524-2529 (published version)
- A Perfors and D Dunbar (2010). Phonetic training makes word learning easier. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society: 1613-1618 (published version)
- A Perfors and DJ Navarro (2010). How does the presence of a label affect attention to other features?. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1834-1839 (published version)
- A Perfors, JB Tenenbaum and E Wonnacott (2010). Variability, negative evidence, and the acquisition of verb argument constructions Journal of Child Language, 37, 607-642 (published version)
- A Perfors, JB Tenenbaum, E Gibson and T Regier (2010). How recursive is language? A Bayesian exploration. In H Hulst (Ed.) Recursion and human language (pp. 159-175)
- RG Stephens, A Perfors and DJ Navarro (2010). Social context effects on the impact of category labels. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 1411-1416) (published version)
- S Foraker, T Regier, N Khetarpal, A Perfors and JB Tenenbaum (2009). Indirect evidence and the poverty of the stimulus: The case of anaphoric one Cognitive Science, 33, 287-300
- L Maurits, AF Perfors and DJ Navarro (2009). Joint acquisition of word order and word reference. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1728-1733 (published version)
- F Xu, K Dewar and A Perfors (2009). Induction, overhypotheses, and the shape bias: Some arguments and evidence for rational constructivism. In BM Hood and L Santos (Ed.) The origins of object knowledge
- S Foraker, T Regier, N Khetarpal, A Perfors and JB Tenenbaum (2007). Indirect evidence and the poverty of the stimulus: The case of anaphoric one. In D McNamara and J Trafton (Ed.) Proceedings of the 29th Annual Conference of the Cognitive Science Society: 275-281 (published version)
- A Fernald, A Perfors and V Marchman (2006). Picking Up Speed in Understanding: Speech Processing Efficiency and Vocabulary Growth Across the 2nd Year Developmental Psychology, 42, 98-116
- A Perfors, J Tenenbaum and T Regier (2006). Poverty of the Stimulus? A rational approach. In R Sun and N Miyake (Ed.) Proceedings of the 28th Annual Conference of the Cognitive Science Society: 663-668 (published version)
- T Wasow, A Perfors and D Beaver (2005). The Puzzle of Ambiguity. In O Orgun and P Sells (Ed.) Morphology and the Web of Grammar
- A Perfors (2002). Simulated Evolution of Language: A Review of the Field Journal of Artificial Societies and Social Simulation, 5, 2 (published version)
Concepts
Understanding human conceptual knowledge is one of the central problems in cognitive science. We focus on a number of key questions. How is knowledge organised in the mind? How do the assumptions people make about how data is generated, especially the social assumptions about the motivations of the providers, drive their conceptual knowledge and what they do with it? What kind of inductive inferences are licenced by our conceptual knowledge, and what are not? What must be "built in" in order to explain human category learning?
Current projects involve understanding how people learn high-dimensional categories, how people identify relevant features, what kind of conceptual inferences people make based on different types of statistical sampling, and how concepts (especially social concepts) change and why. We also study concepts and meaning by looking how words are connected in our mental dictionary. We have been building a semantic network of most of the words a person knows using a word association game. If you're curious and want to help us with a few minutes of your time, go to https://smallworldofwords.org. Besides this, previous work (to which we may return) focuses on the role of labels in concept learning and learning categories with different kinds of structure.
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Relevant publications
- S Langsford, A Hendrickson, A Perfors and DJ Navarro (2017). When do learned transformations influence similarity and categorization? In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 2530-2535 (published version)
- WK Vong, A Hendrickson, A Perfors, and DJ Navarro (2016). Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners. [Marr Prize Winner] In A Papafragou, D Grodner, D Mirman and JC Trueswell (Ed.) Proceedings of the 38th Annual Conference of the Cognitive Science Society: 2471-2476 (published version)
- S De Deyne, A Perfors, and DJ Navarro (2016). Predicting human similarity judgments with distributional models: The value of word associations. [Best Paper Award] 26th International Conference on Computational Linguistics, Osaka, Japan: 1861–1870 (published version)
- D Gokaydin, DJ Navarro, A Ma-Wyatt and A Perfors (2016). The structure of sequential effects. Journal of Experimental Psychology: General, 145, 110-123 (supplementary materials, published version)
- K Ransom, A Perfors and DJ Navarro (2016). Leaping to conclusions: Why premise relevance affects argument strength. Cognitive Science, 40(7), 1775-1796 (published version)
- WK Vong, A Perfors and DJ Navarro (2016). The helpfulness of category labels in semi-supervised learning depends on category structure. Psychonomic Bulletin and Review, 23: 230-238 (published version)
- W Voorspoels, DJ Navarro, A Perfors, K Ransom and G Storms (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 1-25 (published version)
- S Langsford, A Hendrickson, A Perfors and DJ Navarro (2014). People are sensitive to hypothesis sparsity during category discrimination. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 2531-2536 (published version)
- WK Vong, A Perfors and DJ Navarro (2014). The relevance of labels in semi-supervised learning depends on category structure. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 1718-1723 (published version)
- WK Vong, A Hendrickson, A Perfors and DJ Navarro (2013). The role of sampling assumptions in generalization with multiple categories. In M Knauff, M Pauen, N Sebanz and I Wachsmuth (Ed.) Proceedings of the 35th Annual Conference of the Cognitive Science Society: 3699-3704 (published version)
- S De Deyne, DJ Navarro, A Perfors and G Storms (2012). Strong structure in weak semantic similarity: A graph based account. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 1464-1469 (published version)
- DJ Navarro and A Perfors (2012). Anticipating changes: Adaptation and extrapolation in category learning. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 809-814 (supplementary materials, published version)
- D Gokaydin, D., A Ma-Wyatt, A., DJ Navarro and A Perfors (2011). Humans use different statistics for sequence analysis depending on the task. Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 543-548 (published version)
- R Montague, DJ Navarro, A Perfors, R Warner and P Shafto (2011). To catch a liar: The effects of truthful and deceptive testimony on inferential learning. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 1312-1317 (published version)
- S Yuan, A Perfors, J Tenenbaum and F Xu (2011). Learning individual words and learning about words simultaneously. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 3280-3285
- DJ Navarro and A Perfors (2010). Similarity, feature discovery, and the size principle Acta Psychologica, 133, 256-268 (published version)
- A Perfors and DJ Navarro (2010). How does the presence of a label affect attention to other features?. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1834-1839 (published version)
- RG Stephens, A Perfors and DJ Navarro (2010). Social context effects on the impact of category labels. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 1411-1416) (published version)
- DJ Navarro and A Perfors (2009). Learning time-varying categories. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 31st Annual Conference of the Cognitive Science Society: 412-424 (published version)
- A Perfors and JB Tenenbaum (2009). Learning to learn categories. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 31st Annual Conference of the Cognitive Science Society: 136-141 (published version)
- C Kemp, A Perfors and JB Tenenbaum (2007). Learning overhypotheses with hierarchical Bayesian models Developmental Science, 10, 307-321 (published version)
- C Kemp, A Perfors and JB Tenenbaum (2006). Learning overhypotheses. In R Sun and N Miyake (Ed.) Proceedings of the 28th Annual Conference of the Cognitive Science Society: 417-422 (published version)
- A Perfors, C Kemp and JB Tenenbaum (2005). Modeling the acquisition of domain structure and feature understanding. In B Bara, L Barsalou and M Bucciarelli (Ed.) Proceedings of the 27th Annual Conference of the Cognitive Science Society: 1720-1725 (published version)
- C Kemp, A Perfors and J Tenenbaum (2004). Learning domain structures.. In K Forbus, D Gentner and T Regier. (Ed.) Proceedings of the 26th Annual Conference of the Cognitive Science Society: 672-677
Decision Making
Learning about the world is only one half of the problem facing an intelligent agent. The other half is taking actions within that world. How do people take action based on limited information, when operating under time pressure, or when faced with uncertainty? How do people search for information on which to make that action? How do strategies change if the world changes? We are particularly interested in how people reason in a social context or based on social assumptions, including in applied areas like medical decision-making.
Current projects involve understanding how people make decisions in ambiguous or uncertain situations with rare or extreme payoffs, how people take into account the informational value of data when searching, and how trust in information providers affects what decisions people make and what info they pass on. Previous work (to which we may return) investigates how strategies are affected by evidentiary value, number of options, and time pressure.
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Relevant publications
- A Hendrickson, DJ Navarro and A Perfors (2016). Sensitivity to hypothesis size during information search. Decision, 3, 62-80 (published version)
- A Hendrickson, A Perfors and DJ Navarro (2014). Adaptive information source selection during hypothesis testing. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 607-612 (published version)
- DJ Navarro and A Perfors (2011). Hypothesis generation, the positive test strategy, and sparse categories Psychological Review, 118, 120-34 (published version)
- A Ejova, DJ Navarro and AF Perfors (2009). When to walk away: The effect of variability on keeping options viable. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1258-1263 (published version)
- A Perfors and DJ Navarro (2009). Confirmation bias is rational when hypotheses are sparse. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 2471-2476 (published version)
Other Stuff
There is a long tail of “other stuff” which usually stems out of these three areas but doesn’t properly fit into any of them. This usually includes thinking about the role and interpretation of computational and mathematical models in cognitive science, the development of statistical approaches to applied problems, and various other bits and pieces. There is no coherent plan to this: we just write about things when we have good ideas about them!
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Relevant publications
- L Kennedy, DJ Navarro, A Perfors, and N Briggs (in press). Not every credible interval is credible: On the importance of robust methods in Bayesian data analysis. Behavioral Research Methods
- S Tauber, DJ Navarro, A Perfors, and M Steyvers (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410-441 (published version)
- A Perfors (2016). Piaget, probability, causality, and contradiction. Human Development, 59: 26-33 (published version)
- A Perfors (2014). Representations, approximations, and limitations within a computational framework for cognitive science: Commentary on article by Tecumseh Fitch. Physics of Life Reviews, 11, 369-370 (published version)
- A Perfors (2012). Bayesian models of cognition: What's built in after all? Philosophy Compass, 7, 127-138 (published version)
- A Perfors (2012). Levels of explanation and the workings of science. Australian Journal of Psychology, 64, 52-59 (published version)
- DJ Navarro and A Perfors (2011). Enlightenment grows from fundamentals: Comment on Jones and Love Behavioral and Brain Sciences, 34, 207-208 (published version)
- A Perfors, J Tenenbaum, TL Griffiths and F Xu (2011). A tutorial introduction to Bayesian models of cognitive development Cognition, 120, 302-321 (published version)
- T Griffiths, N Chater, C Kemp, A Perfors and J Tenenbaum (2010). Probabilistic models of cognition: Exploring representations and inductive biases Trends in Cognitive Sciences, 14, 357-364 (published version)