Associate Professor of Psychology
For more information about my lab's research, please see the lab webpage.
Why do we perceive
the categories and causal relationships in the world we do? In the domains of categorization and causal reasoning, I address the age-old question
of whether human knowledge derives largely from prior beliefs and biases (rationalism)
or from observations (empiricism). In my research, I investigate how prior beliefs
combine with our observation of property clusters to form our mental representation
of categories, and how prior beliefs about possible causal mechanisms combine
with observations of correlated variables to form our mental representation of
causal relationships. In other areas of higher-level cognition such as skill
acquisition and problem solving I also inquire how prior knowledge
and skills influences current performance. To further the development of precise,
quantitative theories, an important component of my research is the development
of computational models.
Categorization and causal reasoning.
In contrast to a strict empiricist view that categories are induced solely from
observations, prior research has demonstrated that people (even very young children)
often possess extensive (albeit tacit) theoretical beliefs and biases about the
structure of the world that influences the way they acquire, represent, and use
knowledge about categories. The most important form of theoretical knowledge is
causal knowledge about how the world works (e.g., we not only know that birds
fly and have wings, but that birds fly because they have wings). I have established
that an object's category membership is a function of whether or not it conforms
to the causal laws governing one's understanding of the category. I have also
investigated the hypothesis that humans possess an innate tendency to postulate
the existence of an invisible cause (i.e., called by some an essence) to
explain the properties of categories that are directly observed.
In a number
of other research projects I investigate the interplay between theoretical, linguistic,
and empirical knowledge that takes place during the learning, development, and
revision of conceptual systems. First, to evaluate the effects of knowledge versus
empirical structure, I independently manipulate a category's causal knowledge
and the statistical structure of its category members (e.g., the pattern of correlations
among category attributes), and have found that judgments of category membership
tend to be dominated by causal knowledge. Second, I investigate the nature of
higher-level categories such as superordinate categories (e.g., mammal) that are
abstract because do not specify any concrete features. Rather, these structures
have the effect of constraining the allowable combinations of features that may
appear in exemplars of the category. Third, I investigate the issues of disconfirmation
and revision of conceptual systems in light of the theoretical knowledge that
underlies those systems.
Computational modeling. I have three modeling
efforts with respect to accounting for the effects of knowledge on categorization.
First, I have developed a model of categorization named causal-model theory which utilizes Bayesian networks (also known as causal networks,or influence diagrams)
as a representation, or model, of the causal knowledge associated with a category. I have shown that causal-model theory yields better fits to human classification performance in the presence
of causal knowledge than traditional similarity-based models. Second, I have developed
a connectionist model named the knowledge resonance model (KRES) which
employs a constraint satisfaction network to account for the drastic reduction
in the time required to learn a new category when the category is coherent, or
makes sense, in light of prior knowledge. In this network, learning proceeds via
contrastive Hebbian learning (rather than backpropagation) and prior-knowledge
units accelerate learning by amplifying the activation of knowledge-consistent
features and dampening the activation of knowledge-inconsistent features. Contrastive
Hebbian allows the network to learn to suppress those prior knowledge units that
are irrelevant in light of feedback. Third, I have conducted basic research involving
the use of Latent Semantic Analysis (LSA) as a model of human conceptual
knowledge. LSA analyzes large numbers of text documents in order to extract word/document
co-occurrence information, andrepresents the meaning of lexical items in a high-dimensional
semantic space. I am investigating LSA as a model of the statistical category
knowledge that human learners extract from written text.
acquisition and procedural memory. I have investigated the phenomenon of procedural
interference, the interference between items in procedural memory. In a series
of experiments, I established the existence of procedural interference, and demonstrated
that the strength of this interference varies as a function of the strength of
the competing items. The magnitude of interference was undiminished even after
a one-week retention interval, a result attributable to the durability of procedural
memories. This research has implications for the retraining of skills, demonstrating
that obsolete procedural memories can produce errors in performance long after
retraining was thought to have eliminated them. These data are being fit to the
dominant instance-based models of cognitive skill acquisition.
solving and metacognition. I investigate the nature of metacognitive skills
in the realm of problem solving. I have used signal detection theory to study
to what extent are people able to detect that problems (i.e., algebra word problems)
are unsolvable because of missing information. Use of signal detection theory
provides estimates of detection sensitivity (i.e., the ability to discriminate
between solvable and unsolvable problems) that are uncontaminated by response
bias (i.e., the tendency to report that problems are unsolvable). In this
research detection sensitivity and response bias were found to be affected
by whether a "hint" that problems might be unsolvable was provided, indicating
that many individuals possess the metacognitive skill to detect missing information,
but that conscious effort is required for that skill to be deployed.
research. In the past I have been involved in interdisciplinary cognitive
science research on the topics of computer programming and human-computer interaction.
I have also investigated the role of background knowledge during learning from
educational texts using Latent Semantic Analysis (LSA). A patent for the related
task of automatic exam-grading is pending.
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I received a B.A. in Physics and a B.S. Computer Science from Washington University
at St. Louis. After graduation I was employed as a scientific programmer for a
firm developing brain scanners based on the CAT (cranial-axial tomography) technology.
After working for a number of other software companies, I earned a Masters degree
in Artificial Intelligence from Stanford University in 1990, and during this time
worked as a research assistant in a number of labs at Stanford’s psychology department.
I received a Masters (1995) and Ph.D. (1998) in Cognitive Psychology from the
University of Colorado in, where I carried out basic research with Reid Hastie
(my dissertation advisor), Walter Kintsch, Tom Landauer, and others. I was postdoctoral
research associate at the Beckman Institute of the University of Illinois from
1998-1999, working there with Drs. Brian Ross and Greg Murphy.
Manuscripts Submitted for Publication
Hayes, B.K., & Rehder, B. (2010). Causal categorization in children and adults. [pdf]
Williams, J.J., Lombrozo, T., & Rehder, B. (2010). Explanation both enhances and impairs learning: A subsumptive constraints study.
Kim, S. & Rehder, B. (in press). How prior knowledge affects selective attention during category learning: An eyetracking study. Memory & Cognition. [pdf]
Rehder, B. & Kim, S. (in press). Causal status and coherence in causal-based categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition. [pdf]
Hoffman, A.B., & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexbility. Journal of Experimental Psychology: General, 139, 319-340. [pdf]
Rehder, B., Colner, R.M., & Hoffman, A.B. (2009). Feature inference learning and eyetracking. Journal of Memory & Language, 60, 394-419. [pdf]
Rehder, B. (2009). Causal-based property generalization. Cognitive Science, 33, 301-343. [pdf]
Rehder, B. & Kim, S. (2009). Classification as diagnostic reasoning. Memory & Cognition. 37, 715-729. [pdf]
Harris, H.D., Murphy, G.L., & Rehder, B. (2008). Prior knowledge and exemplar frequency. Memory & Cognition, 34, 1335-1350. [pdf]
Rehder, B. & Kim, S. (2006). How causal knowledge affects classification: A generative theory of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 659-683. [pdf]
Rehder, B. (2006). When causality and similarity compete in category-based property induction. Memory & Cognition, 34, 3-16. [pdf]
Rehder, B., & Hoffman, A. B. (2005). Thirty-something categorization results explained: Selective attention, eyetracking, and models of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 811-829. [pdf]
Rehder, B. & Burnett, R. (2005). Feature inference and the causal structure of categories. Cognitive Psychology, 50, 264-314. [pdf]
Rehder, B. & Hoffman, A.B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1-41. [pdf]
Rehder, B., & Hastie, R. (2004). Category coherence and category-based property induction. Cognition, 91, 113-153. [pdf]
Rehder, B., & Murphy, G. L. (2003). A Knowledge-Resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759-784. [pdf]
Rehder, B. (2003). A causal-model theory of conceptual representation and categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1141-59. [pdf]
Rehder, B. (2003). Categorization as causal reasoning. Cognitive Science, 27, 709-748. [pdf]
Rehder, B. & Ross, B.H. (2001). Abstract coherent concepts. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1261-1275. [pdf]
Rehder, B. & Hastie, R. (2001). Causal knowledge and categories: The effects of causal beliefs on categorization, induction, and similarity. Journal of Experimental Psychology: General, 130, 323-360. [pdf]
Rehder, B. (2001). Interference between cognitive skills. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 451-469.
Rehder, B., Schreiner, M.E., Wolfe, M.B., Laham, D. Landauer, T.K., & Kintsch, W. (1998). Using Latent Semantic Analysis to assess knowledge: Some technical considerations. Discourse Processes 25, 337-354.
Rehder, B. & Hastie, R. (1996). The moderating effects of variability on disconfirmation and belief revision. Psychonomic Bulletin & Review, 3, 499-503.
Rehder, B. (1996). A Bayesian analysis of the relationship between category variability and belief revision. Unpublished manuscript. [Word Document]
Harris, H.D. & Rehder, B. (in press). The knowledge and resonance (KRES) model of category learning. In E.M. Pothos, & A.J. Wills (Eds.), Formal approaches in categorization. [Pdf available upon request.]
Rehder, B. (2010). Causal-based classification: A review. In B. Ross,
(Ed.), The Psychology of Learning and Motivation (52), 39-116. [Pdf
available upon request.]
Rehder, B. (2007). Essentialism as a generative theory of classification. In A. Gopnik, & L. Schultz, (Eds.), Causal learning: Psychology, philosophy, and computation, pp. 190-207. Oxford, UK: Oxford University Press. [pdf]
Rehder, B. (2007). Property generalization as causal reasoning. In Feeney, A., & Heit, E. (Eds.), Inductive reasoning: Experimental, developmental, and computational approaches, pp. 81-113. New York: Cambridge University Press. [pdf]
Pennington, N. & Rehder, B. (1995). Looking for transfer and interference. In D.L. Medin (Ed.), The Psychology of Learning and Motivation, (33), 223-289.
Rehder, B. (2010). Attention optimization in probabilistic category learning. Talk presented at the 32nd Annual Conference of the Cognitive Science Society, Austin, TX: Cognitive Science Society.
Williams, J.J., Lombrozo, T., & Rehder, B. (2010). Why does explaining help learning? Insights from an explanation impairment effect. In S. Ohlsson & R. Catrambone (Eds.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2906-2911). Austin, TX: Cognitive Science Society.
Rehder, B. & Kim, S. (2009). Causal status and coherence in causal-based classification. Poster presented at The 50th Annual Meeting of the Psychonomic Society, Washington, D.C.
Kim, S. & Rehder, B. (2009). Knowledge effect the selective attention in category learning: An eyetracking study. In N. Taatgen, H. van Rijn, L. Schomaker, & J. Nerbonne (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 230-235). Mahwah, NJ: Erlbaum. [pdf]
Colner, B. & Rehder, B. (2009). A new theory of classification and feature inference learning: An exemplar fragment model. In N. Taatgen, H. van Rijn, L. Schomaker, & J. Nerbonne (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 371-376). Mahwah, NJ: Erlbaum. [pdf]
Hoffman, A.B. & Rehder, B. (2009). Attentional and representational flexibility of feature inference learning. In N. Taatgen, H. van Rijn, L. Schomaker, & J. Nerbonne (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1864-1869). Mahwah, NJ: Erlbaum. [pdf]
Rehder, B. & Kim, S. (2008). The role of coherence in causal-based categorization. In V. Sloutsky, B. Love, K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 285-290). Mahwah, NJ: Erlbaum. [pdf]
Colner, B. & Rehder, B., & Hoffman, A.B. (2008). Feature inference and eyetracking. In V. Sloutsky, B. Love, K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1170-1175). Mahwah, NJ: Erlbaum. [pdf]
Rehder, B., & Milovanovic, G. (2007). Bias toward sufficiency and completeness in causal explanations. In D. MacNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (p. 1843). [pdf]
Kim, S., & Rehder, B. (2007). Causal status, coherence, and essentialized categories. In D. MacNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (p. 1788). [pdf]
Colner, B., Hoffman, A.B., & Rehder, B. (2007). Attention allocation in inference learning. In D. MacNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (p. 1727).[pdf]
Harris, H.D., & Rehder, B. (2006). Modeling category learning with exemplars and prior knowledge. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1440-1445). Mahwah, NJ: Erlbaum. [pdf]
Cognitive Psychology, 1998. University of Colorado at Boulder.
Psychology, 1995. University of Colorado at Boulder.
M.S. Computer Science
(Artificial Intelligence), 1990. Stanford University.
B.S. Computer Science,
1978. Washington University at St. Louis.
B.A. Physics, 1978. Washington University
at St. Louis.
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Associate Professor of Psychology
New York University
6 Washington Place, Room 858
York, NY 10003
Phone: (212) 992-9586
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