Abstract
Analogical transfer is the ability to transfer knowledge despite significant changes in the surface features of a problem. In categorization, analogical transfer occurs if a classification strategy learned with one set of stimuli can be transferred to a set of novel, perceptually distinct stimuli. Three experiments investigated analogical transfer in rule-based and information-integration categorization tasks. In rule-based tasks, the optimal strategy is easy to describe verbally, whereas in information-integration tasks, accuracy is maximized only if information from two or more stimulus dimensions is integrated in a way that is difficult or impossible to describe verbally. In all three experiments, analogical transfer was nearly perfect in the rule-based conditions, but no evidence for analogical transfer was found in the information-integration conditions. These results were predicted a priori by the COVIS theory of categorization.
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Notes
In all three experiments, each participant was randomly assigned to a condition. Note that this practice led to small sample-size differences across conditions.
For example, if we assume a noise variance equal to the mean estimated noise variance from the GLC model (see the Appendix) across all Experiment 1 participants for whom the GLC provided the best fit to the training data, then an ideal observer (with noise) would achieve 93.7% correct in Experiment 1 and 99.8% correct in Experiment 2.
Although to our knowledge no previous research has addressed the question of whether pigeons show analogical transfer in perceptual categorization, we predict that pigeons run in the present experiments would show little or no transfer in either condition. This is because pigeons presumably have at most a limited rule-learning system.
References
Ashby, F. G. (1992). Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 449–483). Hillsdale: Erlbaum.
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481. doi:10.1037/0033-295X.105.3.442
Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114, 632–656.
Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33–53.
Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.
Ashby, F. G., Maddox, W. T., & Bohil, C. J. (2002). Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30, 666–677.
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). COVIS. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization (pp. 65–87). New York: Cambridge University Press.
Ashby, F. G., & Valentin, V. V. (2005). Multiple systems of perceptual category learning: Theory and cognitive tests. In H. Cohen & C. Lefebvre (Eds.), Categorization in cognitive science. New York: Elsevier.
Ashby, F. G., & Waldron, E. M. (1999). On the nature of implicit categorization. Psychonomic Bulletin & Review, 6, 363–378.
Ashby, F. G., Waldron, E. M., Lee, W. W., & Berkman, A. (2001). Suboptimality in human categorization and identification. Journal of Experimental Psychology. General, 130, 77–96. doi:10.1037/0096-3445.130.1.77
Bassok, M. (1990). Transfer of domain-specific problem-solving procedures. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 522–533.
Beveridge, M., & Parkins, E. (1987). Visual representation in analogical problem solving. Memory & Cognition, 15, 230–237.
Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436. doi:10.1163/156856897X00357
Brooks, L. R., Norman, G. R., & Allen, S. W. (1991). Role of specific similarity in a medical diagnostic task. Journal of Experimental Psychology, 120, 278–287.
Caan, W., Perrett, D. I., & Rolls, E. T. (1984). Responses of striatal neurons in the behaving monkey: 2. Visual processing in the caudal neostriatum. Brain Research, 290, 53–65.
Catrambone, R., Craig, D. L., & Nersessian, N. J. (2006). The role of perceptually represented structure in analogical problem solving. Memory & Cognition, 34, 1126–1132.
Catrambone, R., & Holyoak, K. J. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 1147–1156.
Cho, S., Holyoak, K. J., & Cannon, T. (2007). Analogical reasoning in working memory: Resources shared among relational integration, interference resolution, and maintenance. Memory & Cognition, 35, 1445–1455.
Duncker, K. (1945). On problem solving. Psychological Monographs, 58(5, Whole No. 270).
Ell, S. W., & Ashby, F. G. (2006). The effects of category overlap on information-integration and rule-based category learning. Perception & Psychophysics, 68, 1013–1026. doi:10.3758/BF03193362
Ell, S., Ing, A. D., & Maddox, W. T. (2009). Criterial noise effects on rule-based category learning: The impact of delayed feedback. Attention, Perception, & Psychophysics, 71, 1263–1275.
Filoteo, J. V., Maddox, W. T., Ing, A. D., & Song, D. D. (2007). Characterizing rule-based category learning deficits in patients with Parkinson’s disease. Neuropsychologia, 45, 305–320.
Forbus, K. D., Gentner, D., & Law, K. (1995). MAC/FAC: A model of similarity-based retrieval. Cognitive Science, 19, 141–205.
Gentner, D., & Forbus, K. D. (2011). Computational models of analogy. WIRE’s Cognitive Science, 2, 266–276.
Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993). The roles of similarity in transfer: Separating retrievability from inferential soundness. Cognitive Psychology, 25, 524–575. doi:10.1006/cogp.1993.1013
Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306–355.
Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.
Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72, 1013–1031.
Holyoak, K. J., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory & Cognition, 15, 332–340.
Isaacs, I. D., & Duncan, C. P. (1962). Reversal and nonreversal shifts within and between dimensions in concept formation. Journal of Experimental Psychology, 64, 580–585.
Kaplan, C. A., & Simon, H. A. (1990). In search of insight. Cognitive Psychology, 22, 374–419.
Keane, M. (1985). On drawing analogies when solving problems: A theory and test of solution generation in an analogical problem-solving task. British Journal of Psychology, 76, 449–458.
Keane, M. (1987). On retrieving analogues when solving problems. Quarterly Journal of Experimental Psychology, 39A, 29–41.
Kendler, H. H., & Kendler, T. S. (1970). Developmental processes in discrimination learning. Human Development, 13, 65–89.
Kincaid, A. E., Zheng, T., & Wilson, C. J. (1998). Connectivity and convergence of single corticostriatal axons. Journal of Neuroscience, 18, 4722–4731.
Lane, S. M., & Schooler, J. W. (2004). Skimming the surface: Verbal overshadowing of analogical retrieval. Psychological Science, 15, 715–719.
Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70.
Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66, 309–332. doi:10.1016/j.beproc.2004.03.011
Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32, 582–591. doi:10.3758/BF03195849
Maddox, W. T., Filoteo, J. V., Lauritzen, J. S., Connally, E., & Hejl, K. D. (2005). Discontinuous categories affect information-integration, but not rule-based category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 654–669.
Maddox, W. T., Pacheco, J., Reeves, M., Zhu, B., & Schnyer, D. M. (2010). Rule-based and information-integration category learning in normal aging. Neuropsychologia, 48, 2998–3008. doi:10.1016/j.neuropsychologia.2010.06.008
Novick, L. R., & Holyoak, K. J. (1991). Mathematical problem solving by analogy. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 398–415.
Redington, M., & Chater, N. (2002). Knowledge representation and transfer in artificial grammar learning. In R. M. French & A. Cleeremans (Eds.), Implicit learning and consciousness (pp. 121–143). Hove: Psychology.
Reed, S. K., Dempster, A., & Ettinger, M. (1985). Usefulness of analogous solutions for solving algebra word problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 106–125.
Reeves, L., & Weisberg, R. W. (1994). The role of content and abstract information in analogical transfer. Psychological Bulletin, 115, 381–400.
Roediger, H. L., III. (1990). Implicit memory: A commentary. Bulletin of the Psychonomic Society, 28, 373–380.
Ross, B. H. (1987). This is like that: The use of earlier problems and the separation of similarity effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 629–639.
Ross, B. H., & Kennedy, P. T. (1990). Generalizing from the use of earlier examples in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 42–55.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.
Smith, J. D., Ashby, F. G., Berg, M. E., Murphy, M. S., Spiering, B. J., Cook, R. G., & Grace, R. C. (2011). Pigeons’ categorization may be exclusively nonanalytic. Psychonomic Bulletin and Review, 18, 414–421. doi:10.3758/s13423-010-0047-8
Squire, L. R. (1992). Declarative and nondeclarative memory: Multiple brain systems supporting learning and memory. Journal of Cognitive Neuroscience, 4, 232–243.
Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168–176.
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Appendix
Appendix
A variety of different decision bound models were fit to the responses of each individual participant. Included in this list were three models that assumed an RB decision strategy (two one-dimensional models and a model that assumed a conjunction rule), one that assumed an II strategy (the general linear classifier), and two that assumed random guessing. For more details, see Ashby (1992) or Maddox and Ashby (1993).
Models assuming an RB strategy
The one-dimensional classifier
This model assumes that participants set a decision criterion on a single stimulus dimension. For example, a participant might base his or her categorization decision on the following rule: “Respond ‘A’ if the bar width is small, otherwise respond ‘B’.” Two versions of the model were fit to the data. One version assumed a decision based on bar width, and the other assumed a decision based on orientation. These models had two parameters: a decision criterion along the relevant perceptual dimension, and a perceptual noise variance.
The general conjunctive classifier (GCC)
This model assumes that the rule used by participants is a conjunction of two one-dimensional classifiers (e.g., “Respond ‘A’ if the bar width is small AND the orientation is >45°, otherwise respond ‘B’.”). Although several different versions of the model could be fit to the present data, only the version that seemed plausible based on a visual inspection of the response data was fit. The GCC has three parameters: one for the single decision criterion placed along each stimulus dimension (one for orientation and one for bar width), as well as a perceptual noise variance.
Models assuming an II strategy
The general linear classifier (GLC)
The GLC assumes that participants divide the stimulus space using a linear decision bound. One side of the bound is associated with an “A” response, and the other side is associated with a “B” response. These decision bounds require linear integration of both stimulus dimensions, thereby producing an II decision strategy. The GLC has three parameters: the slope and intercept of the linear decision bound, and a perceptual noise variance.
Random guessing models
Two models assumed that the participant guessed randomly on every trial. One version assumed that each response was equally likely to be selected. This model had no free parameters. A second model assumed that the participant guessed response “A” with probability p and guessed “B” with probability 1 – p, where p was a free parameter. This model is useful for identifying participants who are biased toward pressing one response key.
Goodness-of-fit measure
Model parameters were estimated >using the method of maximum likelihood, and the statistic used for model selection was the Bayesian information criterion (BIC; Schwarz, 1978), which is defined as BIC = r ln N – 2 ln L, where r is the number of free parameters, N is the sample size, and L is the likelihood of the model given the data. The BIC statistic penalizes models for extra free parameters. To determine the best-fitting model within a group of competing models, the BIC statistic is computed for each model, and the model with the smallest BIC value is the winning model.
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Casale, M.B., Roeder, J.L. & Ashby, F.G. Analogical transfer in perceptual categorization. Mem Cogn 40, 434–449 (2012). https://doi.org/10.3758/s13421-011-0154-4
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DOI: https://doi.org/10.3758/s13421-011-0154-4