Skip to main content
Log in

Individual differences in category learning: Memorization versus rule abstraction

  • Published:
Memory & Cognition Aims and scope Submit manuscript

Abstract

Although individual differences in category-learning tasks have been explored, the observed differences have tended to represent different instantiations of general processes (e.g., learners rely upon different cues to develop a rule) and their consequent representations. Additionally, studies have focused largely on participants’ categorizations of transfer items to determine the representations that they formed. In the present studies, we used a convergent-measures approach to examine participants’ categorizations of transfer items in addition to their self-reported learning orientations and response times on transfer items, and in doing so, we garnered evidence that qualitatively distinct approaches in explicit strategies for category learning (i.e., memorization vs. abstracting an articulable rule) and consequent representations might emerge in a single task. Participants categorized instances that followed a categorization rule (in Study 1, we used a relational rule; in Study 2, an additional task with a single-feature rule). Critically, for both tasks, some transfer items differed from trained instances on only one attribute (but otherwise were perceptually similar), rendering the item a member of the opposing category on the basis of the rule (i.e., termed ambiguous items). Some learners categorized ambiguous items on the basis of perceptual similarity, whereas others categorized them on the basis of an abstracted rule. Self-reported learning orientation (i.e., memorization vs. rule abstraction) predicted categorizations and response times on transfer items. Differences in learning orientations were not associated with performance on other cognitive measures (i.e., working memory capacity and Raven’s Advanced Progressive Matrices). This work suggests that individuals may have different predispositions toward memorization versus rule abstraction in a single categorization task.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The OSpan task used for the testing of the earlier participants had an error that rendered the data for those participants unusable. Two participants ran out of time to complete the RAPM.

  2. OSpan scores were not related to how quickly learners reached criterion during training, either in the full sample, r(58) = –.09, p = .5, or among the subgroups of self-reported rule abstractors, r(33) = –.17, p = .34, and self-reported memorizers, r(19) = –.16, p = .51.

  3. As in Experiment 1, OSpan alone did not predict this rate of learning, r(90) = –.13, p = .22.

References

  • Allen, S., & Brooks, L. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120, 3–19. doi:10.1037/0096-3445.120.1.3

    Article  Google Scholar 

  • Anderson, J. R., & Betz, J. (2001). A hybrid model of categorization. Psychonomic Bulletin & Review, 8, 629–647. doi:10.3758/BF03196200

    Article  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Sciences, 5, 204–210. doi:10.1016/S1364-6613(00)01624-7

    Article  PubMed  Google Scholar 

  • 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. doi:10.1037/0278-7393.14.1.33

    PubMed  Google Scholar 

  • Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154–179. doi:10.1037/0033-295X.93.2.154

    Article  PubMed  Google Scholar 

  • Bors, D. A., & Stokes, T. L. (1998). Ravens Advanced Progressive Matrices: Norms for first-year university students and the development of a short form. Educational and Psychological Measurement, 58, 382–398.

    Article  Google Scholar 

  • Bott, L., & Heit, E. (2004). Nonmonotonic extrapolation in function learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 38–50. doi:10.1037/0278-7393.30.1.38

    PubMed  Google Scholar 

  • Bourne, L. E., Jr. (1974). An inference model of conceptual rule learning. In R. L. Solso (Ed.), Theories in cognitive psychology: The Loyola symposium (pp. 231–256). Potomac, MD: Erlbaum.

    Google Scholar 

  • Bourne, L. E., Jr., Healy, A. F., Parker, J. T., & Rickard, T. C. (1999). The strategic basis of performance in binary classification tasks: Strategy choices and strategy transitions. Journal of Memory and Language, 41, 223–252.

    Article  Google Scholar 

  • Bourne, L. E., Jr., Raymond, W. D., & Healy, A. F. (2010). Strategy selection and use during classification skill acquisition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 500–514. doi:10.1037/a0018599

    PubMed  Google Scholar 

  • Clark, V. P., Luger, G. F., McClain, J. T., Verzi, S. J., McDaniel, M. A., Hamilton, D., & Morisette, N. (2006). fMRI analysis of human function learning (Sandia National Laboratories Report). Albuquerque, NM: U.S. Department of Energy.

    Google Scholar 

  • Craig, S., & Lewandowsky, S. (2012). Whichever way you choose to categorize, working memory helps you learn. Quarterly Journal of Experimental Psychology, 65, 439–464. doi:10.1080/17470218.2011.608854

    Article  Google Scholar 

  • DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107, 284–294. doi:10.1016/j.cognition.2007.07.001

    Article  PubMed  Google Scholar 

  • Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140. doi:10.1037/0096-3445.127.2.107

    Article  Google Scholar 

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170. doi:10.1207/s15516709cog0702_3

    Article  Google Scholar 

  • Goldstone, R. L., Medin, D. L., & Gentner, D. (1991). Relational similarity and the nonindependence of features in similarity judgments. Cognitive Psychology, 23, 222–262.

    Article  PubMed  Google Scholar 

  • Johansen, M. K., & Palmeri, T. J. (2002). Are there representational shifts during category learning? Cognitive Psychology, 45, 482–553.

    Article  PubMed  Google Scholar 

  • Juslin, P., Jones, S., Olsson, H., & Winman, A. (2003a). Cue abstraction and exemplar memory in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 924–941. doi:10.1037/0278-7393.29.5.924

    PubMed  Google Scholar 

  • Juslin, P., Olsson, H., & Olsson, A.-C. (2003b). Exemplar effects in categorization and multiple-cue judgment. Journal of Experimental Psychology: General, 132, 133–156. doi:10.1037/0096-3445.132.1.133

    Article  Google Scholar 

  • Kane, M. J., Hambrick, D. Z., & Conway, A. R. A. (2005). Working memory capacity and fluid intelligence are strongly related constructs: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131, 66–71. doi:10.1037/0033-2909.131.1.66

    Article  PubMed  Google Scholar 

  • Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44. doi:10.1037/0033-295X.99.1.22

    Article  PubMed  Google Scholar 

  • Levine, M. (1975). A cognitive theory of learning: Research on hypothesis testing. New York, NY: Wiley.

    Google Scholar 

  • Lewandowsky, S. (2011). Working memory capacity and categorization: Individual differences and modeling. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 720–738. doi:10.1037/a0022639

    PubMed  Google Scholar 

  • Lewandowsky, S., Oberauer, K., Yang, L.-X., & Ecker, U. K. H. (2010). A working memory test battery for MATLAB. Behavior Research Methods, 42, 571–585. doi:10.3758/BRM.42.2.571

    Article  PubMed  Google Scholar 

  • Little, D. R., & Lewandowsky, S. (2009). Beyond nonutilization: Irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35, 530–550. doi:10.1037/0096-1523.35.2.530

    PubMed  Google Scholar 

  • Little, D. R., Nosofsky, R. M., & Denton, S. E. (2011). Response-time tests of logical-rule models of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 1–27. doi:10.1037/a0021330

    PubMed Central  PubMed  Google Scholar 

  • Mack, M. L., Preston, A. R., & Love, B. C. (2013). Decoding the brain’s algorithm for categorization from its neural implementation. Current Biology, 23, 2023–2027. doi:10.1016/j.cub.2013.08.035

    Article  PubMed  Google Scholar 

  • McDaniel, M. A., Cahill, M. J., Robbins, M., & Wiener, C. (2014). Individual differences in learning and transfer: Stable tendencies for learning exemplars versus abstracting rules. Journal of Experimental Psychology: General, 143, 668–693. doi:10.1037/a0032963

    Article  Google Scholar 

  • McDaniel, M. A., Dimperio, E., Griego, J. A., & Busemeyer, J. R. (2009). Predicting transfer performance: A comparison of competing function learning models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 173–195.

    PubMed  Google Scholar 

  • McKinley, S. C., & Nosofsky, R. M. (1995). Investigations of exemplar and decision bound models in large, ill-defined category structures. Journal of Experimental Psychology: Human Perception and Performance, 21, 128–148. doi:10.1037/0096-1523.21.1.128

    PubMed  Google Scholar 

  • Medin, D. L. (1989). Concepts and conceptual structure. American Psychologist, 44, 1469–1481. doi:10.1037/0003-066X.44.12.1469

    Article  PubMed  Google Scholar 

  • Medin, D. L., Altom, M. W., Edelson, S. M., & Freko, D. (1982). Correlated symptoms and simulated medical classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 37–50. doi:10.1037/0278-7393.8.1.37

    PubMed  Google Scholar 

  • Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238. doi:10.1037/0033-295X.85.3.207

    Article  Google Scholar 

  • Medin, D. L., & Smith, E. E. (1981). Strategies and classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 241–253. doi:10.1037/0278-7393.7.4.241

    Google Scholar 

  • Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–114. doi:10.1037/0278-7393.10.1.104

    PubMed  Google Scholar 

  • Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115, 39–57. doi:10.1037/0096-3445.115.1.39

    Article  Google Scholar 

  • Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108. doi:10.1037/0278-7393.13.1.87

    PubMed  Google Scholar 

  • Nosofsky, R. M., Palmeri, T. J., & McKinley, S. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53–79. doi:10.1037/0033-295X.101.1.53

    Article  PubMed  Google Scholar 

  • Pachur, T., & Olsson, H. (2012). Type of learning task impacts performance and strategy selection in decision making. Cognitive Psychology, 65, 207–240. doi:10.1016/j.cogpsych.2012.03.003

    Article  PubMed  Google Scholar 

  • Raijmakers, M. E. J., Schmittmann, V. D., & Visser, I. (2014). Costs and benefits of automatization in category learning of ill-defined rules. Cognitive Psychology, 69, 1–24. doi:10.1016/j.cogpsych.2013.12.002

    Article  PubMed  Google Scholar 

  • Raven, J. C., Raven, J. E., & Court, J. H. (1998). Progressive matrices. Oxford, UK: Oxford Psychologists Press.

    Google Scholar 

  • Regehr, G., & Brooks, L. R. (1993). Perceptual manifestations of an analytic structure: The priority of holistic individuation. Journal of Experimental Psychology: General, 122, 92–114. doi:10.1037/0096-3445.122.1.92

    Article  Google Scholar 

  • Sewell, D. K., & Lewandowsky, S. (2012). Attention and working memory capacity: Insights from blocking, highlighting, and knowledge restructuring. Journal of Experimental Psychology: General, 141, 444–469.

    Article  Google Scholar 

  • Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436. doi:10.1037/0278-7393.24.6.1411

    Google Scholar 

  • Trabasso, T., & Bower, G. H. (1968). Attention in learning: Theory and research. New York, NY: Wiley.

    Google Scholar 

  • Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28, 127–154. doi:10.1016/0749-596X(89)90040-5

    Article  Google Scholar 

  • Wechsler, D. (1997). Wechsler Adult Intelligence Scale—WAIS III (3rd ed.). San Antonio, TX: Psychological Corp.

    Google Scholar 

  • Wiley, J., Jarosz, A. F., Cushen, P. J., & Colflesh, G. J. H. (2011). New rule use drives the relation between working memory capacity and Raven’s Advanced Progressive Matrices. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 256–263.

    PubMed  Google Scholar 

Download references

Author note

We thank Michael Cahill for conversations regarding the design of the materials and procedure and for comments on this article. We appreciate the assistance of Sophie Goloff, Ali Haroon, and Yiyi Liu in the data collection. The research reported here was supported by a Collaborative Activity Award from the James S. McDonnell Foundation’s 21st Century Science Initiative in Bridging Brain, Mind and Behavior.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeri L. Little.

Appendix

Appendix

Following are descriptions of the OSpan, RAPM, and letter–number sequencing tasks used in the present studies.

OSpan task

Participants completed the OSpan task (Turner & Engle, 1989) as an assessment of working memory. Participants were provided with a math problem to validate (e.g., Is 5 × 2 – 1 = 7 ?, Answer: No). Participants were asked to say the math problem aloud and to type their answer into the box provided on the screen. As soon as the participant responded, a to-be-memorized word (e.g., BEAR) was presented on the screen for 1 s. A given trial consisted of two to five sets of math problems and words. After the presentation of all of the words in the trial, participants were asked to recall as many of the words as they could remember in the order that they had been presented by typing their responses into a textbox on the screen. After three practice trials, participants completed 12 more trials in a random order, with three trials being presented for each set size.

RAPM

Participants completed an abbreviated form of the RAPM (Bors & Stokes, 1998). Each problem consists of a 3 × 3 matrix, in which eight of the nine cells in the matrix were filled, and participants’ task was to choose which answer—from eight possible choices—would best fit in the missing cell. Only one choice was appropriate on the basis of both the vertical and horizontal patterns of the matrix. Participants were provided with 12 problems that increased in difficulty and were given unlimited time to complete them, one at a time, on a computer screen.

Letter–number sequencing task

Participants completed a version of the letter–number sequencing task (Wechsler, 1997) between the OSpan task and the RAPM. Letters and digits were presented in alternation on the screen one at a time. The numbers of letters and digits in a given trial ranged from 3 to 12, and 20 trials were included (i.e., two for each span length). After all of the digits and letters were presented for a given trial, participants were to recite the digits and letters in a rearranged order, with all of the digits being recited first and in numerical order and all of the letters being recited second and in alphabetical order. The experimenter sat in the room and recorded participants’ responses.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Little, J.L., McDaniel, M.A. Individual differences in category learning: Memorization versus rule abstraction. Mem Cogn 43, 283–297 (2015). https://doi.org/10.3758/s13421-014-0475-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3758/s13421-014-0475-1

Keywords

Navigation