Single or Multiple Category Learning Systems

The current study has been interpreted under the assumption of multiple category learn- ing systems but instead, one could assume a single category learning system that is able to learn RD and II category sets using a single set of cognitive resources (see, for ex- ample, Newell, Dunn, & Kalish, 2011). On their own, the performance data from this experiment do not provide evidence against a single system account of category learning. The rule-defined and the information integration category sets were similarly affected by the temporary and continuous tasks, potentially providing evidence for a single catego- rization system that is reliant on working memory and executive functions. However,

a single system account cannot explain the pattern of strategy use seen across the two category sets. Without appealing to multiple systems, it is difficult to parsimoniously explain the differential effect of temporary and continuous tasks on optimal strategy use for the two category sets. Specifically, the effect of the temporary task would be expected to be similar for both types of categories, which it was not. Especially if one considers that the information integration category set was more difficult and required attention to two dimensions, it is difficult to explain why optimal strategy use for this category set was less, rather than more, susceptible to the temporary task than was the simpler rule- defined category set. Therefore, when the performance and strategy data are considered together, these data offer support for multiple categorization systems. For the verbal system, taxing executive functions continuously is no more detrimental to learning than taxing them temporarily. For the nonverbal system, continuously taxing executive func- tions is particularly detrimental because this fully restricts access to executive functions, which interferes with the transition to the nonverbal system.

Ours and other recent research (e.g., Huang-Pollock et al., 2011; Maddox et al., 2009, 2010; Schnyer et al., 2009) necessitates that the original description of the category learning systems be updated. The nonverbal system was initially thought to operate independently of executive functions (Ashby et al., 1998; Minda & Miles, 2010) but it is becoming more clear that executive functions do play a role in nonverbal categorization, likely in aiding the transition away from the verbal system. By postulating that both systems are reliant on executive functions, albeit for different purposes, it is possible that the category learning systems are not as different as was initially thought. In some sense, proponents of a single category learning system and proponents of multiple category

learning systems may not be that divergent, in that both groups agree that there is some overlap in the cognitive resources used for learning information integration and rule-defined category sets.

In one version of the single system account, rule-defined and information integration categories may be learned using a single category learning system, but this system may need to implement differentstrategies to learn each type of category. Under such a frame- work, a verbal, single-dimensional strategy may be dominant, and executive functions may be needed to transition away from the dominant strategy and to a nonverbal, two- dimensional strategy. Strictly speaking, this could be a single-systems account, but has many similarities with a multiple-systems framework because different cognitive processes may be important for implementing each type of strategy. Although there is convincing evidence (e.g., Maddox & Filoteo, 2001; Nomura et al., 2007; Nomura & Reber, 2012) that these strategies are subserved by distinct neurobiological systems, this is not the focus of the current paper, nor is it an especially important point for the current find- ings. Instead, the important point is that there are multiple approaches for learning new categories, and executive functions may be important for mediating the transition between these approaches, regardless of whether they are construed as separate systems, strategies, or otherwise.

3.7.3

Conclusions

This study illustrates that continuously taxing executive functions has a different effect on the nonverbal system than temporarily taxing executive functions. When executive

functions were never fully available, the information integration category set was less likely to be learned using the optimal strategy, suggesting that continuously taxing ex- ecutive functions interferes with the transition to the nonverbal system. For the verbal system, whether executive functions were taxed temporarily or continuously did not af- fect categorization performance or strategy use, suggesting that the duration of executive function taxation is not an important factor for the nonverbal system. This study illus- trates that executive functions play an important but different role in each of the category learning systems. For the verbal system, executive functions are used during the cate- gorization trial to support hypothesis testing and process feedback. For the nonverbal system, executive functions are used to facilitate the transition from the dominant verbal system to the nonverbal system. These results also provide more general insight into how multiple cognitive systems may interact during complex cognitive tasks.

References

Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsy- chological theory of multiple systems in category learning. Psychological Review, 105, 442–481.

Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 33-53.

Ashby, F. G., Isen, A. M., & Turken, U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106, 529–550.

Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Reviews in 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., & Valentin, V. V. (2005). Multiple systems of perceptual category learning: Theory and cogntive tests. InCategorization in cognitive science (pp. 16–30). New York, NY: Elsevier.

Buckner, R. L. (2004). Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron,44, 195–208. Bunge, S., & Zelazo, P. (2006). A brain-based account of the development of rule use in

childhood. Current Directions in Psychological Science, 15, 118–121.

Casey, B. J., Davidson, M. C., Hara, Y., Thomas, K. M., Martinez, A., Galvan, A., et al. (2004). Early development of subcortical regions involved in non-cued attention switching. Developmental Science, 7, 534–542.

Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54, 241–257.

Decaro, M. S., Thomas, R. D., Albert, N. B., & Beilock, S. L. (2011). Choking under pressure: Multiple routes to skill failure. Journal of Experimental Psychology: General, 140, 390–406.

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.

Filoteo, J. V., Lauritzen, J. S., & Maddox, W. T. (2010). Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning. Psy- chological Science,21, 415–423.

Fisk, J. E., & Sharp, C. A. (2004). Age-Related impairment in executive functioning: Updating, inhibition, shifting, and access. Journal of Clinical and Experimental Neuropsychology, 26, 874–890.

memory systems by distraction. Proceedings of the National Academy of Sciences, 103, 11778-11783.

Harrison, Y., Horne, J. A., & Rothwell, A. (2000). Prefrontal neuropsychological effects of sleep deprivation in young adults–a model for healthy aging? SLEEP, 23, 1067–1073.

Huang-Pollock, C. L., Maddox, W. T., & Karalunas, S. L. (2011). Development of implicit and explicit category learning. Journal of Experimental Child Psychology, 109, 321–335.

Jurado, M. B., & Rosselli, M. (2007). The elusive nature of executive functions: A review of our current understanding. Neuropsychology Review, 17, 213–233.

Kemler Nelson, D. G. (1984). The effect of intention on what concepts are acquired. Journal of Verbal Learning and Verbal Behavior, 23, 734–759.

Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception and Psychophysics, 53, 49–70.

Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29, 650-662.

Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004). Disrupting feed- back processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32, 582-591.

Maddox, W. T., Bohil, C. J., & Ing, A. D. (2004). Evidence for a procedural-learning based system in perceptual category learning. Psychonomic Bulletin & Review,11, 945–952.

Maddox, W. T., & Filoteo, J. V. (2001). Striatal contributions to category learning: Quantitative modeling of simple linear and complex rule learning in patients with Parkinson’s disease. Journal of the International Neuropsychological Society, 7, 710–727.

Maddox, W. T., Glass, B. D., Wolosin, S. M., Savarie, Z. R., Bowen, C., Matthews, M. D., et al. (2009). The effects of sleep deprivation on information-integration categorization performance. SLEEP,32, 1439–1449.

Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning.Jour- nal of Experimental Psychology: Learning, Memory, & Cognition, 31, 100–107. Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is less:

Feedback effects in perceptual category learning. Cognition, 108, 578–589.

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.

Miles, S. J., & Minda, J. P. (2011). The effects of concurrent verbal and visual tasks on category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 37, 588–607.

Minda, J. P., Desroches, A. S., & Church, B. A. (2008). Learning rule-described and non-rule-described categories: A comparison of children and adults. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 1518-1533.

Minda, J. P., & Miles, S. J. (2010). The influence of verbal and nonverbal processing on category learning. In B. H. Ross (Ed.), The psychology of learning and motivation

(Vol. 52, pp. 117–162). Burlington: Academic Press.

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, A. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. Nadler, R. T., Rabi, R., & Minda, J. P. (2010). Better mood and better performance:

Learning rule-described categories is enhanced by positive mood. Psychological Science,12, 1770–1776.

Newell, B. R., Dunn, J. C., & Kalish, M. (2011). Systems of Category Learning: Fact or Fantasy? In B. H. Ross (Ed.),The psychology of learning and motivation (Vol. 54, pp. 167–215). Burlington: Elsevier Inc.

Nilsson, J. P., S¨oderstr¨om, M., Karlsson, A., Lekander, M., Kerstedt, T. A., Lindroth, N., et al. (2005). Less effective executive functioning after one night’s sleep deprivation. Journal of Sleep Research, 14, 51–56.

Nomura, E. M., Maddox, W. T., Filoteo, J. V., Ing, A. D., Gitelman, D. R., Parrish, T. B., et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex,17, 37-43.

Nomura, E. M., & Reber, P. J. (2008). A review of medial temporal lobe and caudate contributions to visual category learning. Neuroscience and Biobehavioral Reviews, 32, 279–291.

Nomura, E. M., & Reber, P. J. (2012). Combining computational modeling and neu- roimaging to examine multiple category learning systems in the brain. Brain Sci- ences,2, 176–202.

al. (2001). Interactive memory systems in the human brain. Nature,414, 546-550. Poldrack, R. A., & Rodriguez, P. (2004). How do memory systems interact? Evidence

from human classification learning. Neurobiology of Learning and Memory, 84, 324–332.

R Core Team. (2012). R: A language and environment for statistical computing [Com- puter software manual]. Vienna, Austria.

Schnyer, D. M., Maddox, W. T., Ell, S. W., Davis, S., Pacheco, J., & Verfaellie, M. (2009). Prefrontal contributions to rule-based and information-integration category learning. Neuropsychologia, 47, 2995–3006.

Tharp, I. J., & Pickering, A. D. (2009). A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information–integration category learning. Cognition, 111, 410–414.

Visser, I., & Raijmakers, M. E. J. (2012). Developing representations of compound stimuli. Frontiers in Psychology, 3, 1–11.

Voss, J. L., Baym, C., & Paller, K. A. (2008). Accurate forced-choice recognition without awareness of memory retrieval. Learning & Memory, 15, 454–459.

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.

Zeithamova, D., & Maddox, W. T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387-398.

Zeithamova, D., & Maddox, W. T. (2007). The role of visuospatial and verbal working memory in perceptual category learning. Memory & Cognition,35, 1380–1398.

Chapter 4

Biasing the Competition Between

Category Learning Systems

A common theme across many domains of cognition is the existence of multiple cognitive systems dedicated to solving a single task. For example, reasoning can be carried out using heuristics or rules (Evans, 2003; Sloman, 1996), attitudes can be formed through implicit or explicit processes (Gawronski & Bodenhausen, 2006), and memories can be supported by declarative or non-declarative networks (Eichenbaum, 1997). The interac- tion between the systems is an important aspect of any multiple systems theory.

Within the domain of category learning, categorization decisions can be made using a verbal or a nonverbal system (Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Minda & Miles, 2010; Smith & Grossman, 2008). The verbal system learns rule-defined (RD) categories for which category membership can be determined by applying a simple verbal rule (e.g., mammals are animals that possess fur or hair). This type of learning involves active hypothesis testing in order to identify and apply the optimal categorization rule.

Working memory and executive functions are used to identify stimulus dimensions on which a rule could be applied, generate and apply a rule, process feedback, decide whether to switch to a new rule, and remember which rules have already been tested (Miles & Minda, 2011; Waldron & Ashby, 2001; Zeithamova & Maddox, 2006, 2007). Categories based on a single, salient dimension are easiest for the verbal system to learn while categories based on a less salient dimension, multiple dimensions, or complex rules are more difficult for the verbal system to learn (e.g. Filoteo, Maddox, Ing, & Song, 2007; Maddox, Filoteo, & Lauritzen, 2007; Nosofsky, Stanton, & Zaki, 2008; Zeithamova & Maddox, 2007).

The nonverbal system learns non-rule-defined (NRD) categories for which category membership is based on multiple dimensions that must be integrated before the catego- rization decision can be made (e.g., dogs are animals that can sometimes bark, sometimes have fur, sometimes have a tail, etc.). Nonverbal categorization uses an automatic type of learning based on procedural learning mechanisms (Ashby, Maddox, & Bohil, 2002; Mad- dox, Bohil, & Ing, 2004) and dopamine-mediated feedback processing (Maddox, Ashby, & Bohil, 2003; Maddox & Ing, 2005). As such, active cognitive processes like executive functioning and verbal working memory are thought to play little role in learning once the nonverbal system is engaged.

The verbal system is thought to be the default system, in that people tend to approach new categorization tasks by testing out potential categorization rules, and only switch to the nonverbal system when no effective verbal rule is found (Ashby et al., 1998; Ashby & Valentin, 2005; Minda & Miles, 2010). Mathematical modeling of participants’ categorization strategies during NRD learning shows that they tend to begin the task

using RD strategies but switch to NRD strategies as learning progresses (e.g., Maddox, Filoteo, Hejl, & Ing, 2004; Markman, Maddox, & Worthy, 2006; Worthy, Maddox, & Markman, 2009).

4.0.4

Slowing the Transition to the Nonverbal System

Past research has illustrated that the efficiency with which the transition to the nonverbal system is made can be affected by a number of factors. The transition can be slowed by decreasing availability of executive functions, increasing the tendency of the verbal system to maintain control, and perhaps by increasing the initial strength of the verbal system.

Executive functions (i.e., cognitive abilities used to guide effortful behaviour) appear to be important for mediating the transition from the verbal to the nonverbal system. On trials with high competition between the verbal and nonverbal systems (i.e., when each system is confident in its response), fMRI indicates that the dorsolateral prefrontal cortex is particularly active, suggesting that this area may be involved in gating the systems (Nomura & Reber, 2012). Since the dorsolateral prefrontal cortex is involved in executive functioning (Barbey et al., 2012), it may be that executive functions are important for mediating between the systems. Further evidence comes from a series of studies examining NRD categorization of participants with decreased executive function- ing. Children, older adults, sleep-deprived adults and patients with frontal lobe damage all have decreased executive functioning (Buckner, 2004; Bunge & Zelazo, 2006; Casey, Giedd, & Thomas, 2000; Fisk & Sharp, 2004; Harrison, Horne, & Rothwell, 2000; Nils-

son et al., 2005) and have difficulty learning NRD categories (Huang-Pollock, Maddox, & Karalunas, 2011; Maddox et al., 2009; Maddox, Pacheco, Reeves, Zhu, & Schnyer, 2010; Schnyer et al., 2009). Mathematical modeling of their categorization strategies illustrates that these groups tend to use a suboptimal RD strategy to solve the NRD category set, suggesting difficulty making the transition away from the dominant verbal system. Therefore, decreased executive functioning seems to impede the transition to the nonverbal system, resulting in decreased use of appropriate NRD strategies.

Given that the verbal system is initially dominant, manipulations that allow the ver- bal system to maintain its control will slow, and perhaps even stop, the transition to the nonverbal system. For example, the form of categorization feedback can affect whether the verbal system maintains its control even when the system provides suboptimal per- formance. Full feedback (i.e., whether a response was correct or incorrect and the label of the correct category) is conducive to verbal learning because it encourages hypothesis testing whereas minimal feedback (i.e., whether a response was correct or incorrect) is not. Full feedback during NRD categorization encourages prolonged hypothesis testing and allows the verbal system to maintain control over the categorization decisions. This leads to the counterintuitive finding that full feedback causes worse NRD performance and decreases NRD strategy use compared to minimal feedback (Maddox, Love, Glass, & Filoteo, 2008). Similarly, inducing pressure to perform (e.g., when performance will be watched by others) causes explicit, step-by-step monitoring of the categorization process. This too allows the verbal system to persist in hypothesis testing, causing NRD cate- gories to be learned via a suboptimal verbal strategy (Decaro, Thomas, Albert, & Beilock, 2011). Together, these studies show that manipulations allowing the verbal system to

maintain its dominance slow the transition to the more optimal nonverbal system. A prediction that has yet to be tested is that the relative strengths of the verbal and nonverbal systems will also affect the likelihood of transitioning to the nonverbal system. The strength of the systems can be thought of as a set of weightings, constrained to sum to 1.0, so that an increase in one system’s weight causes a decrease in the other system’s weight (Helie, Paul, & Ashby, 2011). Since the verbal system is the default, it has the