Development of implicit and explicit category learning
Introduction
Category formation allows people to make adaptive responses across a wide variety of situations and, therefore, is one of the most fundamental decision-making processes needed for survival. According to the COVIS (competition between verbal and implicit systems) model (Ashby, Alfonso-Reese, Turken, & Waldron, 1998), there exist at least two separate, but partially overlapping, categorization systems to guide correct decision making, and both contribute to performance in day-to-day life.
The first system consciously identifies an explicit rule (i.e., if A, then B) or a set of conjunctive rules (i.e., if A and B, then C) through active hypothesis testing and is a form of explicit learning. This system involves a network of late-developing structures that includes the prefrontal and medial temporal cortices, the anterior cingulate cortex, and the head of the caudate (Ashby et al., 1998, Gabrieli et al., 1997, Schacter and Wagner, 1999). As such, the ability to learn an increasingly complex set of explicit rules over time is dependent on the health and development of these structures to represent such rules. The Wisconsin Card Sorting Test (WCST), a task in which participants learn to sort cards by color, number, or shape, would be an example of a task that not only indexes executive flexibility and set shifting but also taps an explicit category learning system.
The second learning system is procedurally based. It is better suited than the explicit system to handle situations in which hundreds, if not thousands, of exemplars exist and for which the relation among them cannot be expressed easily, if at all, using a verbalizable rule-based algorithm (for reviews, see Ashby and Maddox, 2005, Keri, 2003). The implicit system learns not by active hypothesis testing but rather by automatically and gradually recognizing subtle covariations within the environment. The knowledge base that is formed is often not fully accessible to consciousness.
Information-integration category learning tasks are believed to tap the implicit learning system. In these paradigms, participants are asked to sort into two groups, stimuli that are created by randomly sampling from two bivariate normal distributions (e.g., line orientation and spatial frequency [see Fig. 1]). The optimal strategy requires the participant to combine both values prior to the decision stage (Ashby and Ell, 2001, Filoteo et al., 2005). In the current study, we examined the developmental differences in performance when the decisional bound is quadratic in shape (Study 1 [see Fig. 2]) and when it is linear (Study 2 [see Fig. 5]), neither of which can easily be verbally described.
What developmental differences, if any, might be seen for implicit category formation and why? According to the COVIS model, implicit category learning is dependent on a set of frontal–striatal structures, and the posterior caudate in particular, that develops within the first year of life (Ashby and Ell, 2001, Ashby et al., 1998, Nomura et al., 2007, Seger, 2008, Seger and Cincotta, 2005). Therefore, we might expect implicit concept formation to be age invariant (e.g., Reber, 1992). Indeed, the ability to integrate information across two bivariate normal distributions (e.g., speed and direction [Herbranson, Fremouw, & Shimp, 2002]) and to learn complex artificial grammars is present even in pigeons (Herbranson & Shimp, 2003), which lack the cortical input that would support an explicit hypothesis testing learning strategy.
Critically, however, the COVIS model proposes that a competition exists between the frontally mediated rule-based system and the subcortically mediated information-integration system, the outcome of which determines which system will dominate the response to any given trial. Both humans and nonhuman primates show a clear bias toward using the explicit system even when the optimal strategy is procedural (Ashby and Maddox, in press, Smith et al., 2010; but see Smith, Minda, & Washburn, 2004). For humans to adopt an implicit strategy, the bias to use the explicit system must first be inhibited. Indeed, manipulations that improve implicit learning are those that are known to tax the executive processes and to hinder explicit learning (e.g., increasing working memory load [Zeithamova and Maddox, 2006, Zeithamova and Maddox, 2007], the addition of a concurrent task [Waldron & Ashby, 2001], the addition of irrelevant dimensions [Filoteo, Lauritzen, & Maddox, 2010], sleep deprivation [Maddox et al., 2009]). Thus, even though the neuroanatomical structures that subserve implicit learning are present in early life, we might nevertheless observe age differences in performance due to a failure during the transfer stage, which is dependent on intact and mature inhibitory control over the explicit system.
In one study, Minda, Desroches, and Church (2008) compared category learning among 3-, 5-, and 7-year-olds and college-attending adults. As would be expected, 3-year-olds performed significantly worse than the other three age groups on an explicit learning task that was based on a unidimensional categorization rule (i.e., black objects are in category 1 and white objects are in category 2). In contrast, there were no group differences in learning trajectories on a categorization task believed to tap the implicit learning system (i.e., a family resemblance task). These results would suggest that both the implicit associative learning process (which does not involve executive processes) and the transfer stage (which is theorized to involve executive processes) were intact in preschool and early grade school children.
However, no analyses were reported on whether there was a main effect of practice on accuracy in the implicit condition, and visual inspection of the learning trajectories suggests that the limited number of trials (n = 48, administered in 6 blocks of 8 stimuli) may have provided insufficient practice to improve the performance for any group, including the college-attending adults. Thus, age differences might have eventually become apparent given additional training. But if additional trials had been provided, a second confound to data interpretation would likely have occurred. Information-integration tasks with few exemplars, such as those used in Minda and colleagues’ (2008) study, are solved in a qualitatively different manner from those with many exemplars (Ashby & Ell, 2001). In tasks with only a few repeating exemplars, participants often use simple memorization strategies after approximately 50 trials, bypassing the associative learning mechanisms that the tasks were intended to index (Knowlton, Squire, & Gluck, 1994). Thus, if Minda and colleagues had provided participants with sufficient practice, age differences might have appeared, not because of developmental differences in the ability to acquire implicit knowledge of categories but rather because of developmental differences in the use of memorization strategies.
Regardless, with this example, it becomes clear that to more fully understand category learning in children and why developmental differences might be observed, it is necessary to move beyond examination of accuracy rates alone and to an understanding of the contributing strategies that underlie performance. In the following two studies, we build on Minda and colleagues (2008) to examine developmental differences in implicit and explicit category learning, not only looking for potential age-based differences in performance but also conducting strategy analyses to help explain the developmental differences. Although there exists a large body of work that examines the development of category knowledge and formation during early infancy and preschool (e.g., Ellis and Nelson, 1999, Mareschal and Quinn, 2001), the status of category learning during middle childhood, and implicit categorization specifically, during which time executive processes continue to develop, remains much less well understood.
To best challenge the procedural learning system and the neural structures that subserve it, in Study 1 we chose an information-integration categorization task that used a large number of unique stimuli, provided an extended training period, and followed a nonlinear quadratic rule. Previous research suggests that the requirement to learn a nonlinear decision bound places greater emphasis on striatal involvement than do linear rules (Ashby et al., 2001, Filoteo et al., 2007). In Study 2, we examined developmental differences on an information-integration categorization task that followed a linear bound and expanded the study to include explicit category learning as a point of comparison.
Section snippets
Child participants
Table 1 provides a description of groups. A total of 18 typically developing 8- to 12-year-olds were recruited from local elementary schools and public flyers. All children spoke English as a first language, were attending a regular education classroom, were free of major childhood psychiatric diagnoses (attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder, generalized anxiety disorder, and depression) by parental report on the Diagnostic Interview Schedule
Child participants
A new cohort of 22 typically developing children (11 boys and 11 girls, 9–13 years of age, average age = 10.22 ± 1.00 years) were recruited from local elementary schools and public notices. All spoke English as a first language, were free of parent-reported psychiatric diagnoses according to the DISC-IV, and were not taking any psychoactive medications. Average estimated IQ was 106.41 (66th percentile), as determined by a four-subtest short form of the WISC-IV. Children provided verbal assent and
General discussion
Over the course of two studies, we found consistent evidence in two information-integration paradigms that age-related differences in performance were due to the inability of high-functioning school-age children to transition from a rule-based strategy to an information-integration strategy.
Our results are consistent with the COVIS model, which posits that in humans an initial bias toward the rule-based system must be overcome for successful performance to occur on an implicit category learning
Conclusion
Over the course of two studies in school-age children and college-attending adults, we found evidence of developmental variance in performance on the acquisition of implicit category knowledge. Model-based analyses suggested that the developmental differences in performance are due to children’s inability to inhibit output from the verbal system. Few studies exist on the development of explicit and implicit category learning during middle childhood. The current study, which mapped the
Acknowledgments
This work was supported in part by National Institute of Mental Health Grant R01 MH084947 to Cynthia Huang-Pollock. We thank the children and families who made this work possible.
References (50)
- et al.
The neurobiology of human category learning
Trends in Cognitive Sciences
(2001) - et al.
Differential effects of inactivation of the orbitofrontal cortex on strategy set-shifting and reversal learning
Neurobiology of Learning and Memory
(2008) The cognitive neuroscience of category learning
Brain Research Reviews
(2003)- et al.
Dissociating explicit and procedural-learning based systems of perceptual category learning
Behavioural Processes
(2004) - et al.
Rule-based and information-integration category learning in normal aging
Neuropsychologia
(2010) - et al.
Categorization in infancy
Trends in Cognitive Sciences
(2001) - et al.
Prefrontal contributions to rule-based and information-integration category learning
Neuropsychologia
(2009) How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback
Neuroscience and Biobehavioral Reviews
(2008)- et al.
NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses
Journal of the American Academy of Child and Adolescent Psychiatry
(2000) - et al.
The orbitofrontal cortex and ventral tegmental area are necessary for learning from unexpected outcomes
Neuron
(2009)
A new look at statistical-model identification
IEEE Transactions on Automatic Control
Multivariate probability distributions
A neuropsychological theory of multiple systems in category learning
Psychological Review
Human category learning
Annual Review of Psychology
Suboptimality in human categorization and identification
Journal of Experimental Psychology: General
Visual categorization during childhood: An ERP study
Psychophysiology
How we use rules to select actions: A review of evidence from cognitive neuroscience
Cognitive, Affective, and Behavioral Neuroscience
A brain-based account of the development of rule use in childhood
Current Directions in Psychological Science
Early development of subcortical regions involved in non-cued attention switching
Developmental Science
Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging
Journal of Neuroscience
Category prototypicality judgments in adults and children: Behavioral and electrophysiological correlates
Developmental Neuropsychology
The cognitive neuroscience of human decision making: A review and conceptual framework
Behavioral and Cognitive Neuroscience Reviews
Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning
Psychological Science
Information-integration category learning in patients with striatal dysfunction
Neuropsychology
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