Individual differences in exemplar-based interference during instructed category learning (original) (raw)
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Psychonomic bulletin & review, 2001
Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.
2021
Providing verbal or written instructions on how to perform optimally in a task is one of the most common ways to teach beginners. This practice is so widely accepted that scholarship primarily focuses on how to provide instructions, not whether these instructions help or not. Here we investigate the benefits of prior instruction on rule-based (RB) category-learning, in which the optimal strategy is a simple explicit rule, and information-integration (II) category-learning, in which the optimal strategy is similarity-based. Participants (N = 58) learned either RB or II categories, with or without verbal and written instruction about the optimal categorization strategy. Instructions significantly improved performance with RB categories but had no effect with II categories. The theoretical and practical implication of these results is discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A neuropsychological theory of multiple systems in category learning
Psychological Review, 1998
A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults. Humans are remarkably adept at categorizing objects and events in their environment. In fact, it is now well established that humans can learn some extremely complex (i.e., nonlinear) categorization rules (e.g., Ashby & Maddox, 1992; McKinley & Nosofsky, 1995; Medin& Schwanenfiugel, 1981). One characteristic of demanding categorization problems is that experts use rules that are often difficult or impossible to describe verbally. For example, it is difficult to verbalize the decision rules used by farmers to sex chicks, those used by wine tasters to determine that a certain wine is a Zinfandel or a Cabernet Sanvignon, or those used by artists to categorize unfamiliar paintings according to the Renaissance master who created them. On the other hand, in many cases, contrasting categories are separated perfectly (or nearly so) by some decision rule that can be described verbally. For example, a simple verbal rule separates triangles from rectangles, oranges from lemons, and evergreens from deciduous trees. Current theories of category learning do not discriminate between these two kinds of tasks. Rather, they assume that all rules, whether verbal or nonverbal, are learned by using the same basic processes. Nevertheless, growing evidence indicates a qualitative difference in performance depending on whether the optimal decision rule-that is, the rule that maximizes categorization accuracy-can be described verbally. For example,
The Origin of Exemplar Effects in Rule-Driven Categorization
Journal of Experimental Psychology-learning Memory and Cognition, 2005
- have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts.
Psychological Science, 2008
Previous research has disagreed about whether a difficult cognitive skill is best learned by beginning with easy or difficult examples. Two experiments are described that clarify this debate. Participants in both experiments received one of three types of training on a difficult perceptual categorization task. In one condition participants began with easy examples, then moved to examples of intermediate difficulty, and finished with the most difficult examples. In a second condition this order was reversed, and in a third condition, participants saw examples in a random order. The results depended on the type of categories that participants were learning. When the categories could be learned via explicit reasoning (a rule-based task), all three training procedures were equally effective. However, when the categorization rule was difficult to describe verbally (an information-integration task), participants who began with the most difficult items performed much better than participants in the other two conditions. Conventional wisdom suggests that the best way to learn a difficult cognitive skill is to begin with easy examples, master those, and then gradually increase example difficulty. A variety of evidence supports this general hypothesis. For example, a popular training procedure, called the method of errorless learning (Baddeley, 1992; Terrace, 1964), adopts an extreme form of this strategy in which the initial examples are so easy and each subsequent increase in difficulty is so small that participants never make errors. The basic assumption of this method is that errors that occur during training strengthen incorrect associations and are therefore harmful to the learning process. Errorless learning has proven to be an effective training procedure in a wide variety of tasks (e.g.
Category label and response location shifts in category learning
Psychological Research Psychologische Forschung, 2010
The category shift literature suggests that rulebased classiWcation, an important form of explicit learning, is mediated by two separate learned associations: a stimulus-to-label association that associates stimuli and category labels, and a label-to-response association that associates category labels and responses. Three experiments investigate whether information-integration classiWcation, an important form of implicit learning, is also mediated by two separate learned associations. Participants were trained on a rule-based or an information-integration categorization task and then the association between stimulus and category label, or between category label and response location was altered. For rule-based categories, and in line with previous research, breaking the association between stimulus and category label caused more interference than breaking the association between category label and response location. However, no diVerences in recovery rate emerged. For information-integration categories, breaking the association between stimulus and category label caused more interference and led to greater recovery than breaking the association between category label and response location. These results provide evidence that information-integration category learning is mediated by separate stimulus-to-label and label-to-response associations. Implications for the neurobiological basis of these two learned associations are discussed.
The effects of category overlap on information-integration and rule-based category learning
Perception & Psychophysics, 2006
In the study of category learning, it is often desirable to design tasks in which participants use a particular type of decision strategy. This goal is typically pursued by simply instructing participants to use a specific strategy (see, e.g., Allen & Brooks, 1991) rather than constraining the design of the categorization task. We propose that specifying the amount of overlap between contrasting categories may provide a simple method to constrain decision strategy. Category overlap historically has been manipulated to control task difficulty, and was not thought to affect the qualitative nature of the decision strategy used by participants. This article presents the results of three experiments that challenge this widely held view. Category learning has been investigated using tasks that vary considerably with regard to stimulus materials, category structures, and procedure. For example, in some tasks, the entire stimulus set comprises just nine exemplars (e.g., Medin & Schaffer, 1978), whereas in other tasks, a single category comprises hundreds of exemplars (e.g., Ashby & Gott, 1988). Despite this variability, in the majority of tasks, a trial begins with the presentation of a stimulus, followed by a categorization response, and typically, corrective feedback. Thus, at first glance, one might expect any effect of category overlap on decision strategy to be invariant across tasks. Recent research, however, suggests that the choice of task may be critical in determining the particular categorylearning system and, consequently, the particular decision strategy that is used to learn the categories (Ashby & Ell, 2001). Thus, an alternative hypothesis is that the effect of category overlap on decision strategy may vary as a function of the task. We investigate this alternative using two category-learning tasks that have received the majority of attention in the multiple systems debate: informationintegration and rule-based tasks (see Ashby & Maddox, 2005, and Maddox & Ashby, 2004, for a complete review of the dissociations between these two tasks). Information-integration tasks are those in which accuracy is maximized by implicit, perceptual-integration strategies, which assume that information from two or more dimensions is integrated at some predecisional stage, outside of conscious awareness (Ashby, Alfonso-Reese, Turken, & Waldron, 1998). The type of perceptual integration required could take any number of forms, from a weighted combination of the two dimensions (Ashby & Gott, 1988; Garner, 1974) to more holistic processing (see, e.g., Kemler Nelson, 1993) to the incremental acquisition of stimulus-response associations (Ashby & Waldron, 1999), but the critical point is that the integration is assumed to occur prior to invoking any decision processes.
Category use and category learning
Psychological Bulletin, 2003
Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Researchers have proposed that an explicit reasoning system is responsible for learning rule-based category structures and that a separate implicit, procedural-learning system is responsible for learning information-integration category structures. As evidence for this multiple-system hypothesis, researchers report a dissociation based on category-number manipulations in which rule-based category learning is worse when the category is composed of 4, rather than 2, response categories; however, informationintegration category learning is unaffected by category-number manipulations. We argue that within the reported category-number manipulations, there exists a critical confound: Perceptual clusters used to construct the categories are spread apart in the 4-category condition relative to the 2-category one. The present research shows that when this confound is eliminated, performance on information-integration category learning is worse for 4 categories than for 2 categories, and this finding is demonstrated across 2 different information-integration category structures. Furthermore, model-based analyses indicate that a single-system learning model accounts well for both the original findings and the updated experimental findings reported here.
Delayed feedback effects on rule-based and information-integration category learning
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2003
The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.