Learning and transfer of category knowledge in an indirect categorization task (original) (raw)
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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.
The cognitive neuroscience of implicit category learning
Advances in consciousness research, 2003
There is much recent interest in the question of whether people have available a single category learning system or a number of qualitatively different systems. Most proponents of multiple systems have hypothesized an explicit, rule-based system and some type of implicit system. Although there has been general agreement about the nature of the explicit system, there has been disagreement about the exact nature of the implicit system. This chapter explores the question of whether there is implicit category learning, and if there is, what form it might take. First, we examine what the word "implicit" means in the categorization literature. Next, we review some of the evidence that supports the notion that people have available one or more implicit categorization systems. Finally, we consider the nature of implicit categorization by focusing on three alternatives: an exemplar memory-based system, a procedural memory system, and an implicit system that uses the perceptual representation memory system.
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.
On the nature of implicit categorization
Psychonomic Bulletin & Review, 1999
Current categorization models disagree about whether people make a priori assumptions about the structure of unfamiliar categories. Data from two experiments provided strong evidence that people do not make such assumptions. These results rule out prototype models and many decision bound models of categorization. We review previously published neuropsychological results that favor the assumption that category learning relies on a procedural-memory-based system, rather than on an instance-based system (as is assumed by exemplar models). On the basis ofthese results, a new categorylearning model is proposed that makes no a priori assumptions about category structure and that relies on procedural learning and memory. There is much recent evidence that human category learning relies on multiple systems (e.g.
Procedural learning in perceptual categorization
Memory & Cognition, 2003
In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategyand those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy. However, with the information-integration structures, switching response key assignments interfered with categorization performance, but switching hands did not. These results are consistent with the hypothesis that abstract category labels are learned in rule-based categorization, whereas response positions are learned in information-integration categorization. The association to response positions also supports the hypothesis of a procedural-learning-based component to information integration categorization.
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,
Attention, perception & psychophysics, 2017
When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and coul...
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.
Multiple Systems of Perceptual Category Learning
Handbook of Categorization in Cognitive Science, 2005
There is widespread agreement that multiple qualitatively different category learning systems mediate the learning of different category structures. Two systems that have received support are (1) a frontal-based explicit system that uses logical reasoning, depends on working memory and executive attention, and is mediated primarily by the anterior cingulated, the prefrontal cortex, and the head of the caudate, and (2) a basal ganglia-mediated implicit system that uses procedural learning and requires a dopamine reward signal. This chapter reviews a large body of work conducted in our laboratories that examines the details of the two proposed systems using neurological patients as experimental participants. Collectively the studies suggest little involvement of the medial temporal lobes in category learning with large categories. They also suggest that, in striatal-damaged patients, the need to ignore irrelevant information is predictive of a rule-based category learning deficit, whereas the complexity of the rule is predictive of an information-integration category learning deficit.