Category label and response location shifts in category learning (original) (raw)
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Response processes in information–integration category learning☆
Neurobiology of Learning and Memory, 2008
Much recent evidence suggests that human category learning is mediated by multiple systems. Evidence suggests that at least one of these depends on procedural learning within the basal ganglia. Informationintegration categorization tasks are thought to load heavily on this procedural-learning system. The results of several previous studies were interpreted to suggest that response positions are learned in information-integration tasks. This hypothesis was tested in two experiments. Experiment 1 showed that information-integration category learning was slowed but not disrupted when the spatial location of the responses varied randomly across trials. Experiment 2 showed that information-integration learning was impaired if category membership was signaled by responding to a Yes/No question and the category label had no consistent spatial location. These results suggest that information-integration category learning does not require consistent response locations. In these experiments, a consistent association between a category and a response feature was sufficient. The implication of these results for the neurobiology of information-integration category learning is discussed.
The neurobiology of category learning
Behavioral and cognitive neuroscience reviews, 2004
Many recent studies have examined the neural basis of category learning. Behavioral neuroscience results suggest that both the prefrontal cortex and the basal ganglia play important category-learning roles; neurons that develop category-specific firing properties are found in both regions, and lesions to both areas cause category-learning deficits. Similar studies indicate that the inferotemporal cortex does not mediate the learning of new categories. The cognitive neuroscience literature on category learning appears contradictory until the results are partitioned according to the type of category-learning task that was used. Three major tasks can be identified: rule based, information-integration, and prototype-distortion. Recent results are consistent with the hypotheses that (a) learning in rule-based tasks requires working memory and executive attention and is mediated by frontal-striatal circuits, (b) learning in information-integration tasks requires procedural memory and is m...
Memory & Cognition, 2004
The effect of a sequentially presented memory scanning task on rule-based and informationintegration category learning was investigated. On each trial in the short feedback-processing time condition, memory scanning immediately followed categorization. On each trial in the long feedbackprocessing time condition, categorization was followed by a 2.5-sec delay and then memory scanning. In the control condition, no memory scanning was required. Rule-based category learning was significantly worse in the short feedback-processing time condition than in the long feedback-processing time condition or control condition, whereas information-integration category learning was equivalent across conditions. In the rule-based condition, a smaller proportion of observers learned the task in the short feedback-processing time condition, and those who learned took longer to reach the performance criterion than did those in the long feedback-processing time or control condition. No differences were observed in 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.
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.
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.
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 time course of explicit and implicit categorization
Attention, perception & psychophysics, 2015
Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit cat...
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.