Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis

Researchers have argued that different categorization problems are learned by separate and distinct cognitive systems. They propose that an explicit system is responsible for learning rule-based categories and that a separate implicit system learns information-integration categories. One source of supporting evidence involves experiments in which observers perform a concurrent memory-scanning task that interferes with the processing of feedback. Researchers have reported a dissociation in which this manipulation impairs learning of a rule-based category but not an information-integration category. In the present research, we test the hypothesis that the dissociation was the result of lowered perceptual discriminability in the rule-based structure in comparison with the information-integration one. We demonstrate an example of an alternate rule-based category with easy-to-discriminate stimuli in which performance is unaffected by the interfering memory-scanning task. Furthermore, we demonstrate that learning of an information-integration category with low perceptual discriminability is impaired by the memory-scanning task. These demonstrations of the reverse dissociation challenge the interpretation that rule-based and information-integration category structures are learned by separate cognitive systems.     

Task and distribution sampling affect auditory category learning

There is substantial evidence that two distinct learning systems are engaged in category learning. One is principally engaged when learning requires selective attention to a single dimension (rule-based), and the other is drawn online by categories requiring integration across two or more dimensions (information-integration). This distinction has largely been drawn from studies of visual categories learned via overt category decisions and explicit feedback. Recent research has extended this model to auditory categories, the nature of which introduces new questions for research. With the present experiment, we addressed the influences of incidental versus overt training and category distribution sampling on learning information-integration and rule-based auditory categories. The results demonstrate that the training task influences category learning, with overt feedback generally outperforming incidental feedback. Additionally, distribution sampling (probabilistic or deterministic) and category type (information-integration or rule-based) both affect how well participants are able to learn. Specifically, rule-based categories are learned equivalently, regardless of distribution sampling, whereas information-integration categories are learned better with deterministic than with probabilistic sampling. The interactions of distribution sampling, category type, and kind of feedback impacted category-learning performance, but these interactions have not yet been integrated into existing category-learning models. These results suggest new dimensions for understanding category learning, inspired by the real-world properties of auditory categories.     

Procedural memory effects in categorization: Evidence for multiple systems or task complexity?

According to an influential multiple-systems model of category learning, an implicit procedural system governs the learning of information-integration category structures, whereas a rule-based system governs the learning of explicit rule-based categories. Support for this idea has come in part from demonstrations that motor interference, in the form of inconsistent mapping between response location and category labels, results in observed deficits, but only for learning information-integration category structures. In this article, we argue that this response location manipulation results in a potentially more cognitively complex task in which the feedback is difficult to interpret. In one experiment, we attempted to attenuate the cognitive complexity by providing more information in the feedback, and demonstrated that this eliminates the observed performance deficit for information-integration category structures. In a second experiment, we demonstrated similar interference of the inconsistent mapping manipulation in a rule-based category structure. We claim that task complexity, and not separate systems, might be the source of the original dissociation between performance on rule-based and information-integration tasks.     

Within-category discontinuity interacts with verbal rule complexity in perceptual category learning

A test of the predicted interaction between within-category discontinuity and verbal rule complexity on information-integration and rule-based category learning was conducted. Within-category discontinuity adversely affected information-integration category learning but not rule-based category learning. Model-based analyses suggested that some information-integration participants improved performance by recruiting more "units" in the discontinuous condition. Verbal rule complexity adversely affected rule-based category learning but not information-integration category learning. Model-based analyses suggested that the rule based effect was on both decision criterion learning and variability in decision criterion placement. These results suggest that within-category discontinuity and decision rule complexity differentially impact information-integration and rule-based category learning and provide information regarding the detailed processing characteristics of these two proposed category learning systems.     

Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems

Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than information-integration learning, whereas in the 4 category conditions, unidimensional and conjunctive rule-based learning was worse than information-integration learning. Rule-based strategies were used in the 2-category/rule-based condition, but about half of the observers used rule-based strategies in the 4-category unidimensional and conjunctive rule-based conditions. Information-integration strategies were used in the 4-category/ information-integration condition and by the end of training were used in the 2-category/information-integration condition.     

The effects of category overlap on information-integration and rule-based category learning

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.     

类别学习多重系统理论的研究与展望

介绍了国外学者为验证类别学习的多重系统理论所做的行为实验研究及主要结果,包括反馈训练、延迟反馈、反应位置、间断的类别刺激分布等因素对信息整合的类别学习的影响,和数字Stroop任务、序列记忆扫描任务、类别数量等因素对基于规则的类别学习的影响;同时,作者指出了实验中没有很好地解决两种类别结构之间的难度差异等问题,并提出了今后需要进一步研究的课题     

The effects of positive versus negative feedback on information-integration category learning

A number of studies have shown that in category learning, providing feedback about errors allows faster learning than providing feedback about correct responses. However, these previous studies used explicit, rule-based tasks in which the category structures could be separated by a simple rule that was easily verbalized. Here, the results of the first experiment known to compare the efficacy of positive versus negative feedback during information-integration category learning are reported. Information-integration tasks require participants to integrate perceptual information from incommensurable dimensions, and evidence suggests that optimal responding recruits procedural learning. The results show that although nearly all of the full-feedback control participants demonstrated information-integration learning, participants receiving either positive-only or negative-only feedback generally used explicit, rule-based strategies. It thus appears that, unlike rule-based learning, consistent information-integration learning requires full feedback. The theoretical implications of these findings for current models of information-integration learning are discussed.     

When more is less: feedback effects in perceptual category learning

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed.     

Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.'s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.     

Cross-modal information integration in category learning

An influential theoretical perspective describes an implicit category-learning system that associates regions of perceptual space with response outputs by integrating information preattentionally and predecisionally across multiple stimulus dimensions. In this study, we tested whether this kind of implicit, information-integration category learning is possible across stimulus dimensions lying in different sensory modalities. Humans learned categories composed of conjoint visual–auditory category exemplars comprising a visual component (rectangles varying in the density of contained lit pixels) and an auditory component (in Exp. 1, auditory sequences varying in duration; in Exp. 2, pure tones varying in pitch). The categories had either a one-dimensional, rule-based solution or a two-dimensional, information-integration solution. Humans could solve the information-integration category tasks by integrating information across two stimulus modalities. The results demonstrated an important cross-modal form of sensory integration in the service of category learning, and they advance the field’s knowledge about the sensory organization of systems for categorization.     

Pigeons' categorization may be exclusively nonanalytic

Recent theoretical and empirical developments in human category learning have differentiated an analytic, rule-based system of category learning from a nonanalytic system that integrates information across stimulus dimensions. In the present study, the researchers applied this theoretical distinction to pigeons' category learning. Pigeons learned to categorize stimuli varying in the tilt and width of their internal striping. The matched category problems had either a unidimensional (rule-based) or multidimensional (information-integration) solution. Whereas humans and nonhuman primates strongly dimensionalize these stimuli and learn rule-based tasks far more quickly than information-integration tasks, pigeons learned the two tasks equally quickly to the same accuracy level. Pigeons may represent a cognitive system in which the commitment to dimensional analysis and category rules was not strongly made. Their performance could suggest the character of the ancestral vertebrate categorization system from which that of primates emerged.     

Evidence for a procedural-learning-based system in perceptual category learning

The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve informationintegration tasks. Accuracy rates were examined to isolate global performance deficits, and modelbased analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.     

Automaticity in rule-based and information-integration categorization

Three experiments studied the effects of category structure on the development of categorization automaticity. In Experiment 1, participants were each trained for over 10,000 trials in a simple categorization task with one of three category structures. Results showed that after the first few sessions, there were no significant behavioral differences between participants who learned rule-based versus information-integration category structures. Experiment 2 showed that switching the locations of the response keys after automaticity had developed caused a similar highly significant interference, regardless of category structure. In Experiment 3, a simultaneous dual task that engaged executive functions did not interfere with either rule-based or information-integration categorization. These novel results are consistent with a theory assuming separate processing pathways for initial rule-based and information-integration category learning but a common processing pathway after the development of automaticity.     

Cognitive complexity effects in perceptual classification are dissociable

It has been proposed that a procedural-based classification system mediates the learning of information-integration categories, whereas a hypothesis-testing system mediates the learning of rule-based categories. Ashby, Ell and Waldron (2003) provided support for this claim by showing that a button switch introduced during classification transfer adversely affected information-integration but not rule-based performance. Nosofsky, Stanton and Zaki (2005) showed that increasing "cognitive complexity" can lead to button switch costs on rule-based performance. They argue that "cognitive complexity," and not the existence of separable classification systems, accounts for Ashby et al.'s empirical dissociation. The present study shows that experimental manipulations that increase "cognitive complexity" often have dissociable effects on information-integration and rule-based classification that are predicted a priori from the processing characteristics associated with the procedural-based and hypothesis-testing systems. These results suggest that manipulations of "cognitive complexity" can be dissociated, suggesting that "cognitive complexity" in not a unitary construct that affects a single psychological process.     

The role of visuospatial and verbal working memory in perceptual category learning

The role of verbal and visuospatial working memory in rule-based and information-integration category learning was examined. Previously, Maddox, Ashby, Ing, and Pickering found that a sequentially presented verbal working memory task did not affect information-integration learning, but disrupted rule-based learning when the rule was on the spatial frequency of a Gabor stimulus. This pattern was replicated in Experiment 1, in which the same category structures were used, but in which the verbal working memory task was replaced with a visuospatial analog. Experiment 2A examined rule-based learning on an oblique orientation and also found both verbal and visuospatial working memory tasks disrupting learning. Experiment 2B examined rule-based learning on a cardinal orientation and found a minimal effect of the verbal working memory task, but a large effect of the visuospatial working memory task. The conceptual significance of cardinal orientations and the role of visuospatial and verbal working memory in category learning are discussed.     

The impact of category structure and training methodology on learning and generalizing within-category representations

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 could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.     

Delayed feedback effects on rule-based and information-integration category learning

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.     

反馈延迟与掩蔽类型对知觉类别学习的影响

通过两个实验探讨反馈延迟与掩蔽类型对知觉类别学习的影响。实验1光栅掩蔽条件下,采用2（类别结构：基于规则vs.信息整合）×2（延迟反馈：500vs.3000ms）被试间实验设计,实验2采用同样的实验设计,保持延迟反馈时间不变,但改掩蔽类型为黑屏掩蔽。结果发现：（1）光栅掩蔽条件下,反馈延迟削弱信息整合类别结构的成绩,不影响基于规则类别学习,内隐外显类别结构之间存在分离效应。（2）黑屏掩蔽条件下,信息整合与基于规则类别结构的成绩均不受到延迟反馈的影响,分离效应消失。实验说明“无关因素”掩蔽影响知觉类别学习,知觉噪音与标准噪音在反馈延迟影响知觉类别学习中起到重要作用,COVIS模型理论关于延迟反馈时间影响知觉类别学习的解释具有局限性。