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。     

反馈延迟对信息整合类别学习的影响：视觉项目精准性的调节作用

研究旨在探究更精确的视觉项目表征能否消除延迟反馈对信息整合学习的损害。实验采用2（视觉项目表征精确性：精确/非精确）×2（反馈延迟：即时反馈/延迟反馈）组间设计,结果发现无论有无延迟反馈,精确的项目表征条件下类别学习成绩都显著高于非精确条件,精确性与反馈延迟存在交互作用。采用状态痕迹分析进一步证明精确性显著影响信息整合类别学习的稳定性。在反馈延迟条件下,精确的视觉项目表征能提高信息整合类别学习的成绩。     

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning

Research has identified multiple category-learning systems with each being “tuned” for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus–response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning.     

Critrial noise effects on rule-based category learning: The impact of delayed feedback

Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.     

Comparing the effects of positive and negative feedback in information-integration category learning

Categorical learning is dependent on feedback. Here, we compare how positive and negative feedback affect information-integration (II) category learning. Ashby and O’Brien (2007) demonstrated that both positive and negative feedback are required to solve II category problems when feedback was not guaranteed on each trial, and reported no differences between positive-only and negative-only feedback in terms of their effectiveness. We followed up on these findings and conducted 3 experiments in which participants completed 2,400 II categorization trials across three days under 1 of 3 conditions: positive feedback only (PFB), negative feedback only (NFB), or both types of feedback (CP; control partial). An adaptive algorithm controlled the amount of feedback given to each group so that feedback was nearly equated. Using different feedback control procedures, Experiments 1 and 2 demonstrated that participants in the NFB and CP group were able to engage II learning strategies, whereas the PFB group was not. Additionally, the NFB group was able to achieve significantly higher accuracy than the PFB group by Day 3. Experiment 3 revealed that these differences remained even when we equated the information received on feedback trials. Thus, negative feedback appears significantly more effective for learning II category structures. This suggests that the human implicit learning system may be capable of learning in the absence of positive feedback.     

Feedback and stimulus-offset timing effects in perceptual category learning

We examined how feedback delay and stimulus offset timing affected declarative, rule-based and procedural, information–integration category-learning. We predicted that small feedback delays of several hundred milliseconds would lead to the best information–integration learning based on a highly regarded neurobiological model of learning in the striatum. In Experiment 1 information–integration learning was best with feedback delays of 500 ms compared to delays of 0 and 1000 ms. This effect was only obtained if the stimulus offset following the response. Rule-based learning was unaffected by the length of feedback delay, but was better when the stimulus was present throughout feedback than when it offset following the response. In Experiment 2 we found that a large variance (SD = 150 ms) in feedback delay times around a mean delay of 500 ms attenuated information–integration learning, but a small variance (SD = 75 ms) did not. In Experiment 3 we found that the delay between stimulus offset and feedback is more critical to information–integration learning than the delay between the response and feedback. These results demonstrate the importance of feedback timing in category-learning situations where a declarative, verbalizable rule cannot easily be used as a heuristic to classify members into their correct category.     

The dimensionality of perceptual category learning: A state-trace analysis

State-trace analysis was used to investigate the effect of concurrent working memory load on perceptual category learning. Initial reanalysis of Zeithamova and Maddox (2006, Experiment 1) revealed an apparently two-dimensional state-trace plot consistent with a dual-system interpretation of category learning. However, three modified replications of the original experiment found evidence of a single resource underlying the learning of both rule-based and information integration category structures. Follow-up analyses of the Zeithamova and Maddox data, restricted to only those participants who had learned the category task and performed the concurrent working memory task adequately, revealed a one-dimensional plot consistent with a single-resource interpretation and the results of the three new experiments. The results highlight the potential of state-trace analysis in furthering our understanding of the mechanisms underlying category learning.     

嵌套范式下视空工作记忆对基于规则类别学习

基于COVIS模型与认知加工阶段假设,通过2个实验探讨嵌套范式下, 视空工作记忆对基于规则类别学习的影响。实验1采用类别学习中嵌套视空工作记忆的范式,结果发现视空工作记忆削弱基于规则类别学习成绩,与COVIS模型预测相一致。实验2则采用视空工作记忆中嵌套类别学习任务的范式,结果却发现视空工作记忆对基于规则类别学习的影响消失。实验结果表明嵌套范式下视空工作记忆的位置影响基于规则类别学习,初步验证了类别学习存在多个认知加工阶段的假设,视空工作记忆主要影响基于规则类别学习中规则的发现和检验阶段。     

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.     

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.     

Dual-learning systems during speech category learning

Dual-system models of visual category learning posit the existence of an explicit, hypothesis-testing reflective system, as well as an implicit, procedural-based reflexive system. The reflective and reflexive learning systems are competitive and neurally dissociable. Relatively little is known about the role of these domain-general learning systems in speech category learning. Given the multidimensional, redundant, and variable nature of acoustic cues in speech categories, our working hypothesis is that speech categories are learned reflexively. To this end, we examined the relative contribution of these learning systems to speech learning in adults. Native English speakers learned to categorize Mandarin tone categories over 480 trials. The training protocol involved trial-by-trial feedback and multiple talkers. Experiments 1 and 2 examined the effect of manipulating the timing (immediate vs. delayed) and information content (full vs. minimal) of feedback. Dual-system models of visual category learning predict that delayed feedback and providing rich, informational feedback enhance reflective learning, while immediate and minimally informative feedback enhance reflexive learning. Across the two experiments, our results show that feedback manipulations that targeted reflexive learning enhanced category learning success. In Experiment 3, we examined the role of trial-to-trial talker information (mixed vs. blocked presentation) on speech category learning success. We hypothesized that the mixed condition would enhance reflexive learning by not allowing an association between talker-related acoustic cues and speech categories. Our results show that the mixed talker condition led to relatively greater accuracies. Our experiments demonstrate that speech categories are optimally learned by training methods that target the reflexive learning system.     

Learning and retention through predictive inference and classification

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and classification were compared for fifth-grade students using class-related materials. Making inferences about properties of category members and receiving feedback led to the acquisition of both queried (i.e., tested) properties and nonqueried properties that were correlated with a queried property (e.g., even if not queried, students learned about a species' habitat because it correlated with a queried property, like the species' size). In contrast, classifying items according to their species and receiving feedback led to knowledge of only the property most diagnostic of category membership. After multiple-day delay, the fifth-graders who learned through inference selectively retained information about the queried properties, and the fifth-graders who learned through classification retained information about the diagnostic property, indicating a role for explicit evaluation in establishing memories. Overall, inference learning resulted in fewer errors, better retention, and more liking of the categories than did classification learning. Experiment 2 revealed that querying a property only a few times was enough to manifest the full benefits of inference learning in undergraduate students. These results suggest that classroom teaching should emphasize reasoning from the category to multiple properties rather than from a set of properties to the category.     

That's More Like It: Multiple Exemplars Facilitate Word Learning

Previous research indicates learning words facilitates categorisation. The current study explores how categorisation affects word learning. In the current study, we investigated whether learning about a category facilitates retention of newly learned words by presenting 2‐year‐old children with multiple referent selection trials to the same object category. In Experiment 1, children either encountered the same exemplar repeatedly or encountered multiple exemplars across trials. All children did very well on the initial task; however, only children who encountered multiple exemplars retained these mappings after a short delay. Experiment 2 replicated and extended this finding by exploring the effect of within‐category variability on children's word retention. Children encountered either narrow or broad exemplars across trials. Again, all children did very well on the initial task; however, only children who encountered narrow exemplars retained mappings after a short delay. Overall, these data offer strong evidence that providing children with the opportunity to compare across exemplars during fast mapping facilitates retention. Copyright © 2013 John Wiley & Sons, Ltd.     

One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning

The debate over unitary/multiple category-learning utilities is reminiscent of debates about multiple memory systems and unitary/dual codes in knowledge representation. In categorization, researchers continue to seek paradigms to dissociate explicit learning processes (yielding verbalizable rules) from implicit learning processes (yielding stimulus–response associations that remain outside awareness). We introduce a new dissociation here. Participants learned matched category tasks with a multidimensional, information-integration solution or a one-dimensional, rule-based solution. They received reinforcement immediately (0-Back reinforcement) or after one intervening trial (1-Back reinforcement). Lagged reinforcement eliminated implicit, information-integration category learning but preserved explicit, rule-based learning. Moreover, information-integration learners facing lagged reinforcement spontaneously adopted explicit rule strategies that poorly suited their task. The results represent a strong process dissociation in categorization, broadening the range of empirical techniques for testing the multiple-process theoretical perspective. This and related methods that disable associative learning—fostering a transition to explicit-declarative cognition—could have broad utility in comparative, cognitive, and developmental science.     

Response processes in information-integration category learning

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. Information-integration 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.     

Putting together prior knowledge, verbal arguments, and observations in category learning

Two experiments addressed the novel issue of how people incorporate verbal arguments into category learning. In Experiment 1, at the start of learning, subjects were given verbal arguments, which had an influence equivalent to a fixed number of category members. In Experiment 2, subjects learned under slower paced conditions, and it was found that both prior knowledge and arguments had multiple effects on categorization: a fixed initial influence plus selective weighting of new observations. The results supported the idea that verbally presented arguments can be treated in a similar manner as other forms of prior knowledge, from the perspective of applying models of categorization.     

Kinematic information feedback for learning a rapid arm movement

The experiments reported examine the notion that knowledge of results (KR) about the outcome of a response does not provide the necessary information for optimizing performance in many skilled activities. The effect of traditional KR was contrasted with various kinematic feedback parameters in the acquisition of a single degree of freedom response requiring the minimization of movement time. Experiment 1 showed that the presentation of discrete kinematic information feedback (peak accelaration, time to peak accelaration, and velocity at the target location) did not facilitate performance over movement-time KR. Experiment 2 revealed that presentation of a computer generated velocity-time representation of the movement as terminal information feedback improved performance over movement-time KR. This facilitation occured even without knowledge of the kinematics for optimal performance. The findings suggest that the task criterion specifies the appropriate information feedback for skill learning in that the information feedback must match the constraints imposed upon response output.     

Semisupervised category learning facilitates the development of automaticity

In the human category of learning, learning is studied in a supervised, an unsupervised, or a semisupervised way. The rare human semisupervised category of learning studies all focus on early learning. However, the impact of the semisupervised category learning late in learning, when automaticity develops, is unknown. Therefore, in Experiment 1, all participants were first trained on the information-integration category structure for 2 days until they reached an expert level. Afterwards, half of the participants learned in a supervised way and the other half in a semisupervised way over two successive days. Both groups received an equal number of feedback trials. Finally, all participants took part in a test day where they were asked to respond as quickly as possible. Participants were significantly faster on this test in the semisupervised group than in the supervised group. This difference was not found on day 2, implying that the no-feedback trials in the semisupervised condition facilitated automaticity. Experiment 2 was designed to test whether the higher number of trials in the semisupervised condition of Experiment 1 caused the faster response times. We therefore created an almost supervised condition where participants almost always received feedback (95%) and an almost unsupervised condition where participants almost never received feedback (5%). All conditions now contained an equal number of trials to the semisupervised condition of Experiment 1. The results show that receiving feedback almost always or almost never led to slower response times than the semisupervised condition of Experiment 1. This confirms the advantage of semisupervised learning late in learning.     

Scaffolding feedback to maximize long-term error correction

Scaffolded feedback was tested against three other feedback presentation methods (standard corrective feedback, minimal feedback, and answer-until-correct multiple-choice feedback) over both short- and long-term retention intervals in order to assess which method would produce the most robust gains in error correction. Scaffolded feedback was a method designed to take advantage of the benefits of retrieval practice by providing incremental hints until the correct answer could be self-generated. In Experiments 1 and 3, on an immediate test, final memory for the correct answer was lowest for questions given minimal feedback, moderate for the answer-until-correct condition, and equally high in the scaffolded feedback condition and the standard feedback condition. However, tests of the maintenance of the corrections over a 30-min delay (Experiment 2) and over a 1-day delay (Experiment 3) demonstrated that scaffolded feedback gave rise to the best memory for the correct answers at a delay.