Differential category learning processes: The neural basis of comparison-based learning and induction

This paper explores 6-year-old children's category representation and learning strategies. Category learning is a fundamental ability through which human beings acquire and organize new knowledge about the world (Ashby, 2005), and is critical for normal cognitive development. There are three major theories or models of how categories are represented: Rule-based, Prototype-based, and Exemplar-based models. Rule-based models assume that category learning is a process of discovering an explicit rule to maximize accuracy (Ashby, 2005; Seger, 2006). Prototype-based models assume that stimuli are categorized on the basis of their similarity to category prototypes stored in memory (Rosch Mervis, 1975; Smith, Chapman, Redford, 2010; Coutinho, Redford, Smith, 2010). A category prototype is generally defined as the average, or most typical, member of a category. Exemplar-based models assume that the categorization of a new exemplar is based on the similarity of the new exemplar to the representations of all previously encountered exemplars stored in memory (Medin Schaffer, 1978; Kruschke, 1992; Nosofsky, 1992). Previous studies suggest that 6-year-old children have developed some ability to use category knowledge to solve problems (Wilburn Feeney, 2008; Sloutsky Lo, 1999; Sloutsky Fisher, 2001). Furthermore, several critical aspects of category learning are acquired at this age. Fang, Fang, Xi (1991) pointed out that 6-year-old is a critical period for children to learn to understand the relation between the whole and the part of a subject. Yin (1996) further suggested that 6 years is an important age to learn superordinate categories (for example, the category "furniture"). Two category structures were used in this study. Experiment 1 used the "5/4 category structure" from Medin and Schaffer (1978) and experiment 2 used the "3/3 category structure" from Yamauchi, Love, Markman (2002). The category structures were adapted in order to be able to identify which kinds of representation the children were forming: rule, exemplar or prototype. 62 6-year-old children took part in the experiments. During each trial, an individual exemplar was presented, the participant was asked to infer and indicate which category (A or B) the exemplar belonged to, and feedback as to whether the subject was right or wrong was provided. After a number of such trials of inference and feedback, participants reached the learning criterion and were considered to have formed new category knowledge. A mathematical technique of "Model Fitting" was introduced to analyze the data from two experiments. Different models were used to examine whether childrens’ responses were best fit by exemplar or prototype models, to identify which features the children paid attention to, and to identify which classification strategy children used. Experiment 1 showed that 6-year-old children were able to learn the 5/4 categorization task and reach criterion. Model fitting analyses of category representation found that these children tended to form exemplar representations rather than prototype representations. On measures of distribution of attention, 6-year-old children could identify and pay more attention to the more typical dimensions. Finally, when learning strategy was examined, 6-year-old children used either a single-dimension or rule-plus-exception strategy to classify the items. Experiment 2 using the 3/3 task found similar results to experiment 1, but further found that 6-year-old children could not integrate their processing of the most important distinctive dimensions across the two categories. This result was consistent with the findings of Inhelder Piaget (1958), and Anaki Bentin (2009) that 6-year-old children could not process information across different categories.

The development of category learning strategies: What makes the difference?

The effects of dual verbal and visual tasks on featural vs. relational category learning Wookyoung Jung (jung43@illinois.edu) Department of Psychology, 603 E. Daniel Street Champaign, IL 61820 USA John E. Hummel (jehummel@illinois.edu) Department of Psychology, 603 E. Daniel Street Champaign, IL 61820 USA Abstract Many studies have examined the distinction between feature- and relation-based categories (Gentner, 2005; Genter & Kurtz, 2005; Jung & Hummel, 2009; Tomlinson & Love, 2011). Those findings suggest that featural and relationl categories have fundamentally different learning algorithms, where relational categories rely on explicit representations and thus require working memory and attention, as opposed to featural categories which may be learned more implicitly. In this study, we investigated further the distinction between feature-and relation-based category learning using a dual task methodology. Our results revealed an interaction: featural category learning was more impaired by a visuospatial dual task than by a verbal dual task, whereas relational category learning was more impaired by the verbal dual task. Our results suggest that in contrast to featural category learning, which may involve mainly non-verbal mechanisms, relational category learning appears to place greater demands on more explicit and attention-demanding verbal or verbally-related learning mechanisms. confidence that anything learned about category learning using feature-based categories will generalize at all to the case of relational categories. For example, the kinds of learning algorithms that work well with feature-based categories (i.e., various kinds of statistical learning) are completely incapable of learning relational categories (Doumas, Hummel & Sandhofer, 2008; Hummel & Holyoak, 2003; Jung & Hummel, 2009; Kittur et al., 2004, One of the clearest examples of this difference comes in the form of peoples’ ability to learn probabilistic (aka family resemblance) category structures. It has been known since the 1970s that people have no difficulty learning categories with probabilistic structures, in which any given feature is likely to belong to a given category (e.g., “bugs” in category A are likely to have one kind of head whereas “bugs” in category B are likely to have another), but no feature is deterministically associated with any given category (e.g., sometimes, bugs from category B will have heads typical of bugs from category A and vice-versa; see Murphy, 2002, for a review). However, as noted by Kittur et al. (2004), such prototype effects have always been observed with feature-based categories. With categories defined by the relations between their exemplars’ features, such prototype effects have proven difficult or impossible to observe (Jung & Hummel, 2009, 2011; Kittur et al., 2004, These differences between peoples’ ability to learn featural and relational categories are consistent with the claim that fundamentally different learning algorithms may be at work in the two cases. For example, whereas associative learning may work in the case of featural categories, relational category learning may require a more sophisticated algorithm based, for example, on structured intersection discovery, in which learners compare examples to one another, retaining what the examples have in common and discarding or discounting the details on which they differ (Gick & Holyoak, 1983; Hummel & Holyoak, 2003; Jung & Hummel, 2009, 2011; Kittur et al, 2004, Key words: featural category learning; relational category learning; dual task; verbal dual task; visuospatial dual task; category learning algorithms The ability to categorize plays a central role in human mental life. We use categories to makes sense of the world. They allow us to generalize knowledge form one situation to another, to decide which objects in the world are fundamentally the same, and to infer the unseen properties of novel category members. Research on categorization has mainly focused on feature-based categories—that is, categories defined by their exemplars’ features, as when “bugs” in one category tend to have a particular kind of head, body and tail and “bugs” in the opposite category tend to have a different kind of head, body and tail (e.g., Taylor and Ross, 2009)—and comparatively little on relation- based categories—i.e., categories by the relations between exemplars’ parts, or by relations between category exemplars and other objects in the world (for reviews, see Gentner, 2005; Goldwater, Markman, & Stilwell, 2011; Jung & Hummel, 2009; Kittur, Hummel & Holyoak, 2004). The distinction between featural and relational categories matters because features and relations are very different things—so different that we can have little or no

Incidental experiences can lead to lasting category knowledge, demonstrating that humans forage for information to acquire and consolidate new knowledge even when learning is not strictly necessary for success on an ongoing task.

Incidental experiences can lead to lasting category knowledge, demonstrating that humans forage for information to acquire and consolidate new knowledge even when learning is not strictly necessary for success on an ongoing task.

Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (“between-category separation”) and/or members of the same category come to look more similar (“within-category compression”) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the subjects succeeded in learning the categories (“Learners”: criterion 80% accuracy) and the rest did not (“Non-Learners”). At both lower and higher levels of difficulty, successful Learners showed significant between-category separation—and, to a lesser extent, within-category compression—in pairwise dissimilarity judgments after learning, compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital N1 amplitude (usually interpreted as perceptual) decreased. LPC amplitude increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-Learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model predictive of this effect.

In this Chapter the theoretical framework of the knowledge implementation method is presented. In Chapter 8 the knowledge implementation approach is applied for learning of the knowledge and skills of the different categories of objects. The shape understanding system (SUS) operates in two main modes, the learning mode and understanding mode. Learning and understanding are complementary processes. The ability of SUS to understand depends on the effectiveness of learning process and, in turn, learning of the new knowledge depends on the SUS ability to understand. The knowledge implementation is based on assumption that a system to be able to understand needs to learn and learned knowledge needs to be fully understood, that means, that there is a relation among specific learned facts stored in memory. The knowledge implementation is concerned with learning of the visual and the non-visual knowledge from the different categories of objects. The category of the visual objects was described in Chapter 6. The category of the sensory objects and the category of the text objects are related to the category of the visual objects and these categories will be defined in this Chapter. These categories are described in the context of learning and understanding of the visual object, the sensory object and the text object. Proposed new learning methods, that are part of the knowledge implementation approach, are designed to learn the knowledge of an object from the selected category such as the category of the visual objects, the category of the sensory objects or the category of the text objects. In Section 7.1 understanding and learning of the knowledge of the visual object is presented, in Section 7.2 understanding and learning of the knowledge of the sensory object is presented and in Section 7.3 understanding and learning of the knowledge of the text object is presented. Understanding and learning of the knowledge of the visual object, described in Section 7.1, is considered as acquiring the complex perceptual skills by SUS. Acquiring the complex perceptual skills by SUS, needed in perceiving of the world, is connected with implementation of the sophisticated image processing algorithms and learning of the complex patterns of the visual reasoning sequences. The visual knowledge is learned based on the sample of the visual objects that represent the category of visual objects selected for learning and utilization of the different forms of the visual abstraction. The important forms of the visual abstraction are generalization and specialization described in Sections 7.1.1 and 7.1.2. Learning of the visual knowledge is called the categorical learning and the short description of this method was presented in [1]. In this Section the new developed categorical learning techniques: learning by the small alternation of the visual object (LSA), learning from the simple to complex (LSC), learning by simplification of the complex object (LSCO), learning from parts (LP), and learning parts decomposition (LPD), are described. The new developed categorical learning techniques are applied to learn the knowledge of the selected ontological categories. Learning of the knowledge of the selected ontological categories will be described in Chapter 8.

Categories are learned in many ways, but the focus of much category learning research has been on classification learning. In classification learning, the diagnosticity of features is a primary influence on learning and the category representation. In this paper, we assess this influence of diagnosticity on a different means of category learning, inference learning. In two experiments, each with a different dependent measure, we found the expected result that classification learning led to strong sensitivity to the diagnosticity of the features, even to the exclusion of prototypicality (when controlled for diagnosticity). However, inference learners were significantly less sensitive to the diagnostic value of the features, and were sensitive to the prototypicality. This result provides further evidence for the idea that different types of category learning differentially influence the category representation and provides a better understanding of inference learning.

Everyday behaviors like interpreting a child’s squeal as thrilled or terrified or understandingdiverse acoustic signals from different talkers to each be the word “thanks” rely oncategorization. Learning to treat perceptually distinct objects as functionally equivalent changeshow we structure our knowledge about the world. Category learning is a major areaof research that spans from cellular neuroscience to human behavioral methods to philosophy.Despite the breadth of research on category learning, much still remains unknown about howhumans create and reorganize mental representations of categories. Even so, the majorityof research on perceptual category learning focuses on visual categories. In this dissertation, Iwill focus on auditory category learning. Sound presents unique learning challenges that areimportant for understanding speech learning, music perception, and everyday listening.This dissertation investigates how humans build on existing knowledge to learn new soundcategories. Chapter 1 presents a theoretical framework on the interaction of sensory experience,perceptual representations, and category learning. Chapters 2 and 3 uncover how factors of thecurrent learning context affect category learning. Chapter 4 examines how existing perceptualrepresentations influence how learners form categories within a perceptual environment.Chapters 5 and 6 investigate how experience with statistically structured sensory informationinfluences similarity-based perceptual representations and the effect of this experience onsubsequent category learning. A neural network model presented in Chapter 7 serves as a startingpoint in understanding the underlying computational mechanisms that allow sensory experienceto shape perceptual representations, which then influence higher-level cognition, such as theprocesses involved in category learning. This research advances understanding of both auditoryand visual categorization by revealing how category learning is both constrained by priorexperience and influenced by regularities and the context of the current learning environment.

This paper explores the effects of category size expectations on category learning. The expectation effect is the finding that category learning is improved when subjects are told how many items or exemplars are in each category in advance. There are three major theories or models of how categories are represented: Rule-based, Prototype-based, and Exemplar-based. Rule-based models assume that category learning is a process of discovering an explicit, verbalizable rule that maximizes categorization accuracy (Ashby, 2005; Seger Cincotta, 2006). Prototype-based models assume that stimuli are categorized on the basis of their similarity to category prototypes stored in memory (Rosch Mervis, 1975; Smith, Chapman, Redford, 2010; Coutinho, Redford, Smith, 2010). A category prototype is generally defined as the average, or most typical, member of a category. Exemplar-based models assume that the categorization of a new exemplar is based on the similarity of the new exemplar to the representations of all previously encountered exemplars stored in memory (Medin Schaffer, 1978; Kruschke, 1992; Nosofsky, 1992). According to Rule-based and Prototype-based models, people abstract the rule or prototype as their final representation without regard to the total number of exemplars in each relevant category; therefore, knowing how many exemplars are in each category should not affect learning. However, according to Exemplar-based models categories are represented as all the, specific exemplars that have been previously experienced. This implies that knowledge of category size may improve exemplar based categorization learning. Two learning conditions, Known condition (KC) and Unknown condition (UC) were compared in this experiment. In KC participants were instructed as to how many total exemplars (9) they would see across both categories. In UC participants were given no information about category size. The "5-4 category structure" from Medin and Schaffer (1978) was adapted in order to be able to identify which kinds of representation the participants were forming: rule, exemplar or prototype. 106 undergraduate students took part in the experiment. During each trial, an individual exemplar was presented, the participant was then asked to decide and indicate which category (A or B) the exemplar belonged to, and finally feedback as to whether the response was right or wrong was provided. Training continued until participants reached a learning criterion of three consecutive blocks with a combined accuracy of 90%, or until they completed 40 blocks (360 trials). A mathematical technique of "Model Fitting" was introduced to analyze the data from experiment. Different models were used to examine whether participants’ responses were best fit by exemplar or prototype models, to identify which features the participants paid attention to, and to identify which categorization strategy participants used. The results showed that the expectation effect for category size was significant. Participants who knew the sample size (KC) at the beginning of learning required fewer blocks on average to reach the criterion than the participants who did not know the sample size (UC) in advance; 22 and 27 blocks respectively, t (68)=2.088, p0.05. This result is consistent with the predictions from the Exemplar-based models but not the Rule-based or Prototype-based models. To explore whether the prototype or exemplar model provided the better account of the participant’s representation, we adopted a mathematical method of parameter estimation (Minda Smith, 2002) and fitted two models to each participant’s data: exemplar processing was assessed via a five-parameter version of the General Context Model (GCM), and prototype processing was assessed using the Multiplicative Prototype Model (MPM). We found that the fit of the GCM was quantitatively superior to the MPM model for both learning conditions. We also found that the KC group was more sensitive to the diagnostic dimensions of the category than the UC group. Across the blocks of training, the KC group showed three distinct phases of learning: an early phase in which overall accuracy was consistent with a single-rule strategy, followed by a phase in which accuracy was consistent with a rule-plus-exception strategy, and finally a phase in which accuracy was consistent with an information-integration strategies. This three phase pattern was not present in the UC group.

We explored people's inductive biases in category learning--that is, the factors that make learning category structures easy or hard--using iterated learning. This method uses the responses of one participant to train the next, simulating cultural transmission and converging on category structures that people find easy to learn. We applied this method to four different stimulus sets, varying in the identifiability of their underlying dimensions. The results of iterated learning provide an unusually clear picture of people's inductive biases. The category structures that emerge often correspond to a linear boundary on a single dimension, when such a dimension can be identified. However, other kinds of category structures also appear, depending on the nature of the stimuli. The results from this single experiment are consistent with previous empirical findings that were gleaned from decades of research into human category learning.

We introduce a new framework for measuring the dynamics of category learning using Coupled Hidden Markov Models (CHMMs). The key assumptions of the framework are that people maintain a latent assignment of every stimulus to a category, and that they can update the assignments for all stimuli whenever they encounter any stimulus. These assumptions contrast with many existing accounts of category learning, which either do not allow for what is learned about one stimulus to influence the category association of others, or allow only for indirect influence. The CHMM framework allows tailored models to be developed for specific category learning tasks, taking as input the stimulus sequence and category responses people make, and producing as output inferences about the underlying dynamics of category assignments and the mechanics of the response processes. We demonstrate the framework by applying it to a categorization task considered by Lee and Navarro (2002), showing how the model measures the change in participants’ latent category assignments as they learn the category structure. We conclude by discussing potential applications of the CHMM framework to category learning situations involving prior knowledge, changing category structures, and category learning tasks that involve the consideration of multiple stimuli at one time.

The effect of a working-memory-demanding dual task on perceptual category learning was investigated. In Experiment 1, participants learned unidimensional rule-based or information integration category structures. In Experiment 2, participants learned a conjunctive rule-based category structure. In Experiment 1, unidimensional rule-based category learning was disrupted more by the dual working memory task than was information integration category learning. In addition, rule-based category learning differed qualitatively from information integration category learning in yielding a bimodal, rather than a normal, distribution of scores. Experiment 2 showed that rule-based learning can be disrupted by a dual working memory task even when both dimensions are relevant for optimal categorization. The results support the notion of at least two systems of category learning a hypothesis-testing system that seeks verbalizable rules and relies on working memory and selective attention, and an implicit system that is procedural-learning based and is essentially automatic.

Common and distinct neural substrates of rule- and similarity-based category learning.

Over the past two decades the topic of category learning has received considerable attention. Category learning evolved from the earlier study of concept formation but differs from it in how concepts (or categories) are viewed. In concept formation, concepts were assumed to be strictly defined by necessary and sufficient characteristics whereas category learning extends consideration to more natural categories that are ‘fuzzy’ and ill‐defined. A number of category‐learning models have been proposed to explain how human participants categorize stimuli. Most of the models require external information in addition to the stimuli themselves in order to learn. This additional information may take the form of external feedback following decisions, information about the number of categories to use, or a ‘preview’ of a batch of category exemplars. In contrast, human participants appear to be able to learn category structures without these forms of additional information, although their success in doing so varies directly with the clarity of the classification to be learned. The article proposes mechanisms for category learning without external information which can be added to the majority of category‐learning models. The main mechanism is ‘self‐generated feedback’, where the learner provides feedback internally by assuming decisions to be correct. Using a model, self‐feedback was found to facilitate learning of two‐ and three‐category problems across varying levels of clarity of category structure. Conditions were used where the model was given (i) exemplars of each category with their category labels, (ii) the number of categories to use and (iii) neither. In all cases, learning occurred without external feedback. The degree of learning increased with increasing clarity of category structure. In this respect, as in most others, the results were similar to those reported for human participants.