Interleaved effects are widely documented. Research demonstrates that interleaved presentation orders, as opposed to blocked orders typically benefit inductive category learning. What drives interleaved effects is less straightforward. Interleaved presentations provide both the opportunity to compare and contrast between different types of category exemplars, which are temporally juxtaposed, and the opportunity to space study of the same type of category exemplars, which are temporally separated within the presentation span. Accordingly, interleaved effects might be driven by enhanced discrimination, enhanced memory retention, or both in some measure. Though recent studies have largely endorsed enhanced discrimination as the critical mechanism driving interleaved effects, there is no strong evidence to controvert the contribution of enhanced memory retention for interleaved effects. I further examined the role of memory retention by manipulating both presentation order and category structure. Across two experiments I found that memory retention may drive interleaved effects in categorization tasks.

In tasks with more than one path to succeed, it is possible that participants’ strategies vary and therefore, participants should not be analyzed as a homogeneous group. This thesis investigates individual differences in a two-dimensional categorization task with redundancy (i.e., a task where any of the two dimensions by itself suffices for perfect performance). Individual differences in learned knowledge and used knowledge are considered and studied. Participants first performed a categorization task with redundancy (training phase), and afterward were asked to do categorizations in which the previously redundant knowledge becomes decisive (testing phase). Using the data from the testing phase, dimension(s) learned by each participant were determined and the response patterns of each participant in the training phase was used to determine which dimension(s) were used. The used knowledge was assessed using two separate analyses, both of which look at accuracy and response time patterns, but in different ways. Analysis 1 uses iterative decision bound modeling and RT-distance hypothesis and Analysis 2 uses the stochastic version of general recognition theory. In Analysis 1, more errors and slower response times close to a decision bound perpendicular to a dimension indicate that a participant is using that dimension. Analysis 2 goes a step further and in addition to determining which dimension(s) are used, specifies in what way they were used (i.e., identifying the strategy of each participant). Possible strategies are described heuristically (unidimensional, time efficient and conservative) and then each heuristic is translated into a drift diffusion model by the unique way that strategy is assumed to affect trial-by-trial difficulty of the task. Finally, a model selection criterion is used to pick the strategy that is used by each participant.

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A recurring theme in the cognitive development literature is the notion that people restructure their task knowledge as they develop increasingly sophisticated strategies. A large body of empirical literature spanning several domains suggests that in some cases, the process of knowledge restructuring is best characterized by a process of sequentially replacing old strategies with newer ones. In other cases, restructuring appears to be better characterized as a process involving changes in the way partial knowledge elements are selectively applied to a task. Critically, the former, but not the latter position, suggests that it may be quite difficult for people to revert to using an old strategy after restructuring has already occurred. The three experiments reported herein suggest that knowledge restructuring observed in experimental settings is aptly characterized by a process of strategy retention. Specifically, people are shown to readily revert to using an old categorization strategy even after demonstrably having restructured their knowledge, suggesting that knowledge is best conceptualized as having a heterogeneous structure. Formal modeling further supports this interpretation of the empirical results, and highlights the important role of selective attention in determining the manifest response strategy. The implications of these findings are discussed in terms of an overarching mixture-of-experts framework of knowledge representation.

Central to human intelligence, visual categorization is a skill that is both remarkably fast and accurate. Although there have been numerous studies in primates regarding how information flows in inferiortemporal (ITC) and prefrontal (PFC) cortices during online discrimination of visual categories, there has been little comparable research on the human cortex. To bridge this gap, this thesis explores how visual categories emerge in prefrontal cortex and the ventral stream, which is the human homologue of ITC. In particular, cortical spatiotemporal plasticity in visual category learning was investigated using behavioral experiments, magnetoencephalographic (MEG) imaging, and statistical machine learning methods. From a theoretical perspective, scientists from work on non-human primates have posited that PFC plays a primary role in the encoding of visual categories. Much of the extant research in the cognitive neuroscience literature, however, emphasizes the role of the ventral stream. Despite their apparent incompatibility, no study has evaluated these theories in the human cortex by examining the roles of the ventral stream and PFC in online discrimination and acquisition of visual categories. To address this question, I conducted two learning experiments using visually-similar categories as stimuli and recorded cortical response using MEG—a neuroimaging technique that offers a millisecond temporal resolution. Across both experiments, categorical information was found to be available during the period of cortical activity. Moreover, late in the learning process, this information is supplied increasingly in the ventral stream but less so in prefrontal cortex. These findings extend previous theories by suggesting that the ventral stream is crucial to long-term encoding of visual categories when categorical perception is proficient, but that PFC jointly encodes visual categories early on during learning. From a methodological perspective, MEG is limited as a technique because it can lead to false discoveries in a large number of spatiotemporal regions of interest (ROIs) and, typically, can only coarsely reconstruct the spatial locations of cortical responses. To address the first problem, I developed an excursion algorithm that identified ROIs contiguous in time and space. I then used a permutation test to measure the global statistical significance of the ROIs. To address the second problem, I developed a method that incorporates domainspecific and experimental knowledge in the modeling process. Utilizing faces as a model category, I used a predefined “face” network to constrain the estimation of cortical activities by applying differential shrinkages to regions within and outside this network. I proposed and implemented a trial-partitioning approach which uses trials in the midst of learning for model estimation. Importantly, this renders localizing trials more precise in both the initial and final phases of learning. In summary, this thesis makes two significant contributions. First, it methodologically improves the way we can characterize the spatiotemporal properties of the human cortex using MEG. Second, it provides a combined theory of visual category learning by incorporating the large time scales that encompass the course of the learning.

Sloutsky (2010; Kloos與Sloutsky, 2008) 操弄不同的類別結構亂度 (categorical entropy) 進行類別學習作業，藉此提出了雙系統理論，認為人們會啟動不同的系統，濃縮式系統 (compression-based system)或選擇式系統 (selection-based system)，以適應不同的類別結構組成之刺激材料。本研究回顧了Sloutsky的研究證據與過去類別學習領域的相關文獻，認為此雙系統理論可能只適用在向度數目較多的情境之下，因此設計了三個實驗，使用和Kloos與Sloutsky (2008) 相同的實驗派典，欲說明刺激材料的向度個數確實會影響到人們的類別學習行為。實驗一發現，Sloutsky所預測的類別結構與學習方式之交互作用只出現在向度個數較多的情境，向度個數少時則無此交互作用。實驗二得到與實驗一相同的結果，並排除了刺激材料本身特性（幾何圖形或類自然類別材料）此一混淆變項。實驗三採用特別設計的依變項，直接觀察受試者採用相似性(similarity)或規則(rule)的方式進行分類判斷，集群分析的結果顯示在向度數目少的情境時，不管何種類別結構受試者均傾向使用以規則為基礎的選擇式系統學習。因此，綜合以上發現，本研究認為Sloutsky的雙系統理論必須考慮到向度數目此一變項，才能更廣泛的應用於各種類別學習情境之中。 / The goal of this research is to point out that the dimensions of experimental materials can influence human category learning, which is neglected by traditional models of category learning. Three experiments in this research examined the effect of stimuli complexity by following the paradigms of Kloos and Sloutsky (2008). In Experiment 1, the prediction of Sloutsky’s theory (2010) on the interaction effect between category structures and learning conditions succeeds only at high complexity of materials, but fails in the low complexity condition. Experiment 2 was conducted by the same experimental setting as Experiment 1, but the natural-like stimuli were replaced by well-defined artificial geometrics. The result of Experiment 2 is the same as Experiment 1, suggesting that the complexity of materials plays a critical role in category learning no matter what kind of stimuli are used. Experiment 3 found that various materials complexity had distinct effects on human category representations. Namely, when experimental stimuli are relatively complex, people would use the corresponding category learning system to represent stimuli to learn dense categories or sparse ones. In contrast, when the stimuli are relatively simple, participants would represent the stimuli in a rule-based manner both in dense and sparse category structures.

In category learning, explicit processes function through the prefrontal cortex (PFC) and implicit processes function through the basal ganglia. Research suggested that these two systems compete with each other. The goal of this study was to shed light on this theory. 15 undergraduate subjects took part in an event-related experiment that required them to categorize computer-generated line-stimuli, which varied in length and/or angle depending on condition. Subjects participated in an explicit "rule-based" (RB) condition and an implicit "information-integration" (II) condition while connected to a functional near-infrared spectroscopy (fNIRS) apparatus, which measured the hemodynamic response (HR) in their PFC. Each condition contained 2 blocks. We hypothesized that the competition between explicit and implicit systems (COVIS) would be demonstrated if, by block 2, task-accuracy was approximately equal across conditions with PFC activity being comparatively higher in the II condition. This would indicate that subjects could learn the categorization task in both conditions but were only able to decipher an explicit rule in the RB condition; their PFC would struggle to do so in the II condition, resulting in perpetually high activation. In accordance with predictions, results revealed no difference in accuracy across conditions with significant difference in channel activation. There were channel trends (p < .1) which showed PFC activation decrease in the RB condition and increase in the II condition by block 2. While these results support our predictions, they are largely nonsignificant, which could be attributed to the event-related design. Future research should utilize a larger samples size for improved statistical power.