Current Search: category learning (x)
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(1 - 7 of 7)
- Title
- TAXING WORKING MEMORY: THE EFFECTS ON CATEGORY LEARNING.
- Creator
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Ercolino, Ashley, Bohil, Corey, University of Central Florida
- Abstract / Description
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In the past decade, the COVIS model (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) has emerged as the only neuropsychological theory for the existence of multiple brain systems for category learning. COVIS postulates that there are two systems, explicit and implicit, which compete against one another. These two systems reply on two discrete networks: explicit, or rule based categorization relies on executive function and working memory while implicit, or information integration...
Show moreIn the past decade, the COVIS model (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) has emerged as the only neuropsychological theory for the existence of multiple brain systems for category learning. COVIS postulates that there are two systems, explicit and implicit, which compete against one another. These two systems reply on two discrete networks: explicit, or rule based categorization relies on executive function and working memory while implicit, or information integration categorization is mediated by dopaminergic pathways. The purpose of this pilot study was to further provide evidence for the existence of multiple systems of category learning. In all three experiments, we interrupted feedback processing using a modified Sternberg task. In Experiment 1 and 2, participants were separated into four conditions, rule based (RB) categorization with a short delay between feedback and the modified Sternberg task, RB categorization with a long delay, information integration (II) categorization with a short delay, and II categorization with a long delay. Participants in the RB conditions performed worse than those in the II conditions in Experiment 1 and 2. After determining there was no significant difference between the short and long delay manipulations, only the short delay was used for Experiment 3. Consistent with Experiment 1 and 2, participants in the RB condition performed worse than those in the II condition. Functional near-infrared spectroscopy (fNIRS) technology was also used in Experiment 3 to determine the difference in prefrontal activation between RB and II conditions. Although statistically not significant, across blocks, the difference in prefrontal activation increased.
Show less - Date Issued
- 2015
- Identifier
- CFH0004870, ucf:45471
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004870
- Title
- NEUROIMAGING IN HUMAN CATEGORY LEARNING: A COMPARISON BETWEEN FUNCTIONAL NEAR-INFRARED SPECTROSCOPY (FNIR) AND FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI).
- Creator
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Viegas, Carina, Bohil, Corey, University of Central Florida
- Abstract / Description
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The objective of this thesis is to examine the validity of functional near-infrared spectroscopy (fNIR) to examine brain regions involved in rule based (RB) and information integration (II) category learning. We predicted similar patterns of activation found by past studies that used fMRI scans. Our goal was to test if fNIR would be able to detect changes in blood oxygenation levels of participants who learned to categorize (learners) vs. those that did not (non learners). The stimulus set...
Show moreThe objective of this thesis is to examine the validity of functional near-infrared spectroscopy (fNIR) to examine brain regions involved in rule based (RB) and information integration (II) category learning. We predicted similar patterns of activation found by past studies that used fMRI scans. Our goal was to test if fNIR would be able to detect changes in blood oxygenation levels of participants who learned to categorize (learners) vs. those that did not (non learners). The stimulus set comprised of lines that differed in length and orientation. Participants had to learn to categorize by trial and error based on the feedback provided. Behavioral and neuroimaging data was recorded for both RB and II conditions. Results showed an upward trend in response accuracy over trials for participants identified as learners. Furthermore, blood oxygenation levels reported by fNIR indicated a systematic increase in oxygen consumption for learners as compared to non learners. These areas of increased prefrontal cortex activity recorded by fNIR correspond to the same areas found to be involved in categorization by fMRI. This paper reviews the background of category learning, explores various neuroimaging techniques in categorization research, and investigates the efficacy of fNIR as a relatively new neuroimaging modality by comparing it to fMRI.
Show less - Date Issued
- 2014
- Identifier
- CFH0004591, ucf:45231
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004591
- Title
- AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS.
- Creator
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Kaylani, Assem, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing...
Show moreThis dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have focused on the evolutionary optimization of neural networks, but no research has been performed on the evolutionary optimization of ART neural networks, prior to 2006, when Daraiseh has used evolutionary techniques for the optimization of ART structures. This dissertation extends in many ways and expands in different directions the evolution of ART architectures, such as: (a) uses a multi-objective optimization of ART structures, thus providing to the user multiple solutions (ART networks) with varying degrees of merit, instead of a single solution (b) uses GA parameters that are adaptively determined throughout the ART evolution, (c) identifies a proper size of the validation set used to calculate the fitness function needed for ART's evolution, thus speeding up the evolutionary process, (d) produces experimental results that demonstrate the evolved ART's effectiveness (good accuracy and small size) and efficiency (speed) compared with other competitive ART structures, as well as other classifiers (CART (Classification and Regression Trees) and SVM (Support Vector Machines)). The overall methodology to evolve ART using a multi-objective approach, the chromosome representation of an ART neural network, the genetic operators used in ART's evolution, and the automatic adaptation of some of the GA parameters in ART's evolution could also be applied in the evolution of other exemplar based neural network classifiers such as the probabilistic neural network and the radial basis function neural network.
Show less - Date Issued
- 2008
- Identifier
- CFE0002212, ucf:47907
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002212
- Title
- Categorical Change: Exploring the Effects of Concept Drift in Human Perceptual Category Learning.
- Creator
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Wismer, Andrew, Bohil, Corey, Szalma, James, Neider, Mark, Gluck, Kevin, University of Central Florida
- Abstract / Description
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Categorization is an essential survival skill that we engage in daily. A multitude of behavioral and neuropsychological evidence support the existence of multiple learning systems involved in category learning. COmpetition between Verbal and Implicit Systems (COVIS) theory provides a neuropsychological basis for the existence of an explicit and implicit learning system involved in the learning of category rules. COVIS provides a convincing account of asymptotic performance in human category...
Show moreCategorization is an essential survival skill that we engage in daily. A multitude of behavioral and neuropsychological evidence support the existence of multiple learning systems involved in category learning. COmpetition between Verbal and Implicit Systems (COVIS) theory provides a neuropsychological basis for the existence of an explicit and implicit learning system involved in the learning of category rules. COVIS provides a convincing account of asymptotic performance in human category learning. However, COVIS (-) and virtually all current theories of category learning (-) focus solely on categories and decision environments that remain stationary over time. However, our environment is dynamic, and we often need to adapt our decision making to account for environmental or categorical changes. Machine learning addresses this significant challenge through what is termed concept drift. Concept drift occurs any time a data distribution changes over time. This dissertation draws from two key characteristics of concept drift in machine learning known to impact the performance of learning models, and in-so-doing provides the first systematic exploration of concept drift (i.e., categorical change) in human perceptual category learning. Four experiments, each including one key change parameter (category base-rates, payoffs, or category structure [RB/II]), investigated the effect of rate of change (abrupt, gradual) and awareness of change (foretold or not) on decision criterion adaptation. Critically, Experiments 3 and 4 evaluated differences in categorical adaptation within explicit and implicit category learning tasks to determine if rate and awareness of change moderated any learning system differences. The results of these experiments inform current category learning theory and provide information for machine learning models of decision support in non-stationary environments.
Show less - Date Issued
- 2018
- Identifier
- CFE0007114, ucf:51947
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007114
- Title
- Perceptual Judgment: The Impact of Image Complexity and Training Method on Category Learning.
- Creator
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Curtis, Michael, Jentsch, Florian, Salas, Eduardo, Szalma, James, Boloni, Ladislau, University of Central Florida
- Abstract / Description
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The goal of this dissertation was to bridge the gap between perceptual learning theory and training application. Visual perceptual skill has been a vexing topic in training science for decades. In complex task domains, from aviation to medicine, visual perception is critical to task success. Despite this, little, if any, emphasis is dedicated to developing perceptual skills through training. Much of this may be attributed to the perceived inefficiency of perceptual training. Recent applied...
Show moreThe goal of this dissertation was to bridge the gap between perceptual learning theory and training application. Visual perceptual skill has been a vexing topic in training science for decades. In complex task domains, from aviation to medicine, visual perception is critical to task success. Despite this, little, if any, emphasis is dedicated to developing perceptual skills through training. Much of this may be attributed to the perceived inefficiency of perceptual training. Recent applied research in perceptual training with discrimination training, however, holds promise for improved perceptual training efficiency. As with all applied research, it is important to root application in solid theoretical bases. In perceptual learning, the challenge is connecting the basic science to more complex task environments. Using a common aviation task as an applied context, participants were assigned to a perceptual training condition based on variation of image complexity and training type. Following the training, participants were tested for transfer of skill. This was intended to help to ground a potentially useful method of perceptual training in a model category learning, while offering qualitative testing of model fit in increasingly complex visual environments. Two hundred and thirty-one participants completed the computer based training module. Results indicate that predictions of a model of category learning largely extend into more complex training stimuli, suggesting utility of the model in more applied contexts. Although both training method conditions showed improvement across training blocks, the discrimination training condition did not transfer to the post training transfer tasks. Lack of adequate contextual information related to the transfer task in training was attributed to this outcome. Further analysis of the exposure training condition showed that individuals training with simple stimuli performed as well as individuals training on more complex stimuli in a complex transfer task. On the other hand, individuals in the more complex training conditions were less accurate when presented with a simpler representation of the task in transfer. This suggests training benefit to isolating essential task cues from irrelevant information in perceptual judgment tasks. In all, the study provided an informative look at both the theory and application associated with perceptual category learning. Ultimately, this research can help inform future research and training development in domains where perceptual judgment is critical for success.
Show less - Date Issued
- 2011
- Identifier
- CFE0004096, ucf:49139
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004096
- Title
- EVALUATING COMPETITION BETWEEN VERBAL AND IMPLICIT SYSTEMS WITH FUNCTIONAL NEAR-INFRARED SPECTROSCOPY.
- Creator
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Schiebel, Troy A, Bohil, Corey, University of Central Florida
- Abstract / Description
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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...
Show moreIn 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.
Show less - Date Issued
- 2016
- Identifier
- CFH2000086, ucf:45502
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000086
- Title
- Modeling social norms in real-world agent-based simulations.
- Creator
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Beheshti, Rahmatollah, Sukthankar, Gita, Boloni, Ladislau, Wu, Annie, Swarup, Samarth, University of Central Florida
- Abstract / Description
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Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors....
Show moreStudying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing.
Show less - Date Issued
- 2015
- Identifier
- CFE0005577, ucf:50244
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005577
