THE NEUROPSYCHOLOGY OF PERCEPTUAL CATEGORY LEARNING**This research was supported in part by National Institute of Health Grant R01 MH59196 to WTM, National Institute of Neurological Disorders and Stroke Grant R01 41372 to JVF, and a James McDonnell Foundation Grant.

Maddox, W. Todd; Filoteo, J. Vincent

doi:10.1016/b978-008044612-7/50081-0

Cited by 10 publications

(4 citation statements)

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“…Accurate performance in reflexive categorization requires predecisional integration of stimulus components, and it is therefore often referred to as an information-integration (II) strategy. Learning in this system does not rely on working memory and executive attention, and the critical structures are the posterior caudate, putamen and the supplementary motor area (SMA; Ashby and Waldron, 1999; Maddox and Filoteo, 2001; Poldrack et al, 2001; Aron et al, 2004; Filoteo et al, 2005b; Maddox and Filoteo, 2005; Seger and Cincotta, 2005; Nomura et al, 2007; Seger, 2008; Ashby and Crossley, 2011). Figure 1B displays a simple two-category problem.…”

Section: Single System Vs Multiple Systems Of Category Learningmentioning

confidence: 99%

Toward a dual-learning systems model of speech category learning

Chandrasekaran¹,

Koslov²,

Maddox³

2014

Front. Psychol.

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More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

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Section: Single System Vs Multiple Systems Of Category Learningmentioning

confidence: 99%

Toward a dual-learning systems model of speech category learning

Chandrasekaran¹,

Koslov²,

Maddox³

2014

Front. Psychol.

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“…In categorization, the COVIS model developed by Ashby et al (1998, 2011) is a good example of this approach. In COVIS, category learning involves multiple systems that are localized in different brain regions (Ashby et al, 2003; Filoteo et al, 2005; Maddox and Filoteo, 2005) with an implicit procedural-based system that mediates category learning when it is necessary to integrate information from multiple dimensions and an explicit hypothesis-testing system that mediates rule-based category learning. Although offering a potential framework within which to explore perceptual processing mechanisms in categorization, the notion of multiple systems has been repeatedly questioned (Gureckis et al, 2011; Newell et al, 2011; Dunn et al, 2012).…”

Section: Future Directionsmentioning

confidence: 99%

Stochastic accumulation of feature information in perception and memory

Kent¹,

Guest²,

Adelman³

et al. 2014

Front. Psychol.

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It is now well established that the time course of perceptual processing influences the first second or so of performance in a wide variety of cognitive tasks. Over the last 20 years, there has been a shift from modeling the speed at which a display is processed, to modeling the speed at which different features of the display are perceived and formalizing how this perceptual information is used in decision making. The first of these models (Lamberts, 1995) was implemented to fit the time course of performance in a speeded perceptual categorization task and assumed a simple stochastic accumulation of feature information. Subsequently, similar approaches have been used to model performance in a range of cognitive tasks including identification, absolute identification, perceptual matching, recognition, visual search, and word processing, again assuming a simple stochastic accumulation of feature information from both the stimulus and representations held in memory. These models are typically fit to data from signal-to-respond experiments whereby the effects of stimulus exposure duration on performance are examined, but response times (RTs) and RT distributions have also been modeled. In this article, we review this approach and explore the insights it has provided about the interplay between perceptual processing, memory retrieval, and decision making in a variety of tasks. In so doing, we highlight how such approaches can continue to usefully contribute to our understanding of cognition.

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“…These results have profoundly affected the categorization field, partly because no single-system theory has been able to account for more than one or two of these dissociations simultaneously. Second, there has been an explosion of new knowledge about the neural basis of category learning (Ashby & Ennis, 2006; Ashby, Noble, Filoteo, Waldron, & Ell, 2003; Filoteo & Maddox, 2007; Maddox & Filoteo, 2005, 2007; Nomura et al, 2007; Nomura & Reber, 2008; Seger, 2008; Seger & Cincotta, 2005, 2006). These new data come from a variety of sources, including fMRI, electroencephalography (EEG), single-unit recordings, and behavioral studies with a variety of different neuropsychological patient populations.…”

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confidence: 99%

Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa.

et al. 2014

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Objective To examine set shifting in a group of women previously diagnosed with anorexia nervosa who are now weight-restored (AN-WR) and then apply a biologically-based computational model (Competition between Verbal and Implicit Systems; COVIS) to simulate the pattern of category learning and set shifting performances observed. Method Nineteen AN-WR women and 35 control women (CW) were administered an explicit category learning task that required rule acquisition and then a set shift following a rule change. COVIS was first fit to the behavioral results of the controls and then parameters of the model theoretically relevant to AN were altered to mimic the behavioral results. Results Relative to CW, the AN-WR group displayed steeper learning curves (i.e., hyper learning) prior to the rule shift, but greater difficulty in learning the new categories after the rule shift (i.e., a deficit in set shifting).Hyper learning and set shifting deficits in the AN-WR group were not associated and differentially correlated with clinical measures. Hyper learning in the AN-WR group was simulated by increasing the model parameter that represents sensitivity to negative feedback (δ parameter), whereas the deficit in set shifting was simulated by altering the parameters that represent changes in rule selection and flexibility (λ and γ parameters, respectively). Conclusions These simulations suggest that multiple factors can impact category learning and set shifting in AN-WR individuals (e.g., alterations in sensitivity to negative feedback, rule selection deficits, and inflexibility) and provide an important starting point to further investigate this pervasive deficit in adult AN.

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THE NEUROPSYCHOLOGY OF PERCEPTUAL CATEGORY LEARNING**This research was supported in part by National Institute of Health Grant R01 MH59196 to WTM, National Institute of Neurological Disorders and Stroke Grant R01 41372 to JVF, and a James McDonnell Foundation Grant.

Cited by 10 publications

References 51 publications

Toward a dual-learning systems model of speech category learning

Toward a dual-learning systems model of speech category learning

Stochastic accumulation of feature information in perception and memory

Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa.

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