Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels

Maddox, W. Todd; Molis, Michelle R.; Diehl, Randy L.

doi:10.3758/bf03194728

Cited by 34 publications

(40 citation statements)

References 56 publications

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“…It has also been suggested that the body of the caudate may be involved in classification learning independently of the role of the head of the caudate in feedback processing (Seger & Cincotta, 2005). In visual classification research, the role of the tail and sometimes body of the caudate is often emphasized (e.g., Seger & Cincotta, 2005;Ashby & Ell, 2002;Maddox, Molis, & Diehl, 2002). Similarly, Ashby and Ell (2002) posit that auditory stimulus-category associations should activate the body of the caudate.…”

Section: Discussionmentioning

confidence: 94%

Performance Feedback Drives Caudate Activation in a Phonological Learning Task

Tricomi

Delgado

McCandliss

et al. 2006

Journal of Cognitive Neuroscience

189

178

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Adults have difficulty discriminating nonnative phonetic contrasts, but under certain circumstances training can lead to improvement in this ability. Despite the ubiquitous use of performance feedback in training paradigms in this and many other domains, the mechanisms by which feedback affects learning are not well understood. In this event-related functional magnetic resonance imaging study, we examined how performance feedback is processed during perceptual learning. Thirteen Japanese speakers for whom the English phonemes [r] and [l] were nondistinct performed an identification task of the words "road" and "load" that has been shown to be effective in inducing learning only when performance feedback is present. Each subject performed alternating runs of training with and without feedback, followed by performance of a card-guessing task with monetary reward and punishment outcomes. We found that the caudate nucleus was more robustly activated bilaterally when performing the perceptual identification task with feedback than without feedback, and the right caudate nucleus also showed a differential response to positive and negative feedback. Moreover, using a within-subjects design, we found that the caudate nucleus also showed a similar activation pattern to monetary reward and punishment outcomes in the card-guessing task. These results demonstrate that the caudate responds to positive and negative feedback during learning in a manner analogous to its processing of extrinsic affective reinforcers and indicate that this region may be a critical moderator of the influence of feedback on learning. These findings impact our broader understanding of the mechanisms underlying nondeclarative learning and language acquisition.

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Section: Discussionmentioning

confidence: 94%

Performance Feedback Drives Caudate Activation in a Phonological Learning Task

Tricomi

Delgado

McCandliss

et al. 2006

Journal of Cognitive Neuroscience

189

178

View full text Add to dashboard Cite

show abstract

“…This approach is similar to evaluations of model performance described by Maddox et al ͑2002͒ andNearey andKiefte ͑2003͒ and some of the data modeled here also served as the basis for a comparison of visual and auditory models of categorization by Maddox and colleagues ͑2002͒. Through logistic regression analysis, any number of categorical or continuous independent variables can be used to model the categorical outcome of a dependent variable ͑Hosmer and Lemeshow, 1989͒.…”

Section: B Data Acquisition and Stimulus Representationsmentioning

confidence: 97%

“…In previous investigations, Nearey and Kiefte ͑2003͒ found that adding nonlinear ͑quadratic͒ formant terms improved the fit of the multinomial logistic regression analysis of a large F1-F2-F3 vowel space relative to the linear terms alone. Maddox et al ͑2002͒ investigated decision processes in the categorization of vowels using both linear and nonlinear formant models and also found improved fits when nonlinear boundaries were allowed. Hillenbrand and Gayvert ͑1993͒ evaluated a nonlinear classification method for spoken vowels, and compared their results with earlier studies that used linear analyses.…”

Section: A Formant-based Representationsmentioning

confidence: 99%

Evaluating models of vowel perception

Molis

2005

The Journal of the Acoustical Society of America

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There is a long-standing debate concerning the efficacy of formant-based versus whole spectrum models of vowel perception. Categorization data for a set of synthetic steady-state vowels were used to evaluate both types of models. The models tested included various combinations of formant frequencies and amplitudes, principal components derived from excitation patterns, and perceptually scaled LPC cepstral coefficients. The stimuli were 54 five-formant synthesized vowels that had a common F1 frequency and varied orthogonally in F2 and F3 frequency. Twelve speakers of American English categorized the stimuli as the vowels /I/, /[symbol: see text]/, or /[symbol: see text]/. Results indicate that formant frequencies provided the best account of the data only if nonlinear terms, in the form of squares and cross products of the formant values, were also included in the analysis. The excitation pattern principal components also produced reasonably accurate fits to the data. Although a wish to use the lowest-dimensional representation would dictate that formant frequencies are the most appropriate vowel description, the relative success of richer, more flexible, and more neurophysiologically plausible whole spectrum representations suggests that they may be preferred for understanding human vowel perception.

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“…The concept of multiple learning systems mediating categorization has been recently applied in the auditory domain, in studying vowel processing (Maddox, Molis, & Diehl, 2002), as well as learning tonal categories in adulthood . These studies have shown that successful learning of speech categories activates the neural circuitry involved in implicit, procedural-learning .…”

Section: Introductionmentioning

confidence: 99%

The role of age and executive function in auditory category learning

Reetzke

Maddox

Chandrasekaran

2015

Journal of the Acoustical Society of America

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Auditory categorization is a natural and adaptive process that allows for the organization of highdimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesistesting process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study is two-fold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence, into early adulthood; and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. Sixty participants with normal hearing, 20 children (age range, 7-12), 21 adolescents (age range, 13-19), and 19 young adults (age range, 20-23), learned to categorize novel dynamic ripple sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied Gabor patches in the visual domain. Results revealed that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e. a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning.

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Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels

Cited by 34 publications

References 56 publications

Performance Feedback Drives Caudate Activation in a Phonological Learning Task

Performance Feedback Drives Caudate Activation in a Phonological Learning Task

Evaluating models of vowel perception

The role of age and executive function in auditory category learning

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