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Performance pressure enhances speech learning

Published online by Cambridge University Press:  23 December 2015

W. TODD MADDOX*
Affiliation:
University of Texas
SETH KOSLOV
Affiliation:
University of Texas
HAN-GYOL YI
Affiliation:
University of Texas
BHARATH CHANDRASEKARAN
Affiliation:
University of Texas
*
ADDRESS FOR CORRESPONDENCE W. Todd Maddox, Department of Psychology, University of Texas, 1 University Station, A8000, Austin, TX 78712. E-mail: maddox@psy.utexas.edu

Abstract

Real-world speech learning often occurs in high-pressure situations such as trying to communicate in a foreign country. However, the impact of pressure on speech learning success is largely unexplored. In this study, adult, native speakers of English learned nonnative speech categories under pressure or no-pressure conditions. In the pressure conditions, participants were informed that they were paired with a (fictitious) partner, and that each had to independently exceed a performance criterion for both to receive a monetary bonus. They were then informed that their partner had exceeded the bonus and the fate of both bonuses depended upon the participant's performance. Our results demonstrate that pressure significantly enhanced speech learning success. In addition, neurobiologically inspired computational modeling revealed that the performance advantage was due to faster and more frequent use of procedural learning strategies. These results integrate two well-studied research domains and suggest a facilitatory role of motivational factors in speech learning performance that may not be captured in traditional training paradigms.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Akaike, H. (1974). A new look at the statistical model identification. Transactions on Automatic Control, 19, 716723.CrossRefGoogle Scholar
Allen, S. W., & Brooks, L. R. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120, 319.Google Scholar
Arnauld, E., Jeantet, Y., Arsaut, J., & Desmotes-Mainard, J. (1996). Involvement of the caudal striatum in auditory processing: c-fos response to cortical application of picrotoxin and to auditory stimulation. Molecular Brain Research, 41, 2735.CrossRefGoogle ScholarPubMed
Aron, A. R., Shohamy, D., Clark, J., Myers, C., Gluck, M. A., & Poldrack, R. A. (2004). Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning. Journal of Neurophysiology, 92, 11441152.Google Scholar
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442481.CrossRefGoogle ScholarPubMed
Ashby, F. G., & Crossley, M. J. (2011). A computational model of how cholinergic interneurons protect striatal-dependent learning. Journal of Cognitive Neuroscience, 23, 15491566.Google Scholar
Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005). FROST: A distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17, 17281743.Google Scholar
Ashby, F. G., & Ennis, J. M. (2006). The role of the basal ganglia in category learning. In Ross, B. H. & Markman, A. (Eds.), Psychology of learning and motivation: Vol. 47. Categories in use (pp. 136). New York: Elsevier.Google Scholar
Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149178.Google Scholar
Ashby, F. G., & Maddox, W. T. (2010). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147161.CrossRefGoogle ScholarPubMed
Ashby, F. G., Paul, E., & Maddox, W. T. (2011). COVIS 2.0. In Wills, E. P. A. (Ed.), Formal approaches in categorization. Cambridge: Cambridge University Press.Google Scholar
Ashby, F. G., & Valentin, V. V. (2005). Multiple systems of perceptual category learning: Theory and cognitive tests. In Cohen, H. & Lefebvre, C. (Eds.), Categorization in cognitive science. New York: Elsevier.Google Scholar
Ashby, F. G., & Waldron, E. M. (1999). On the nature of implicit categorization. Psychonomic Bulletin and Review, 6, 363378.CrossRefGoogle ScholarPubMed
Bates, D., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 classes [Computer software]. Retrieved from http://cran.R-project.org/package=lme4 Google Scholar
Beilock, S. L., Bertenthal, B. I., McCoy, A. M., & Carr, T. H. (2004). Haste does not always make waste: Expertise, direction of attention, and speed versus accuracy in performing sensorimotor skills. Psychonomic Bulletin and Review, 11, 373379.Google Scholar
Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail: Working memory and “choking under pressure” in math. Psychological Science, 16, 101105.Google Scholar
Best, C. T., & Tyler, M. D. (2007). Nonnative and second-language speech perception: Commonalities and complementarities. In Bohn, O. S. & Munro, M. J. (Eds.), Language experience in second language speech learning: In honor of James Emil Flege (pp. 1334). Amsterdam: John Benjamins.Google Scholar
Boersma, P., & Weenink, D. (2015). Praat: Doing phonetics by computer (Version 5.2 18) [Computer software]. Retrieved from http://www.fon.hum.uva.nl/praat/ Google Scholar
Bradlow, A. R., Akahane-Yamada, R., Pisoni, D. B., & Tohkura, Y. (1999). Training Japanese listeners to identify English /r/ and /l/: Long-term retention of learning in perception and production. Perception & Psychophysics, 61, 977985.CrossRefGoogle Scholar
Bradlow, A. R., & Bent, T. (2008). Perceptual adaptation to non-native speech. Cognition, 106, 707729.Google Scholar
Bradlow, A. R., Pisoni, D. B., Akahane-Yamada, R., & Tohkura, Y. (1997). Training Japanese listeners to identify English /r/ and /l/: IV. Some effects of perceptual learning on speech production. Journal of the Acoustical Society of America, 101, 22992310.CrossRefGoogle Scholar
Brooks, L. (1978). Nonanalytic concept formation and memory for instances. Hillsdale, NJ: Erlbaum.Google Scholar
Casale, M. B., & Ashby, F. G. (2008). A role for the perceptual representation memory system in category learning. Perception & Psychophysics, 70, 983999.Google Scholar
Chandrasekaran, B., Gandour, J. T., & Krishnan, A. (2007). Neuroplasticity in the processing of pitch dimensions: A multidimensional scaling analysis of the mismatch negativity. Restorative Neurology and Neuroscience, 25, 195210.Google Scholar
Chandrasekaran, B., Koslov, S. R., & Maddox, W. T. (2014). Toward a dual-learning systems model of speech category learning. Frontiers in Psychology, 5, 825.CrossRefGoogle Scholar
Chandrasekaran, B., Sampath, P. D., & Wong, P. C. (2010). Individual variability in cue-weighting and lexical tone learning. Journal of the Acoustical Society of America, 128, 456465.Google Scholar
Chandrasekaran, B., Yi, H. G., Blanco, N. J., McGeary, J. E., & Maddox, W. T. (2015). Enhanced procedural learning of speech sound categories in a genetic variant of FOXP2. Journal of Neuroscience, 35, 78087812.Google Scholar
Chandrasekaran, B., Yi, H. G., & Maddox, W. T. (2014). Dual-learning systems during speech category learning. Psychonomic Bulletin and Review, 21, 488495.Google Scholar
Cooper, J. A., Worthy, D. A., Gorlick, M. A., & Maddox, W. T. (2013). Scaffolding across the lifespan in history-dependent decision-making. Psychology of Aging, 28, 505514.Google Scholar
DeCaro, M. S., Thomas, R. D., Albert, N. B., & Beilock, S. L. (2011). Choking under pressure: Multiple routes to skill failure. Journal of Experimental Psychology: General, 140, 390406.Google Scholar
DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107, 284294.Google Scholar
Dornyei, Z. (1994). Motivation and motivating in the foreign language classroom. Modern Language Journal, 78, 273284.Google Scholar
Dornyei, Z., & Otto, I. (1998). Motivation in action: A process model of L2 motivation. Working Papers in Applied Linguistics, 4, 4369.Google Scholar
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 127, 107140.Google Scholar
Filoteo, J. V., Lauritzen, J. S., & Maddox, W. T. (2010). Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning. Psychological Science, 21, 415423.CrossRefGoogle ScholarPubMed
Filoteo, J. V., Maddox, W. T., Salmon, D. P., & Song, D. D. (2005). Information-integration category learning in patients with striatal dysfunction. Neuropsychology, 19, 212222.Google Scholar
Filoteo, J. V., Maddox, W. T., Simmons, A. N., Ing, A. D., Cagigas, X. E., Matthews, S., et al. (2005). Cortical and subcortical brain regions involved in rule-based category learning. NeuroReport, 16, 111115.Google Scholar
Folstein, J. R., & Van Petten, C. (2004). Multidimensional rule, unidimensional rule, and similarity strategies in categorization: Event-related brain potential correlates. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 10261044.Google Scholar
Francis, A. L., Ciocca, V., Ma, L., & Fenn, K. (2008). Perceptual learning of Cantonese lexical tones by tone and non-tone language speakers. Journal of Phonetics, 36, 268294.Google Scholar
Gandour, J. T., & Harshman, R. A. (1978). Crosslanguage differences in tone perception: A multidimensional scaling investigation. Language and Speech, 21, 133.Google Scholar
Gardner, R. C. (1985). Social psychology and second language learning: The role of attitudes and motivation. London: Edward Arnold.Google Scholar
Gardner, R. C. (2007). Motivation and second language acquisition. Porta Linguarum, 8, 920.Google Scholar
Grimm, L. R., Markman, A. B., & Maddox, W. T. (2012). End-of-semester syndrome: How situational regulatory fit affects test performance over an academic semester. Basic and Applied Social Psychology, 34, 376385.Google Scholar
Grimm, L. R., Markman, A. B., Maddox, W. T., & Baldwin, G. C. (2009). Stereotype threat reinterpreted as a regulatory mismatch. Journal of Personality and Social Psychology, 96, 288304.Google Scholar
Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 12801300.Google Scholar
Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55, 12171230.Google Scholar
Hikosaka, O., Sakamoto, Y., & Usui, S. (1989). Functional properties of monkey caudate neruons: III. Activities related to expectation of target and reward. Journal of Neurophysiology, 61, 814832.Google Scholar
Holt, L. L., & Lotto, A. J. (2008). Speech perception within an auditory cognitive science framework. Current Directions in Psychological Science, 17, 4246.Google Scholar
Holt, L. L., & Lotto, A. J. (2010). Speech perception as categorization. Attention, Perception, and Psychophysics, 72, 12181227.Google Scholar
Iverson, P., Kuhl, P. K., Akahane-Yamada, R., Diesch, E., Tohkura, Y., Kettermann, A., et al. (2003). A perceptual interference account of acquisition difficulties for non-native phonemes. Cognition, 87, B47B57.Google Scholar
Keri, S. (2003). The cognitive neuroscience of category learning. Brain Research Reviews, 43, 85109.Google Scholar
Knowlton, B. J., & Squire, L. R. (1993). The learning of categories: Parallel brain systems for item memory and category level knowledge. Science, 262, 17471749.Google Scholar
Knowlton, B. J., Squire, L. R., & Gluck, M. A. (1994). Probabilistic classification learning in amnesia. Learning and Memory, 1, 106120.Google Scholar
Lee, C. Y., & Hung, T. H. (2008). Identification of Mandarin tones by English-speaking musicians and nonmusicians. Journal of the Acoustical Society of America, 124, 32353248.Google Scholar
Lim, S. J., & Holt, L. L. (2011). Learning foreign sounds in an alien world: Videogame training improves non-native speech categorization. Cognitive Science, 35, 13901405.Google Scholar
Lively, S. E., Pisoni, D. B., Yamada, R. A., Tohkura, Y., & Yamada, T. (1994). Training Japanese listeners to identify English /r/ and /l/: III. Long-term retention of new phonetic categories. Journal of the Acoustical Society of America, 96, 20762087.Google Scholar
Lombardi, W. J., Andreason, P. J., Sirocco, K. Y., Rio, D. E., Gross, R. E., Umhau, J. C., et al. (1999). Wisconsin Card Sorting Test performance following head injury: Dorsolateral fronto-striatal circuit activity predicts perseveration. Journal of Clinical and Experimental Neuropsychology, 21, 216.Google Scholar
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309332.Google Scholar
Maddox, W. T. (1999). On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization. Perception & Psychophysics, 61, 354375.Google Scholar
Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66, 309332.Google Scholar
Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cogniion, 29, 650662.Google Scholar
Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory and Cognition, 32, 582591.Google Scholar
Maddox, W. T., & Chandrasekaran, B. (2014). Tests of a dual-systems model of speech category learning. Bilingualism (Cambridge, England), 17, 709728.Google ScholarPubMed
Maddox, W. T., Chandrasekaran, B., Smayda, K., & Yi, H. G. (2013). Dual systems of speech category learning across the lifespan. Psychology and Aging, 28, 10421056.Google Scholar
Maddox, W. T., Chandrasekaran, B., Smayda, K., Yi, H. G., Koslov, S., & Beevers, C. G. (2014). Elevated depressive symptoms enhance reflexive but not reflective auditory category learning. Cortex, 58, 186198.CrossRefGoogle Scholar
Maddox, W. T., & Filoteo, J. V. (2001). Striatal contributions to category learning: Quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease. Journal of the International Neuropsychological Society, 7, 710727.Google Scholar
Maddox, W. T., & Filoteo, J. V. (2005). The neuropsychology of perceptual category learning. In Cohen, H. & Lefebvre, C. (Eds.), Handbook of categorization in cognitive science (pp. 573599). New York: Elsevier.Google Scholar
Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 100107.Google Scholar
Maddox, W. T., Ing, A. D., & Lauritzen, J. S. (2006). Stimulus modality interacts with category structure in perceptual category learning. Perception & Psychophysics, 68, 11761190.Google Scholar
Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is less: Feedback effects in perceptual category learning. Cognition, 108, 578589.Google Scholar
Maddox, W. T., & Markman, A. B. (2010). The motivation–cognition interface in learning and decision-making. Current Directions in Psychological Science, 19, 106110.Google Scholar
Maddox, W. T., Molis, M. R., & Diehl, R. L. (2002). Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels. Perception & Psychophysics, 64, 584597.Google Scholar
Markman, A. B., Maddox, W. T., & Worthy, D. A. (2006). Choking and excelling under pressure. Psychological Science, 17, 944948.Google Scholar
McCoy, S. K., Hutchinson, S., Hawthorne, L., Cosley, B. J., & Ell, S. W. (2014). Is pressure stressful? The impact of pressure on the stress response and category learning. Cognitive, Affective, and Behavioral Neuroscience, 14, 769781.Google Scholar
Monchi, O., Petrides, M., Petre, V., Worsley, K., & Dagher, A. (2001). Wisconsin Card Sorting revisited: Distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. Journal of Neuroscience, 21, 77337741.Google Scholar
Nomura, E. M., Maddox, W. T., Filoteo, J. V., Ing, A. D., Gitelman, D. R., Parrish, T. B., et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17, 3743.CrossRefGoogle ScholarPubMed
Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). A rule-plus-exception model of classification learning. Psychological Review, 101, 5379.Google Scholar
Perrachione, T. K., Lee, J., Ha, L. Y., & Wong, P. C. (2011). Learning a novel phonological contrast depends on interactions between individual differences and training paradigm design. Journal of the Acoustical Society of America, 130, 461472.Google Scholar
Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Creso Moyano, J., Myers, C., et al. (2001). Interactive memory systems in the human brain. Nature, 414, 546550.Google Scholar
Poldrack, R. A., & Foerde, K. (2008). Category learning and the memory systems debate. Neuroscience & Biobehavioral Reviews, 32, 197205.Google Scholar
Reber, P. J., Gitelman, D. R., Parrish, T. B., & Mesulam, M. M. (2003). Dissociating explicit and implicit category knowledge with fMRI. Journal of Cognitive Neuroscience, 15, 574583.Google Scholar
Regehr, G., & Brooks, L. R. (1993). Perceptual manifestations of an analytic structure: The priority of holistic individuation. Journal of Experimental Psychology: General, 122, 92114.Google Scholar
Schnyer, D. M., Maddox, W. T., Ell, S., Davis, S., Pacheco, J., & Verfaellie, M. (2009). Prefrontal contributions to rule-based and information-integration category learning. Neuropsychologia, 47, 29953006.CrossRefGoogle ScholarPubMed
Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neuroscience & Biobehavioral Reviews, 32, 265278.Google Scholar
Seger, C. A., & Cincotta, C. M. (2002). Striatal activity in concept learning. Cognitive, Affective, and Behavioral Neuroscience, 2, 149161.Google Scholar
Seger, C. A., & Cincotta, C. M. (2005). The roles of the caudate nucleus in human classification learning. Journal of Neuroscience, 25, 29412951.Google Scholar
Seger, C. A., & Cincotta, C. M. (2006). Dynamics of frontal, striatal, and hippocampal systems during rule learning. Cerebral Cortex, 16, 15461555.Google Scholar
Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203219.Google Scholar
Shohamy, D., Myers, C. E., Grossman, S., Sage, J., Gluck, M. A., & Poldrack, R. A. (2004). Cortico-striatal contributions to feedback-based learning: Converging data from neuroimaging and neuropsychology. Brain: A Journal of Neurology, 127, 851859.Google Scholar
Shohamy, D., Myers, C. E., Onlaor, S., & Gluck, M. A. (2004). Role of the basal ganglia in category learning: How do patients eith Parkinson's disease learn? Behavioral Neuroscience, 118, 676686.Google Scholar
Smayda, K. E., Chandrasekaran, B., & Maddox, W. T. (2015). Enhanced cognitive and perceptual processing: A computational basis for the musician advantage in speech learning. Frontiers in Psychology, 6, 682.Google Scholar
Smith, J. D., Berg, M. E., Cook, R. G., Murphy, M. S., Crossley, M. J., Boomer, J., et al. (2012). Implicit and explicit categorization: A tale of four species. Neuroscience & Biobehavioral Reviews, 36, 23552369.Google Scholar
Smith, J. D., Johnston, J. J., Musgrave, R. D., Zakrzewski, A. C., Boomer, J., Church, B. A., et al. (2014). Cross-modal information integration in category learning. Attention, Perception, and Psychophysics, 76, 14731484.Google Scholar
Tharp, I. J., & Pickering, A. D. (2008). A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information-integration category learning. Cognition, 111, 411415.Google Scholar
Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin and Review, 8, 168176.Google Scholar
Wang, Y., Jongman, A., & Sereno, J. A. (2003). Acoustic and perceptual evaluation of Mandarin tone productions before and after perceptual training. Journal of the Acoustical Society of America, 113, 10331043.Google Scholar
Wang, Y., Sereno, J. A., Jongman, A., & Hirsch, J. (2003). fMRI evidence for cortical modification during learning of Mandarin lexical tone. Journal of Cognitive Neuroscience, 15, 10191027.Google Scholar
Wang, Y., Spence, M. M., Jongman, A., & Sereno, J. A. (1999). Training American listeners to perceive Mandarin tones. Journal of the Acoustical Society of America, 106, 36493658.Google Scholar
Whalen, D. H., & Xu, Y. (1992). Information for Mandarin tones in the amplitude contour and in brief segments. Phonetica, 49, 2547.Google Scholar
Wilson, C. J. (1995). The contribution of cortical neurons to the firing pattern of striatal spiny neurons. Cambridge, MA: MIT Press.Google Scholar
Wong, P., & Perrachione, T. K. (2007). Learning pitch patterns in lexical identification by native English-speaking adults. Applied Psycholinguistics, 28, 565585.Google Scholar
Worthy, D. A., Markman, A. B., & Maddox, W. T. (2009a). Choking and excelling under pressure in experienced classifiers. Attention, Perception, and Psychophysics, 71, 924935.Google Scholar
Worthy, D. A., Markman, A. B., & Maddox, W. T. (2009b). What is pressure? Evidence for social pressure as a type of regulatory focus. Psychonomic Bulletin and Review, 16, 344349.Google Scholar
Worthy, D. A., Markman, A. B., & Maddox, W. T. (2013). Feedback and stimulus-offset timing effects in perceptual category learning. Brain and Cognition, 81, 283293.Google Scholar
Yeterian, E. H., & Pandya, D. N. (1998). Corticostriatal connections of the superior temporal region in rhesus monkeys. Journal of Comparative Neurology, 399, 384402.3.0.CO;2-X>CrossRefGoogle ScholarPubMed
Yi, H. G., Maddox, W. T., Mumford, J. A., & Chandrasekaran, B. (2014). The role of corticostriatal systems in speech category learning. Cerebral Cortex. Advance online publication.Google Scholar
Zeithamova, D., & Maddox, W. T. (2006). Dual task interference in perceptual category learning. Memory and Cognition, 34, 387398.Google Scholar
Zeithamova, D., & Maddox, W. T. (2007). The role of visuo-spatial and verbal working memory in perceptual category learning. Memory and Cognition, 35, 13801398.Google Scholar
Zeithamova, D., Maddox, W. T., & Schnyer, D. M. (2008). Dissociable prototype learning systems: Evidence from brain imaging and behavior. Journal of Neuroscience, 28, 1319413201.Google Scholar