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Part III - Methods for Studying the Structure of Expertise

Published online by Cambridge University Press:  10 May 2018

K. Anders Ericsson
Affiliation:
Florida State University
Robert R. Hoffman
Affiliation:
Florida Institute for Human and Machine Cognition
Aaron Kozbelt
Affiliation:
Brooklyn College, City University of New York
A. Mark Williams
Affiliation:
University of Utah
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Print publication year: 2018

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References

Anderson, M. L. (2007). The massive redeployment hypothesis and the functional topography of the brain. Philosophical Psychology, 20, 143174.Google Scholar
Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.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.CrossRefGoogle Scholar
Biederman, I., & Shiffrar, M. M. (1987). Sexing day-old chicks: A case study and expert systems analysis of a difficult perceptual-learning task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 640645.Google Scholar
Braithwaite, D. W., Goldstone, R. L., van der Maas, H. L., & Landy, D. H. (2016). Non-formal mechanisms in mathematical cognitive development: The case of arithmetic. Cognition, 149, 4055.CrossRefGoogle ScholarPubMed
Brants, M., Wagemans, J., & Op de Beeck, H. P. (2011). Activation of fusiform face area by Greebles is related to face similarity but not expertise. Journal of Cognitive Neuroscience, 23, 39493958.Google Scholar
Bruner, J. S. (1992). Another look at New Look 1. American Psychologist, 47, 780783.CrossRefGoogle Scholar
Bruner, J. S., & Goodman, C. C. (1947). Value and need as organizing factors in perception. Journal of Abnormal and Social Psychology, 42, 3344.CrossRefGoogle ScholarPubMed
Bukach, C. M., Gauthier, I., & Tarr, M. J. (2006). Beyond faces and modularity: The power of an expertise framework. Trends in Cognitive Sciences, 10, 159166.Google Scholar
Bukach, C. M., Phillips, W. S., & Gauthier, I. (2010). Limits of generalization between categories and implications for theories of category specificity. Attention, Perception, & Psychophysics, 72, 18651874.Google Scholar
Changizi, M. A., Zhang, Q., Ye, H., & Shimojo, S. (2006). The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes. American Naturalist, 167, E117E139.CrossRefGoogle ScholarPubMed
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 5581.Google Scholar
Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121152.CrossRefGoogle Scholar
Clark, A. (1998). Magic words: How language augments human computation. In Carruthers, P. & Boucher, J. (eds.), Language and thought: Interdisciplinary themes (pp. 162183). Cambridge University Press.Google Scholar
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58, 719.Google Scholar
Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaff, M. A., & Michel, F. (2000). The visual word form area. Brain, 123, 291307.Google Scholar
Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology: General, 115, 107117.Google Scholar
Dobson, V., & Teller, D. Y. (1978). Visual acuity in human infants: A review and comparison of behavioral and electrophysiological studies. Vision Research, 18, 14691483.CrossRefGoogle ScholarPubMed
Dorfman, D. D., & Zajonc, R. B. (1963). Some effects of sound, background brightness, and economic status on the perceived size of coins and discs. Journal of Abnormal and Social Psychology, 66, 8790.Google Scholar
Firestone, C., & Scholl, B. J. (2015). Cognition does not affect perception: Evaluating the evidence for ‘top-down’ effects. Behavioral and Brain Sciences, 20, 177.Google Scholar
Fodor, J. (1984). Observation reconsidered. Philosophy of Science, 51, 2343.CrossRefGoogle Scholar
Gauthier, I., Behrmann, M., & Tarr, M. J. (1999). Can face recognition really be dissociated from object recognition? Journal of Cognitive Neuroscience, 11, 349370.CrossRefGoogle ScholarPubMed
Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3, 191197.Google Scholar
Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform “face area” increases with expertise in recognizing novel objects. Nature Neuroscience, 2, 568573.CrossRefGoogle ScholarPubMed
Gleicher, M., Correll, M., Nothelfer, C., & Franconeri, S. (2013). Perception of average value in multiclass scatterplots. IEEE Transactions on Visualization and Computer Graphics, 19, 23162325.Google Scholar
Glenberg, A. M. (2010). Embodiment as a unifying perspective for psychology. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 586596.Google Scholar
Goldstone, R. L., Landy, D. H., & Son, J. Y. (2010). The education of perception. Topics in Cognitive Science, 2, 265284.Google Scholar
Goldstone, R. L., Weitnauer, E., Ottmar, E., Marghetis, T., & Landy, D. H. (2016). Modeling mathematical reasoning as trained perception-action procedures. In Sottilare, R., Graesser, A., Hu, X., Olney, A., Nye, B., & Sinatra, A. (eds.), Design recommendations for intelligent tutoring systems, Volume 4: Domain modeling (pp. 213233). Orlando, FL: U.S. Army Research Laboratory.Google Scholar
Goodwin, C. (1994). Professional vision. American Anthropologist, 96, 606633.Google Scholar
Grill-Spector, K., Knouf, N., and Kanwisher, N. (2004). The fusiform face area subserves face perception, not generic within-category identification. Nature Neuroscience, 7, 555562.CrossRefGoogle Scholar
Grill-Spector, K., Sayres, R., & Ress, D. (2006). High-resolution imaging reveals highly selective nonface clusters in the fusiform face area. Nature Neuroscience, 9, 11771185.Google Scholar
Haegerstrom-Portnoy, G., Schneck, M. E., & Brabyn, J. A. (1999). Seeing into old age: Vision function beyond acuity. Optometry & Vision Science, 76, 141158.CrossRefGoogle ScholarPubMed
Hansen, M., Goldstone, R. L., & Lumsdaine, A. (2013). What makes code hard to understand? arXiv preprint arXiv:1304.5257.Google Scholar
Harel, A., Kravitz, D., & Baker, C. I. (2013). Beyond perceptual expertise: Revisiting the neural substrates of expert object recognition. Neural Implementations of Expertise, 7, 885.Google Scholar
Harel, A., Kravitz, D. J., & Baker, C. I. (2014). Task context impacts visual object processing differentially across the cortex. Proceedings of the National Academy of Sciences USA, 111, E962E971.CrossRefGoogle ScholarPubMed
Hatano, G., Miyake, Y., & Binks, M. G. (1977). Performance of expert abacus operators. Cognition, 5, 4755.Google Scholar
Hegarty, M., Canham, M. S., & Fabrikant, S. I. (2010). Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 3753.Google Scholar
Hegarty, M., Keehner, M., Khooshabeh, P., & Montello, D. R. (2009). How spatial abilities enhance, and are enhanced by, dental education. Learning and Individual Differences, 19, 6170.Google Scholar
Hole, G. J. (1994). Configurational factors in the perception of unfamiliar faces. Perception, 23, 6574.Google Scholar
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
James, K. H. (2010). Sensori-motor experience leads to changes in visual processing in the developing brain. Developmental Science, 13, 279288.Google Scholar
Johnson, K. E., & Mervis, C. B. (1997). Effects of varying levels of expertise on the basic level of categorization. Journal of Experimental Psychology: General, 126, 248277.Google Scholar
Kanwisher, N. (2010). Functional specificity in the human brain: A window into the functional architecture of the mind. Proceedings of the National Academy of Sciences USA, 107, 1116311170.CrossRefGoogle Scholar
Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 43024311.Google Scholar
Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2, 285305.Google Scholar
Kirsh, D. (2010). Thinking with external representations. AI & Society, 25, 441454.Google Scholar
Kirsh, D., & Maglio, P. (1994). On distinguishing epistemic from pragmatic action. Cognitive Science, 18, 513549.Google Scholar
Kirshner, D. (1989). The visual syntax of algebra. Journal for Research in Mathematics Education, 20, 274287.Google Scholar
Kirshner, D., & Awtry, T. (2004). Visual salience of algebraic transformations. Journal for Research in Mathematics Education, 35, 224257.Google Scholar
Klein, G. S., Schlesinger, H. J., & Meister, D. E. (1951). The effect of personal values on perception: An experimental critique. Psychological Review, 58, 96112.Google Scholar
Konar, Y., Bennett, P. J., & Sekuler, A. B. (2010). Holistic processing is not correlated with face-identification accuracy. Psychological Science, 21, 3843.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350, 13321338.Google Scholar
Landy, D., Allen, C. & Zednik, C. (2014). A perceptual account of symbolic reasoning. Frontiers in Psychology, 5, 275.Google Scholar
Landy, D., & Goldstone, R. L. (2007a). How abstract is symbolic thought? Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 720733.Google Scholar
Landy, D., & Goldstone, R. L. (2007b). Formal notations are diagrams: Evidence from a production task. Memory & Cognition, 35, 20332040.Google Scholar
Landy, D., & Goldstone, R. L. (2010). Proximity and precedence in arithmetic. Quarterly Journal of Experimental Psychology, 63, 19531968.Google Scholar
Landy, D. H., Jones, M. N., & Goldstone, R. L. (2008). How the appearance of an operator affects its formal precedence. In Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 21092114). Washington, DC.Google Scholar
Le Grand, R., Mondloch, C. J., Maurer, D., & Brent, H. P. (2001). Neuroperception: Early visual experience and face processing. Nature, 410, 890.Google Scholar
Li, J. X., & James, K. H. (2016). Handwriting generates variable visual output to facilitate symbol learning. Journal of Experimental Psychology: General, 145, 298313.CrossRefGoogle ScholarPubMed
Lupyan, G. (2008). The conceptual grouping effect: Categories matter (and named categories matter more). Cognition, 108, 566577.Google Scholar
Lupyan, G. (2015). Cognitive penetrability of perception in the age of prediction: Predictive systems are penetrable systems. Review of Philosophy and Psychology, 6, 547569.CrossRefGoogle Scholar
Marghetis, T., Landy, D., & Goldstone, R. L. (2016). Mastering algebra retrains the visual system to perceive hierarchical structure in equations. Cognitive Research: Principles and Implications, 1, 25.Google Scholar
Maruyama, M., Pallier, C., Jobert, A., Sigman, M., & Dehaene, S. (2012). The cortical representation of simple mathematical expressions. NeuroImage, 61, 14441460.CrossRefGoogle ScholarPubMed
McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Sciences, 7, 293299.Google Scholar
McClelland, J. L., Mirman, D., Bolger, D. J., & Khaitan, P. (2014). Interactive activation and mutual constraint satisfaction in perception and cognition. Cognitive Science, 38, 11391189.CrossRefGoogle ScholarPubMed
McGugin, R. W., Gatenby, J. C., Gore, J. C., & Gauthier, I. (2012). High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance. Proceedings of the National Academy of Sciences USA, 109, 1706317068.CrossRefGoogle ScholarPubMed
McKone, E., Kanwisher, N., & Duchaine, B. C. (2007). Can generic expertise explain special processing for faces? Trends in Cognitive Sciences, 11, 815.Google Scholar
Michal, A. L., Uttal, D., Shah, P., & Franconeri, S. L. (2016). Visual routines for extracting magnitude relations. Psychonomic Bulletin & Review, 23, 18021809.Google Scholar
Miller, G. A., Bruner, J. S., & Postman, L. (1954). Familiarity of letter sequences and tachistoscopic identification. Journal of General Psychology, 50, 129139.CrossRefGoogle Scholar
Newcombe, N. S., & Shipley, T. F. (2015). Thinking about spatial thinking: New typology, new assessments. In Gero, J. S. (ed.), Studying visual and spatial reasoning for design creativity (pp. 179192). Amsterdam: Springer.Google Scholar
Nisbett, R. E., & Miyamoto, Y. (2005). The influence of culture: Holistic versus analytic perception. Trends in Cognitive Sciences, 9, 467473.Google Scholar
Op de Beeck, H. P., Baker, C. I., Dicarlo, J. J., and Kanwisher, N. G. (2006). Discrimination training alters object representations in human extrastriate cortex. Journal of Neuroscience, 26, 1302513036.Google Scholar
Patsenko, E. G., & Altmann, E. M. (2010). How planful is routine behavior? A selective-attention model of performance in the Tower of Hanoi. Journal of Experimental Psychology: General, 139, 95116.Google Scholar
Perky, C. W. (1910). An experimental study of imagination. American Journal of Psychology, 2, 432452.Google Scholar
Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron, 72, 692697.CrossRefGoogle ScholarPubMed
Proffitt, D. R., & Linkenauger, S. A. (2013). Perception viewed as a phenotypic expression. In Prinz, W., Beisert, M., & Herwig, A. (eds.), Action science: Foundations of an emerging discipline (pp. 171198). Cambridge, MA: MIT Press.Google Scholar
Reinke, K., Fernandes, M., Schwindt, G., O’Craven, K., & Grady, C. L. (2008). Functional specificity of the visual word form area: General activation for words and symbols but specific network activation for words. Brain and Language, 104, 180189.Google Scholar
Rhodes, G., Brake, S., Taylor, K., & Tan, S. (1989). Expertise and configural coding in face recognition. British Journal of Psychology, 80, 313331.Google Scholar
Richler, J. J., Cheung, O. S., & Gauthier, I. (2011). Holistic processing predicts face recognition. Psychological Science, 22, 464471.Google Scholar
Rosch, E. (1978). Principles of categorization. In Rosch, E.. & Lloyd, B. B. (eds.), Cognition and categorization (pp. 2748). Hillsdale, NJ: Erlbaum.Google Scholar
Rossion, B. (2013). The composite face illusion: A whole window into our understanding of holistic face perception. Visual Cognition, 21, 139253.Google Scholar
Salvi, C., Bricolo, E., Franconeri, S. L., Kounios, J., & Beeman, M. (2015). Sudden insight is associated with shutting out visual inputs. Psychonomic Bulletin & Review, 22, 18141819.Google Scholar
Schneider, E., Maruyama, M., Dehaene, S., & Sigman, M. (2012). Eye gaze reveals a fast, parallel extraction of the syntax of arithmetic formulas. Cognition, 125, 475490.Google Scholar
Segal, S. J., & Nathan, S. (1964). The Perky effect: Incorporation of an external stimulus into an imagery experience under placebo and control conditions. Perceptual & Motor Skills, 18, 385395.Google Scholar
Shayan, S., Abrahamson, D., Bakker, A., Duijzer, A. C. G., & Van der Schaaf, M. F. (2015). The emergence of proportional reasoning from embodied interaction with a tablet application: An eyetracking study. In Chova, L. Gómez, Martínez, A. López, & Torres, I. Candel (eds.), Proceedings of the 9th International Technology, Education, and Development Conference (INTED 2015) (pp. 57325741). Madrid: IATED.Google Scholar
Shipley, T., Manduca, C., Resnick, I., & Schilling, C. (2009). Expertise in spatial visualization: Can geologists reverse time? Psychonomic Society, November, Boston.CrossRefGoogle Scholar
Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29, 123145.Google Scholar
Stieff, M., & Raje, S. (2010). Expert algorithmic and imagistic problem solving strategies in advanced chemistry. Spatial Cognition & Computation, 10, 5381.CrossRefGoogle Scholar
Stull, A. T., Hegarty, M., Dixon, B., & Stieff, M. (2012). Representational translation with concrete models in organic chemistry. Cognition and Instruction, 30, 404434.Google Scholar
Suwa, M., & Tversky, B. (1997). What do architects and students perceive in their design sketches? A protocol analysis. Design Studies, 18, 385403.Google Scholar
Szafir, D. A., Haroz, S., Gleicher, M., & Franconeri, S. (2016). Four types of ensemble coding in data visualizations. Journal of Vision, 16, 11.Google Scholar
Tanaka, J. W. (2001). The entry point of face recognition: Evidence for face expertise. Journal of Experimental Psychology: General, 130, 534543.Google Scholar
Tanaka, J. W., Kiefer, M., & Bukach, C. M. (2004). A holistic account of the own-race effect in face recognition: Evidence from a cross-cultural study. Cognition, 93, B1B9.Google Scholar
Ullman, S. (1984). Visual routines. Cognition, 18, 97159.Google Scholar
Wong, A. C. N., Palmeri, T. J., & Gauthier, I. (2009). Conditions for facelike expertise with objects: Becoming a ziggerin expert—but which type? Psychological Science, 20, 11081117.Google Scholar
Wong, Y. K., & Gauthier, I. (2010). A multimodal neural network recruited by expertise with musical notation. Journal of Cognitive Neuroscience, 22, 695713.Google Scholar
Yin, R. K. (1969). Looking at upside-down faces. Journal of Experimental Psychology, 81, 141145.Google Scholar
Young, A. W., Hellawell, D., & Hay, D. C. (1987). Configurational information in face perception. Perception, 16, 747759.Google Scholar
Zemel, R. S., Behrmann, M., Mozer, M. C., & Bavelier, D. (2002). Experience-dependent perceptual grouping and object-based attention. Journal of Experimental Psychology: Human Perception and Performance, 28, 202217.Google Scholar
Zhang, J., & Norman, D. A. (1994). Representations in distributed cognitive tasks. Cognitive Science, 18, 87122.Google Scholar

References

Beach, L. R., & Lipshitz, R. (1993). Why classical decision theory is an inappropriate standard for evaluating and aiding most human decision making. In Klein, G., Orasanu, J., Calderwood, R., & Zsambok, C. E. (eds.), Decision making in action: Models and methods (pp. 2135). Norwood, NJ: Ablex.Google Scholar
Bennett, K. B., & Flach, J. M. (2011). Display and interface design: Subtle science, exact art. Boca Raton, FL: CRC Press.Google Scholar
Bennett, K. B., & Hoffman, R. R. (2015). Principles for interaction design, Part 3: Spanning the creativity gap. IEEE Intelligent Systems, 30, 8291.CrossRefGoogle Scholar
Bradshaw, J. M., Hoffman, R. R., Johnson, M., & Woods, D. D. (2013). The seven deadly myths of “autonomous systems.” IEEE Intelligent Systems, 28, 5461.Google Scholar
Cañas, A. J., Coffey, J. W., Carnot, M. J., Feltovich, P., Hoffman, R. R., Feltovich, J., & Novak, J. D. (2003). A summary of literature pertaining to the use of concept mapping techniques and technologies for education and performance support. Report to the Chief of Naval Education and Training, prepared by the Institute for Human and Machine Cognition, Pensacola, FL.Google Scholar
Challenger, R., Clegg, C. W., & Shepherd, C. (2013). Function allocation in complex systems: Reframing an old problem. Ergonomics, 56, 10511069.Google Scholar
Cook, R. I., Woods, D. D., Walters, M., & Christoffersen, K. (1996). The cognitive systems engineering of automated medical evacuation scheduling. In Proceedings of the Third Annual Symposium on Human Interaction with Complex Systems (pp. 202207). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar
Cooke, N. J., & Durso, F. (2007). Stories of modern technology failures and cognitive engineering successes. Boca Raton, FL: CRC Press.Google Scholar
Crandall, B., & Getchell-Reiter, K. (1993). Critical decision method: A technique for eliciting concrete assessment indicators from the intuition of NICU nurses. Advances in Nursing Science, 16, 4251.Google Scholar
Crandall, B., Klein, G., & Hoffman, R. R. (2006). Working minds: A practitioner’s guide to cognitive task analysis. Cambridge, MA: MIT Press.Google Scholar
Cyterra (2015). Handheld Standoff Mine Detection System. www.cyterra.com/products/anpss14.htm.Google Scholar
Defense Science Board (2016). Summer study on autonomy final report. Washington, DC: Office of the Under Secretary for Defense, Department of Defense.Google Scholar
Dominguez, C., Strouse, R., Papautsky, L., & Moon, B. (2015). Cognitive design of an application enabling remote bases to receive unmanned helicopter resupply. Journal of Human–Robot Interaction, 4, 5060.Google Scholar
Duncker, K. (1945). On problem solving. Psychological Monographs, 58, 1113.Google Scholar
Ericsson, K. A., Whyte, J., & Ward, P. (2007). Expert performance in nursing: Reviewing research on expertise in nursing within the framework of the expert performance approach. Advances in Nursing Science, 30, E58E71.Google Scholar
Hambrick, D. Z., Ferreira, F., & Henderson, J. M. (2014). The 10,000 hour rule is wrong and perpetuates a cruel myth. www.slate.com/articles/health_and_science/science/192014/09/malcolm_gladwell_s_10_000_hour_rule_for_deliberate_practice_is_wrong_genes.htmlslate.com.Google Scholar
Hintze, N. R. (2008). First responder problem solving and decision making in today’s asymmetrical environment. Master’s thesis, Naval Postgraduate School Monterey, CA.Google Scholar
Hoffman, R. R., Crandall, B., & Shadbolt, N. (1998). Use of the critical decision method to elicit expert knowledge: A case study in the methodology of cognitive task analysis. Human Factors, 40, 254276.Google Scholar
Hoffman, R. R., Cullen, T. M., & Hawley, J. K. (2016). Rhetoric and reality of autonomous weapons: Getting a grip on the myths and costs of automation. Bulletin of the Atomic Scientists, 72, 247255.Google Scholar
Hoffman, R. R., Henderson, S., Moon, B., Moore, D. T., & Litman, J. A. (2011a). Reasoning difficulty in analytical activity. Theoretical Issues in Ergonomic Science, 12, 225240.Google Scholar
Hoffman, R. R., LaDue, D. S., Mogil, H. M., Roebber, P. J., & Trafton, J. G. (2017). Minding the weather: How expert forecasters think. Cambridge, MA: MIT Press.Google Scholar
Hoffman, R. R., & Lintern, G. (2006). Eliciting and representing the knowledge of experts. In Ericsson, K. A., Charness, N., Hoffman, R. R., & Feltovich, P. J. (eds.), The Cambridge handbook of expertise and expert performance (pp. 203222). Cambridge University Press.Google Scholar
Hoffman, R. R., & Militello, L. G. (2008). Perspectives on cognitive task analysis: Historical origins and modern communities of practice. Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
Hoffman, R. R., Norman, D., & Vagners, J. (2009). Complex sociotechnical joint cognitive work systems IEEE Intelligent Systems, 24, 8283.Google Scholar
Hoffman, R. R., Schraagen, J. M., van de Ven, J., & Moon, B. M. (2011b). Influencing the business model and innovations of a research organization through concept mapping. In Moon, B. M., Hoffman, R. R., Novak, J. D., and Cañas, A. J. (eds.), Applied concept mapping: Capturing, analyzing, and organizing knowledge (pp. 169192). Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
Hoffman, R. R., Ward, P., DiBello, L., Feltovich, P. J., Fiore, S. M., & Andrews, D. (2014). Accelerated expertise: Training for high proficiency in a complex world. Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
Hoffman, R. R., & Yates, J. (2005). Decision (?) making (?). IEEE Intelligent Systems, 20, 2229.Google Scholar
Hoffman, R. R., Ziebell, D., Feltovich, P., Moon, B., & Fiore, S. (2011c). Franchise experts. IEEE Intelligent Systems, 26, 7277.Google Scholar
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
Hutchins, S. (2007). What makes intelligence analysis difficult? A cognitive task analysis. Monterey, CA: Dudley Knox Library, Naval Postgraduate School.Google Scholar
Hutton, R. J. B., & Militello, L. G. (1996). Applied cognitive task analysis (ACTA): A practitioner’s window into skilled decision making. In Harris, D. (ed.), Engineering psychology and cognitive ergonomics: Job design and product design (Vol. 2, pp. 1723). Aldershot: Ashgate.Google Scholar
Johnston, R. (2005). Analytic culture in the US intelligence community: An ethnographic study. Washington, DC: Center for the Study of Intelligence.Google Scholar
Jordan, B. (1989). Cosmopolitical obstetrics: Some insights from the training of traditional midwives. Social Science Medicine, 28, 925944.Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus & Giroux.Google Scholar
Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64, 515526.Google Scholar
Klein, G. A. (1989). Recognition-primed decisions. In Rouse, W. B. (ed.), Advances in man–machine systems research (Vol. 5, pp. 4792). Greenwich, CT: JAI Press.Google Scholar
Klein, G. (1998). Sources of power: How people make decisions. Cambridge, MA: MIT Press.Google Scholar
Klein, G., & Borders, J. (2016). The ShadowBox approach to cognitive skills training: An empirical evaluation. Journal of Cognitive Engineering and Decision Making, 10, 268280.Google Scholar
Klein, G. A., & Calderwood, R. (1991). Decision models: Some lessons from the field. IEEE Transactions on Systems, Man, and Cybernetics, 21, 10181026.Google Scholar
Klein, G. A., Calderwood, R., & MacGregor, D. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man, and Cybernetics, 19, 462472.Google Scholar
Klein, G., Hintze, N., & Saab, D. (2013). Thinking inside the box: The ShadowBox method for cognitive skill development. In Proceedings of the 11th International Conference on Naturalistic Decision Making (NDM-11), Marseille, France. Paris: Arpege Science Publishing.Google Scholar
Klein, G., & Hoffman, R. R. (2008). Macrocognition, mental models, and cognitive task analysis methodology. In Schraagen, J. M., Militello, L. G., Ormerod, T., & Lipshitz, R. (eds.), Naturalistic decision making and macrocognition (pp. 5780). Aldershot: Ashgate.Google Scholar
Klein, G., Militello, L. G., Dominguez, C. O., & Lintern, G. (in press). A one-day workshop for teaching cognitive systems engineering skills. In Hoffman, R. R. & Smith, P. (eds.), Cognitive systems engineering: The future for a changing world. Boca Raton, FL: Taylor & Francis.Google Scholar
Klein, G., Moon, B., & Hoffman, R. R. (2006). Making sense of sensemaking 2: A macrocognitive model. IEEE Intelligent Systems, 21, 8892.Google Scholar
Klinger, D. W., & Klein, G. (1999). Emergency response organizations: An accident waiting to happen. Ergonomics in Design, 7, 2025.Google Scholar
Kruger, C., & Cross, N. (2006). Solution driven versus problem driven design: Strategies and outcomes. Design Studies, 27, 527548.Google Scholar
Kuipers, B., & Kassirer, J. P. (1987). Knowledge acquisition by analysis of verbatim protocols. In Kidd, A. L. (ed.), Knowledge acquisition for expert systems: A practical handbook (pp. 4571). New York: Plenum Press.Google Scholar
Lave, J. (1988). Cognition in practice. Cambridge University Press.Google Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.Google Scholar
Lintern, G. (2010). A comparison of the decision ladder and the recognition-primed decision model. Journal of Cognitive Engineering and Decision Making, 4, 304327.Google Scholar
Lintern, G. (2013). Joker one: A tutorial in cognitive work analysis. Melbourne: Cognitive Systems Design. www.cognitivesystemsdesign.net.Google Scholar
Militello, L. G., Dominguez, C. O., Lintern, G., & Klein, G. (2010). The role of cognitive systems engineering in the systems engineering design process. Systems Engineering, 13, 261273.Google Scholar
Miller, A., Moon, B., Anders, S., Walden, R., & Montella, D. (2015). Integrating computerized clinical decision support systems into electronic health records: A metasynthesis. International Journal of Medical Informatics, 84, 10091018.Google Scholar
Moon, B. M., & Hoffman, R. R. (2005). How might “transformational” technologies and concepts be barriers to sensemaking in intelligence analysis. In Schraagen, J. M. C. (ed.), Proceedings of the Seventh International Naturalistic Decision Making Conference (pp. 111). Amsterdam.Google Scholar
Moon, B. M., Hoffman, R. R., Eskridge, T., & Coffey, J. (2011b). Skills in concept mapping. In Moon, B. M., Hoffman, R. R., Novak, J. D., & Cañas, A. J. (eds.), Applied concept mapping: Capturing, analyzing, and organizing knowledge (pp. 2346). Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
Moon, B. M., Hoffman, R. R., Lacroix, M., Fry, E., & Miller, A. (2014). Exploring macrocognitive healthcare work: Discovering seeds for design guidelines for clinical decision support. Presentation at the 5th International Conference on Applied Human Factors and Ergonomics. Krakow, Poland.Google Scholar
Moon, B. M., Hoffman, R. R., Novak, J. D., & Cañas, A. J. (eds.) (2011a). Applied concept mapping: Capturing, analyzing, and organizing knowledge. Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
Moon, B., Way, A., Fry, E., Miller, A., & Montella, D. (2015). Tracing a user-centered design process to validate guidelines for clinical decision support. Poster presentation at the International Symposium on Human Factors and Ergonomics in Health Care. Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
Newell, A. (1985). Duncker on thinking: An inquiry into progress on cognition. In Koch, S. & Leary, D. E. (eds.), A century of psychology as a science (pp. 392419). Oxford University Press.Google Scholar
Novak, J. D. (1998). Learning, creating, and using knowledge. Mahwah, NJ: Erlbaum.Google Scholar
Papautsky, E. L., Dominguez, C., Strouse, R., & Moon, B. (2015). Integration of cognitive task analysis and design thinking for autonomous helicopter displays. Journal of Cognitive Engineering and Decision Making, 9.Google Scholar
Pliske, R. M., Crandall, B., & Klein, G. (2004). Competence in weather forecasting. In Smith, K., Shanteau, J., & Johnson, P. (eds.), Psychological investigations of competence in decision making (pp. 4068). Cambridge University Press.Google Scholar
Rasmussen, J. (1986). Information processing and human–machine interaction: An approach to cognitive engineering. New York: Science Publishing.Google Scholar
Rasmussen, J., Petjersen, A. M., & Goodstein, L. P. (1994). Cognitive systems engineering. New York: John Wiley.Google Scholar
Read, G. J. M., Salmon, P. M., Lenné, M. G., & Jenkins, D. P. (2015). Designing a ticket to ride with the Cognitive Work Analysis Design Toolkit. Ergonomics, 58, 12661286.Google Scholar
Regan, M. A., Lintern, G., Hutchinson, R., & Turetschek, C. (2014). Use of cognitive work analysis for exploration of safety management in the operation of motorcycles and scooters. Accident Analysis and Prevention, 74, 279289.Google Scholar
Schnittker, R., Marshall, S. D., Horberry, T., Young, K., & Lintern, G. (2016). Examination of anesthetic practitioners’ decisions for the design of a cognitive tool for airway management. In Proceedings of the 60th Human Factors and Ergonomics Society Annual Meeting (pp. 17651769). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
Staszewski, J. (2004). Models of expertise as blueprints for cognitive engineering: Applications to landmine detection. In Proceedings of the 48th Annual Meeting of the Human Factors and Ergonomics Society (pp. 458462). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
Vicente, K. H. (1999). Cognitive work analysis: Towards safe, productive, and healthy computer-based work. Mahwah, NJ: Erlbaum.Google Scholar
Woods, D. D., & Roth, E. M. (1986). Models of cognitive behavior in nuclear power plant personnel. Washington, DC: U.S. Nuclear Regulatory Commission.Google Scholar
Zakay, D., & Wooler, S. (1984). Time pressure, training, and decision effectiveness. Ergonomics, 27, 273284.Google Scholar

References

Abernethy, B., Neal, R. J., & Koning, P. (1994). Visual-perceptual and cognitive differences between expert, intermediate, and novice snooker players. Applied Cognitive Psychology, 18, 185211.Google Scholar
Afonso, J., Garganta, J., McRobert, A., Williams, A. M., & Mesquita, I. (2014). Visual search behaviours and verbal reports during film-based and in situ representative tasks in volleyball. European Journal of Sport Science, 14, 177184.Google Scholar
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
Anzai, Y., & Simon, H. A. (1979). The theory of learning by doing. Psychological Review, 86, 124140.Google Scholar
Arsal, G., Eccles, D. W., & Ericsson, K. A. (2016). Cognitive mediation of putting: Use of a think-aloud measure and implications for studies of golf-putting in the laboratory. Psychology of Sport and Exercise, 27, 1827.Google Scholar
Austin, J., & Delaney, P. F. (1998). Protocol analysis as a tool for behavior analysis. The Analysis of Verbal Behavior, 15, 4156.Google Scholar
Azevedo, R., Faremo, S., & Lajoie, S. P. (2007). Expert–novice differences in mammogram interpretation. In McNamara, D. S. & Trafton, J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 6570). Nashville, TN: Cognitive Science Society.Google Scholar
Balslev, T., Jarodzka, H., Holmqvist, K., de Grave, W., Muijtjens, A. M. M., Eika, B., … & Scherpbier, A. J. J. A. (2011). Visual expertise in paediatric neurology. European Journal of Paediatric Neurology, 16, 161166.Google Scholar
Binet, A. (1966). Mnemonic virtuosity: A study of chess players (trans. Simmel, M. L. and Barron, S. B.). Genetic Psychology Monographs, 74, 127162. (Original work published 1893)Google Scholar
Boring, E. B. (1950). A history of experimental psychology. New York: Appleton-Century Crofts.Google Scholar
Boshuizen, H. P. A., & Schmidt, H. G. (1992). On the role of biomedical knowledge in clinical reasoning by experts, intermediates and novices. Cognitive Science, 16, 153184.Google Scholar
Bryan, W. L., & Harter, N. (1899). Studies on the telegraphic language: The acquisition of a hierarchy of habits. Psychological Review, 6, 345375.Google Scholar
Burns, B. D. (2004). The effects of speed on skilled chess performance. Psychological Science, 15, 442447.Google Scholar
Calderwood, R., Klein, G. A., & Crandall, B. W. (1988). Time pressure, skill, and move quality in chess. American Journal of Psychology, 101, 481493.Google Scholar
Calmeiro, L., & Tenenbaum, G. (2011). Concurrent verbal protocol analysis in sport: Illustration of thought process during a golf-putting task. Journal of Clinical Sport Psychology, 5, 223236.Google Scholar
Carayon, P., Kianfar, S., Li, Y., Xie, A., Alyousef, B., & Wooldridge, A. (2015). A systematic review of mixed methods research on human factors and ergonomics in health care. Applied Ergonomics, 51, 291321.Google Scholar
Chabris, C. F., & Hearst, E. S. (2003). Visualization, pattern recognition, and forward search: Effects of playing speed and sight of the position on grandmaster chess errors. Cognitive Science, 27, 637648.Google Scholar
Chaffin, R., Imreh, G., Lemieux, A. F., & Chen, C. (2003). “Seeing the big picture”: Piano practice as expert problem solving. Music Perception, 20, 465490.Google Scholar
Charness, N. (1981). Search in chess: Age and skill differences. Journal of Experimental Psychology: Human Perception and Performance, 7, 467476.Google Scholar
Chase, W. G., & Ericsson, K. A. (1981). Skilled memory. In Anderson, J. R. (ed.), Cognitive skills and their acquisition (pp. 141189). Hillsdale, NJ: Erlbaum.Google Scholar
Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In Bower, G. H. (ed.), The psychology of learning and motivation (Vol. 16, pp. 158). New York: Academic Press.Google Scholar
Collins, D., & Dunn, M. (2011). Problem-solving strategies and processes in musical composition: Observations in real time. Journal of Music, Technology and Education, 4, 4776.Google Scholar
Cooke, L. (2010). Assessing concurrent think-aloud protocol as a usability test method: A technical communication approach. IEEE Transactions on Professional Communication, 53, 202215.Google Scholar
Crovitz, H. F. (1970). Galton’s walk: Methods for the analysis of thinking, intelligence, and creativity. New York: Harper & Row.Google Scholar
Crowley, R. S., Naus, G. J., Stewart, J., & Friedman, C. P. (2003). Development of visual diagnostic expertise in pathology: An information-processing study. Journal of the American Medical Informatics Association, 10, 3951.Google Scholar
Dansereau, D. F., & Gregg, L. W. (1966). An information processing analysis of mental multiplication. Psychonomic Science, 6, 7172.Google Scholar
Darker, C. D., & French, D. P. (2009). What sense do people make of a theory of planned behaviour questionnaire? A think aloud study. Journal of Health Psychology, 14, 861871.Google Scholar
de Groot, A. (1978). Thought and choice in chess (2nd edn.). The Hague: Mouton. (Original work published 1946)Google Scholar
Den Hartigh, R. J. R., Van Der Steen, S., De Meij, M., Van Yperen, N. W., Gernigon, C. & Van Geert, P. L. C. (2014). Characterising expert representations during real-time action: A Skill Theory application to soccer. Journal of Cognitive Psychology, 26, 754767.Google Scholar
Duncker, K. (1945). On problem solving. Psychological Monographs, 58, 1113.CrossRefGoogle Scholar
Ericsson, K. A. (1988). Concurrent verbal reports on reading and text comprehension. Text, 8, 295325.Google Scholar
Ericsson, K. A. (1996). The acquisition of expert performance: An introduction to some of the issues. In Ericsson, K. A. (ed.), The road to excellence: The acquisition of expert performance in the arts and sciences, sports, and games (pp. 150). Mahwah, NJ: Erlbaum.Google Scholar
Ericsson, K. A. (2002). Toward a procedure for eliciting verbal expression of nonverbal experience without reactivity: Interpreting the verbal overshadowing effect within the theoretical framework for protocol analysis. Applied Cognitive Psychology, 16, 981987.Google Scholar
Ericsson, K. A. (2003). Valid and non-reactive verbalization of thoughts during performance of tasks: Toward a solution to the central problems of introspection as a source of scientific data. Journal of Consciousness Studies, 10, 118.Google Scholar
Ericsson, K. A. (2004). Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Academic Medicine, 79, S70S81.Google Scholar
Ericsson, K. A. (2015). Acquisition and maintenance of medical expertise: A perspective from the expert-performance approach with deliberate practice. Academic Medicine, 90, 14711486.Google Scholar
Ericsson, K. A., & Charness, N. (1994). Expert performance: Its structure and acquisition. American Psychologist, 49, 725747.Google Scholar
Ericsson, K. A., Chase, W., & Faloon, S. (1980). Acquisition of a memory skill. Science, 208, 11811182.Google Scholar
Ericsson, K. A., Cheng, X., Pan, Y., Ku, Y., Ge, Y., & Hu, Y. (2017). Memory skills mediating superior memory in a world-class memorist. Memory, 25, 12941302.Google Scholar
Ericsson, K. A., Delaney, P. F., Weaver, G., & Mahadevan, R. (2004). Uncovering the structure of a memorist’s superior “basic” memory capacity. Cognitive Psychology, 49, 191237.Google Scholar
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211245.Google Scholar
Ericsson, K. A., Patel, V. L., & Kintsch, W. (2000). How experts’ adaptations to representative task demands account for the expertise effect in memory recall: Comment on Vicente and Wang (1998). Psychological Review, 107, 578592.Google Scholar
Ericsson, K. A., & Polson, P. G. (1988). Memory for restaurant orders. In Chi, M., Glaser, R., & Farr, M. (eds.), The nature of expertise (pp. 2370). Hillsdale, NJ: Erlbaum.Google Scholar
Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215251.Google Scholar
Ericsson, K. A., & Simon, H. A. (1984). Protocol analysis: Verbal reports as data. Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (revised edn.). Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Ericsson, K. A., & Smith, J. (1991). Prospects and limits in the empirical study of expertise: An introduction. In Ericsson, K. A. and Smith, J. (eds.), Toward a general theory of expertise: Prospects and limits (pp. 138). Cambridge University Press.Google Scholar
Ericsson, K. A., & Staszewski, J. (1989). Skilled memory and expertise: Mechanisms of exceptional performance. In Klahr, D. and Kotovsky, K. (eds.), Complex information processing: The impact of Herbert A. Simon (pp. 235267). Hillsdale, NJ: Erlbaum.Google Scholar
Ericsson, K. A., & Ward, P. (2007). Capturing the naturally occurring superior performance of experts in the laboratory: Toward a science of expert and exceptional performance. Current Directions in Psychological Science, 16, 346350.Google Scholar
Ericsson, K. A., & Williams, A. M. (2007). Capturing naturally-occurring superior performance in the laboratory: Translational research on expert performance. Journal of Experimental Psychology: Applied, 13, 115123.Google Scholar
Fayena-Tawil, F., Kozbelt, A., & Sitaras, L. (2011). Think global, act local: A protocol analysis comparison of artists’ and nonartists’ cognitions, metacognitions, and evaluations while drawing. Psychology of Aesthetics, Creativity, and the Arts, 5, 135145.Google Scholar
Fox, M. C., Ericsson, K. A., & Best, R. (2011a). Do procedures for verbal reporting of thinking have to be reactive? A meta-analysis and recommendations for best reporting methods. Psychological Bulletin, 137, 316344.Google Scholar
Fox, M. C., Ericsson, K. A., & Best, R. (2011b). Supplemental materials. http://dx.doi.org/10.1037/a0021663.supp.Google Scholar
French, K. E., Nevett, M. E., Spurgeon, J. H., Graham, K. C., Rink, J. E., & McPherson, S. L. (1996). Knowledge representation and problem solution in expert and novice youth baseball players. Research Quarterly for Exercise and Sport, 67, 386395.Google Scholar
Gagné, R. H., & Smith, E. C. (1962). A study of the effects of verbalization on problem solving. Journal of Experimental Psychology, 63, 1218.Google Scholar
Galton, F. (1879). Psychometric experiments. Brain, 2, 148162.Google Scholar
Gilhooly, K. J., McGeorge, P., Hunter, J., Rawles, J. M., Kirby, I. K., Green, C., & Wynn, V. (1997). Biomedical knowledge in diagnostic thinking: The case of electrocardiogram (ECG) interpretation. European Journal of Cognitive Psychology, 9, 199223.Google Scholar
Hu, Y., & Ericsson, K. A. (2012). Memorization and recall of very long lists accounted for within the Long-Term Working Memory framework. Cognitive Psychology, 64, 236266.Google Scholar
Jaarsma, T., Jarodzka, H., Nap, M., Merriënboer, J., & Boshuizen, H. (2014). Expertise under the microscope: Processing histopathological slides. Medical Education, 48, 292300.Google Scholar
Karpov, A. (1995). Grandmaster musings. Chess Life (November), 3233.Google Scholar
Keller, F. S. (1958). The phantom plateau. Journal of the Experimental Analysis of Behavior, 1, 113.Google Scholar
Koltanowski, G. (1985). In the dark. Coraopolis, PA: Chess Enterprises.Google Scholar
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 13351342.Google Scholar
Lesgold, A., Rubinson, H., Feltovitch, P., Glaser, R., Klopher, D., & Wang, Y. (1988). Expertise in a complex skill: Diagnosing X-ray pictures. In Chi, M. T. H., Glaser, R., & Farr, M. J. (eds.), The nature of expertise (pp. 311342). Hillsdale, NJ: Erlbaum.Google Scholar
Lundgrén-Laine, H., & Salanterä, S. (2010). Think-aloud technique and protocol analysis in clinical decision making research. Qualitative Health Research, 20, 565575.Google Scholar
MacDonald, S., Edwards, H. M., & Zhao, T. (2012). Exploring think-alouds in usability testing: An international survey. IEEE Transactions on Professional Communication, 55, 219.Google Scholar
Mann, D. T. Y., Williams, A. M., Ward, P., & Janelle, C. M. (2007). Perceptual-cognitive expertise in sport: A meta analysis. Journal of Sport & Exercise Psychology, 29, 457478.Google Scholar
McLaughlin, K., Novac, K., Rikers, R., & Schmidt, H. (2010). Does applying biomedical knowledge improve diagnostic performance when solving electrolyte problems? Canadian Medical Education Journal, 1, e4e9.Google Scholar
McPherson, S., & Kernodle, M. (2007). Mapping two new points on the tennis expertise continuum: Tactical skills of adult advanced beginners and entry-level professionals during competition. Journal of Sports Sciences, 25, 945959.Google Scholar
McRobert, A. P., Causer, J., Vassiliadis, J., Watterson, L., Kwan, J., & Williams, M. A. (2013). Contextual information influences diagnosis accuracy and decision making in simulated emergency medicine emergencies. BMJ Quality & Safety, 22, 478484.Google Scholar
Meissner, C. A., & Brigham, J. C. (2001). A meta-analysis of the verbal overshadowing effect in face identification. Applied Cognitive Psychology, 15, 603616.Google Scholar
Miglioretti, D. L., Gard, C. C., Carney, P. A., Onega, T. L., Buist, D. S., Sickles, E. A., … & Elmore, J. G. (2009). When radiologists perform best: The learning curve in screening mammogram interpretation. Radiology, 253, 632640.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt, Rinehart, & Winston.Google Scholar
Moxley, J. H., Ericsson, K. A., Charness, N., & Krampe, R. T. (2012). The role of intuition and deliberative thinking in experts’ superior tactical decision-making. Cognition, 124, 7278.Google Scholar
Neuman, Y., & Schwarz, B. (1998). Is self-explanation while solving problems helpful? The case of analogical problem-solving. British Journal of Educational Psychology, 68, 1524.Google Scholar
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Nielsen, S. G. (1999) Learning strategies in instrumental music practice. British Journal of Music Education, 16, 275291.Google Scholar
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231259.Google Scholar
Nodine, C. F., Kundel, H. L., Mello-Thoms, C., Weinstein, S. P., Orel, S. G., Sullivan, D. C., & Conant, E. F. (1999). How experience and training influence mammography expertise. Academic Radiology, 6, 575585.Google Scholar
Norman, G. R., Trott, A. D., Brooks, L. R., & Smith, E. K. M. (1994). Cognitive differences in clinical reasoning related to postgraduate training. Teaching and Learning in Medicine, 6, 114120.Google Scholar
Patel, V. L., Arocha, J. F., & Kaufmann, D. R. (1994). Diagnostic reasoning and medical expertise. In Medin, D. (ed.), The psychology of learning and motivation (Vol. 30, pp. 187251). New York: Academic Press.Google Scholar
Patel, V. L., & Groen, G. J. (1991). The general and specific nature of medical expertise: A critical look. In Ericsson, K. A. & Smith, J. (eds.), Toward a general theory of expertise: Prospects and limits (pp. 93125). Cambridge University Press.Google Scholar
Roca, A., Ford, P., McRobert, A., & Williams, A. M. (2011). Identifying the processes underpinning anticipation and decision-making in a dynamic time-constrained-task. Cognitive Processing, 12, 301310.Google Scholar
Roca, A., Williams, A. M., & Ford, P. R. (2013). Capturing and testing perceptual-cognitive expertise: A comparison of stationary and movement response methods. Behavior Research Methods, 46, 173177.Google Scholar
Saariluoma, P. (1991). Aspects of skilled imagery in blindfold chess. Acta Psychologica, 77, 6589.Google Scholar
Saariluoma, P. (1992). Error in chess: The apperception-restructuring view. Psychological Research/Psychologische Forschung, 54, 1726.Google Scholar
Saariluoma, P. (1995). Chess players’ thinking. London: Routledge.Google Scholar
Schmidt, H. G., & Boshuizen, H. (1993). On acquiring expertise in medicine. Educational Psychology Review, 5, 205221.Google Scholar
Schooler, J. W., & Engstler-Schooler, T. Y. (1990). Verbal overshadowing of visual memories: Some things are better left unsaid. Cognitive Psychology, 22, 3671.Google Scholar
Schraagen, J. M. (2006). Task analysis. In Ericsson, K. A., Charness, N., Hoffman, R. R., & Feltovich, P. J. (eds.), The Cambridge handbook of expertise and expert performance (pp. 185201). Cambridge University Press.Google Scholar
St. Germain, J., & Tenenbaum, G. (2011) Decision-making and thought processes among poker players. High Ability Studies, 22, 317.Google Scholar
Thompson, C. P., Cowan, T. M., & Frieman, J. (1993). Memory search by a memorist. Hillsdale, NJ: Erlbaum.Google Scholar
Tuffiash, M., Roring, R. W., & Ericsson, K. A. (2007). Expert word play: Capturing and explaining reproducibly superior verbal task performance. Journal of Experimental Psychology: Applied, 13, 124134.Google Scholar
van der Maas, H. L. J., & Wagenmakers, E. J. (2005). A psychometric analysis of chess expertise. American Journal of Psychology, 118, 2960.Google Scholar
Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158177.Google Scholar
Watson, J. B. (1920). Is thinking merely the action of language mechanisms? British Journal of Psychology, 11, 87104.Google Scholar
Whitehead, A. E., Taylor, J. A., & Polman, R. C. J. (2015). Examination of the suitability of collecting in event cognitive processes using Think Aloud protocol in golf. Frontiers in Psychology, 6, 1083.Google Scholar
Whitehead, A. E., Taylor, J. A., & Polman, R. C. (2016). Evidence for skill level differences in the thought processes of golfers during high and low pressure situations. Frontiers in Psychology, 6, 112.Google Scholar
Willis, G. (2005). Cognitive interviewing: A tool for improving questionnaire design. Thousand Oaks, CA: Sage Publications.Google Scholar
Willis, G. (2015). Analysis of the cognitive interview in questionnaire design: Understanding qualitative research. Oxford University Press.Google Scholar

References

Ackerman, P. L. (1987). Individual differences in skill learning: An integration of psychometric and information processing perspectives. Psychological Bulletin, 102, 327.Google Scholar
Ackerman, P. L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 117, 288318.Google Scholar
Ackerman, P. L. (1990). A correlational analysis of skill specificity: Learning, abilities, and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 883901.Google Scholar
Ackerman, P. L. (1992). Predicting individual differences in complex skill acquisition: Dynamics of ability determinants. Journal of Applied Psychology, 77, 598614.Google Scholar
Ackerman, P. L. (1996). A theory of adult intellectual development: Process, personality, interests, and knowledge. Intelligence, 22, 229259.Google Scholar
Ackerman, P. L. (2000). Domain-specific knowledge as the “dark matter” of adult intelligence: Gf/Gc, personality and interest correlates. Journal of Gerontology: Psychological Sciences, 55B, P69–P84.Google Scholar
Ackerman, P. L. (2014). Nonsense, common sense, and science of expert performance: Talent and individual differences. Intelligence, 45, 617.Google Scholar
Ackerman, P. L., Bowen, K. R., Beier, M. B., & Kanfer, R. (2001). Determinants of individual differences and gender differences in knowledge. Journal of Educational Psychology, 93, 797825.Google Scholar
Ackerman, P. L., & Cianciolo, A. T. (2000). Cognitive, perceptual speed, and psychomotor determinants of individual differences during skill acquisition. Journal of Experimental Psychology: Applied, 6, 259290.Google Scholar
Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219245.Google Scholar
Ackerman, P. L., & Kanfer, R. (1993). Integrating laboratory and field study for improving selection: Development of a battery for predicting air traffic controller success. Journal of Applied Psychology, 78, 413432.Google Scholar
Ackerman, P. L., Kanfer, R., & Beier, M. E. (2013). Trait complex, cognitive ability, and domain knowledge predictors of baccalaureate success, STEM persistence, and gender differences. Journal of Educational Psychology, 105, 911927.Google Scholar
Ackerman, P. L., Kanfer, R., & Goff, M. (1995). Cognitive and noncognitive determinants and consequences of complex skill acquisition. Journal of Experimental Psychology: Applied, 1, 270304.Google Scholar
Ackerman, P. L., & Rolfhus, E. L. (1999). The locus of adult intelligence: Knowledge, abilities, and non-ability traits. Psychology and Aging, 14, 314330.Google Scholar
Ackerman, P. L., & Woltz, D. J. (1994). Determinants of learning and performance in an associative memory/substitution task: Task constraints, individual differences, and volition. Journal of Educational Psychology, 86, 487515.Google Scholar
Adams, J. A. (1987). Historical review and appraisal of research on the learning, retention, and transfer of human motor skills. Psychological Bulletin, 101, 4174.Google Scholar
Anastasi, A., & Urbina, S. (1997). Psychological testing (7th edn.). New York: Prentice-Hall.Google Scholar
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191215.Google Scholar
Bar-Eli, M., Avugos, S., & Raab, M. (2006). Twenty years of “hot hand” research: Review and critique. Psychology of Sports and Exercise, 7, 525553.Google Scholar
Barrett, G. V., Alexander, R. A., & Doverspike, D. (1992). The implications for personnel selection of apparent declines in predictive validities over time: A critique of Hulin, Henry, and Noon. Personnel Psychology, 45, 601617.Google Scholar
Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 126.Google Scholar
Beier, M. E., & Ackerman, P. L. (2012). Time in personnel selection. In Schmitt, N. (ed.), The Oxford handbook of personnel selection and assessment (pp. 721739). Oxford University Press.Google Scholar
Binet, A., & Simon, Th. (1973). The development of intelligence in children (trans. Kite, E.). New York: Arno Press.Google Scholar
Byrk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage Publications.Google Scholar
Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In Sternberg, R. J. (ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 776). Hillsdale, NJ: Erlbaum.Google Scholar
Cronbach, L. J., & Furby, L. (1970). How we should measure “change” – or should we? Psychological Bulletin, 74, 6880.Google Scholar
Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment: An individual differences model and its applications. Minneapolis, MN: University of Minnesota Press.Google Scholar
Feltz, D. L. (1982). Path analysis of the causal elements in Bandura’s theory of self-efficacy and anxiety-based model of avoidance behavior. Journal of Personality and Social Psychology, 42, 764781.Google Scholar
Ferguson, L. W. (1952). A look across the years 1920 to 1950. In Thurstone, L. L. (ed.), Applications of psychology (pp. 117). New York: Harper & Brothers.Google Scholar
Fleishman, E. A., & Hempel, W. E. Jr. (1955). The relation between abilities and improvement with practice in a visual discrimination reaction task. Journal of Experimental Psychology, 49, 301312.Google Scholar
Guilford, J. P., Christensen, P. R., Bond, N. A. Jr., & Sutton, M. A. (1954). A factor analysis study of human interests. Psychological Monographs, 68 (Whole no. 375).Google Scholar
Guttman, L. (1954). A new approach to factor analysis: The radex. In Lazarsfeld, P. F. (ed.), Mathematical thinking in the social sciences (pp. 258348). New York: Free Press.Google Scholar
Hoffman, R. R. (1987). The problem of extracting the knowledge of experts from the perspective of experimental psychology. The AI Magazine, 8, 5367.Google Scholar
Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 3545.Google Scholar
Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd edn.). Odessa, FL: Psychological Assessment Resources.Google Scholar
Honzik, M. P., MacFarlane, J. W., & Allen, L. (1948). The stability of mental test performance between two and eighteen years. Journal of Experimental Education, 17, 309324.Google Scholar
Humphreys, L. G. (1960). Investigations of the simplex. Psychometrika, 25, 313323.Google Scholar
Hunt, E. (1995). Will we be smart enough? A cognitive analysis of the coming workforce. New York: Russell Sage Foundation.Google Scholar
Jensen, A. R. (1998). The G factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Jones, M. B. (1962). Practice as a process of simplification. Psychological Review, 69, 274294.Google Scholar
Kanfer, R. (1987). Task-specific motivation: An integrative approach to issues of measurement, mechanisms, processes, and determinants. Journal of Social and Clinical Psychology, 5, 251278.Google Scholar
Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative/aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74, 657690.Google Scholar
Kant, I. (1987). Critique of judgment (trans. Pluhar, W. S.). Indianapolis, IN: Hackett. (Original work published 1790)Google Scholar
Lohman, D. F. (1999). Minding our P’s and Q’s: On finding relationships between learning and intelligence. In Ackerman, P. L., Kyllonen, P. C., & Roberts, R. D. (eds.), Learning and individual differences: Process, trait, and content determinants (pp. 5576). Washington, DC: American Psychological Association.Google Scholar
McClelland, D. C., & Boyatzis, R. E. (1982). Leadership motive pattern and long-term success in management. Journal of Applied Psychology, 67, 737743.Google Scholar
McNemar, Q. (1940). A critical examination of the University of Iowa studies of environmental influences upon the IQ. Psychological Bulletin, 37, 6392.Google Scholar
Meehl, P. E., & Rosen, A. (1955). Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores. Psychological Bulletin, 52, 194216.Google Scholar
Motowidlo, S. J., & Beier, M. E. (2010). Differentiating specific job knowledge from implicit trait policies in procedural knowledge measured by a situational judgment test. Journal of Applied Psychology, 95, 321333.Google Scholar
Murray, H. A. (1938). Explorations in personality: A clinical and experimental study of fifty men of college age. Oxford University Press.Google Scholar
Ployhart, R. E., & Hakel, M. D. (1998). The substantive nature of performance variability: Predicting interindividual differences in intraindividual performance. Personnel Psychology, 51, 859901.Google Scholar
Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: Theory, design, and analysis of change. Journal of Management, 36, 94120.Google Scholar
Roe, A. (1956). The psychology of occupations. New York: John Wiley.Google Scholar
Simonton, D. K. (1994). Greatness: Who makes history and why. New York: Guilford Press.Google Scholar
Snow, R. E. (1989). Aptitude-treatment interaction as a framework for research on individual differences in learning. In Ackerman, P. L., Sternberg, R. J., & Glaser, R. (eds.), Learning and individual differences: Advances in theory and research (pp. 1359). New York: W. H. Freeman.Google Scholar
Spangler, W. D. (1992). Validity of questionnaire and TAT measures of need of achievement: Two meta-analyses. Psychological Bulletin, 112, 140154.Google Scholar
Stanovich, K. E. (1986). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360406.Google Scholar
Super, D. E. (1940). Avocational interest patterns: A study in the psychology of avocations. Stanford, CA: Stanford University Press.Google Scholar
Terman, L. M. (1926). Genetic studies of genius: Mental and physical traits of a thousand gifted children. Stanford, CA: Stanford University Press.Google Scholar
Thorndike, E. L. (1908). The effect of practice in the case of a purely intellectual function. American Journal of Psychology, 19, 374384.Google Scholar
Thorndike, R. L. (1949). Personnel selection. New York: John Wiley.Google Scholar
Warr, P. (1994). Age and employment. In Dunnette, M. D. & Triandis, H. C. (eds.), Handbook of industrial and organizational psychology (Vol. 4, pp. 485550). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Watson, J. D. (2001). The double helix: A personal account of the discovery of the structure of DNA. New York: Simon & Schuster.Google Scholar
Willingham, W. W. (1974). Predicting success in graduate education. Science, 183, 273278.Google Scholar
Wittmann, W. W., & Süß, H.-M. (1999). Investigating the paths between working memory, intelligence, knowledge, and complex problem-solving performances via Brunswik symmetry. In Ackerman, P. L., Kyllonen, P. C., & Roberts, R. D. (eds.), Learning and individual differences: Process, trait, and content determinants (pp. 77108). Washington, DC: American Psychological Association.Google Scholar

References

Abreu, A. M., Macaluso, E., Azevedo, R. T., Cesari, P., Urgesi, C., & Aglioti, S. M. (2012). Action anticipation beyond the action observation network: A functional magnetic resonance imaging study in expert basketball players. European Journal of Neuroscience, 35, 16461654.Google Scholar
Aglioti, S. M., Cesari, P., Romani, M., & Urgesi, C. (2008). Action anticipation and motor resonance in elite basketball players. Nature Neuroscience, 11, 11091116.Google Scholar
Amidzic, O., Riehle, H. J., Fehr, T., Wienbruch, C., & Elbert, T. (2001). Pattern of focal gamma-bursts in chess players. Nature, 412, 603.Google Scholar
Atherton, M., Zhuang, J., Bart, W. M., Hu, X., & He, S. (2003). A functional MRI study of high-level cognition. I. The game of chess. Cognitive Brain Research, 16, 2631.Google Scholar
Balser, N., Lorey, B., Pilgramm, S., Naumann, T., Kindermann, S., Stark, R., … & Munzert, J. (2014a). The influence of expertise on brain activation of the action observation network during anticipation of tennis and volleyball serves. Frontiers in Human Neuroscience, 8.Google Scholar
Balser, N., Lorey, B., Pilgramm, S., Stark, R., Bischoff, M., Zentgraf, K., … & Munzert, J. (2014b). Prediction of human actions: Expertise and task-related effects on neural activation of the action observation network. Human Brain Mapping, 35, 40164034.Google Scholar
Bartlett, J., Boggan, A. L., & Krawczyk, D. C. (2013). Expertise and processing distorted structure in chess. Frontiers in Human Neuroscience, 7, 825.Google Scholar
Bilalić, M. (2016) Revisiting the role of the Fusiform Face Area (FFA) in expertise. Journal of Cognitive Neuroscience, 28, 13451357.Google Scholar
Bilalić, M. (2017). The neuroscience of expertise. Cambridge University Press.Google Scholar
Bilalić, M., Grottenthaler, T., Nägele, T., & Lindig, T. (2016). The faces in radiological images: Fusiform face area supports radiological expertise. Cerebral Cortex, 26, 10041014.Google Scholar
Bilalić, M., Kiesel, A., Pohl, C., Erb, M., & Grodd, W. (2011). It takes two: Skilled recognition of objects engages lateral areas in both hemispheres. PLoS ONE, 6, e16202.Google Scholar
Bilalić, M., Langner, R., Erb, M., & Grodd, W. (2010). Mechanisms and neural basis of object and pattern recognition: A study with chess experts. Journal of Experimental Psychology: General, 139, 728742.Google Scholar
Bilalić, M., Langner, R., Ulrich, R., & Grodd, W. (2011). Many faces of expertise: Fusiform face area in chess experts and novices. Journal of Neuroscience, 31, 1020610214.Google Scholar
Bilalić, M., Turella, L., Campitelli, G., Erb, M., & Grodd, W. (2012). Expertise modulates the neural basis of context dependent recognition of objects and their relations. Human Brain Mapping, 33, 27282740.Google Scholar
Binet, A. (1894). Psychologie des grands calculateurs et joueurs d’échecs. Paris: Hachette.Google Scholar
Bishop, D. T., Wright, M. J., Jackson, R. C., & Abernethy, B. (2013). Neural bases for anticipation skill in soccer: An FMRI study. Journal of Sport & Exercise Psychology, 35, 98109.Google Scholar
Buschhüter, D., Smitka, M., Puschmann, S., Gerber, J. C., Witt, M., Abolmaali, N. D., & Hummel, T. (2008). Correlation between olfactory bulb volume and olfactory function. NeuroImage, 42, 498502.Google Scholar
Busey, T. A., & Vanderkolk, J. R. (2005). Behavioral and electrophysiological evidence for configural processing in fingerprint experts. Vision Research, 45, 431448.Google Scholar
Calvo-Merino, B. (2004). Action observation and acquired motor skills: An fMRI study with expert dancers. Cerebral Cortex, 15, 12431249.Google Scholar
Calvo-Merino, B., Grèzes, J., Glaser, D. E., Passingham, R. E., & Haggard, P. (2006). Seeing or doing? Influence of visual and motor familiarity in action observation. Current Biology, 16, 19051910.Google Scholar
Campitelli, G., Gobet, F., Head, K., Buckley, M., & Parker, A. (2007). Brain localization of memory chunks in chessplayers. International Journal of Neuroscience, 117, 16411659.Google Scholar
Campitelli, G., Gobet, F., & Parker, A. (2005). Structure and stimulus familiarity: A study of memory in chess-players with functional magnetic resonance imaging. Spanish Journal of Psychology, 8, 238245.Google Scholar
Cannonieri, G. C., Bonilha, L., Fernandes, P. T., Cendes, F., & Li, L. M. (2007). Practice and perfect: Length of training and structural brain changes in experienced typists. Neuroreport, 18, 10631066.Google Scholar
Carter, C. S., & van Veen, V. (2007). Anterior cingulate cortex and conflict detection: An update of theory and data. Cognitive, Affective & Behavioral Neuroscience, 7, 367379.Google Scholar
Castriota-Scanderbeg, A., Hagberg, G. E., Cerasa, A., Committeri, G., Galati, G., Patria, F., … & Frackowiak, R. (2005). The appreciation of wine by sommeliers: A functional magnetic resonance study of sensory integration. NeuroImage, 25, 570578.Google Scholar
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 5581.Google Scholar
Cross, E. S., Hamilton, A. F., & Grafton, S. T. (2006). Building a motor simulation de novo: Observation of dance by dancers. NeuroImage, 31, 12571267.Google Scholar
Cross, E. S., Kraemer, D. J. M., Hamilton, A. F., Kelley, W. M., & Grafton, S. T. (2009). Sensitivity of the action observation network to physical and observational learning. Cerebral Cortex, 19, 315326.Google Scholar
de Groot, A. (1978). Thought and choice in chess (2nd edn.). The Hague: Mouton. (Original work published 1946)Google Scholar
Di, X., Zhu, S., Jin, H., Wang, P., Ye, Z., Zhou, K., … & Rao, H. (2012). Altered resting brain function and structure in professional badminton players. Brain Connectivity, 2, 225233.Google Scholar
Downing, P. E., Jiang, Y., Shuman, M., & Kanwisher, N. (2001). A cortical area selective for visual processing of the human body. Science, 293, 24702473.Google Scholar
Duan, X., He, S., Liao, W., Liang, D., Qiu, L., Wei, L., … & Chen, H. (2012). Reduced caudate volume and enhanced striatal-DMN integration in chess experts. NeuroImage, 60, 12801286.Google Scholar
Duan, X., Long, Z., Chen, H., Liang, D., Qiu, L., Huang, X., … & Gong, Q. (2014). Functional organization of intrinsic connectivity networks in Chinese-chess experts. Brain Research, 1558, 3343.Google Scholar
Duchaine, B., & Yovel, G. (2015). A revised neural framework for face processing. Annual Review of Vision Science, 1, 393416.Google Scholar
Epstein, R. A. (2008). Parahippocampal and retrosplenial contributions to human spatial navigation. Trends in Cognitive Sciences, 12, 388396.Google Scholar
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363406.Google Scholar
Frank, M. C., & Barner, D. (2012). Representing exact number visually using mental abacus. Journal of Experimental Psychology: General, 141, 134149.Google Scholar
Frasnelli, J., Lundström, J. N., Boyle, J. A., Djordjevic, J., Zatorre, R. J., & Jones-Gotman, M. (2010). Neuroanatomical correlates of olfactory performance. Experimental Brain Research, 201, 111.Google Scholar
Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I., & Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5, 236243.Google Scholar
Goldstein, R., Almenberg, J., Dreber, A., Emerson, J. W., Herschkowitsch, A., & Katz, J. (2008). Do more expensive wines taste better? Evidence from a large sample of blind tastings. Journal of Wine Economics, 3, 19.Google Scholar
Grafton, S. T. (2009). Embodied cognition and the simulation of action to understand others. Annals of the New York Academy of Sciences, 1156, 97117.Google Scholar
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role in object recognition. Vision Research, 41, 14091422.Google Scholar
Hadjikhani, N., & de Gelder, B. (2002). Neural basis of prosopagnosia: An fMRI study. Human Brain Mapping, 16, 176182.Google Scholar
Haller, S., & Radue, E. W. (2005). What is different about a radiologist’s brain? Radiology, 236, 983989.Google Scholar
Hanakawa, T., Honda, M., Okada, T., Fukuyama, H., & Shibasaki, H. (2003). Neural correlates underlying mental calculation in abacus experts: A functional magnetic resonance imaging study. NeuroImage, 19, 296307.Google Scholar
Hänggi, J., Brütsch, K., Siegel, A. M., & Jäncke, L. (2014). The architecture of the chess player’s brain. Neuropsychologia, 62, 152162.Google Scholar
Harel, A. (2015). What is special about expertise? Visual expertise reveals the interactive nature of real-world object recognition. Neuropsychologia, 83, 8899.Google Scholar
Harel, A., Gilaie-Dotan, S., Malach, R., & Bentin, S. (2010). Top-down engagement modulates the neural expressions of visual expertise. Cerebral Cortex, 20, 23042318.Google Scholar
Harley, E. M., Pope, W. B., Villablanca, J. P., Mumford, J., Suh, R., Mazziotta, J. C., … & Engel, S. A. (2009). Engagement of fusiform cortex and disengagement of lateral occipital cortex in the acquisition of radiological expertise. Cerebral Cortex, 19, 27462754.Google Scholar
Hu, Y., Geng, F., Tao, L., Hu, N., Du, F., Fu, K., & Chen, F. (2011). Enhanced white matter tracts integrity in children with abacus training. Human Brain Mapping, 32, 1021.Google Scholar
Jäncke, L., Koeneke, S., Hoppe, A., Rominger, C., & Hänggi, J. (2009). The architecture of the golfer’s brain. PloS ONE, 4, e4785.Google Scholar
Jung, W. H., Kim, S. N., Lee, T. Y., Jang, J. H., Choi, C.-H., Kang, D.-H., & Kwon, J. S. (2013). Exploring the brains of Baduk (Go) experts: Gray matter morphometry, resting-state functional connectivity, and graph theoretical analysis. Frontiers in Human Neuroscience, 7.Google Scholar
Kalamangalam, G. P., & Ellmore, T. M. (2014). Focal cortical thickness correlates of exceptional memory training in Vedic priests. Frontiers in Human Neuroscience, 8.Google Scholar
Krupinski, E. A. (2000). The importance of perception research in medical imaging. Radiation Medicine, 18, 329334.Google Scholar
Ku, Y., Hong, B., Zhou, W., Bodner, M., & Zhou, Y.-D. (2012). Sequential neural processes in abacus mental addition: An EEG and fMRI case study. PLoS ONE, 7, e36410.Google Scholar
Kundel, H. L., & Nodine, C. F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527532.Google Scholar
Kundel, H. L., Nodine, C. F., Conant, E. F., & Weinstein, S. P. (2007). Holistic component of image perception in mammogram interpretation: Gaze-tracking study. Radiology, 242, 396402.Google Scholar
Kupers, R., Beaulieu-Lefebvre, M., Schneider, F. C., Kassuba, T., Paulson, O. B., Siebner, H. R., & Ptito, M. (2011). Neural correlates of olfactory processing in congenital blindness. Neuropsychologia, 49, 20372044.Google Scholar
Kupers, R., & Ptito, M. (2014). Compensatory plasticity and cross-modal reorganization following early visual deprivation. Neuroscience & Biobehavioral Reviews, 41, 3652.Google Scholar
Lu, L. H., Dapretto, M., O’Hare, E. D., Kan, E., McCourt, S. T., Thompson, P. M., … & Sowell, E. R. (2009). Relationships between brain activation and brain structure in normally developing children. Cerebral Cortex, 19, 25952604.Google Scholar
Lucan, J. N., Foxe, J. J., Gomez-Ramirez, M., Sathian, K., & Molholm, S. (2010). Tactile shape discrimination recruits human lateral occipital complex during early perceptual processing. Human Brain Mapping, 31, 18131821.Google Scholar
Maguire, E. A., Frackowiak, R. S., & Frith, C. D. (1997). Recalling routes around London: Activation of the right hippocampus in taxi drivers. Journal of Neuroscience, 17, 71037110.Google Scholar
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences USA, 97, 43984403.Google Scholar
Maguire, E. A., Valentine, E. R., Wilding, J. M., & Kapur, N. (2003). Routes to remembering: The brains behind superior memory. Nature Neuroscience, 6, 9095.Google Scholar
Maguire, E. A., Woollett, K., & Spiers, H. J. (2006). London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis. Hippocampus, 16, 10911101.Google Scholar
Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L., & Ungerleider, L. G. (1995). Discrete cortical regions associated with knowledge of color and knowledge of action. Science, 270, 102105.Google Scholar
Melo, M., Scarpin, D. J., Amaro, E., Passos, R. B., Sato, J. R., Friston, K. J., & Price, C. J. (2011). How doctors generate diagnostic hypotheses: A study of radiological diagnosis with functional magnetic resonance imaging. PloS ONE, 6, e28752.Google Scholar
Morrot, G., Brochet, F., & Dubourdieu, D. (2001). The color of odors. Brain and Language, 79, 309320.Google Scholar
Nichelli, P., Grafman, J., Pietrini, P., Alway, D., Carton, J. C., & Miletich, R. (1994). Brain activity in chess playing. Nature, 369, 191.Google Scholar
Olsson, C.-J., & Lundström, P. (2013). Using action observation to study superior motor performance: A pilot fMRI study. Frontiers in Human Neuroscience, 7, 819.Google Scholar
Park, I. S., Lee, K. J., Han, J. W., Lee, N. J., Lee, W. T., Park, K. A., & Rhyu, I. J. (2009). Experience-dependent plasticity of cerebellar vermis in basketball players. The Cerebellum, 8, 334339.Google Scholar
Pazart, L., Comte, A., Magnin, E., Millot, J.-L., & Moulin, T. (2014). An fMRI study on the influence of sommeliers’ expertise on the integration of flavor. Frontiers in Behavioral Neuroscience, 8.Google Scholar
Pesenti, M., Zago, L., Crivello, F., Mellet, E., Samson, D., Duroux, B., … & Tzourio-Mazoyer, N. (2001). Mental calculation in a prodigy is sustained by right prefrontal and medial temporal areas. Nature Neuroscience, 4, 103107.Google Scholar
Plailly, J., Delon-Martin, C., & Royet, J.-P. (2012). Experience induces functional reorganization in brain regions involved in odor imagery in perfumers. Human Brain Mapping, 33, 224234.Google Scholar
Poldrack, R. A., Prabhakaran, V., Seger, C. A., & Gabrieli, J. D. (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564574.Google Scholar
Raz, A., Packard, M. G., Alexander, G. M., Buhle, J. T., Zhu, H., Yu, S., & Peterson, B. S. (2009). A slice of π: An exploratory neuroimaging study of digit encoding and retrieval in a superior memorist. Neurocase, 15, 361372.Google Scholar
Reingold, E. M., & Sheridan, H. (2011). Eye movements and visual expertise in chess and medicine. In Liversedge, S. P., Gilchrist, I. D., & Everling, S. (eds.), The Oxford handbook of eye movements (pp. 523550). Oxford University Press.Google Scholar
Renier, L., Cuevas, I., Grandin, C. B., Dricot, L., Plaza, P., Lerens, E., … & De Volder, A. G. (2013). Right occipital cortex activation correlates with superior odor processing performance in the early blind. PLoS ONE, 8, e71907.Google Scholar
Rennig, J., Bilalić, M., Huberle, E., Karnath, H.-O., & Himmelbach, M. (2013). The temporo-parietal junction contributes to global gestalt perception: Evidence from studies in chess experts. Frontiers in Human Neuroscience, 7, 513.Google Scholar
Richler, J., Palmeri, T. J., & Gauthier, I. (2012). Meanings, mechanisms, and measures of holistic processing. Frontiers in Psychology, 3, 553.Google Scholar
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169192.Google Scholar
Roberts, R. E., Bain, P. G., Day, B. L., & Husain, M. (2013). Individual differences in expert motor coordination associated with white matter microstructure in the cerebellum. Cerebral Cortex, 23, 22822292.Google Scholar
Rombaux, P., Huart, C., De Volder, A. G., Cuevas, I., Renier, L., Duprez, T., & Grandin, C. (2010). Increased olfactory bulb volume and olfactory function in early blind subjects. NeuroReport, 21, 10691073.Google Scholar
Ross, D., Tamber-Rosenau, B., Palmeri, T., Zhang, J., Xu, Y., & Gauthier, I. (2015). High resolution fMRI reveals holistic car representations in the anterior FFA of car experts. Journal of Vision, 15, 614.Google Scholar
Rossion, B., & Jacques, C. (2008). Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170. NeuroImage, 39, 19591979.Google Scholar
Sadeh, B., Podlipsky, I., Zhdanov, A., & Yovel, G. (2010). Event-related potential and functional MRI measures of face-selectivity are highly correlated: A simultaneous ERP-fMRI investigation. Human Brain Mapping, 31, 14901501.Google Scholar
Seubert, J., Freiherr, J., Frasnelli, J., Hummel, T., & Lundstrom, J. N. (2013). Orbitofrontal cortex and olfactory bulb volume predict distinct aspects of olfactory performance in healthy subjects. Cerebral Cortex, 23, 24482456.Google Scholar
Stilla, R., Deshpande, G., LaConte, S., Hu, X., & Sathian, K. (2007). Posteromedial parietal cortical activity and inputs predict tactile spatial acuity. Journal of Neuroscience, 27, 1109111102.Google Scholar
Stilla, R., & Sathian, K. (2008). Selective visuo-haptic processing of shape and texture. Human Brain Mapping, 29, 11231138.Google Scholar
Swensson, R. G. (1980). A two-stage detection model applied to skilled visual search by radiologists. Attention, Perception, & Psychophysics, 27, 1116.Google Scholar
Tanaka, S., Michimata, C., Kaminaga, T., Honda, M., & Sadato, N. (2002). Superior digit memory of abacus experts: An event-related functional MRI study. Neuroreport, 13, 2187.Google Scholar
Tanaka, S., Seki, K., Hanakawa, T., Harada, M., Sugawara, S. K., Sadato, N., … & Honda, M. (2012). Abacus in the brain: A longitudinal functional MRI study of a skilled abacus user with a right hemispheric lesion. Frontiers in Psychology, 3.Google Scholar
Thompson, P. (1980). Margaret Thatcher: A new illusion. Perception, 9, 483484.Google Scholar
Turella, L., Wurm, M. F., Tucciarelli, R., & Lingnau, A. (2013). Expertise in action observation: Recent neuroimaging findings and future perspectives. Frontiers in Human Neuroscience, 7, 637.Google Scholar
Wagner, A. D., Shannon, B. J., Kahn, I., & Buckner, R. L. (2005). Parietal lobe contributions to episodic memory retrieval. Trends in Cognitive Sciences, 9, 445453.Google Scholar
Wan, X., Nakatani, H., Ueno, K., Asamizuya, T., Cheng, K., & Tanaka, K. (2011). The neural basis of intuitive best next-move generation in board game experts. Science, 331, 341346.Google Scholar
Wan, X., Takano, D., Asamizuya, T., Suzuki, C., Ueno, K., Cheng, K., … & Tanaka, K. (2012). Developing intuition: Neural correlates of cognitive-skill learning in caudate nucleus. Journal of Neuroscience, 32, 1749217501.Google Scholar
Weissman, D. H., & Banich, M. T. (2000). The cerebral hemispheres cooperate to perform complex but not simple tasks. Neuropsychology, 14, 4159.Google Scholar
Wenzel, U., Taubert, M., Ragert, P., Krug, J., & Villringer, A. (2014). Functional and structural correlates of motor speed in the cerebellar anterior lobe. PLoS ONE, 9, e96871.Google Scholar
Wong, Y. K., & Gauthier, I. (2010). A multimodal neural network recruited by expertise with musical notation. Journal of Cognitive Neuroscience, 22, 695713.Google Scholar
Woollett, K., & Maguire, E. A. (2011). Acquiring “the knowledge” of London’s layout drives structural brain changes. Current Biology, 21, 21092114.Google Scholar
Wright, M. J., Bishop, D. T., Jackson, R. C., & Abernethy, B. (2010). Functional MRI reveals expert–novice differences during sport-related anticipation. Neuroreport, 21, 9498.Google Scholar
Wright, M. J., Bishop, D. T., Jackson, R. C., & Abernethy, B. (2011). Cortical fMRI activation to opponents’ body kinematics in sport-related anticipation: Expert–novice differences with normal and point-light video. Neuroscience Letters, 500, 216221.Google Scholar
Wright, M. J., Bishop, D. T., Jackson, R. C., & Abernethy, B. (2013). Brain regions concerned with the identification of deceptive soccer moves by higher-skilled and lower-skilled players. Frontiers in Human Neuroscience, 7, 851.Google Scholar
Yin, L.-J., Lou, Y.-T., Fan, M.-X., Wang, Z.-X., & Hu, Y. (2015). Neural evidence for the use of digit-image mnemonic in a superior memorist: An fMRI study. Frontiers in Human Neuroscience, 9.Google Scholar

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