Abstract
According to Hubert Dreyfus’s famous claim that expertise is fundamentally “mindless,” experts in any domain perform most effectively when their activity is automatic and unmediated by concepts or cognitive processes like attention and memory. While several scholars have recently challenged the plausibility of Dreyfus’s “mindless” account of expertise for explaining a wide range of expert activities, there has been little consideration of the one form of expertise which might be most amenable to Dreyfus’s account – namely, perceptual expertise. Indeed, Dreyfus’s account of expert coping is ultimately an account of perceptual expertise, in that an expert’s intuitive situational responses are thought to rely on a sophisticated repertoire of perceptual skills. In this paper, I examine the feedforward model of sensory processing that Dreyfus uses to illustrate the perceptual underpinnings of expert action, and consider its resonance with psychological research that characterizes perceptual expertise as being automatic, holistic, pre-attentive, and non-cognitive in nature. However, citing competing empirical research, I argue instead that Dreyfus’s model of perceptual expertise cannot adequately explain the integral roles of attention, memory, and conceptual knowledge in expert object recognition. I conclude that the Dreyfusian model of perceptual expertise fails – the perceptual repertoire of skills that grounds expert object recognition is not operative in isolation from the expert’s conceptual repertoire.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
For discussion of how recent developments of chunking theory respond to Dreyfus’s objections, see Gobet and Chassy 2009.
To clarify, the ventral visual pathway is hierarchically structured into low-level and high-level areas according to the level of abstraction and complexity at which visual stimuli are processed. The ventral stream starts in the occipital lobe, branches off from the primary or “striate” visual cortex (V1) and secondary or “prestriate” visual cortex (V2), and then proceeds through to “extrastriate” areas V3 and V4. These four visual regions are considered to be low-level areas because they are responsive to rudimentary sensory features like color and shape. An intermediate state of processing occurs at the lateral occipital complex, where sensory features are integrated into the representation of a coherent three-dimensional object. The ventral stream culminates in the inferior temporal cortex located in the temporal lobe. Here, high-level regions are responsible for identifying objects under categories (e.g., “car,” “face,” “dog”) that abstract away from variations in low-level stimuli. Of course, there are also recurrent feedback signals from high-level areas back down to low-level areas. See Grill-Spector 2010 for a more detailed overview of the high-level and low-level visual regions involved in object processing.
The “Einstellung effect” refers to cases where a given problem automatically triggers certain cognitive states and habitual responses that prevent one from detecting a better solution. Bilalic et al. studied the occurrence of this effect among expert chess players, by presenting them with a board situation where checkmate could be reached through a familiar five-move sequence or an uncommon three-move sequence. The experts easily found the five-move sequence; but even when they reported that they were still looking for another solution, they were observed through eye-tracking technology to still be focusing on the features of the problem that were relevant to the solution they had already given. The conclusion drawn was that the problem activated a memory-schema which directs the experts’ attention to the features relevant to the familiar sequence, thereby distracting them from the features that would be relevant to another, more simple solution.
See Gauthier and Bukach 2007 for discussion of “composite task” tests that measure the interference to expert recognition caused by the holistic processing of incongruous images.
The orbitofrontal cortex has been associated with a wide range of cognitive functions, the most relevant for our purposes being the processing of affective value and reward, decision-making, guessing and hypothesis-testing, and the formation of expectations. See Bar et al. 2006: 453 for discussion of how the rapid detection of coarse-grained gist and the formation of top-down predictions about object-identities may subserve these cognitive functions in a way that would have conferred evolutionary benefits.
Dreyfus (2002: 376) mentions three ways in which, without any conceptual rules, the network could learn to process disparate inputs as being relevantly similar for producing a certain output response: There could be innate, non-cognitive gestalt structures that group inputs together; the temporal order and frequency of inputs could come to signify a shared relevance for a certain output-response, like how nearby objects which afford a reaching response would be detected more early and often than things which do not afford reaching; and inputs could be grouped as similar according to whether they tend to produce a practically satisfactory response.
It is worth noting that the patterns of activation in the early visual cortex (V1) were relatively task-independent – that is to say, experimenters could determine which object was being seen from the pattern of activation in the early visual cortex regardless of which task was being performed. Harel, Kravitz, and Baker hence write that “in all visual regions it was still possible to decode object identity across tasks, suggesting that although representations are perturbed, they are not completely changed” (2014: 968). We might say, then, that the top-down influence of behavioral goals or observer intent do not construct an object representation out of whole cloth – they may penetrate the visual processing of visual object representations, but (at least in non-hallucinatory cases) they do not fully replace the bottom-up object information delivered from the early visual cortex.
References
Aminoff, E. M., Kveraga, K., & Bar, M. (2013). The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17(8), 379–390.
Bar, M. (2004). Visual objects in context. Nature Reviews Neuroscience, 5(8), 617–629.
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., Hämäläinen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R., & Halgren, E. (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences of the United States of America, 103(2), 449–454.
Bilalić, M. (2016). Revisiting the role of the fusiform face area in expertise. Journal of Cognitive Neuroscience, 28(9), 1345–1357.
Bilalic, M., McLeod, P., & Gobet, F. (2008). Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition, 108(3), 652–661.
Bläsing, B. E., Güldenpenning, I., Koester, D., & Schack, T. (2014). Expertise affects representation structure and categorical activation of grasp postures in climbing. Frontiers in Psychology, 5(1008), 1–11.
Breivik, G. (2013). Zombie-like or superconscious? A phenomenological and conceptual analysis of consciousness in elite sport. Journal of the Philosophy of Sport, 40(1), 85–106.
Campitelli, G., Gobet, F., Head, K., Buckley, M., & Parker, A. (2007). Brain localisation of memory chunks in chess players. International Journal of Neuroscience, 117(2), 1641–1659.
Chaumon, M., Kveraga, K., Barrett, L. F., & Bar, M. (2014). Visual predictions in the orbitofrontal dortex rely on associative content. Cerebral Cortex, 24(11), 2899–2907.
Cheung, O. S., & Bar, M. (2012). Visual prediction and perceptual expertise. International Journal of Psychophysiology, 83(2), 156–163.
Christensen, W., Sutton, J., & McIlwain, D. J. F. (2015). Putting pressure on theories of choking: Towards an expanded perspective on breakdown in skilled performance. Phenomenology and the Cognitive Sciences, 14(2), 253–293.
Christensen, W., Sutton, J., & McIlwain, D. J. F. (2016). Cognition in skilled action: Meshed control and the varieties of skill experience. Mind & Language, 31(1), 37–66.
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.
Donovan, T. (2010). Attention and medical diagnosis. In E. B. Goldstein (Ed.), Encyclopedia of perception (pp. 119–121). Los Angeles: SAGE.
Dreyfus, H. L. (1972). What computers can’t do: A critique of artificial reason. New York: Harper & Row.
Dreyfus, H. L. (2002). Intelligence without representation: Merleau-Ponty’s critique of mental representation. Phenomenology and the Cognitive Sciences, 1(4), 367–383.
Dreyfus, S. E. (2004). The five-stage model of adult skill acquisition. Bulletin of Science, Technology & Society, 24(3), 177–181.
Dreyfus, H. L. (2005). Overcoming the myth of the mental: How philosophers can profit from the phenomenology of everyday expertise. Proceedings and Addresses of the American Philosophical Association, 79(2), 47–65.
Dreyfus, H. L. (2007a). The return of the myth of the mental. Inquiry, 50(4), 352–365.
Dreyfus, H. L. (2007b). Response to McDowell. Inquiry, 50(4), 371–377.
Dreyfus, H. L. (2013). The myth of the pervasiveness of the mental. In J. K. Schear (Ed.), Mind, reason, and being-in-the-world: The McDowell-Dreyfus debate (pp. 15–40). London: Routledge.
Dreyfus, H. L., & Dreyfus, S. E. (1988). Mind over machine: The power of human intuition and expertise in the era of the computer (2nd ed.). New York: Free Press.
Fahle, Manfred. 2002. Introduction. In M. Fahle & T. Poggio, Perceptual learning (pp. ix-xx). Cambridge: MIT press.
Feltovich, P. J., Prietula, M. J., & Ericsson, K. A. (2006). Studies of expertise from psychological perspectives. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 41–67). New York: Cambridge University Press.
Fridland, E. (2017). Automatically minded. Synthese, 194(11), 4337–4363.
Gauthier, I., & Bukach, C. (2007). Should we reject the expertise hypothesis? Cognition, 103(2), 322–330.
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(2), 191–197.
Geeves, A., McIlwain, D. J. F., Sutton, J., & Christensen, W. (2014). To think or not to think: The apparent paradox of expert skill in music performance. Educational Philosophy and Theory, 46(6), 674–691.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
Gilaie-Dotan, S., Harel, A., Bentin, S., Kanai, R., & Rees, G. (2012). Neuroanatomical correlates of visual car expertise. NeuroImage, 62-334(1), 147–153.
Gilbert, C. D., & Li, W. (2013). Top-down influences on visual processing. Nature Reviews Neuroscience, 14(5), 350–363.
Gobet, F. (2005). Chunking models of expertise: Implications for education. Applied Cognitive Psychology, 19(2), 183–204.
Gobet, F., & Chassy, P. (2009). Expertise and intuition: A tale of three theories. Minds and Machines, 19(2), 151–180.
Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49(1), 585–612.
Goldstone, R. L., & Byrge, L. (2015). Perceptual learning. In M. Matthen (Ed.), The Oxford handbook of philosophy of perception (pp. 812–832). Oxford: Oxford University Press.
Grill-Spector, K. (2010). Object perception: Physiology. In E. B. Goldstein (Ed.), Encyclopedia of perception (pp. 648–653). Los Angeles: SAGE.
Harel, A. (2016). What is special about expertise? Visual expertise reveals the interactive nature of real-world object recognition. Neuropsychologia, 83, 88–99.
Harel, A., Ullman, S., Harari, D., & Bentin, S. (2011). Basic-level categorization of intermediate complexity fragments reveals top-down effects of expertise in visual perception. Journal of Vision, 11(8), 1–13.
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, 111(10), 962–971.
Hughson, A. L., & Boakes, R. A. (2002). The knowing nose: The role of knowledge in wine expertise. Food Quality and Preference, 13(7–8), 463–472.
James, T. W., & Cree, G. (2010). Perceptual and conceptual interactions in object recognition and expertise. In I. Gauthier, M. J. Tarr, & D. Bub (Eds.), Perceptual expertise: Bridging brain and behavior (pp. 333–352). New York: Oxford University Press.
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(3), 248–277.
Karni, A., & Sagi, D. (1995). A memory system in the adult visual cortex. In B. Julesz & I. Kovacs (Eds.), Maturational windows and adult cortical plasticity (pp. 95–111). Reading: Addison-Wesley.
Kellman, P. J., & Garrigan, P. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53–84.
Kok, P., Jehee, J. F. M., & de Lange, F. P. (2012). Less is more: Expectation sharpens representations in the primary visual cortex. Neuron, 75(2), 265–270.
Kok, P., Failing, M. F., & de Lange, F. P. (2014). Prior expectations evoke stimulus templates in the primary visual cortex. Journal of Cognitive Neuroscience, 26(7), 1546–1554.
Kveraga, K., Ghuman, A., & Bar, M. (2007). Top-down predictions in the cognitive brain. Brain and Cognition, 65(2), 145–168.
Land, M. F. (2006). Eye movements and the control of actions in everyday life. Progress in Retinal and Eye Research, 25(3), 296–324.
Land, M. F., & Hayhoe, M. (2001). In what ways do eye movements contribute to everyday activities? Vision Research, 41(25–26), 3559–3565.
Lu, Z. L., Hua, T., Huang, C. B., Zhou, Y., & Dosher, B. A. (2011). Visual perceptual learning. Neurobiology of Learning and Memory, 95(2), 145–151.
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(4), 457–478.
Montero, B. (2016). Thought in action: Expertise and the conscious mind. Oxford: Oxford University Press.
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57.
Oliva, A., & Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive Sciences, 11(12), 520–527.
Palmer, S. E. (1975). The effects of contextual scenes on the identification of objects. Memory & Cognition, 3(5), 519–526.
Palmeri, T. J., & Gauthier, I. (2004). Visual object understanding. Nature Reviews Neuroscience, 5(4), 291–303.
Raftopoulos, A. (2001). Perceptual learning meets philosophy: Cognitive penetrability of perception and its philosophical implications. In J. Moore & K. Stemming (Eds.), Proceedings of the 23rd annual conference of the cognitive science society (pp. 802–808). Mahwah: Lawrence Erlbaum.
Reingold, E. M., & Sheridan, H. (2011). Eye movements and visual expertise in chess and medicine. In S. Liversedge, I. D. Gilchrist, & S. Everling (Eds.), The Oxford handbook on eye movements (pp. 528–550). Oxford: Oxford University Press.
Richler, J. J., Wong, Y. K., & Gauthier, I. (2011). Perceptual expertise as a shift from strategic interference to automatic holistic processing. Current Directions in Psychological Science, 20(2), 129–134.
Smith, L. B., & Heise, D. (1992). Perceptual similarity and conceptual structure. In B. Burns (Ed.), Percepts, concepts and categories: The representation and processing of information (pp. 233–272). Amsterdam: Elsevier.
Sutton, J., McIlwain, D., Christensen, W., & Geeves, A. (2011). Applying intelligence to the reflexes: Embodied skills and habits between Dreyfus and Descartes. Journal of the British Society for Phenomenology, 42(1), 78–103.
Tarr, M. J., & Gauthier, I. (2000). FFA: A flexible fusiform area for subordinate-level visual processing automatized by expertise. Nature Neuroscience, 3(8), 764–769.
Toner, J., Montero, B. G., & Moran, A. (2015). The perils of automaticity. Review of General Psychology, 19(4), 431–442.
Toner, J., Montero, B. G., & Moran, A. (2016). Reflective and prereflective bodily awareness in skilled action. Psychology of Consciousness: Theory, Research, and Practice, 3(4), 303–315.
Wang, R., Wang, J., Zhang, J. Y., Xie, X. Y., Yang, Y. X., Luo, S. H., Yu, C., & Li, W. (2016). Perceptual learning at a conceptual level. The Journal of Neuroscience, 36(7), 2238–2246.
Wisniewski, E. J., & Medin, D. L. (1994). On the interaction of theory and data in concept learning. Cognitive Science, 18(2), 221–281.
Wulf, G. (2013). Attentional focus and motor learning: A review of 15 years. International Review of Sport and Exercise Psychology, 6(1), 77–104.
Yarbus, A. (1967). Eye movements and vision. New York: Plenum Press.
Acknowledgments
I would like to thank the two anonymous reviewers for generously providing suggestions for correction and clarification.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chaturvedi, A. Against a “mindless” account of perceptual expertise. Phenom Cogn Sci 18, 509–531 (2019). https://doi.org/10.1007/s11097-018-9557-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11097-018-9557-z