Abstract
The cognitive penetrability of perceptual experiences has been a long-standing topic of disagreement among philosophers and psychologists. Although the notion of cognitive penetrability itself has also been under dispute, the debate has mainly focused on the cases in which cognitive states allegedly penetrate perceptual experiences. This paper concerns the plausibility of two prominent cases. The first one originates from Susanna Siegel’s claim that perceptual experiences can represent natural kind properties. If this is true, then the concepts we possess change the way things appear to us. The second candidate for cognitive penetration is Fiona Macpherson’s claim that, in addition to concepts, our beliefs can penetrate perceptual experiences. It is argued that neither candidate is a case of cognitive penetration. In doing so, I provide an explanation to both that is based on perceptual learning, a non-cognitive phenomenon where relatively slow and long-lasting modifications to an organism’s perceptual system bring about changes in perception. This explanation is theoretically more plausible and remains closer to the empirical data than the explanations based on cognitive penetration.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Pylyshyn does not appear to discard the causal notion altogether however. Rather, although he explicates mainly the notion of semantic coherence, he can also be understood to as maintaining that, if cognitive penetration takes place, such coherence is a consequence of cognitive states causally influencing the penetrates states (e.g. Pylyshyn 1999, 343, 405).
Indeed, it appears as if most of the time cognitive penetration is simply assumed and philosophers are more interested in addressing the consequences of cognitive penetration than providing convincing argument that it actually takes place. This is peculiar, however, since even if cognitive penetration does occur in some form, the details of these cases determine what their consequences are (see Sect. 5).
The processing in the early visual areas does not determine the perceptual experiences, but it is plausible to assume that the altered processing in the early visual areas affects the latter processing states too, which in turn would change perceptual experiences in some respect.
For what it’s worth, it appears that perceptual learning is not considered to be part of cognitive processes in the psychology textbooks on cognition (e.g. Matlin 2008; Reisberg 2012). Likewise, although “declarative learning” was regarded as a cognitive term in a recent study (Whissell et al. 2013) analyzing the prevalence of cognitive terms in psychology journals, “learning” per se and “perceptual learning” were not. This concurs with the fact that even though Pylyshyn accepted that perceptual learning occurs, he did not regard it as cognitive penetration of early visual areas either. Then again, on the broadest definition, any information processing appears to qualify as cognition. Thus, for instance, snails’ brains in vitro that are observed to learn to distinguish odors are said to possess micro-cognition (Watanabe et al. 2008). In short, perceptual learning appears to be a part of cognition at least under some of the definitions (even if it is not explicitly discussed in them) and not under others.
The first type is the possession of concept: the concepts we possess determine in part our cognitive states and hence the concepts we possess belong to cognition too. The second example Macpherson (2012, 27) gives is that of “the cognitive system being primed so that certain concepts are likely to be triggered or activated”.
Contrary to this conclusion, Ariel S. Cecchi (2014) has argued that cognitive penetration occurs because of perceptual learning. The disagreement between our conclusions is only apparent though, because our conception of cognitive penetration differs. In more detail, Cecchi argues that perceptual learning influences the allocation of attention that in turn affects the early stages of the visual processing. Thus he maintains that the differences between perceptual experiences before and after perceptual learning are due to attentional mechanisms. However, as mentioned in the introduction, philosophers (including Macpherson whose candidate for a case of cognitive penetration is under scrutiny in this paper) usually do not think that the differences caused by attentional mechanism constitute a case of cognitive penetration. Hence, in the current framework, Cecchi does not establish that perceptual learning leads to cognitive penetration either.
The argument assumes that experiences have contents and that the difference in content is reflected in phenomenological difference. Since these assumptions are not central for the point that Siegel tries to make—and are often assumed in the debate related to cognitive penetrability (e.g. Macpherson 2012)—they are not discussed here.
This does not exclude the likely possibility that expertise also improves one’s skills to verbalize the contents of experiences and make correct judgments based on them.
For example, by enhancing the difference of the percepts of physically similar stimuli, differentiation enhances the salience of distinguishing dimensions too. It is worth noting that the described effects and mechanisms are compatible with possible additional attentional effects, and Goldstone (1998) in fact argues that attentional weighting aids to sensitize perceptual processes related to the category-relevant dimensions (especially those that are at the category boundary).
Moreover, the idea that that the increased saliency results from the concepts we possess somehow yet not through attentional effects (even though Siegel writes about attention) does not help Siegel. After all, this would still not demonstrate Thesis K, because the difference between E1 and E2 could be in the represented low-level properties.
For this reason, it is odd that Macpherson (2012, 46, my emphasis) writes inaccurately that “Delk and Fillenbaum explicitly say that they tried to ensure that the only relevant difference between the cases where the subjects were presented with cutout shapes of characteristically red objects and non-characteristically red objects was the subject’s beliefs”.
The main modifications in the more recent studies include the following: (i) the intersubjective differences in color perception are taken into account, (ii) the experiment is conducted on a computer screen, (iii) the task of the subject is to adjust the color of a stimulus to match with the gray background, (iv) the perception of figures is not reduced by placing them behind waxed paper, and (v) more stimuli and more varied stimuli are used.
In addition to considering the possibility of low-level recognition of the apple, Macpherson also elaborated on similar phenomena as regards the color of faces of members of different races. However, empirical evidence does not concur with Macpherson’s consideration here either because it has been shown that race categorization can occur very early in visual processing.
See Lyons (2011) for a similar claim according to which cognitive penetration can increase the reliability of perception.
References
Ahissar, M., & Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends in Cognitive Sciences, 8(10), 457–464.
Cecchi, A. S. (2014). Cognitive penetration, perceptual learning, and neural plasticity. Dialectica, 68(1), 63–95.
Churchland, P. M. (1988). Perceptual plasticity and theoretical neutrality: A reply to Jerry Fodor. Philosophy of Science, 55(2), 167–187.
Delk, J. L., & Fillenbaum, S. (1965). Differences in perceived color as a function of characteristic color. The American Journal of Psychology, 78(2), 290–293.
Fahle, M. (2002). Introduction. In M. Fahle & T. A. Poggio (Eds.), Perceptual learning (ix–xx). Cambridge: The MIT Press.
Fisher, S., Hull, C., & Holtz, P. (1956). Past experience and perception: Memory color. The American Journal of Psychology, 69, 216–227.
Fodor, J. (1984). Observation reconsidered. Philosophy of Science, 51(1), 23–43.
Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49, 585–612.
Goldstone, R. L., Braithwaite, D. W., & Byrge, L. A. (2012). Perceptual learning. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning (pp. 2580–2583). Heidelberg, German: Springer Verlag GmbH.
Grill-Spector, K., & Kushnir, T. (1998). A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Human Brain Mapping, 6, 316–328.
Hansen, T., Olkkonen, M., Walter, S., & Gegenfurtner, K. R. (2006). Memory modulates color appearance. Nature Neuroscience, 9(11), 1367–1368.
Kirchner, H., & Thorpe, S. (2006). Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited. Vision Research, 46(10), 1762–1776.
Kuhn, T. (1996). The structure of scientific revolutions (3rd ed., p. 212). Chicago, IL: University of Chicago Press.
Lamme, V. A. F. (2006). Towards a true neural stance on consciousness. Trends in Cognitive Sciences, 10(11), 494–501.
Liu, H., Agam, Y., Madsen, J., & Kreiman, G. (2009). Timing, timing, timing: Fast decoding of object information from intracranial field potentials in human visual cortex. Neuron, 62, 281–290.
Lyons, J. (2011). Circularity, reliability, and the cognitive penetrability of perception. Philosophical Issues, 21(1), 289–311.
Macpherson, F. (2012). Cognitive penetration of colour experience: Rethinking the issue in light of an indirect mechanism. Philosophy and Phenomenological Research, 84(1), 24–62.
Matlin, M. (2008). Cognition (7th ed.). Hoboken, New Jersey: Wiley.
McClelland, J., & Johnston, J. (1977). The role of familiar units in perception of words and nonwords. Perception and Psychophysics, 22(3), 249–261.
Naor-Raz, G., Tarr, M. J., & Kersten, D. (2003). Is color an intrinsic property of object representation? Perception, 32(6), 667–680.
Olkkonen, M., Hansen, T., & Gegenfurtner, K. R. (2008). Color appearance of familiar objects: Effects of object shape, texture, and illumination changes. Journal of Vision, 8(5), 1–16.
Pylyshyn, Z. (1999). Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. The Behavioral and Brain Sciences, 22(3), 341–365.
Raftopoulos, A. (2001). Is perception informationally encapsulated? The issue of the theory-ladenness of perception. Cognitive Science, 25(3), 423–451.
Reisberg, D. (2012). Cognition: Exploring the science of the mind (5th ed.). New York, NY: W. W. Norton & Company.
Roelfsema, P. R., van Ooyen, A., & Watanabe, T. (2010). Perceptual learning rules based on reinforcers and attention. Trends in Cognitive Sciences, 14(2), 64–71.
Shea, N. (2014). Distinguishing top-down from bottom-up effects. In S. Biggs, M. Matthen, & D. Stokes (Eds.), Perception and its modalities. Oxford: Oxford University Press.
Shuren, J. E., Brott, T. G., Schefft, B. K., & Houston, W. (1996). Preserved color imagery in an achromatopsic. Neuropsychologia, 34(6), 485–489.
Siegel, S. (2005). Which properties are represented in perception? In T. Szabo Gendler & J. Hawte (Eds.), Perceptual experience (pp. 481–503). Oxford: Oxford University Press.
Siegel, S. (2012). Cognitive penetrability and perceptual justification. Noûs, 46(2), 201–222.
Snowden, P. T., Davies, I. R. L., & Roling, P. (2000). Perceptual learning of the detection of features in X-ray images: A functional role for improvements in adults’ visual sensitivity? Journal of Experimental Psychology: Human Perception and Performance, 26(1), 379–390.
Stokes, D. (2012). Perceiving and desiring: A new look at the cognitive penetrability of experience. Philosophical Studies, 158(3), 477–492.
Stokes, D. (2013). Cognitive penetrability of perception. Philosophy Compass, 8(7), 646–663.
Stokes, D., & Bergeron, V. (2015). Modular architectures and informational encapsulation: A dilemma. European Journal for Philosophy of Science. doi:10.1007/s13194-015-0107-z.
Tanaka, J., Weiskopf, D., & Williams, P. (2001). The role of color in high-level vision. Trends in Cognitive Sciences, 5(5), 211–215.
Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5(7), 682–687.
Watanabe, S., Kirino, Y., & Gelperin, A. (2008). Neural and molecular mechanisms of microcognition in Limax. Learning & Memory, 15(9), 633–642.
Whissell, C., Abramson, C., & Barber, K. (2013). The search for cognitive terminology: An analysis of comparative psychology journal titles. Behavioral Sciences, 3(1), 133–142.
Winawer, J., Witthoft, N., Frank, M. C., Wu, L., Wade, A. R., & Boroditsky, L. (2007). Russian blues reveal effects of language on color discrimination. PNAS, 104(19), 7780–7785.
Witthoft, N., Winawer, J., Wu, L., Frank, M., Wade, A., & Boroditsky, L. (2003). Effects of language on color discriminability. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th annual meeting of the cognitive science society. Mahwah, NJ: Lawrence Erlbaum Associates.
Witzel, C., Valkova, H., Hansen, T., & Gegenfurtner, K. (2011). Object knowledge modulates colour appearance. i-Perception, 2(1), 13–49.
Zeki, S., & Marini, L. (1998). Three cortical stages of colour processing in the human brain. Brain, 121, 1669–1685.
Author information
Authors and Affiliations
Corresponding author
Additional information
An erratum to this article is available at http://dx.doi.org/10.1007/s10670-016-9827-5.
Rights and permissions
About this article
Cite this article
Arstila, V. Perceptual Learning Explains Two Candidates for Cognitive Penetration. Erkenn 81, 1151–1172 (2016). https://doi.org/10.1007/s10670-015-9785-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10670-015-9785-3