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The Metacognitive Role of Familiarity in Artificial Grammar Learning: Transitions from Unconscious to Conscious Knowledge

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Trends and Prospects in Metacognition Research

Abstract

We present two methods by which people could learn (e.g., artificial grammars): learning by a single updating model that has the function to reflect how reality is (e.g., the standard types of connectionist models in the implicit learning literature), and learning by the use of considering hypotheticals (hypothesis testing). The first method results in unconscious knowledge of the structure of a domain. Such unconscious structural knowledge can lead to conscious knowledge that new items do (or do not) have that structure (“judgment knowledge”). When unconscious structural knowledge produces conscious judgment knowledge, the phenomenology is of intuition, a common phenomenology in implicit learning experiments. We propose a mechanism by which one becomes aware of judgment knowledge, turning feelings of guessing into those of intuition: feedback in calibrating the accuracy of one’s knowledge of the distribution of familiarity of the test strings. Accurate predictions lead to awareness of knowing, that is, to conscious knowledge. Contrary to some popular beliefs, we argue fluency plays little role in either the expression of unconscious structural knowledge or in the formation of conscious judgment knowledge. The individual difference variable Faith in Intuition was not associated with better implicit learning but it was associated with sensitivity to familiarity and the metacognitive processes by which judgment knowledge can be made conscious: that is, by which feelings of intuition are formed.

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Notes

  1. 1.

    Familiarity ratings were standardised (z-transformed).

  2. 2.

    For each participant, familiarity was regressed on seven measures of the structural similarity between training and test strings. The adjusted R 2 from those regressions was used as the measure of how well familiarity was predicted by structural similarity. FI was significantly related to the adjusted R 2 values.

  3. 3.

    FI was also found to correlate with reported experience of déjà vu, ρ(80) = 0.25, p = 0.024, consistent with the theory that déjà vu experiences may result from misattributed familiarity (Jacoby & Whitehouse, 1989).

References

  • Buchner, A. (1994). Indirect effects of synthetic grammar learning in an identification task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(3), 550–566.

    Article  Google Scholar 

  • Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

    Article  Google Scholar 

  • Carruthers, P. (2000). Phenomenal consciousness: A naturalistic theory. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Chomsky, N. (1957). Syntactic structures. The Hague, The Netherlands: Mouton.

    Google Scholar 

  • Chrisley, R., & Parthemore, J. (2007) Synthetic phenomenology: Exploiting embodiment to specify the non-conceptual content of visual experience. Journal of Consciousness Studies 14, 44–58.

    Google Scholar 

  • Cleeremans, A., & Dienes, Z. (2008). Computational models of implicit learning. In R. Sun (Ed.), Handbook of computational cognitive modeling (pp. 396–421). Cambridge, UK: Cambridge: University Press.

    Google Scholar 

  • Cleeremans, A., & Jiménez, L. (2002). Implicit learning and consciousness: A graded, dynamic perspective. In R. M. French & A. Cleeremans (Eds.), Implicit learning and consciousness (pp. 1–40). Hove, UK: Psychology Press.

    Google Scholar 

  • Destrebecqz, A., & Cleeremans, A. (2001). Can sequence learning be implicit? New evidence with the process dissociation procedure. Psychonomic Bulletin and Review, 8(2), 343–350.

    Article  Google Scholar 

  • Dienes, Z. (2008). Subjective measures of unconscious knowledge. Models of Brain and Mind: Physical, Computational and Psychological Approaches, 168, 49–64.

    Article  Google Scholar 

  • Dienes, Z., Altmann, G. T. M., Kwan, L., & Goode, A. (1995). Unconscious knowledge of artificial grammars is applied strategically. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(5), 1322–1338.

    Article  Google Scholar 

  • Dienes, Z., & Perner, J. (1999) A theory of implicit and explicit knowledge. Behavioural and Brain Sciences, 22, 735–755.

    Article  Google Scholar 

  • Dienes, Z., & Scott, R. (2005). Measuring unconscious knowledge: Distinguishing structural knowledge and judgment knowledge. Psychological Research, 69(5–6), 338–351.

    Article  Google Scholar 

  • Dulany, D. E. (1997). Consciousness in the explicit (deliberative) and implicit (evocative). In J. Cohen & J. Schooler (Eds.), Scientific approaches to consciousness (pp. 179–212). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Dulany, D. E., Carlson, R. A., & Dewey, G. I. (1984). A case of syntactical learning and judgment: How conscious and how abstract? Journal of Experimental Psychology: General, 113(4), 541–555.

    Article  Google Scholar 

  • Epstein, S. (1983). The unconscious, the preconscious and the self-concept. In J. Suls & A. Greenwald (Eds.), Psychological perspectives on the self (Vol. 2, pp. 219–247). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Epstein, S., Pacini, R., Denes Raj, V., & Heier, H. (1996). Individual differences in intuitive-experiential and analytical-rational thinking styles. Journal of Personality and Social Psychology, 71(2), 390–405.

    Article  Google Scholar 

  • Evans, J. S. B. T., & Over, D. E. (1999). Rationality and reasoning. Hove, UK: Psychology Press.

    Google Scholar 

  • Fu, Q., Fu, X., & Dienes, Z. (2008). Implicit sequence learning and conscious awareness. Consciousness and Cognition, 17(1), 185–202.

    Article  Google Scholar 

  • Gray, J. A. (1995). The contents of consciousness: A neuropsychological conjecture. Behavioral and Brain Sciences, 18(4), 659–722.

    Article  Google Scholar 

  • Higham, P. A., Vokey, J. R., & Pritchard, J. (2000). Beyond dissociation logic: Evidence for controlled and automatic influences in artificial grammar learning. Journal of Experimental Psychology: General, 129(4), 457–470.

    Article  Google Scholar 

  • Jacoby, L. L., & Dallas, M. (1981). On the relationship between autobiographical memory and perceptual learning. Journal of Experimental Psychology: General, 110, 306–340.

    Article  Google Scholar 

  • Jacoby, L. L., & Whitehouse, K. (1989). An illusion of memory: False recognition influenced by unconscious perception. Journal of Experimental Psychology: General, 118(2), 126–135.

    Article  Google Scholar 

  • Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30(5), 513–541.

    Article  Google Scholar 

  • Jiménez, L. (Ed.). (2003). Attention and implicit learning. Amsterdam: Benjamins.

    Google Scholar 

  • Kinder, A., & Assmann, A. (2000). Learning artificial grammars: No evidence for the acquisition of rules. Memory and Cognition, 28(8), 1321–1332.

    Article  Google Scholar 

  • Kinder, A., Shanks, D. R., Cock, J., & Tunney, R. J. (2003). Recollection, fluency, and the explicit/implicit distinction in artificial grammar learning. Journal of Experimental Psychology: General, 132(4), 551–565.

    Article  Google Scholar 

  • Knowlton, B. J., & Squire, L. R. (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 169–181.

    Article  Google Scholar 

  • Kuhn, G., & Dienes, Z. (2005). Implicit learning of nonlocal musical rules: Implicitly learning more than chunks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(6), 1417–1432.

    Article  Google Scholar 

  • Lau, H. C. (2008). A higher order Bayesian decision theory of consciousness. Models of Brain and Mind: Physical, Computational and Psychological Approaches, 168, 35–48.

    Article  Google Scholar 

  • Lotz, A., & Kinder, A. (2006). Transfer in artificial grammar learning: The role of repetition information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 707–715.

    Article  Google Scholar 

  • Meulemans, T., & Van der Linden, M. (1997). Associative chunk strength in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(4), 1007–1028.

    Article  Google Scholar 

  • Norman, E., Price, M. C., & Duff, S. C. (2006). Fringe consciousness in sequence learning: The influence of individual differences. Consciousness and Cognition, 15, 723–760.

    Article  Google Scholar 

  • Norman, E., Price, M. C., Duff, S. C., & Mentzoni, R. A. (2007). Gradations of awareness in a modified sequence learning task. Consciousness and Cognition, 16, 809–837.

    Article  Google Scholar 

  • O’Regan, J. K., & Noe, A. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24(5), 939–973.

    Article  Google Scholar 

  • Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. Journal of Personality and Social Psychology, 76(6), 972–987.

    Article  Google Scholar 

  • Perner, J. (1991). Understanding the representational mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Perruchet, P., & Vinter, A. (2002). The self-organizing consciousness. Behavioral and Brain Sciences, 25(3), 297–388.

    Google Scholar 

  • Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6(6), 855–863.

    Article  Google Scholar 

  • Reber, A. S. (1976). Implicit learning of synthetic languages: The role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2, 88–94.

    Article  Google Scholar 

  • Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118, 219–235.

    Article  Google Scholar 

  • Reber, A. S., & Lewis, S. (1977). Implicit learning: An analysis of the form and structure of a body of tacit knowledge. Cognition, 114, 14–24.

    Google Scholar 

  • Redington, M., Friend, M., & Chater, N. (1996, July). Confidence judgments, performance, and practice, in artificial grammar learning. Paper presented at the Eighteenth Annual Conference of the Cognitive Science Society, Mawah, New Jersey.

    Google Scholar 

  • Rosenthal, D. (1986). Two concepts of consciousness. Philosophical Studies, 49(3), 329–359.

    Article  Google Scholar 

  • Rosenthal, D. M. (2005). Consciousness and mind. Oxford, England: Clarendon.

    Google Scholar 

  • Scott, R., & Dienes, Z. (2008). The conscious, the unconscious, and familiarity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(5), 1264–1288.

    Article  Google Scholar 

  • Scott, R., & Dienes, Z. (2009). Fluency does not express implicit knowledge of artificial grammars. Manuscript submitted for publication.

    Google Scholar 

  • Servan Schreiber, E., & Anderson, J. R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 592–608.

    Article  Google Scholar 

  • Shanks, D. R. (2005a). Connectionist models of basic human learning processes. In G. Houghton (Ed.), Connectionist models in cognitive psychology (pp. 45–82). Hove, UK: Psychology Press.

    Google Scholar 

  • Shanks, D. R. (2005b). Implicit learning. In K. Lamberts & R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.

    Google Scholar 

  • Shanks, D. R., & St. John, M. F. (1994). Characteristics of dissociable human learning systems. Behavioral & Brain Sciences, 17, 367–447.

    Article  Google Scholar 

  • Sun, R. (2002). Duality of the mind. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Tanaka, D., Kiyokawa, S., Yamada, A., Dienes, Z., & Shigemasu, K. (2008). Role of selective attention in artificial grammar learning. Psychonomic Bulletin and Review, 15, 1154–1159.

    Article  Google Scholar 

  • Wan, L. L., Dienes, Z., & Fu, X. L. (2008). Intentional control based on familiarity in artificial grammar learning. Consciousness and Cognition, 17(4), 1209–1218.

    Article  Google Scholar 

  • Wilkinson, L., & Shanks, D. R. (2004). Intentional control and implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 354–369.

    Article  Google Scholar 

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Scott, R., Dienes, Z. (2010). The Metacognitive Role of Familiarity in Artificial Grammar Learning: Transitions from Unconscious to Conscious Knowledge. In: Efklides, A., Misailidi, P. (eds) Trends and Prospects in Metacognition Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6546-2_3

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