Trends in Cognitive Sciences
Implicit learning and statistical learning: one phenomenon, two approaches
Introduction
There is no doubt that many of our most fundamental abilities, whether they concern language, perception, motor skill, or social behavior, reflect some kind of adaptation to the regularities of the world that evolves without intention to learn, and without a clear awareness of what we know. This ubiquitous phenomenon was called ‘implicit learning’ (IL) by Reber 1, 2 40 years ago. Since then, several studies have explored this form of learning with several experimental paradigms (mainly finite-state grammars and serial reaction time tasks; for reviews, see 3, 4).
Originating from a different research tradition, the term ‘statistical learning’ (SL) was proposed 10 years ago by Saffran and collaborators [4] to designate the ability of infants to discover the words embedded in a continuous artificial language, and this field of research is now growing exponentially. There are obvious similarities between SL and IL. As in IL, participants in SL experiments are faced with structured material without being instructed to learn. They learn merely from exposure to positive instances, without engaging in analytical processes or hypothesis-testing strategies. Researchers have pointed out that SL proceeds automatically 5, 6, 7, 8, incidentally [9], spontaneously [6], or by simple observation [9], and that participants in SL settings were unaware of the statistical structure of the material [7].
This article first describes how recent evolution in IL and SL research fields has made them closer to one another, leading to a growing number of cross-references and to the occasional use of the two expressions as synonymous. Conway and Christiansen [10] even now propose the term ‘implicit statistical learning’ to cover the two domains. However, we then go on to show that beyond the similarity of paradigms and results, the two domains emphasize different interpretations of the data. We suggest that this divergence, which has not been highlighted as yet, opens up a deep challenge for future studies.
Section snippets
The recent evolution of IL and SL studies
Ten years ago, it seemed possible to contrast IL and SL on their main issues of interest, namely syntax acquisition and lexicon formation, respectively. Indeed, the to-be-learned material used in artificial grammar learning research is typically governed by rules, that is by organizing principles which are independent of the specific material used in a given instance. If participants learned the rules, then this form of learning would be out of the scope of SL studies, in which the notion of
A new question: chunk formation versus statistical computation
Although the similarities between IL and SL are impressive, comparing the interpretations favored in both fields leads us to a thought-provoking observation. In the IL literature, several models have been developed as alternatives to the initial rule-based view. The first alternative idea in artificial grammar learning research was that participants memorized the displayed strings of letters, then performed their grammaticality judgments on the basis of the similarity between the test items and
Combining chunks and statistics: three possible scenarios
Nobody denies the existence of chunk knowledge. The advocates of statistical approaches claim themselves that learning shapes some kind of psychological units. For instance, Saffran and collaborators 15, 35 have shown that training with unsegmented speech results in the formation of word-like units, rather than in strings of sounds the probability of which varies on a continuous dimension. Likewise, Baker and collaborators [26] and Fiser and Aslin [9] emphasize that the end result of SL with
Does efficient chunking need prior statistical computations?
We are not aware of empirical arguments from proponents of chunk-based theories against the models assuming statistical computations, except that this assumption could be unnecessary. By contrast, SL researchers have occasionally argued that chunk models are only sensitive to the raw frequency of co-occurrences [14], whereas studies in SL have shown that participants were sensitive to more subtle statistics, such as conditional (or transitional) probabilities (e.g. 14, 42). Indeed, most
Implications for the issue of consciousness
One of the major implication of the debate outlined above is the function of consciousness in the learning process. If the chunks are inferred from the results of statistical computations, then most of the learning process must be thought of as unconscious, because statistical computations are not performed consciously in the context of incidental learning paradigms. Of course, this does not mean that chunks, once formed, are functionally inert in further steps of conscious activities, but
Conclusion
Recent evolution of research on both IL, initially aimed at studying rule abstraction in complex situations, and SL, initially focused on word segmentation, suggests that the two lines of research explore the same domain-general incidental learning processes. Bringing together these two domains of research, however, reveals a divergence between the interpretation favored in IL, which focuses on the formation of chunks, and the interpretation favored in SL, which relies on statistical
Acknowledgements
This work was supported by grants from the Centre National de la Recherche Scientifique (CNRS, UMR 5022 and FRE 2987), from the Université de Bourgogne, from the Région de Bourgogne (AAFE), and from the Université Paris V. The authors thank Stephanie Chambaron, Suzanne Filipic, Bob French, Barbara Tillmann, and the anonymous reviewers of a first draft for their help at various stages of elaboration.
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