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Prediction during statistical learning, and implications for the implicit/explicit divide.

Dale R, Duran ND, Morehead JR - Adv Cogn Psychol (2012)

Bottom Line: We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events.Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction.We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.

View Article: PubMed Central - PubMed

ABSTRACT
Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.

No MeSH data available.


Panel A. Test sequence match score (%) as a function ofG, with means grouped by stimulus condition.Panel B. Using the same stimulus list means, the match score as afunction of predictiveness.
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Figure 5: Panel A. Test sequence match score (%) as a function ofG, with means grouped by stimulus condition.Panel B. Using the same stimulus list means, the match score as afunction of predictiveness.

Mentions: We calculated the grammatical regularity of the participants’ testingoutput of 24 clicks using the same statistic as in Jamieson and Mewhort(2009). In the expecteddirection, there is a strong relationship between theG-score regularity of the training sequence and the testingrecall, r = .42, p < .0001. We alsofound that the testing sequences more closely matched the original trainingsequences in the same direction, r = .48,p < .0001. This matching score was generated using asequence-alignment method known as cross-recurrence (seeDale & Spivey, 2005), where apercentage score reflects the overall match between the original and testingsequences by calculating the percentage of position sequences that are thesame (similar to Levenshtein distance). In addition, we tested therelationship between how predictive a participant is in the final two blocksof the experiment (12 trials, using distance from previous with 275-pixelthreshold), and the matching score, controlling for the training sequenceG-scores (included as a covariate in a linearmultiple-regression model). In the total model, training sequenceG-score is a significant predictor (p< .01), but amount of prediction also strongly relates to the matchingscore (p < .005; multiple-R2= .29, p < .0001). The relationship between matchingscore, and G, and predictiveness, is shown in Figure 5.


Prediction during statistical learning, and implications for the implicit/explicit divide.

Dale R, Duran ND, Morehead JR - Adv Cogn Psychol (2012)

Panel A. Test sequence match score (%) as a function ofG, with means grouped by stimulus condition.Panel B. Using the same stimulus list means, the match score as afunction of predictiveness.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376885&req=5

Figure 5: Panel A. Test sequence match score (%) as a function ofG, with means grouped by stimulus condition.Panel B. Using the same stimulus list means, the match score as afunction of predictiveness.
Mentions: We calculated the grammatical regularity of the participants’ testingoutput of 24 clicks using the same statistic as in Jamieson and Mewhort(2009). In the expecteddirection, there is a strong relationship between theG-score regularity of the training sequence and the testingrecall, r = .42, p < .0001. We alsofound that the testing sequences more closely matched the original trainingsequences in the same direction, r = .48,p < .0001. This matching score was generated using asequence-alignment method known as cross-recurrence (seeDale & Spivey, 2005), where apercentage score reflects the overall match between the original and testingsequences by calculating the percentage of position sequences that are thesame (similar to Levenshtein distance). In addition, we tested therelationship between how predictive a participant is in the final two blocksof the experiment (12 trials, using distance from previous with 275-pixelthreshold), and the matching score, controlling for the training sequenceG-scores (included as a covariate in a linearmultiple-regression model). In the total model, training sequenceG-score is a significant predictor (p< .01), but amount of prediction also strongly relates to the matchingscore (p < .005; multiple-R2= .29, p < .0001). The relationship between matchingscore, and G, and predictiveness, is shown in Figure 5.

Bottom Line: We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events.Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction.We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.

View Article: PubMed Central - PubMed

ABSTRACT
Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.

No MeSH data available.