Implicit learning of fifth- and sixth-order sequential probabilities

Remillard, Gilbert

doi:10.3758/mc.38.7.905

Cited by 24 publications

(29 citation statements)

References 38 publications

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“…A correlation would not be expected if the RT difference was a product of implicit processes. For this An effect of successor in the absence of a Successor 9 Session interaction occurs frequently in the traditional SRTT when there are six target locations and each target location has two possible successors (Remillard, 2008a(Remillard, , 2010Remillard & Clark, 2001). Thus, this pattern of results is not unusual.…”

Section: Resultsmentioning

confidence: 58%

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Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities

Remillard

2010

Psychological Research

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There is evidence that sequence learning in the traditional serial reaction time task (SRTT), where target location is the response dimension, and sequence learning in the perceptual SRTT, where target location is not the response dimension, are handled by different mechanisms. The ability of the latter mechanism to learn sequential contingencies that can be learned by the former mechanism was examined. Prior research has established that people can learn second-, third-, and fourth-order probabilities in the traditional SRTT. The present study reveals that people can learn such probabilities in the perceptual SRTT. This suggests that the two mechanisms may have similar architectures. A possible neural basis of the two mechanisms is discussed.

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Section: Resultsmentioning

confidence: 58%

“…Nonadjacent probabilities involve events that skip over at least one trial (e.g., lag 2-x and lag 4-x-2-x probabilities). More recently, Remillard (2010) has shown that people can learn fifth-and sixth-order probabilities. Thus, the mechanism for learning a sequence of responses in the traditional SRTT is remarkably powerful.…”

Section: Introductionmentioning

confidence: 98%

Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities

Remillard

2010

Psychological Research

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“…In the Perruchet task a long sequence of non-reinforced trials leads to an expectation that the next trial will be reinforced and vice-versa. Participants also respond to trial sequence information so that reaction time is reduced if the sequence is predictive of the response requirement and responses on trial n can be influenced by events on trial n − 5 or n − 6 (Remillard, 2010). Models for sequence learning include neural networks (Simple Recurrent Networks, SRNs) as well as in memory buffer frameworks similar to that used in the MECA model.…”

Section: Discussionmentioning

confidence: 99%

“…Whilst the local learning environment assumption is common in studies of associative learning there are some examples of non-local influences in learning (e.g. the Perruchet Effect and sequence learning effects, McAndrew, Jones, McLaren, & McLaren, 2012;Perruchet, 1985;Remillard, 2010). Examples such as these suggest that unexplained learning curve variability might be understood as an effect of non-local influences.…”

Section: Non-local Influences On Associative Learning: New Data and Fmentioning

confidence: 99%

Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation

Glautier¹

2017

Preprint

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Previous work (Glautier, 2013) showed that the responses made by humans on trial n in simple associative learning tasks were influenced by events that took place on trial n − 1 and a simple extension of the Rescorla-Wagner Model (RWM Rescorla & Wagner, 1972), the Memory Environment Cue Array (MECA) model, was presented to account for those results. In the current work further evidence of non-local influences on responding during associative learning tasks is presented. The Rescorla-Wagner model and the MECA model are evaluated as models for the observed data using qualitative, when the CS is presented and this is said to occur because the associations between the CS and the US representations allow excitation to spread from one representation to the other. Thus, presenting the CS excites the representation of the US and produces the observed CRs. Informally, the presence of the CS generates an expectancy of the US.The Rescorla-Wagner model principles are sufficiently general to have been successfully imported into new domains. Since its development as a model of Pavlovian conditioning in animals the Rescorla-Wagner model has been considered a viable candidate model in a variety of human learning tasks including predictive, causal, and Pavlovian learning (e.g. Chapman & Robbins, 1990;Dickinson, Shanks, & Evenden, 1984;Lachnit, 1988).Equation 1 The primary purpose of the current paper is to extend the work presented by There is no mechanism in the standard Rescorla-Wagner model to take account of this non-local information. Therefore, if participants actually make use of non-local information, the the MECA model should provide a better model of participant behaviour than the Rescorla-Wagner model in these designs. Experiment 1In Glautier (2013) noisy learning curves were better described by the MECA model than by the Rescorla-Wagner model. The noise in those learning curves was attributed to the influence of trial n − 1 events on trial n responding. In the current experiment a more direct attempt was made to show how the events of trial n − 1 could exert an associative influence on the response observed on trial n. Experimental sequences were set-up so that whenever cue A appeared on trial n − 1 then the outcome occurred on trial n. It was predicted that likelihood of response would be greater on trials involving cue B, which was partially reinforced, if the previous trial had involved cue A. NON-LOCAL INFLUENCES ON LEARNING 7 MethodProcedures were approved by the University of Southampton ResearchGovernance Office and the School of Psychology's Ethics Committee.Participants. One hundred and seven participants took part. One hundred and six were high school students and one was a teacher who accompanied the students.Recruitment took place at local colleges or during college trips to the University of Southampton for open days. The mean age of the participants was 17.1 years (minimum 16, maximum 39) and they included 20 males. Participants were tested in groups, in computer workstation suites, spread ove...

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“…Finally, it is clear that young children (Saffran et al, 1996) and adults (Remillard, 2010) are sensitive to statistical regularities over a range of items. It also appears that being skilled at learning an artificial grammar in a sequential reaction time task is associated with strong reading skills (Misyak et al, 2010).…”

Section: Measuring Local Contextmentioning

confidence: 99%

Measuring local context as context–word probabilities

Hahn

2011

Behav Res

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Context enables readers to quickly recognize a related word but disturbs recognition of unrelated words. The relatedness of a final word to a sentence context has been estimated as the probability (cloze probability) that a participant will complete a sentence with a word. In four studies, I show that it is possible to estimate local contextword relatedness based on common language usage. Conditional probabilities were calculated for sentences with published cloze probabilities. Four-word contexts produced conditional probabilities significantly correlated with cloze probabilities, but usage statistics were unavailable for some sentence contexts. The present studies demonstrate that a composite context measure based on conditional probabilities for one-to four-word contexts and the presence of a final period represents all of the sentences and maintains significant correlations (.25, .52, .53) with cloze probabilities. Finally, the article provides evidence for the effectiveness of this measure by showing that local context varies in ways that are similar to the N400 effect and that are consistent with a role for local context in reading. The Supplemental materials include local context measures for three cloze probability data sets.

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Implicit learning of fifth- and sixth-order sequential probabilities

Cited by 24 publications

References 38 publications

Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities

Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities

Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation

Measuring local context as context–word probabilities

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