Characterizing sequence knowledge using online measures and hidden Markov models

Visser, Ingmar; Raijmakers, Maartje E. J.; Molenaar, Peter

doi:10.3758/bf03193619

Cited by 21 publications

(23 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, implicit sequence learning can arise from statistical learning (Hunt & Aslin, 2001)-a process where the probabilistic structure underlying the entire sequence is learned and encoded in the form of an internal model that represents the stimulus contingencies (Bornstein & Daw, 2012;Visser, Raijmakers, & Molenaar, 2007).…”

Section: Introductionmentioning

confidence: 99%

New insights into statistical learning and chunk learning in implicit sequence acquisition

Clark

2016

Psychon Bull Rev

View full text Add to dashboard Cite

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

show abstract

Section: Introductionmentioning

confidence: 99%

New insights into statistical learning and chunk learning in implicit sequence acquisition

Clark

2016

Psychon Bull Rev

View full text Add to dashboard Cite

show abstract

“…Here we model response times and accuracies from an SRTT reported in Visser et al . (); the data set is included in the glba package. The data consist of 12 blocks of trials from a single participant in Experiment 2.…”

Section: Illustration: Implicit Learningmentioning

confidence: 99%

“…After some training with one particular sequence, the sequence of locations switches to a different or a random pattern (see, for example, Pronk & Visser, 2010;Reed & Johnson, 1994, for discussion about these different options). Visser et al (2007); the data set is included in the glba package. The data consist of 12 blocks of trials from a single participant in Experiment 2.…”

Section: Illustration: Implicit Learningmentioning

confidence: 99%

“…Although in this task it is common to find Block RT (s) Figure 3. Implicit learning data from a single participant responding to a sequence of locations; data from experiment 2 in Visser et al (2007). Error bars around the means denote standard errors.…”

Section: Modelsmentioning

confidence: 99%

“…Fourth, we present a simulation study that compares two methods of computing standard errors of the same basic model and their use in parameter inference. Finally, we illustrate the model by applying it to a data set from an implicit learning experiment (Visser, Raijmakers, & Molenaar, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter recovery, bias and standard errors in the linear ballistic accumulator model

Visser

Poessé

2017

Brit J Math & Statis

Self Cite

View full text Add to dashboard Cite

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model.

show abstract

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother–Infant Interactions During Infant Immunization

Stifter

Rovine

2015

Infant and Child Development

View full text Add to dashboard Cite

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

show abstract

Characterizing sequence knowledge using online measures and hidden Markov models

Cited by 21 publications

References 59 publications

New insights into statistical learning and chunk learning in implicit sequence acquisition

New insights into statistical learning and chunk learning in implicit sequence acquisition

Parameter recovery, bias and standard errors in the linear ballistic accumulator model

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother–Infant Interactions During Infant Immunization

Contact Info

Product

Resources

About