Structural priming as implicit learning: Cumulative priming effects and individual differences

Kaschak, Michael P.; Kutta, Timothy J.; Jones, John L.

doi:10.3758/s13423-011-0157-y

Cited by 85 publications

(99 citation statements)

References 23 publications

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“…The first set of experiments reported here represents an effort to replicate and extend Kaschak, Kutta, and Jones' (2011) finding of an inverse frequency effect. One of the experiments is a straight replication of the original study using the same subject population.…”

Section: Mixed Logit Modelmentioning

confidence: 99%

“…Finally, participants completed another set of target stems to assess how much the completion of the prime stems moved their rates of DO and PO production. Kaschak, Kutta, and Jones (2011) demonstrated an inverse frequency effect. The base rates of production were slanted toward the DO construction (~ 60% DO completions), establishing the DO as the more frequently used construction in our participant population.…”

mentioning

confidence: 99%

“…The experiments reported here represent an effort to examine further the influence of implicit learning and explicit memory on structural priming. We begin with an initial set of two experiments that had the same design as Kaschak, Kutta, and Jones (2011). Participants completed several sentence stems.…”

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confidence: 99%

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Implicit and Explicit Memory Factors in Cumulative Structural Priming

Kutta

Kaschak

Porcellini

et al. 2017

Collabra: Psychology

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Although researchers have argued that short-term structural priming is driven by both implicit and explicit memory processes, accounts of longer-term structural priming tend to focus on implicit memory processes. We explore this claim in five experiments. In the first two experiments, we replicate the finding that stronger cumulative structural priming is observed for the prepositional object (PO) construction than for the double object (DO) construction. The next three experiments explore the possibility that explicit memory effects may contribute to cumulative priming. The results of these experiments suggest that any explicit memory effects on cumulative priming are likely to be very weak. We conclude by conducting an analysis across all 5 experiments, and find that an examination of individual participants' base rates of DO production helps to explain the circumstances under which cumulative priming for the DO and PO will be observed.

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Section: Mixed Logit Modelmentioning

confidence: 99%

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confidence: 99%

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Implicit and Explicit Memory Factors in Cumulative Structural Priming

Kutta

Kaschak

Porcellini

et al. 2017

Collabra: Psychology

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“…This state of affairs may be partly due to the fact that until recently (e.g., [27]), there has been some doubt as to whether substantive individual variation in SL ability (or, more generally, implicit learning of probabilistic structure) actually existed (e.g., [2]). A handful of studies on individual differences in SL ability have been reported over the past several years (e.g., [27,28,29]), with some studies reporting significant correlations between SL and language outcomes (e.g., [30]) and others reporting null or weak correlations (e.g., [31]). …”

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confidence: 99%

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

et al. 2016

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The ability to adapt to statistical structure (often referred to as "statistical learning") has been proposed to play a major role in the acquisition and use of natural languages. Several recent studies have explored the relationship between individual differences in statistical learning and language outcomes. These studies have produced mixed results, with some studies finding a significant relationship between statistical learning and language outcomes, and others finding weak or null results. Furthermore, the few studies that have used multiple measures of statistical learning have reported that they are not correlated (e.g., [1]). The current study assesses the reliability of various measures of auditory statistical segmentation, and their consistency over time. That is, do the generally low correlations observed between measures of statistical learning stem from task demands, the psychometric properties of the measures, or the fact that statistical learning may be a highly fragmented construct? Our results confirm previous reports that individual measures of statistical learning tend not to correlate with each other, and suggest that the somewhat weak reliability of the measures may be an important factor in the low correlations. Our data also suggest that aggregating performance across tasks may be an avenue for improving the reliability of the measures.

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“…In an experimental study published later by another lab (Kaschak et al, 2011a), this turned out to be the case.…”

Section: Predictionsmentioning

confidence: 92%

Toward Cognitively Constrained Models of Language Processing: A Review

et al. 2017

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Language processing is not an isolated capacity, but is embedded in other aspects of our cognition. However, it is still largely unexplored to what extent and how language processing interacts with general cognitive resources. This question can be investigated with cognitively constrained computational models, which simulate the cognitive processes involved in language processing. The theoretical claims implemented in cognitive models interact with general architectural constraints such as memory limitations. This way, it generates new predictions that can be tested in experiments, thus generating new data that can give rise to new theoretical insights. This theory-model-experiment cycle is a promising method for investigating aspects of language processing that are difficult to investigate with more traditional experimental techniques. This review specifically examines the language processing models of Lewis and Vasishth (2005), Reitter et al. (2011), andVan Rij et al. (2010), all implemented in the cognitive architecture Adaptive Control of Thought-Rational (Anderson et al., 2004). These models are all limited by the assumptions about cognitive capacities provided by the cognitive architecture, but use different linguistic approaches. Because of this, their comparison provides insight into the extent to which assumptions about general cognitive resources influence concretely implemented models of linguistic competence. For example, the sheer speed and accuracy of human language processing is a current challenge in the field of cognitive modeling, as it does not seem to adhere to the same memory and processing capacities that have been found in other cognitive processes. Architecturebased cognitive models of language processing may be able to make explicit which language-specific resources are needed to acquire and process natural language. The review sheds light on cognitively constrained models of language processing from two angles: we discuss (1) whether currently adopted cognitive assumptions meet the requirements for language processing, and (2) how validated cognitive architectures can constrain linguistically motivated models, which, all other things being equal, will increase the cognitive plausibility of these models. Overall, the evaluation of cognitively constrained models of language processing will allow for a better understanding of the relation between data, linguistic theory, cognitive assumptions, and explanation.

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Structural priming as implicit learning: Cumulative priming effects and individual differences

Cited by 85 publications

References 23 publications

Implicit and Explicit Memory Factors in Cumulative Structural Priming

Implicit and Explicit Memory Factors in Cumulative Structural Priming

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

Toward Cognitively Constrained Models of Language Processing: A Review

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