The Neuropharmacology of Implicit Learning

Uddén, Julia; Folia, Vasiliki; Petersson, Karl Magnus

doi:10.2174/157015910793358178

Cited by 12 publications

(7 citation statements)

References 101 publications

(181 reference statements)

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“…Another example mechanism is AMPA receptor modulation of synapse strength. For example, an increasing or decreasing number of AMPA receptors results in strengthening or weakening of the synapse (Kessels & Malinow, 2009; for a review, see Uddén, Folia, & Petersson, 2010).…”

Section: Discussionmentioning

confidence: 99%

Implicit Acquisition of Grammars With Crossed and Nested Non‐Adjacent Dependencies: Investigating the Push‐Down Stack Model

et al. 2012

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A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finitestate and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.Correspondence should be sent to Julia Uddén, Stockholm Brain Institute, A2:3, Retzius väg 8, 171 72

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Section: Discussionmentioning

confidence: 99%

Implicit Acquisition of Grammars With Crossed and Nested Non‐Adjacent Dependencies: Investigating the Push‐Down Stack Model

et al. 2012

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“…Note, however, that explicit learning is the exception and implicit learning the rule; hence, implicit learning processes will form the bulk of what is learned also during adult language learning. One of the clearer definitions of implicit learning is that by Seger (1994), reviewed in Uddén et al (2010). In her view, implicit learning has four characteristics: (1) no or limited explicit access to the knowledge acquired and how it is put to use; (2) the acquired knowledge does not consist of simple association, but is more complex; (3) it is an incidental consequence of information processing and not explicit hypothesis testing; and (4) it does not rely on declarative memory mechanisms for learning-far from all AGL studies which describe learning processes that have all of these characteristics.…”

Section: Which Learning (Implicit/explicit)?mentioning

confidence: 99%

Artificial Grammar Learning and Its Neurobiology in Relation to Language Processing and Development

Uddén

Männel

2018

The Oxford Handbook of Psycholinguistics

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The artificial grammar learning (AGL) paradigm enables systematic investigation of the acquisition of linguistically relevant structures. It is a paradigm of interest for language processing research, interfacing with theoretical linguistics, and for comparative research on language acquisition and evolution. This chapter presents a key for understanding major variants of the paradigm. An unbiased summary of neuroimaging findings of AGL is presented, using meta-analytic methods, pointing to the crucial involvement of the bilateral frontal operculum and regions in the right lateral hemisphere. Against a background of robust posterior temporal cortex involvement in processing complex syntax, the evidence for involvement of the posterior temporal cortex in AGL is reviewed. Infant AGL studies testing for neural substrates are reviewed, covering the acquisition of adjacent and non-adjacent dependencies as well as algebraic rules. The language acquisition data suggest that comparisons of learnability of complex grammars performed with adults may now also be possible with children.

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“…Animal and human studies of implicit sequence learning 1 show that experimental enhancement and inhibition of DA release lead to corresponding enhancements and impairments of sequence learning (Kumari et al, 1997; Miyachi et al, 1997; Dunnett et al, 2012; for a review, see Udden et al, 2010). Moreover, human research participants show increased DA release in the striatum, including the caudate head, during implicit learning on the SRT (Badgaiyan et al, 2007).…”

Section: A Striatal Basis Of Npower-dependent Implicit Learningmentioning

confidence: 99%

The role of the dorsoanterior striatum in implicit motivation: the case of the need for power

Schultheiss¹,

Schiepe-Tiska²

2013

Front. Hum. Neurosci.

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Implicit motives like the need for power (nPower) scale affective responses to need-specific rewards or punishments and thereby influence activity in motivational-brain structures. In this paper, we review evidence specifically supporting a role of the striatum in nPower. Individual differences in nPower predict (1) enhanced implicit learning accuracy, but not speed, on serial-response tasks that are reinforced by power-related incentives (e.g., winning or losing a contest; dominant or submissive emotional expressions) in behavioral studies and (2) activation of the anterior caudate in response to dominant emotional expressions in brain imaging research. We interpret these findings on the basis of Hikosaka et al.'s (2002a) model of central mechanisms of motor skill learning. The model assigns a critical role to the dorsoanterior striatum in dopamine-driven learning of spatial stimulus sequences. Based on this model, we suggest that the dorsoanterior striatum is the locus of nPower-dependent reinforcement. However, given the centrality of this structure in a wide range of motivational pursuits, we also propose that activity in the dorsoanterior striatum may not only reflect individual differences in nPower, but also in other implicit motives, like the need for achievement or the need for affiliation, provided that the proper incentives for these motives are present during reinforcement learning. We discuss evidence in support of such a general role of the dorsoanterior striatum in implicit motivation.

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The Neuropharmacology of Implicit Learning

Cited by 12 publications

References 101 publications

Implicit Acquisition of Grammars With Crossed and Nested Non‐Adjacent Dependencies: Investigating the Push‐Down Stack Model

Implicit Acquisition of Grammars With Crossed and Nested Non‐Adjacent Dependencies: Investigating the Push‐Down Stack Model

Artificial Grammar Learning and Its Neurobiology in Relation to Language Processing and Development

The role of the dorsoanterior striatum in implicit motivation: the case of the need for power

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