A common neural hub resolves syntactic and non-syntactic conflict through cooperation with task-specific networks

Hsu, Nina S.; Jaeggi, Susanne M.; Novick, Jared M.

doi:10.1016/j.bandl.2016.12.006

Cited by 54 publications

(55 citation statements)

References 114 publications

(210 reference statements)

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“…According to the Memory-Unification-Control (MUC) model (Hagoort, 2013(Hagoort, , 2016, a control mechanism in language processing is located in the left dorsolateral prefrontal cortex (DLPFC). The MUC model is supported by functional magnetic resonance imaging (fMRI) studies reporting activation of the left DLPFC during sentence comprehension (Cooke et al, 2006;Hashimoto & Sakai, 2002;Hsu, Jaeggi, & Novick, 2017;Makuuchi, Bahlmann, Anwander, & Friederici, 2009;Stephens, Silbert, & Hasson, 2010) and sentence production (Humphreys & Gennari, 2014). To examine the functional nature of the neural activation patterns more directly, transcranial direct current stimulation (tDCS) is used to directly influence the cortical areas during language processing (Joyal & Fecteau, 2016;Price, McAdams, Grossman, & * Address correspondence to Jana Klaus (j.klaus@donders.ru.nl).…”

mentioning

confidence: 98%

The Role of Left Dorsolateral Prefrontal Cortex in Language Processing

Klaus¹,

Schutter²

2017

Preprint

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In addition to the role of left frontotemporal areas in language processing, there is increasing evidence that language comprehension and production require control and working memory resources involving the left dorsolateral prefrontal cortex (DLPFC). The aim of this study was to investigate the role of the left DLPFC in both language comprehension and production. In a double-blind, sham-controlled crossover experiment, thirty-two participants received cathodal or sham transcranial direct current stimulation (tDCS) to the left DLPFC while performing a language comprehension and a language production task. Results showed that cathodal tDCS increases reaction times in the language comprehension task, but decreases naming latencies in the language production task. However, additional analyses revealed that the polarity of tDCS effects was highly correlated across tasks, implying differential individual susceptibility to the effect of tDCS within participants. Overall, our findings demonstrate that left DLPFC is part of the complex cortical network associated with language processing.

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mentioning

confidence: 98%

The Role of Left Dorsolateral Prefrontal Cortex in Language Processing

Klaus¹,

Schutter²

2017

Preprint

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“…Broca's region has been traditionally associated with verb processing across tasks such as verb-specific morphological transformation (Sahin, Pinker, & Halgren, 2006;Shapiro et al, 2005Shapiro et al, , 2006, action processing (Caspers et al, 2010), lesion studies of action picture naming (Damasio & Tranel, 1993) and verb semantics (Kemmerer et al, 2012). Although Broca's area activations were specific to verbs in the present ALE analysis, it is important to point out that Broca's area is associated with numerous cognitive operations, including language selective and domain general operations, such as math, music, cognitive control and working memory (Fedorenko, Behr, & Kanwisher, 2011;Fedorenko, Duncan, & Kanwisher, 2012;Hsu, Jaeggi, & Novick, 2017). Given Broca's area's role in numerous neuroimaging studies of syntactic processing (see Friederici, 2011 for a review) and patients with syntactic deficits (Faroqi-Shah et al, 2014), it is likely that these two verb selective regions subserve different aspects of verb processing: BA21 for verb representations and BA44/45 for implementing a verb's syntactic constraints.…”

Section: Verbs and Nouns Have Unique Representationsmentioning

confidence: 62%

“…Given Broca's area's role in numerous neuroimaging studies of syntactic processing (see Friederici, 2011 for a review) and patients with syntactic deficits (Faroqi-Shah et al, 2014), it is likely that these two verb selective regions subserve different aspects of verb processing: BA21 for verb representations and BA44/45 for implementing a verb's syntactic constraints. Although Broca's area activations were specific to verbs in the present ALE analysis, it is important to point out that Broca's area is associated with numerous cognitive operations, including language selective and domain general operations, such as math, music, cognitive control and working memory (Fedorenko, Behr, & Kanwisher, 2011;Fedorenko, Duncan, & Kanwisher, 2012;Hsu, Jaeggi, & Novick, 2017). This cluster included parts of the insula (BA13), whose lateral aspect is interspersed between BA44 and BA45.…”

Section: Verbs and Nouns Have Unique Representationsmentioning

confidence: 64%

Neural representation of word categories is distinct in the temporal lobe: An activation likelihood analysis

Faroqi‐Shah¹,

Sebastian²,

Woude³

2018

Human Brain Mapping

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The distinction between nouns and verbs is a language universal. Yet, functional neuroimaging studies comparing noun and verb processing have yielded inconsistent findings, ranging from a complete frontal(verb)-temporal(noun) dichotomy to a complete overlap in activation patterns. The current study addressed the debate about neural distinctions between nouns and verbs by conducting an activation likelihood estimation (ALE) meta-analysis of probabilistic cytoarchitectonic maps. Two levels of analysis were conducted: simple effects (Verbs vs. Baseline, Nouns vs. Baseline), and direct comparisons (Verbs vs. Nouns, Nouns vs. Verbs). Nouns were uniquely associated with a left medial temporal cluster (BA37). Activation foci for verbs included extensive inferior frontal (BA44-47) and mid-temporal (BA22, 21) regions in the left hemisphere. These findings confirm that the two grammatical classes have distinct neural architecture in supra-modal brain regions. Further, nouns and verbs overlapped in a small left lateral inferior temporal activation cluster (BA37), which is a region for modality-independent, grammatical class-independent lexical representations. These findings are most consistent with the view that as one acquires language, linguistic representations for a lexical category shift from the modality specific cortices which represent prototypical members of that category (e.g., motion for verbs) to abstract amodal representations in close proximity to modality specific cortices.

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“…In addition, we focused on two cognitive domains which have shown to be particularly susceptible to the effects of N-back training; specifically, we included measures of inhibitory control, interference resolution (Soveri et al 2017; Novick et al 2014), and visuospatial reasoning (Au et al 2015; Jaeggi et al 2014). Previous work has demonstrated that those domains share common variance with the N-back task and that they rely on similar neural networks (Hsu et al 2017; Jaeggi et al 2010; Szmalec et al 2011). Furthermore, an exploratory portion of our study investigated the generalization potential of N-back training to more applied measures.…”

Section: Methodsmentioning

confidence: 99%

“…Given the shared variance of N-back and various measures of inhibitory control and interference resolution (Hsu et al 2017; Szmalec et al 2011), we included two measures to capture training-related improvements in those domains and any differential effects as a function of training type.…”

Section: Methodsmentioning

confidence: 99%

The Benefits and Challenges of Implementing Motivational Features to Boost Cognitive Training Outcome

et al. 2017

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In the current literature, there are a number of cognitive training studies that use N-back tasks as their training vehicle; however, the interventions are often bland, and many studies suffer from considerable attrition rates. An increasingly common approach to increase participant engagement has been the implementation of motivational features in training tasks; yet, the effects of such “gamification” on learning have been inconsistent. To shed more light on those issues, here, we report the results of a training study conducted at two Universities in Southern California. A total of 115 participants completed 4 weeks (20 sessions) of N-back training in the laboratory. We varied the amount of “gamification” and the motivational features that might make the training more engaging and, potentially, more effective. Thus, 47 participants trained on a basic color/identity N-back version with no motivational features, whereas 68 participants trained on a gamified version that translated the basic mechanics of the N-back task into an engaging 3D space-themed “collection” game (Deveau et al. Frontiers in Systems Neuroscience, 8, 243, 2015). Both versions used similar adaptive algorithms to increase the difficulty level as participants became more proficient. Participants’ self-reports indicated that the group who trained on the gamified version enjoyed the intervention more than the group who trained on the non-gamified version. Furthermore, the participants who trained on the gamified version exerted more effort and also improved more during training. However, despite the differential training effects, there were no significant group differences in any of the outcome measures at post-test, suggesting that the inclusion of motivational features neither substantially benefited nor hurt broader learning. Overall, our findings provide guidelines for task implementation to optimally target participants’ interest and engagement to promote learning, which may lead to broader adoption and adherence of cognitive training.

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A common neural hub resolves syntactic and non-syntactic conflict through cooperation with task-specific networks

Cited by 54 publications

References 114 publications

The Role of Left Dorsolateral Prefrontal Cortex in Language Processing

The Role of Left Dorsolateral Prefrontal Cortex in Language Processing

Neural representation of word categories is distinct in the temporal lobe: An activation likelihood analysis

The Benefits and Challenges of Implementing Motivational Features to Boost Cognitive Training Outcome

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