ERP Characterization of Sustained Attention Effects in Visual Lexical Categorization

Martin, Clara D.; Thierry, Guillaume; Démonet, Jean‐François

doi:10.1371/journal.pone.0009892

Cited by 15 publications

(12 citation statements)

References 64 publications

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“…As is common in the literature, we analyzed this effect by looking at the mean amplitude in the time window between 380 and 800 ms after feedback onset, based on visual inspection of the grand average. To compare P300 components for positive and negative feedback, we performed analyses on three electrode clusters along the midline (based on methodology described in Martin et al, 2010 ) for which averages were computed over 5 electrodes each: fronto-central (F1, F2, FC1, FCz, FC2), parieto-central (C1, Cz, C2, CP1, CP2), and parieto-occipital (CPz, P1, Pz, P2, POz). A repeated measures ANOVA was performed on the mean amplitudes per cluster with feedback type (2 levels), round (3 levels), and region (3 levels) as factors.…”

Section: Resultsmentioning

confidence: 99%

Electrophysiological Correlates of Error Monitoring and Feedback Processing in Second Language Learning

Bultena

Danielmeier

Bekkering

et al. 2017

Front. Hum. Neurosci.

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Humans monitor their behavior to optimize performance, which presumably relies on stable representations of correct responses. During second language (L2) learning, however, stable representations have yet to be formed while knowledge of the first language (L1) can interfere with learning, which in some cases results in persistent errors. In order to examine how correct L2 representations are stabilized, this study examined performance monitoring in the learning process of second language learners for a feature that conflicts with their first language. Using EEG, we investigated if L2 learners in a feedback-guided word gender assignment task showed signs of error detection in the form of an error-related negativity (ERN) before and after receiving feedback, and how feedback is processed. The results indicated that initially, response-locked negativities for correct (CRN) and incorrect (ERN) responses were of similar size, showing a lack of internal error detection when L2 representations are unstable. As behavioral performance improved following feedback, the ERN became larger than the CRN, pointing to the first signs of successful error detection. Additionally, we observed a second negativity following the ERN/CRN components, the amplitude of which followed a similar pattern as the previous negativities. Feedback-locked data indicated robust FRN and P300 effects in response to negative feedback across different rounds, demonstrating that feedback remained important in order to update memory representations during learning. We thus show that initially, L2 representations may often not be stable enough to warrant successful error monitoring, but can be stabilized through repeated feedback, which means that the brain is able to overcome L1 interference, and can learn to detect errors internally after a short training session. The results contribute a different perspective to the discussion on changes in ERN and FRN components in relation to learning, by extending the investigation of these effects to the language learning domain. Furthermore, these findings provide a further characterization of the online learning process of L2 learners.

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Section: Resultsmentioning

confidence: 99%

Electrophysiological Correlates of Error Monitoring and Feedback Processing in Second Language Learning

Bultena

Danielmeier

Bekkering

et al. 2017

Front. Hum. Neurosci.

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“…The influence of attention on perception was studied using event related potential (ERP) recordings [ 27 ] in electroencephalographic (EEG) or magnetoencephalographic (MEG) data by averaging over a large number of EEG (or MEG) traces associated with the perception of stimuli. The ERP approach was widely used for the analysis of visual attention, in particular, for studying selective attention of humans [ 28 ], including neural mechanisms of spatial selective attention [ 29 ], effects of mental fatigue on the attention [ 30 ], and effects of attention on visual lexical categorization [ 31 ]. According to Elmer [ 32 ], a specific brain response to a particular stimulus is too small to be distinguished in a single EEG.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between different scenarios would allow us to estimate the degree of alertness of the observer during visual perception and analyze the effect of motivation and task complexity, which would be impossible to do using approaches described in Refs. [ 55 – 59 ] or methods based on the event-related potentials (ERP) [ 28 – 31 ].…”

Section: Introductionmentioning

confidence: 99%

Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface

et al. 2017

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The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8–12 Hz) with a simultaneous increase in beta-wave activity (20–30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time.

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“…In general, neural representations of sensory inputs in early cortical areas are modulated by attention to accommodate task demands (e.g. Woods et al ., ; Martin et al ., ), and it is well known that the onset‐type N1 elicited by pitch onset is enhanced by attention (Hillyard et al ., ; Näätänen & Picton, ). Also regarding auditory change‐evoked responses, the P1–N1–P2 complex elicited by a formant shift in synthesized speech signal was enhanced by attention in a cochlear implant recipient (Martin, ), and an N1m‐like response to ‘auditory edges’ defined by changes in acoustic regularity was also enhanced by attention (Chait et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Effects of musical training on the early auditory cortical representation of pitch transitions as indexed by change‐N1

Itoh

Okumiya-Kanke

Nakayama

et al. 2012

Eur J of Neuroscience

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The effects of musical training on the early auditory cortical response to pitch transitions in music were investigated by use of the change-N1 component of auditory event-related potentials. Musicians and non-musicians were presented with music stimuli comprising a melody and a harmony under various listening conditions. First, when the subjects played a video game and were instructed to ignore the auditory stimuli, the onset of stimuli elicited a typical, fronto-central onset-N1, whereas melodic and harmonic pitch transitions within the stimuli elicited so-called change-N1s that were more posterior in scalp distribution. The pitch transition change-N1s, but not onset-N1, were enhanced in musicians. Second, when the listeners attended to the same stimuli as above to detect infrequently occurring target stimuli, the change-N1 elicited by pitch changes (in non-target stimuli) was augmented, in non-musicians only when the target was easily detectable, and in both musicians and non-musicians when it was difficult to detect. Thus, the early, obligatory cortical response to pitch transitions during passive listening was chronically enhanced by training in musicians, and, reflecting this training-induced enhancement, the task-related modulation of this response was also different between musicians and non-musicians. These results are the first to demonstrate the long-term effects of training, short-term effects of task and the effects of their interaction on the early (~100-ms) cortical processing of pitch transitions in music. The scalp distributions of these enhancement effects were generally right dominant at temporal electrode sites, suggesting contributions from the radially oriented subcomponent of change-N1, namely, the Tb (N1c) wave of the T-complex.

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ERP Characterization of Sustained Attention Effects in Visual Lexical Categorization

Cited by 15 publications

References 64 publications

Electrophysiological Correlates of Error Monitoring and Feedback Processing in Second Language Learning

Electrophysiological Correlates of Error Monitoring and Feedback Processing in Second Language Learning

Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface

Effects of musical training on the early auditory cortical representation of pitch transitions as indexed by change‐N1

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