Brain Activity Patterns Suggest Prosodic Influences on Syntactic Parsing in the Comprehension of Spoken Sentences

We address the question of how syntactic and prosodic factors interact during the comprehension of spoken sentences. Previous studies using event-related brain potential measures have revealed that syntactic phrase structure violations elicit an early left anterior negativity followed by a late posterior positivity (P600). We present recent experimental evidence showing that prosodic information can modulate these components and thus the syntactic processes they reflect. We conclude that the initiation of first-pass parsing processes is affected by the appropriateness of the prosodic realization of the preceding element.

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Electrophysiological Evidence for Two Steps in Syntactic Analysis: Early Automatic and Late Controlled Processes

Journal of Cognitive Neuroscience ◽

10.1162/089892999563328 ◽

1999 ◽

Vol 11 (2) ◽

pp. 194-205 ◽

Cited By ~ 371

Author(s):

Anja Hahne ◽

Angela D. Friederici

Keyword(s):

Structural Analysis ◽

Phrase Structure ◽

Syntactic Analysis ◽

Strategic Control ◽

Brain Potential ◽

Controlled Processes ◽

Late Positivity ◽

Electrophysiological Evidence ◽

First Pass ◽

Anterior Negativity

In this study we examined the properties of the processes involved in the structural analysis of sentences using event-related brain potential measures (ERP). Previous research had shown two ERP components to correlate with phrase structure violations: an early left anterior negativity (ELAN), which is assumed to reflect first-pass parsing processes, and a late parietally distributed positivity (P600), assumed to reflect second-pass parsing processes. We hypothesized that the first-pass parsing processes are highly automatic, whereas second-pass parsing processes are more controlled. To test this hypothesis we varied the proportion of correct sentences and sentences containing phrase structure violations with incorrect sentences being either of a low (20% violation) or a high (80% violation) proportion. Results showed that the early left anterior negativity was elicited and equally pronounced under both proportion conditions. By contrast, the late positivity was elicited for a low proportion of incorrect sentences only. This data pattern suggests that first-pass parsing processes are automatic, whereas second-pass parsing processes are under participants' strategic control.

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Neural Correlates of Syntactic Processing in Two-Year-Olds

Journal of Cognitive Neuroscience ◽

10.1162/089892905774597236 ◽

2005 ◽

Vol 17 (10) ◽

pp. 1667-1678 ◽

Cited By ~ 54

Author(s):

Regine Oberecker ◽

Manuela Friedrich ◽

Angela D. Friederici

Keyword(s):

Sentence Processing ◽

Phrase Structure ◽

Neural Correlates ◽

Neural Mechanisms ◽

Syntactic Processing ◽

Early Language ◽

Late Positivity ◽

Early Language Development ◽

First Pass ◽

Anterior Negativity

Event-related brain potential (ERP) studies of sentence processing in adults have shown that phrase-structure violations are associated with two ERP components: an early left anterior negativity (ELAN) and a late, centro-parietal positivity (P600). Although the ELAN reflects highly automatic first-pass sentence parsing, the P600 has been interpreted to reflect later, more controlled processes. The present ERP study investigates the processing of phrase-structure violations in children below three years of age. Both children (mean age of 2.8 years) and adults passively listened to short active sentences that were either correct or syntactically incorrect. Adults displayed an ELAN that was followed by a P600 to the syntactic violation. Children also demonstrated a biphasic ERP pattern consisting of an early left hemispheric negativity and a late positivity. Both components, however, started later and persisted longer than those observed in adults. The left lateralization of the children's negativity suggests that this component can be interpreted as a child-specific precursor to the ELAN observed in adults. The appearance of the early negativity indicates that the neural mechanisms of syntactic parsing are present, in principle, during early language development.

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A New Technique to Measure the Level of Interest Using Heartbeat-Evoked Brain Potential

Journal of Psychophysiology ◽

10.1027/0269-8803/a000257 ◽

2021 ◽

Vol 35 (1) ◽

pp. 15-22

Author(s):

Kohei Fuseda ◽

Jun’ichi Katayama

Keyword(s):

Brain Activity ◽

Random Order ◽

Overt Response ◽

Brain Potential ◽

Probe Technique ◽

Dynamic Stimuli ◽

Attentional Resources ◽

Opposite Sex ◽

A New Technique ◽

Task Irrelevant

Abstract. Interest is a positive emotion related to attention. The event-related brain potential (ERP) probe technique is a useful method to evaluate the level of interest in dynamic stimuli. However, even in the irrelevant probe technique, the probe is presented as a physical stimulus and steals the observer’s attentional resources, although no overt response is required. Therefore, the probe might become a problematic distractor, preventing deep immersion of participants. Heartbeat-evoked brain potential (HEP) is a brain activity, time-locked to a cardiac event. No probe is required to obtain HEP data. Thus, we aimed to investigate whether the HEP can be used to evaluate the level of interest. Twenty-four participants (12 males and 12 females) watched attractive and unattractive individuals of the opposite sex in interesting and uninteresting videos (7 min each), respectively. We performed two techniques each for both the interesting and the uninteresting videos: the ERP probe and the HEP techniques. In the former, somatosensory stimuli were presented as task-irrelevant probes while participants watched videos: frequent (80%) and infrequent (20%) stimuli were presented at each wrist in random order. In the latter, participants watched videos without the probe. The P2 amplitude in response to the somatosensory probe was smaller and the positive wave amplitudes of HEP were larger while watching the videos of attractive individuals than while watching the videos of unattractive ones. These results indicate that the HEP technique is a useful method to evaluate the level of interest without an external probe stimulus.

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Brain Activity Patterns in Anxiety-Prone People Suggest Deficits in Handling Fear

PsycEXTRA Dataset ◽

10.1037/e614992011-001 ◽

2011 ◽

Keyword(s):

Brain Activity ◽

Activity Patterns

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Neural alignment predicts learning outcomes in students taking an introduction to computer science course

Nature Communications ◽

10.1038/s41467-021-22202-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Meir Meshulam ◽

Liat Hasenfratz ◽

Hanna Hillman ◽

Yun-Fei Liu ◽

Mai Nguyen ◽

...

Keyword(s):

Computer Science ◽

Learning Outcomes ◽

Brain Activity ◽

Activity Patterns ◽

Real Life ◽

Final Exam ◽

Neural Representations ◽

Real Life Setting ◽

The Right ◽

Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

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Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽

10.3390/info12060226 ◽

2021 ◽

Vol 12 (6) ◽

pp. 226

Author(s):

Lisa-Marie Vortmann ◽

Leonid Schwenke ◽

Felix Putze

Keyword(s):

Augmented Reality ◽

Eye Tracking ◽

Brain Activity ◽

Activity Patterns ◽

Training Data ◽

Machine Learning Techniques ◽

Neural Net ◽

Dependent Manner ◽

Tracking Data ◽

Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

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