scholarly journals Can the meaning of multiple words be integrated unconsciously?

2014 ◽  
Vol 369 (1641) ◽  
pp. 20130212 ◽  
Author(s):  
Simon van Gaal ◽  
Lionel Naccache ◽  
Julia D. I. Meuwese ◽  
Anouk M. van Loon ◽  
Alexandra H. Leighton ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Language Processing ◽  
Masked Priming ◽  
Rule Based ◽  
Negative Valence ◽  
Priming Paradigm ◽  
Specific Rule ◽  
Visible Target ◽  
Eeg Recordings

What are the limits of unconscious language processing? Can language circuits process simple grammatical constructions unconsciously and integrate the meaning of several unseen words? Using behavioural priming and electroencephalography (EEG), we studied a specific rule-based linguistic operation traditionally thought to require conscious cognitive control: the negation of valence. In a masked priming paradigm, two masked words were successively (Experiment 1) or simultaneously presented (Experiment 2), a modifier (‘not’/‘very’) and an adjective (e.g. ‘good’/‘bad’), followed by a visible target noun (e.g. ‘peace’/‘murder’). Subjects indicated whether the target noun had a positive or negative valence. The combination of these three words could either be contextually consistent (e.g. ‘very bad - murder’) or inconsistent (e.g. ‘not bad - murder’). EEG recordings revealed that grammatical negations could unfold partly unconsciously, as reflected in similar occipito-parietal N400 effects for conscious and unconscious three-word sequences forming inconsistent combinations. However, only conscious word sequences elicited P600 effects, later in time. Overall, these results suggest that multiple unconscious words can be rapidly integrated and that an unconscious negation can automatically ‘flip the sign’ of an unconscious adjective. These findings not only extend the limits of subliminal combinatorial language processes, but also highlight how consciousness modulates the grammatical integration of multiple words.

Efficient but less mobilized monitoring system in individuals with high aggressive predispositions

2019 ◽  
Author(s):  
Fanny Grisetto ◽  
Yvonne N. Delevoye-Turrell ◽  
Clémence Roger
Keyword(s):  
Cognitive Control ◽  
Monitoring System ◽  
Congruency Effect ◽  
Performance Monitoring ◽  
Simon Task ◽  
Aggressive Behaviors ◽  
Eeg Recordings ◽  
Global Reduction

Aggressive behaviors in pathological and healthy populations have been largely related to poor cognitive control functioning. However, few studies investigated the influence of aggressive traits (i.e., aggressiveness) on cognitive control. In the current study, we investigated the effects of aggressiveness on cognitive control abilities and particularly, on performance monitoring. Thirty-two participants performed a Simon task while electroencephalography (EEG) and electromyography (EMG) were recorded. Participants were classified as high and low aggressive using the BPAQ questionnaire (Buss & Perry, 1992). EMG recordings were used to reveal three response types by uncovering small incorrect muscular activations in ~15% of correct trials (i.e., partial-errors) that have to be distinguished from full-error and pure-correct responses. For these three response types, EEG recordings were used to extract fronto-central negativities indicative of performance monitoring, the error and correct (-related) negativities (ERN/Ne and CRN/Nc). Behavioral results indicated that the high aggressiveness group had a larger congruency effect compared to the low aggressiveness group, but there were no differences in accuracy. EEG results revealed a global reduction in performance-related negativities amplitudes in all the response types in the high aggressiveness group compared to the low aggressiveness group. Interestingly, the distinction between the ERN/Ne and the CRN/Nc components was preserved both in high and low aggressiveness groups. In sum, high aggressive traits did not affect the capacity to self-evaluate erroneous from correct actions but are associated with a decrease in the importance given to one’s own performance. The implication of these findings are discussed in relation to pathological aggressiveness.

An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

Second language processing of English past tense morphology: The role of working memory

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Filiz Rızaoğlu ◽  
Ayşe Gürel
Keyword(s):  
Working Memory ◽  
Second Language ◽  
Language Processing ◽  
Native Speakers ◽  
Negative Relationship ◽  
Masked Priming ◽  
Morphological Processing ◽  
Target Language ◽  
Past Tense ◽  
L2 Learners

AbstractThis study examines, via a masked priming task, the processing of English regular and irregular past tense morphology in proficient second language (L2) learners and native speakers in relation to working memory capacity (WMC), as measured by the Automated Reading Span (ARSPAN) and Operation Span (AOSPAN) tasks. The findings revealed quantitative group differences in the form of slower reaction times (RTs) in the L2-English group. While no correlation was found between the morphological processing patterns and WMC in either group, there was a negative relationship between English and Turkish ARSPAN scores and the speed of word recognition in the L2 group. Overall, comparable decompositional processing patterns found in both groups suggest that, like native speakers, high-proficiency L2 learners are sensitive to the morphological structure of the target language.

scholarly journals Eyes wide open: Pupil size as a proxy for inhibition in the masked-priming paradigm

2015 ◽  
Vol 44 (4) ◽  
pp. 554-564 ◽  
Author(s):  
Jason Geller ◽  
Mary L. Still ◽  
Alison L. Morris
Keyword(s):  
Pupil Size ◽  
Masked Priming ◽  
Priming Paradigm

Triage and diagnosis of COVID-19 from medical social media (Preprint)

2021 ◽  
Author(s):  
Abul Hasan ◽  
Mark Levene ◽  
David Weston ◽  
Renate Fromson ◽  
Nicolas Koslover ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Rule Based ◽  
Additional Information ◽  
Processing Pipeline ◽  
Machine Learning Models

BACKGROUND The COVID-19 pandemic has created a pressing need for integrating information from disparate sources, in order to assist decision makers. Social media is important in this respect, however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. In particular, machine learning techniques for triage and diagnosis could allow for a better understanding of what social media may offer in this respect. OBJECTIVE This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts, in order to provide researchers and other interested parties with additional information on the symptoms, severity and prevalence of the disease. METHODS The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients’ posts using conditional random fields. An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations are subsequently used to construct two different vector representations of each post. These vectors are applied separately to build support vector machine learning models to triage patients into three categories and diagnose them for COVID-19. RESULTS We report that Macro- and Micro-averaged F_{1\ }scores in the range of 71-96% and 61-87%, respectively, for the triage and diagnosis of COVID-19, when the models are trained on human labelled data. Our experimental results indicate that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. Also, we highlight important features uncovered by our diagnostic machine learning models and compare them with the most frequent symptoms revealed in another COVID-19 dataset. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from natural language narratives using a machine learning pipeline, in order to provide additional information on the severity and prevalence of the disease through the eyes of social media.

Testing the automaticity of syntax using masked visual priming

2021 ◽  
Author(s):  
Elena Pyatigorskaya ◽  
Matteo Maran ◽  
Emiliano Zaccarella
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Syntactic Priming ◽  
Visual Words ◽  
Visual Priming ◽  
Priming Paradigm ◽  
Syntactic Information ◽  
Fast Pace ◽  
Behavioural Experiments

Language comprehension proceeds at a very fast pace. It is argued that context influences the speed of language comprehension by providing informative cues for the correct processing of the incoming linguistic input. Priming studies investigating the role of context in language processing have shown that humans quickly recognise target words that share orthographic, morphological, or semantic information with their preceding primes. How syntactic information influences the processing of incoming words is however less known. Early syntactic priming studies reported faster recognition for noun and verb targets (e.g., apple or sing) following primes with which they form grammatical phrases or sentences (the apple, he sings). The studies however leave open a number of questions about the reported effect, including the degree of automaticity of syntactic priming, the facilitative versus inhibitory nature, and the specific mechanism underlying the priming effect—that is, the type of syntactic information primed on the target word. Here we employed a masked syntactic priming paradigm in four behavioural experiments in German language to test whether masked primes automatically facilitate the categorization of nouns and verbs presented as flashing visual words. Overall, we found robust syntactic priming effects with masked primes—thus suggesting high automaticity of the process—but only when verbs were morpho-syntactically marked (er kau-t; he chew-s). Furthermore, we found that, compared to baseline, primes slow down target categorisation when the relationship between prime and target is syntactically incorrect, rather than speeding it up when the prime-target relationship is syntactically correct. This argues in favour of an inhibitory nature of syntactic priming. Overall, the data indicate that humans automatically extract abstract syntactic features from word categories as flashing visual words, which has an impact on the speed of successful language processing during language comprehension.

Sign in / Sign up

Export Citation Format

Share Document