scholarly journals Differential effects of learned associations with words and pseudowords on event-related brain potentials

2017 ◽  
Author(s):  
Louisa Kulke ◽  
Mareike Bayer ◽  
Anna-Maria Grimm ◽  
Annekathrin Schacht
Keyword(s):  
Language Processing ◽  
Word Association ◽  
Semantic Content ◽  
Written Language ◽  
Visual Features ◽  
List Type ◽  
Learning Session ◽  
Low Level ◽  
Early Processing ◽  
Gain Loss

Associated stimulus valence affects neural responses at an early processing stage. However, in the field of written language processing, it is unclear whether semantics of a word or low-level visual features affect early neural processing advantages. The current study aimed to investigate the role of semantic content on reward and loss associations. Participants completed a learning session to associate either words (Experiment 1, N=24) or pseudowords (Experiment 2, N=24) with different monetary outcomes (gain-associated, neutral or loss-associated). Gain-associated stimuli were learned fastest. Behavioural and neural response changes based on the associated outcome were further investigated in separate test sessions. Responses were faster towards gain- and loss-associated than neutral stimuli if they were words, but not pseudowords. Early P1 effects of associated outcome occurred for both pseudowords and words. Specifically, loss-association resulted in increased P1 amplitudes to pseudowords, compared to decreased amplitudes to words. Although visual features are likely to explain P1 effects for pseudowords, the inversed effect for words suggests that semantic content affects associative learning, potentially leading to stronger associations.Highlights- Neural mechanisms of gain/loss association to pseudowords and words were investigated- Loss effects can be observed for the P1 component- Words and pseudowords differ in the direction of loss effects- Semantic content may play a role during word association- Low-level visual features may play a role during pseudoword association

scholarly journals Electrophysiological correlates of the interplay between low-level visual features and emotional content during word reading

2018 ◽  
Author(s):  
Sebastian Schindler ◽  
Antonio Schettino ◽  
Gilles Pourtois
Keyword(s):  
Word Processing ◽  
Word Reading ◽  
Electrophysiological Study ◽  
Semantic Content ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Visual Features ◽  
Low Contrast ◽  
Attentional Processes ◽  
Low Level

Processing affectively charged visual stimuli typically results in increased amplitude of specific event-related potential (ERP) components. Low-level features similarly modulate electrophysiological responses, with amplitude changes proportional to variations in stimulus size and contrast. However, it remains unclear whether emotion-related amplifications during visual word processing are necessarily intertwined with changes in specific low-level features or, instead, may act independently.In this pre-registered electrophysiological study, we varied font size and contrast of neutral and negative words while participants were monitoring their semantic content. We examined ERP responses associated with early sensory and attentional processes as well as later stages of stimulus processing. Results showed amplitude modulations by low-level visual features early on following stimulus onset – i.e., P1 and N1 components –, while the LPP was independently modulated by these visual features. Independent effects of size and emotion were observed only at the level of the EPN. Here, larger EPN amplitudes for negative were observed only for small high contrast and large low contrast words. These results suggest that early increase in sensory processing at the EPN level for negative words is not automatic, but bound to specific combinations of low-level features, occurring presumably via attentional control processes.

scholarly journals Visual features influence thought content in the absence of overt semantic information

2019 ◽  
Author(s):  
Kathryn E Schertz ◽  
Omid Kardan ◽  
Marc Berman
Keyword(s):  
Cognitive Processes ◽  
Semantic Content ◽  
Visual Features ◽  
Edge Density ◽  
Low Level ◽  
Life Journey ◽  
Edge Content ◽  
High Level ◽  
Scene Identification ◽  
Selection Of

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

scholarly journals Hemispheric and executive influences on low-level language processing after traumatic brain injury

Brain Injury ◽  
2012 ◽  
Vol 26 (7-8) ◽  
pp. 984-995 ◽  
Author(s):  
Kathryn C. Russell ◽  
Patricia M. Arenth ◽  
Joelle M. Scanlon ◽  
Lauren Kessler ◽  
Joseph H. Ricker
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Language Processing ◽  
Low Level

scholarly journals Ekstraksi Informasi Halaman Web Menggunakan Pendekatan Bootstrapping pada Ontology-Based Information Extraction

2015 ◽  
Vol 9 (2) ◽  
pp. 111 ◽  
Author(s):  
Erma Susanti ◽  
Khabib Mustofa
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
Semantic Content ◽  
Extraction Process ◽  
Web Pages ◽  
Structured Information ◽  
Improved Performance ◽  
Types Of Information ◽  
Unstructured Information

AbstrakEkstraksi  informasi  merupakan suatu bidang ilmu untuk pengolahan bahasa alami, dengan cara mengubah teks tidak terstruktur menjadi informasi dalam bentuk terstruktur. Berbagai jenis informasi di Internet ditransmisikan secara tidak terstruktur melalui website, menyebabkan munculnya kebutuhan akan suatu teknologi untuk menganalisa teks dan menemukan pengetahuan yang relevan dalam bentuk informasi terstruktur. Contoh informasi tidak terstruktur adalah informasi utama yang ada pada konten halaman web. Bermacam pendekatan untuk ekstraksi informasi telah dikembangkan oleh berbagai peneliti, baik menggunakan metode manual atau otomatis, namun masih perlu ditingkatkan kinerjanya terkait akurasi dan kecepatan ekstraksi. Pada penelitian ini diusulkan suatu penerapan pendekatan ekstraksi informasi dengan mengkombinasikan pendekatan bootstrapping dengan Ontology-based Information Extraction (OBIE). Pendekatan bootstrapping dengan menggunakan sedikit contoh data berlabel, digunakan untuk memimalkan keterlibatan manusia dalam proses ekstraksi informasi, sedangkan penggunakan panduan ontologi untuk mengekstraksi classes (kelas), properties dan instance digunakan untuk menyediakan konten semantik untuk web semantik. Pengkombinasian kedua pendekatan tersebut diharapkan dapat meningkatan kecepatan proses ekstraksi dan akurasi hasil ekstraksi. Studi kasus untuk penerapan sistem ekstraksi informasi menggunakan dataset “LonelyPlanet”. Kata kunci—Ekstraksi informasi, ontologi, bootstrapping, Ontology-Based Information Extraction, OBIE, kinerja Abstract Information extraction is a field study of natural language processing by converting unstructured text into structured information. Several types of information on the Internet is transmitted through unstructured information via websites, led to emergence of the need a technology to analyze text and found relevant knowledge into structured information. For example of unstructured information is existing main information on the content of web pages. Various approaches  for information extraction have been developed by many researchers, either using manual or automatic method, but still need to be improved performance related accuracy and speed of extraction. This research proposed an approach of information extraction that combines bootstrapping approach with Ontology-Based Information Extraction (OBIE). Bootstrapping approach using small seed of labelled data, is used to minimize human intervention on information extraction process, while the use of guide ontology for extracting classes, properties and instances, using for provide semantic content for semantic web. Combining both approaches expected to increase speed of extraction process and accuracy of extraction results. Case study to apply information extraction system using “LonelyPlanet” datasets. Keywords— Information extraction, ontology, bootstrapping, Ontology-Based Information Extraction, OBIE, performance

scholarly journals Incrementality and intention-recognition in utterance processing

2011 ◽  
Vol 2 (1) ◽  
pp. 199-233 ◽  
Author(s):  
Eleni Gregoromichelaki ◽  
Ruth Kempson ◽  
Matthew Purver ◽  
Gregory J. Mills ◽  
Ronnie Cann ◽  
...  
Keyword(s):  
Predictive Model ◽  
Language Processing ◽  
Mental States ◽  
Higher Order ◽  
Low Level ◽  
Intention Recognition ◽  
Dialogue Modelling ◽  
Open Question ◽  
Dynamic Syntax ◽  
Do So

Ever since dialogue modelling first developed relative to broadly Gricean assumptions about utter-ance interpretation (Clark, 1996), it has remained an open question whether the full complexity of higher-order intention computation is made use of in everyday conversation. In this paper we examine the phenomenon of split utterances, from the perspective of Dynamic Syntax, to further probe the necessity of full intention recognition/formation in communication: we do so by exploring the extent to which the interactive coordination of dialogue exchange can be seen as emergent from low-level mechanisms of language processing, without needing representation by interlocutors of each other’s mental states, or fully developed intentions as regards messages to be conveyed. We thus illustrate how many dialogue phenomena can be seen as direct consequences of the grammar architecture, as long as this is presented within an incremental, goal-directed/predictive model.

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