scholarly journals Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network

2020 ◽  
Author(s):  
Leila Wehbe ◽  
Idan Asher Blank ◽  
Cory Shain ◽  
Richard Futrell ◽  
Roger Levy ◽  
...  
Keyword(s):  
Language Comprehension ◽  
Processing Load ◽  
Behavioral Measures ◽  
Comprehension Difficulty ◽  
Domain Specific ◽  
Incremental Processing ◽  
Story Listening ◽  
Core Language ◽  
The Relationship ◽  
Language Network

AbstractWhat role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general “multiple demand” (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, fMRI data collected during naturalistic story listening are compared to theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant-or trial-level variability, the neuroimaging and behavioral datasets were collected in non-overlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal “core language network”, in both left hemispheric areas and their right hemispheric homologues. These results argue against strong involvement of domain-general executive circuits in language comprehension.

Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension

2021 ◽  
Vol 30 (6) ◽  
pp. 526-534
Author(s):  
Evelina Fedorenko ◽  
Cory Shain
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Fluid Intelligence ◽  
Linguistic Knowledge ◽  
Domain Specific ◽  
Cognitive Operations ◽  
High Level ◽  
General Circuits ◽  
The Mind ◽  
Language Network

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s lexicon and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension and that past findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation of memory and computation in the mind and brain.

The relationship between acute stress and neurophysiological and behavioral measures of food-related inhibitory control: An event-related potential (ERP) study

Appetite ◽  
2021 ◽  
pp. 105862
Author(s):  
Whitney D. Allen ◽  
Rebekah E. Rodeback ◽  
Kaylie A. Carbine ◽  
Ariana M. Hedges-Muncy ◽  
James D. LeCheminant ◽  
...  
Keyword(s):  
Inhibitory Control ◽  
Acute Stress ◽  
Event Related Potential ◽  
Behavioral Measures ◽  
The Relationship

scholarly journals Comparison of Electrically Evoked Compound Action Potential Thresholds and Loudness Estimates for the Stimuli Used to Program the Advanced Bionics Cochlear Implant

2010 ◽  
Vol 21 (01) ◽  
pp. 016-027 ◽  
Author(s):  
Eun Kyung Jeon ◽  
Carolyn J. Brown ◽  
Christine P. Etler ◽  
Sara O'Brien ◽  
Li-Kuei Chiou ◽  
...  
Keyword(s):  
Cochlear Implant ◽  
Dynamic Range ◽  
Compound Action Potential ◽  
Behavioral Measures ◽  
Speech Processor ◽  
Compound Action Potentials ◽  
Evoked Compound Action Potential ◽  
The Individual ◽  
The Relationship ◽  
Compound Action

Background: In the mid-1990s, Cochlear Corporation introduced a cochlear implant (CI) to the market that was equipped with hardware that made it possible to record electrically evoked compound action potentials (ECAPs) from CI users of all ages. Over the course of the next decade, many studies were published that compared ECAP thresholds with levels used to program the speech processor of the Nucleus CI. In 2001 Advanced Bionics Corporation introduced the Clarion CII cochlear implant (the Clarion CII internal device is also known as the CII Bionic Ear). This cochlear implant was also equipped with a system that allowed measurement of the ECAP. While a great deal is known about how ECAP thresholds compare with the levels used to program the speech processor of the Nucleus CI, relatively few studies have reported comparisons between ECAP thresholds and the levels used to program the speech processor of the Advanced Bionics CI. Purpose: To explore the relationship between ECAP thresholds and behavioral measures of perceptual dynamic range for the range of stimuli commonly used to program the speech processor of the Advanced Bionics CI. Research Design: This prospective and experimental study uses correlational and descriptive statistics to define the relationship between ECAP thresholds and perceptual dynamic range measures. Study Sample: Twelve postlingually deafened adults participated in this study. All were experienced users of the Advanced Bionics CI system. Data Collection and Analysis: ECAP thresholds were recorded using the commercially available SoundWave software. Perceptual measures of threshold (T-level), most comfortable level (M-level), and maximum comfortable level (C-level) were obtained using both “tone bursts” and “speech bursts.” The relationship between these perceptual and electrophysiological variables was defined using paired t-tests as well as correlation and linear regression. Results: ECAP thresholds were significantly correlated with the perceptual dynamic range measures studied; however, correlations were not strong. Analysis of the individual data revealed considerable discrepancy between the contour of ECAP threshold versus electrode function and the behavioral loudness estimates used for programming. Conclusion: ECAP thresholds recorded from Advanced Bionics cochlear implant users always indicated levels where the programming stimulus was audible for the listener. However, the correlation between ECAP thresholds and M-levels (the primary metric used to program the speech processor of the Advanced Bionics CI), while statistically significant, was quite modest. If programming levels are to be determined on the basis of ECAP thresholds, care should be taken to ensure that stimulation is not uncomfortably loud, particularly on the basal electrodes in the array.

scholarly journals Precise quantification of behavioral individuality from 80 million decisions across 183,000 flies

2021 ◽  
Author(s):  
Benjamin L de Bivort ◽  
Seaan M Buchanan ◽  
Kyobi J Skutt-Kakaria ◽  
Erika Gajda ◽  
Chelsea J O'Leary ◽  
...  
Keyword(s):  
Large Data ◽  
Behavioral Variability ◽  
Biological Mechanisms ◽  
Behavioral Experiments ◽  
Behavioral Measures ◽  
Data Set ◽  
Precise Quantification ◽  
Heterogametic Sex ◽  
High Throughput Experiments ◽  
The Relationship

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

scholarly journals Towards Reading Comprehension for Long Documents

2018 ◽  
Author(s):  
Yuanxing Zhang ◽  
Yangbin Zhang ◽  
Kaigui Bian ◽  
Xiaoming Li
Keyword(s):  
Reading Comprehension ◽  
Language Comprehension ◽  
Exact Match ◽  
Attention Model ◽  
Comprehension Task ◽  
Proposed Model ◽  
Short Span ◽  
Machine Reading ◽  
The Relationship

Machine reading comprehension has gained attention from both industry and academia. It is a very challenging task that involves various domains such as language comprehension, knowledge inference, summarization, etc. Previous studies mainly focus on reading comprehension on short paragraphs, and these approaches fail to perform well on the documents. In this paper, we propose a hierarchical match attention model to instruct the machine to extract answers from a specific short span of passages for the long document reading comprehension (LDRC) task. The model takes advantages from hierarchical-LSTM to learn the paragraph-level representation, and implements the match mechanism (i.e., quantifying the relationship between two contexts) to find the most appropriate paragraph that includes the hint of answers. Then the task can be decoupled into reading comprehension task for short paragraph, such that the answer can be produced. Experiments on the modified SQuAD dataset show that our proposed model outperforms existing reading comprehension models by at least 20% regarding exact match (EM), F1 and the proportion of identified paragraphs which are exactly the short paragraphs where the original answers locate.

scholarly journals Emotional aspects of language

ANALES RANM ◽  
2018 ◽  
Vol 135 (135(02)) ◽  
pp. 41-46
Author(s):  
J.A. Hinojosa ◽  
E.M. Moreno ◽  
P. Ferré ◽  
M.A. Pozo
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Developmental Psychology ◽  
Brain Regions ◽  
Emotional Content ◽  
Current Work ◽  
Subcortical Structures ◽  
Emotional Aspects ◽  
The Relationship ◽  
And Linguistics

Up to date the study of the relationship between language and emotion has received little attention from researchers. In the current work we will summarize evidence coming from the fields of developmental psychology and affective neurolinguistics. The results from different studies indicate that learning emotional language has its own idiosyncrasy. Also, the emotional content of words, sentences and texts modulates several levels of language processing, including phonological, lexico-semantic and morpho-syntactic aspects of language comprehension and production. Finally, the interactions between language and emotion involve the activation of several brain regions linked to distinct affective and linguistics processes, like parts of frontal and temporal cortices or subcortical structures such as the amygdala. Overall, the results of these studies clearly show that emotional content determines certain aspects of how we acquire and process language.

scholarly journals Forensic Feature-Comparison Expertise: Statistical Learning Facilitates Visual Comparison Performance

2020 ◽  
Author(s):  
Bethany Growns ◽  
Kristy Martire
Keyword(s):  
Statistical Learning ◽  
High Performance ◽  
Comparison Task ◽  
Domain Specific ◽  
Visual Evidence ◽  
Visual Comparison ◽  
Distributional Information ◽  
Distributional Learning ◽  
The Impact ◽  
The Relationship

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing visual evidence samples. This paper examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relation-ship between distributional learning and visual comparison performance, and the impact of training about the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accu-rate training (informed novices; n = 32) or inaccurate training (misinformed novices; n = 32) in Experiment 1; and between forensic examiners (n = 26), informed novices (n = 29) and unin-formed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and nov-ices performed significantly above chance in a visual comparison task where distributional learning was required for high performance. However, informed novices outperformed all par-ticipants and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners’ expertise is domain-specific and doesn’t generalise to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance.

Telemetered Heart Rate as a Psychophysiological Correlate of Mother-Child Interaction, by K. Michael Hong, M.D., Douglas M. Bowden, M.D., and Kate L. Kogan, Ph.D

PEDIATRICS ◽  
1979 ◽  
Vol 63 (4) ◽  
pp. 601-601
Keyword(s):  
Heart Rate ◽  
Three Dimensions ◽  
Behavioral Measures ◽  
Transition Analysis ◽  
Physiological Variables ◽  
Cardiac Deceleration ◽  
Behavioral Interaction ◽  
New Findings ◽  
Naturalistic Setting ◽  
The Relationship

Heart rate was telemetered from 6 preschool children during play sessions with their mothers. Their behavioral interaction was simultaneously recorded on videotape and rated on three dimensions of interaction: status (submission- dominance), affect (hostility-warmth), and degree of involvement; 100 specific behaviors were coded in consecutive 4-second epochs. In exploring the relationship between heart rate and the behavioral measures, we applied two kinds of analysis—state analysis and transition analysis. The usefulness of recording heart rate in a naturalistic setting was demonstrated by replicating the finding from more rigidly defined experiments that intense looking at an object is associated with cardiac deceleration. New findings were that submissive status and warm affect of the child and dominant status and warm affect of the mother were associated with low heart rate in the child and that the onset of smiling was associated with cardiac deceleration in most situations. The study demonstrates the feasibility and some of the potential uses of continuously telemetered heart rate for analyzing interactional and physiological variables in a naturalistic setting.

What Are the Nodes? Unitization and Configural Learning vs. Selective Attention

2018 ◽  
pp. 69-98
Author(s):  
Vsevolod Kapatsinski
Keyword(s):  
Selective Attention ◽  
Language Change ◽  
Context Effects ◽  
Future Research ◽  
Extensive Experience ◽  
Configural Learning ◽  
Incremental Processing ◽  
Promising Area ◽  
Dual Route ◽  
The Relationship

This chapter introduces the debate between elemental and configural learning models. Configural models represent both a whole pattern and its parts as separate nodes, which are then both associable, i.e. available for wiring with other nodes. This necessitates a kind of hierarchical inference at the timescale of learning and motivates a dual-route approach at the timescale of processing. Some patterns of language change (semanticization and frequency-in-a-favourable-context effects) are argued to be attributable to hierarchical inference. The most prominent configural pattern in language is argued to be a superadditive interaction. However, such interactions are argued to often be unstable in comprehension due to selective attention and incremental processing. Selective attention causes the learner to focus on one part of a configuration over others. Incremental processing favors the initial part, which can then overshadow other parts and drive the recognition decision. Only with extensive experience, can one can learn to integrate multiple cues. When cues are integrated, the weaker cue can cue the outcome directly or can serve as an occasion-setter to the relationship between the outcome and the primary cue. The conditions under which occasion-setting arises in language acquisition is a promising area for future research.

Application of Semantic Web Based on the Domain-Specific Ontology for Global KM

2007 ◽  
pp. 287-309
Author(s):  
Jaehun Joo ◽  
Sang Lee ◽  
Yongil Jeong
Keyword(s):  
Knowledge Management ◽  
Semantic Web ◽  
Web Applications ◽  
Web Based ◽  
Domain Specific ◽  
Promising Area ◽  
The Relationship

This chapter introduces an application of the Semantic Web based on ontology to the tourism business. Tourism business is one promising area for Semantic Web applications. To realize the potential of the Semantic Web, we need to find a killer application of the Semantic Web in the knowledge management (KM) area. The ontology as a key enabler is deigned and implemented under a framework of the Semantic-Web-driven KM system in a tourism domain. Finally, we discussed the relationship between the Semantic Web and KM processes.

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