scholarly journals Complex dynamics of semantic memory access in reading

2011 ◽  
Vol 9 (67) ◽  
pp. 328-338 ◽  
Author(s):  
Giosué Baggio ◽  
André Fonseca
Keyword(s):  
Language Processing ◽  
Complex Dynamics ◽  
Semantic Representation ◽  
Onset Time ◽  
Memory Systems ◽  
Prediction Errors ◽  
N400 Effect ◽  
Discourse Model ◽  
Lower Entropy ◽  
Critical Words

Understanding a word in context relies on a cascade of perceptual and conceptual processes, starting with modality-specific input decoding, and leading to the unification of the word's meaning into a discourse model. One critical cognitive event, turning a sensory stimulus into a meaningful linguistic sign, is the access of a semantic representation from memory. Little is known about the changes that activating a word's meaning brings about in cortical dynamics. We recorded the electroencephalogram (EEG) while participants read sentences that could contain a contextually unexpected word, such as ‘cold’ in ‘In July it is very cold outside’. We reconstructed trajectories in phase space from single-trial EEG time series, and we applied three nonlinear measures of predictability and complexity to each side of the semantic access boundary, estimated as the onset time of the N400 effect evoked by critical words. Relative to controls, unexpected words were associated with larger prediction errors preceding the onset of the N400. Accessing the meaning of such words produced a phase transition to lower entropy states, in which cortical processing becomes more predictable and more regular. Our study sheds new light on the dynamics of information flow through interfaces between sensory and memory systems during language processing.

scholarly journals Coercion and Compositionality

2010 ◽  
Vol 22 (9) ◽  
pp. 2131-2140 ◽  
Author(s):  
Giosuè Baggio ◽  
Travis Choma ◽  
Michiel van Lambalgen ◽  
Peter Hagoort
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Cognitive Neuroscience ◽  
Reading Time ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Syntactic Processing ◽  
Neural Basis ◽  
N400 Effect ◽  
Semantic Element

Research in psycholinguistics and in the cognitive neuroscience of language has suggested that semantic and syntactic processing are associated with different neurophysiologic correlates, such as the N400 and the P600 in the ERPs. However, only a handful of studies have investigated the neural basis of the syntax–semantics interface, and even fewer experiments have dealt with the cases in which semantic composition can proceed independently of the syntax. Here we looked into one such case—complement coercion—using ERPs. We compared sentences such as, “The journalist wrote the article” with “The journalist began the article.” The second sentence seems to involve a silent semantic element, which is expressed in the first sentence by the head of the verb phrase (VP) “wrote the article.” The second type of construction may therefore require the reader to infer or recover from memory a richer event sense of the VP “began the article,” such as began writing the article, and to integrate that into a semantic representation of the sentence. This operation is referred to as “complement coercion.” Consistently with earlier reading time, eye tracking, and MEG studies, we found traces of such additional computations in the ERPs: Coercion gives rise to a long-lasting negative shift, which differs at least in duration from a standard N400 effect. Issues regarding the nature of the computation involved are discussed in the light of a neurocognitive model of language processing and a formal semantic analysis of coercion.

Language processing is not a race against time

2016 ◽  
Vol 39 ◽  
Author(s):  
Giosuè Baggio ◽  
Carmelo M. Vicario
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Language Processing ◽  
Cognitive Functions ◽  
Memory Systems ◽  
Language System ◽  
Moderating Factors

AbstractWe agree with Christiansen & Chater (C&C) that language processing and acquisition are tightly constrained by the limits of sensory and memory systems. However, the human brain supports a range of cognitive functions that mitigate the effects of information processing bottlenecks. The language system is partly organised around these moderating factors, not just around restrictions on storage and computation.

EXPRESS: Selective adaptation in sentence comprehension: Evidence from event-related brain potentials

2020 ◽  
pp. 174702182098462
Author(s):  
Masataka Yano ◽  
Shugo Suwazono ◽  
Hiroshi Arao ◽  
Daichi Yasunaga ◽  
Hiroaki Oishi
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Processing System ◽  
Selective Adaptation ◽  
Event Related Potential ◽  
Equal Probability ◽  
N400 Effect ◽  
Mixed Effect ◽  
Processing Cost ◽  
Low Probability

The present study conducted two event-related potential experiments to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we manipulated the probability of morphosyntactically/semantically grammatical and anomalous sentence occurrence through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effect models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude’s decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. No evidence was found in favor of adaptation to semantic violations in that the processing cost of semantic violations did not decrease over the course of the experiment. Therefore, the present study demonstrated a dynamic aspect of language-processing system. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations.

scholarly journals Polysomnographic Sleep Is Associated With Time to Develop Dementia: A Study Using 19-Year VA National EHR Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 469-470
Author(s):  
Sara Nowakowski ◽  
Javad Razjouyan ◽  
Amir Sharafkhaneh ◽  
Mark Kunik ◽  
Aanand Naik
Keyword(s):  
Language Processing ◽  
Univariate Analysis ◽  
Sleep Onset ◽  
Onset Time ◽  
Late Time ◽  
Sleep Onset Latency ◽  
One Year ◽  
Objectively Measured ◽  
Onset Group ◽  
Dementia Onset

Abstract Few studies have longitudinally investigated the association between objectively measured sleep and time to develop dementia. This study leverages polysomnography (PSG) sleep data extracted from the VA national electronic health records (VA-EHR) to assess the association between sleep and time to develop dementia. We identified 61,165 PSG reports from the VA-EHR from 2000 to 2019 using CPT codes. Patients who developed dementia were identified using all-cause dementia ICD-9/10 codes documented on two separate visits starting one year after the PSG study until the end of 2019 in a 1-year sliding period (n=1,534). Using the first appearance of ICD-9/10 code as dementia onset time, patients were clustered into 3 groups of early-, mid-, and late time to develop dementia (mean = 2.7, 7.5, 12.8 years, respectively). Natural language processing was used to extract sleep efficiency (SE) and sleep onset latency (SOL). Univariate analysis was used to compare the groups. After adjusting for age, SE was significantly higher in the late (76%) vs early (69%) group and SOL was significantly shorter in late (21m) versus early (33m) group. SE was higher and SOL was shorter in patients who developed dementia later compared to those who developed dementia earlier. Greater sleep continuity in late dementia onset group suggests that sleep may be a modifiable risk factor that could potentially delay the onset of dementia.

scholarly journals Automatically Representing TExt Meaning via an Interlingua-based System (ARTEMIS). A further step towards the computational representation of RRG

2017 ◽  
Vol 1 (1) ◽  
pp. 61 ◽  
Author(s):  
Ricardo Mairal-Usón ◽  
Francisco Cortés-Rodríguez
Keyword(s):  
Language Processing ◽  
Semantic Representation ◽  
Analysis Data ◽  
Logical Structure ◽  
Automatic Generation ◽  
Role And Reference Grammar ◽  
Reference Grammar ◽  
Level 1 ◽  
Computational Resources ◽  
Computational Representation

Within the framework of FUNK Lab – a virtual laboratory for natural language processing inspired on a functionally-oriented linguistic theory like Role and Reference Grammar-, a number of computational resources have been built dealing with different aspects of language and with an application in different scientific domains, i.e. terminology, lexicography, sentiment analysis, document classification, text analysis, data mining etc. One of these resources is ARTEMIS (<span style="text-decoration: underline;">A</span>utomatically <span style="text-decoration: underline;">R</span>epresenting <span style="text-decoration: underline;">TE</span>xt <span style="text-decoration: underline;">M</span>eaning via an <span style="text-decoration: underline;">I</span>nterlingua-Based <span style="text-decoration: underline;">S</span>ystem), which departs from the pioneering work of Periñán-Pascual (2013) and Periñán-Pascual &amp; Arcas (2014).  This computational tool is a proof of concept prototype which allows the automatic generation of a conceptual logical structure (CLS) (cf. Mairal-Usón, Periñán-Pascual and Pérez 2012; Van Valin and Mairal-Usón 2014), that is, a fully specified semantic representation of an input text on the basis of a reduced sample of sentences. The primary aim of this paper is to develop the syntactic rules that form part of the computational grammar for the representation of simple clauses in English. More specifically, this work focuses on the format of those syntactic rules that account for the upper levels of the RRG Layered Structure of the Clause (LSC), that is, the <em>core</em> (and the level-1 construction associated with it), the <em>clause</em> and the <em>sentence </em>(Van Valin 2005). In essence, this analysis, together with that in Cortés-Rodríguez and Mairal-Usón (2016), offers an almost complete description of the computational grammar behind the LSC for simple clauses.

scholarly journals Cognitive Control Processes and Defense Mechanisms That Influence Aggressive Reactions: Toward an Integration of Socio-Cognitive and Psychodynamic Models of Aggression

2022 ◽  
Vol 15 ◽  
Author(s):  
Jean Gagnon ◽  
Joyce Emma Quansah ◽  
Paul McNicoll
Keyword(s):  
Cognitive Control ◽  
Response Inhibition ◽  
Defense Mechanisms ◽  
Stop Signal Task ◽  
Psychological Processes ◽  
N400 Effect ◽  
Defense Style ◽  
Prepotent Response ◽  
Aversive Behavior ◽  
Critical Words

Research on cognitive processes has primarily focused on cognitive control and inhibitory processes to the detriment of other psychological processes, such as defense mechanisms (DMs), which can be used to modify aggressive impulses as well as self/other images during interpersonal conflicts. First, we conducted an in-depth theoretical analysis of three socio-cognitive models and three psychodynamic models and compared main propositions regarding the source of aggression and processes that influence its enactment. Second, 32 participants completed the Hostile Expectancy Violation Paradigm (HEVP) in which scenarios describe a hostile vs. non-hostile social context followed by a character's ambiguous aversive behavior. The N400 effect to critical words that violate expected hostile vs. non-hostile intent of the behavior was analyzed. Prepotent response inhibition was measured using a Stop Signal task (SST) and DMs were assessed with the Defense Style Questionnaire (DSQ-60). Results showed that reactive aggression and HIA were not significantly correlated with response inhibition but were significantly positively and negatively correlated with image distorting defense style and adaptive defense style, respectively. The present article has highlighted the importance of integrating socio-cognitive and psychodynamic models to account for the full complexity underlying psychological processes that influence reactive aggressive behavior.

Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

2018 ◽  
Vol 24 (6) ◽  
pp. 861-886 ◽  
Author(s):  
ABDULGABBAR SAIF ◽  
UMMI ZAKIAH ZAINODIN ◽  
NAZLIA OMAR ◽  
ABDULLAH SAEED GHAREB
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Language Processing ◽  
Knowledge Engineering ◽  
Semantic Representation ◽  
Semantic Similarity Measure ◽  
Topological Parameters ◽  
Research Areas ◽  
Comparison Results ◽  
Feature Based

AbstractSemantic measures are used in handling different issues in several research areas, such as artificial intelligence, natural language processing, knowledge engineering, bioinformatics, and information retrieval. Hierarchical feature-based semantic measures have been proposed to estimate the semantic similarity between two concepts/words depending on the features extracted from a semantic taxonomy (hierarchy) of a given lexical source. The central issue in these measures is the constant weighting assumption that all elements in the semantic representation of the concept possess the same relevance. In this paper, a new weighting-based semantic similarity measure is proposed to address the issues in hierarchical feature-based measures. Four mechanisms are introduced to weigh the degree of relevance of features in the semantic representation of a concept by using topological parameters (edge, depth, descendants, and density) in a semantic taxonomy. With the semantic taxonomy of WordNet, the proposed semantic measure is evaluated for word semantic similarity in four gold-standard datasets. Experimental results show that the proposed measure outperforms hierarchical feature-based semantic measures in all the datasets. Comparison results also imply that the proposed measure is more effective than information-content measures in measuring semantic similarity.

scholarly journals Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things

2019 ◽  
Vol 9 (16) ◽  
pp. 3283 ◽  
Author(s):  
Zhenhao Luo ◽  
Baosheng Wang ◽  
Yong Tang ◽  
Wei Xie
Keyword(s):  
Internet Of Things ◽  
Language Processing ◽  
Semantic Representation ◽  
Compiler Optimization ◽  
Symbolic Execution ◽  
Detection Methods ◽  
Intermediate Representation ◽  
Clone Detection ◽  
Code Reuse ◽  
Representation Model

Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities.

scholarly journals What Limits Our Capacity to Process Nested Long-Range Dependencies in Sentence Comprehension?

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 446
Author(s):  
Yair Lakretz ◽  
Stanislas Dehaene ◽  
Jean-Rémi King
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Sentence Processing ◽  
Sentence Comprehension ◽  
Semantic Representation ◽  
A Priori ◽  
Syntactic Structures ◽  
Capacity Limitation ◽  
Capacity Limit ◽  
Alternative Approach

Sentence comprehension requires inferring, from a sequence of words, the structure of syntactic relationships that bind these words into a semantic representation. Our limited ability to build some specific syntactic structures, such as nested center-embedded clauses (e.g., “The dog that the cat that the mouse bit chased ran away”), suggests a striking capacity limitation of sentence processing, and thus offers a window to understand how the human brain processes sentences. Here, we review the main hypotheses proposed in psycholinguistics to explain such capacity limitation. We then introduce an alternative approach, derived from our recent work on artificial neural networks optimized for language modeling, and predict that capacity limitation derives from the emergence of sparse and feature-specific syntactic units. Unlike psycholinguistic theories, our neural network-based framework provides precise capacity-limit predictions without making any a priori assumptions about the form of the grammar or parser. Finally, we discuss how our framework may clarify the mechanistic underpinning of language processing and its limitations in the human brain.

scholarly journals Positive reward prediction errors strengthen incidental memory encoding

2018 ◽  
Author(s):  
Anthony I. Jang ◽  
Matthew R. Nassar ◽  
Daniel G. Dillon ◽  
Michael J. Frank
Keyword(s):  
Prediction Error ◽  
Memory Systems ◽  
Prediction Errors ◽  
Memory Encoding ◽  
Dopamine System ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Incidental Memory ◽  
Episodic Memories ◽  
The Impact

AbstractThe dopamine system is thought to provide a reward prediction error signal that facilitates reinforcement learning and reward-based choice in corticostriatal circuits. While it is believed that similar prediction error signals are also provided to temporal lobe memory systems, the impact of such signals on episodic memory encoding has not been fully characterized. Here we develop an incidental memory paradigm that allows us to 1) estimate the influence of reward prediction errors on the formation of episodic memories, 2) dissociate this influence from other factors such as surprise and uncertainty, 3) test the degree to which this influence depends on temporal correspondence between prediction error and memoranda presentation, and 4) determine the extent to which this influence is consolidation-dependent. We find that when choosing to gamble for potential rewards during a primary decision making task, people encode incidental memoranda more strongly even though they are not aware that their memory will be subsequently probed. Moreover, this strengthened encoding scales with the reward prediction error, and not overall reward, experienced selectively at the time of memoranda presentation (and not before or after). Finally, this strengthened encoding is identifiable within a few minutes and is not substantially enhanced after twenty-four hours, indicating that it is not consolidation-dependent. These results suggest a computationally and temporally specific role for putative dopaminergic reward prediction error signaling in memory formation.

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