scholarly journals Eye-tracking as a proxy for coherence and complexity of texts

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260236
Author(s):  
Débora Torres ◽  
Wagner R. Sena ◽  
Humberto A. Carmona ◽  
André A. Moreira ◽  
Hernán A. Makse ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Maximum Entropy ◽  
Language Processing ◽  
Entropy Method ◽  
Internet Survey ◽  
Entropy Model ◽  
The Gaze ◽  
Attention Focus ◽  
Average Magnetization ◽  
High Level

Reading is a complex cognitive process that involves primary oculomotor function and high-level activities like attention focus and language processing. When we read, our eyes move by primary physiological functions while responding to language-processing demands. In fact, the eyes perform discontinuous twofold movements, namely, successive long jumps (saccades) interposed by small steps (fixations) in which the gaze “scans” confined locations. It is only through the fixations that information is effectively captured for brain processing. Since individuals can express similar as well as entirely different opinions about a given text, it is therefore expected that the form, content and style of a text could induce different eye-movement patterns among people. A question that naturally arises is whether these individuals’ behaviours are correlated, so that eye-tracking while reading can be used as a proxy for text subjective properties. Here we perform a set of eye-tracking experiments with a group of individuals reading different types of texts, including children stories, random word generated texts and excerpts from literature work. In parallel, an extensive Internet survey was conducted for categorizing these texts in terms of their complexity and coherence, considering a large number of individuals selected according to different ages, gender and levels of education. The computational analysis of the fixation maps obtained from the gaze trajectories of the subjects for a given text reveals that the average “magnetization” of the fixation configurations correlates strongly with their complexity observed in the survey. Moreover, we perform a thermodynamic analysis using the Maximum-Entropy Model and find that coherent texts were closer to their corresponding “critical points” than non-coherent ones, as computed from the Pairwise Maximum-Entropy method, suggesting that different texts may induce distinct cohesive reading activities.

Statistical Methods

2012 ◽  
Author(s):  
Christer Samuelsson
Keyword(s):  
Language Learning ◽  
Maximum Entropy ◽  
Statistical Methods ◽  
Language Processing ◽  
Mathematical Statistics ◽  
Semantic Interpretation ◽  
Entropy Model ◽  
Heterogeneous Information ◽  
Part Of Speech ◽  
Maximum Entropy Modelling

Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

A Modified Markov Based Maximum-entropy Model for POS Tagging of Odia Text

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Maximum Entropy ◽  
Language Processing ◽  
Conditional Random Field ◽  
Entropy Model ◽  
Text Corpus ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Linguistic Rules ◽  
The Rich ◽  
Pos Tagger

POS (Parts of Speech) tagging, a vital step in diverse Natural Language Processing (NLP) tasks has not drawn much attention in case of Odia a computationally under-developed language. The proposed hybrid method suggests a robust POS tagger for Odia. Observing the rich morphology of the language and unavailability of sufficient annotated text corpus a combination of machine learning and linguistic rules is adopted in the building of the tagger. The tagger is trained on tagged text corpus from the domain of tourism and is capable of obtaining a perceptible improvement in the result. Also an appreciable performance is observed for news articles texts of varied domains. The performance of proposed algorithm experimenting on Odia language shows its manifestation in dominating over existing methods like rule based, hidden Markov model (HMM), maximum entropy (ME) and conditional random field (CRF).

scholarly journals Geographic distribution and future expansion of Aedes albopictus in the Democratic Republic of the Congo

2021 ◽  
Author(s):  
Fabien cedric Zimbombe Vulu ◽  
Thierry Lengu Bobanga ◽  
Toshihiko Sunahara ◽  
Kyoko Futami ◽  
Hu Jinping ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Aedes Albopictus ◽  
Vegetation Index ◽  
Democratic Republic ◽  
Mosquito Species ◽  
Entropy Method ◽  
Maximum Temperature ◽  
African Countries ◽  
Entropy Model ◽  
Republic Of The Congo

Aedes albopictus with an Asian origin has been reported from central African countries. The establishment of this mosquito species poses a serious threat as the vector of various infectious diseases. Since information about Ae. albopictus in Democratic Republic of the Congo (DRC) is scarce, we investigated the current distribution of this mosquito species. Based on the factors affecting the distribution, we predicted future distribution. We conduced entomological surveys in Kinshasa and three neighboring cities from May 2017 to September 2019. The survey was extended to seven inland cities. A total of 19 environmental variables were examined using the maximum entropy method to identify areas suitable for Ae. albopictus to establish a population. We found Ae. albopictus at 21 of 23 sites in Kinshasa and three neighboring cities. For the first time Ae. albopictus was also found from three of seven inland cities, while it was not found in four cities located in the eastern and southeastern parts of DRC. A maximum entropy model revealed that the occurrence of Ae. albopictus was positively associated with maximum temperature of the warmest month, and negatively associated with wider mean diurnal temperature range and enhanced vegetation index. The model predicted that most parts of DRC are suitable for the establishment of the mosquito. The unsuitable areas were the eastern and southeastern highlands, which have low temperatures and long dry seasons. We confirmed that Ae. albopictus is well established in Kinshasa and its neighboring cities. The expansion of Ae. albopictus to the inland is ongoing, and in the future the mosquito may establish in most parts of DRC.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Language and Aging

2019 ◽  
pp. 294-316
Author(s):  
Jonathan E. Peelle
Keyword(s):  
Language Processing ◽  
Brain Function ◽  
Neural Systems ◽  
Language Abilities ◽  
Physiological Mechanisms ◽  
Cognitive Changes ◽  
Older Adulthood ◽  
Neural Organization ◽  
Age Related ◽  
High Level

Language processing in older adulthood is a model of balance between preservation and decline. Despite widespread changes to physiological mechanisms supporting perception and cognition, older adults’ language abilities are frequently well preserved. At the same time, the neural systems engaged to achieve this high level of success change, and individual differences in neural organization appear to differentiate between more and less successful performers. This chapter reviews anatomical and cognitive changes that occur in aging and popular frameworks for age-related changes in brain function, followed by an examination of how these principles play out in the context of language comprehension and production.

Machine learning for rediscovering revolutionary ideas of the past

2021 ◽  
pp. 105971232098304
Author(s):  
R Alexander Bentley ◽  
Joshua Borycz ◽  
Simon Carrignon ◽  
Damian J Ruck ◽  
Michael J O’Brien
Keyword(s):  
Language Processing ◽  
Scientific Progress ◽  
Phylogenetic Inference ◽  
The Past ◽  
New Methods ◽  
Inherent Nature ◽  
Social Learning Processes ◽  
High Level ◽  
Scientific Ideas ◽  
Revolutionary Science

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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