scholarly journals Computational models of retrieval processes in sentence processing

2019 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Felix Engelmann ◽  
Frank Burchert
Keyword(s):  
Sentence Processing ◽  
Sentence Comprehension ◽  
Computational Models ◽  
Predictive Performance ◽  
Data Set ◽  
Benchmark Data ◽  
Retrieval Processes ◽  
Empirical Coverage ◽  
Competing Models ◽  
Retrieval From Memory

Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval, and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.

scholarly journals Variability in sentence comprehension in aphasia in German

2020 ◽  
Author(s):  
Dorothea Pregla ◽  
Paula Lisson ◽  
Shravan Vasishth ◽  
Frank Burchert ◽  
Nicole Stadie
Keyword(s):  
Sentence Processing ◽  
Large Scale ◽  
Sentence Comprehension ◽  
Control Group ◽  
Data Set ◽  
Control Structures ◽  
Individual Level ◽  
Competing Models ◽  
Test Retest Reliability ◽  
And Control

An important property of aphasia is the variability in the performance between and within individual patients. However, there have been only a few systematic large-scale studies in a range of syntactic constructions and tasks that make it possible to investigate variability and to evaluate the quantitative predictions of competing models of sentence comprehension in aphasia (Lissón et al., under review). This is the first comprehensive investigation of variability in sentence comprehension in German, testing 18 individuals with aphasia and a control group and involving (a) several construction (canonical / non-canonical declarative sentences, subject / object relative clauses, subject / object control structures, near / distant antecedents of pronouns), (b) three tasks (object manipulation, sentence-picture matching with / without self-paced listening), and (c) two test phases (to investigate test-retest reliability). This data-set provides a detailed investigation of individual-level variation in individuals with aphasia and control participants along several dimensions of sentence processing difficulty.

scholarly journals Sentence Comprehension as a Cognitive Process

2021 ◽  
Author(s):  
Shravan Vasishth ◽  
Felix Engelmann
Keyword(s):  
Computational Linguistics ◽  
Sentence Processing ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Sentence Comprehension ◽  
Computational Models ◽  
Model Comparison ◽  
Written Language ◽  
Retrieval Processes ◽  
New Research

Sentence comprehension - the way we process and understand spoken and written language - is a central and important area of research within psycholinguistics. This book explores the contribution of computational linguistics to the field, showing how computational models of sentence processing can help scientists in their investigation of human cognitive processes. It presents the leading computational model of retrieval processes in sentence processing, the Lewis and Vasishth cue-based retrieval mode, and develops a principled methodology for parameter estimation and model comparison/evaluation using benchmark data, to enable researchers to test their own models of retrieval against the present model. It also provides readers with an overview of the last 20 years of research on the topic of retrieval processes in sentence comprehension, along with source code that allows researchers to extend the model and carry out new research. Comprehensive in its scope, this book is essential reading for researchers in cognitive science.

scholarly journals A computational evaluation of two models of retrieval processes in sentence processing in aphasia

2020 ◽  
Author(s):  
Paula Lissón ◽  
Dorothea Pregla ◽  
Bruno Nicenboim ◽  
Dario Paape ◽  
Mick L. van het Nederend ◽  
...  
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Sentence Comprehension ◽  
Model Comparison ◽  
Predictive Performance ◽  
Direct Access ◽  
Computational Evaluation ◽  
Retrieval Processes ◽  
Using Data ◽  
Access Model

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependencycompletion difficulty are investigated, the Lewis and Vasishth (2005) activation-based model, and the direct-access model (McElree, 2000). These models’ predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self-paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k-fold cross validation. For both IWAs and controls, the activation model furnishes a somewhat better quantitativefit to the data than the direct-access model. Model comparison using Bayes factors shows that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs. This is the first computational evaluation of different models of dependency completion using data from impaired andunimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing.

scholarly journals Computational Models of Retrieval Processes in Sentence Processing

2021 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Felix Engelmann ◽  
Frank Burchert
Keyword(s):  
Sentence Processing ◽  
Computational Models ◽  
Retrieval Processes

scholarly journals Comparison of the Performance of the SANN, SARIMA and ARIMA Models for Forecasting Quarterly GDP of Nigeria

2021 ◽  
pp. 1-20
Author(s):  
Chukwudike C. Nwokike ◽  
Emmanuel W. Okereke
Keyword(s):  
Mean Squared Error ◽  
Predictive Performance ◽  
Arima Models ◽  
Data Set ◽  
Variance Stabilization ◽  
Augmented Dickey Fuller ◽  
Chi Squared ◽  
Competing Models ◽  
Hidden Layer ◽  
Adf Test

This research aimed at modelling and forecasting the quarterly GDP of Nigeria using the Seasonal Artificial Neural Network (SANN), SARIMA and Box-Jenkins models as well as comparing their predictive performance. The three models mentioned earlier were successfully fitted to the data set. Tentative architecture for the SANN was suggested by varying the number of neurons in the hidden layer while that of the input and output layer remained constant at 4. It was observed that the best architecture was when the hidden layer had 10 neurons and thus SANN (4-10-4) was chosen as the best. In fitting the ARIMA/SARIMA models, the Augmented Dickey Fuller (ADF) test was used to check for stationarity. Variance stabilization and Stationarity were achieved after logarithm transformation and first regular differencing. The ARIMA/SARIMA model with lowest AIC, BIC and HQIC values was chosen as the best amongst the competing models and fitted to the data. The adequacy of the fitted models was confirmed observing the correlogram of the residuals and the Ljung-Box Chi-Squared test result. The SANN model performed better than the SARIMA and ARIMA models as it had a Mean Squared Error value of 0.004 while SARIMA and ARIMA had mean squared errors of 0.527 and 0.705 respectively. It was concluded that the SANN which is a non-linear model be used in modelling the quarterly GDP of Nigeria. Hybrid models which combine the strength of individual models are recommended for further research.

scholarly journals Computational Models of Retrieval Processes in Sentence Processing

2019 ◽  
Vol 23 (11) ◽  
pp. 968-982 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Felix Engelmann ◽  
Frank Burchert
Keyword(s):  
Sentence Processing ◽  
Computational Models ◽  
Retrieval Processes

scholarly journals Individual differences in cue-weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

2021 ◽  
Author(s):  
Himanshu Yadav ◽  
Dario Paape ◽  
Garrett Smith ◽  
Brian Dillon ◽  
Shravan Vasishth
Keyword(s):  
Individual Differences ◽  
Sentence Processing ◽  
Sentence Comprehension ◽  
Computational Models ◽  
Approximate Bayesian Computation ◽  
Reading Proficiency ◽  
Bayesian Computation ◽  
Individual Level ◽  
Cue Weighting ◽  
Approximate Bayesian

Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for processing speed and cue weighting using 13 published datasets; hierarchical Approximate Bayesian Computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the higher weighting for structural cues, suggesting that reading proficiency might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.

The Effects of Focal Brain Damage on Sentence Processing: an examination of the neurological organization of a mental module

1989 ◽  
Vol 1 (1) ◽  
pp. 25-37 ◽  
Author(s):  
David Swinney ◽  
Edgar Zurif ◽  
Janet Nicol
Keyword(s):  
Brain Damage ◽  
Sentence Processing ◽  
Sentence Comprehension ◽  
A Priori ◽  
Cognitive Architecture ◽  
Decision Task ◽  
Visual Words ◽  
Retrieval Processes ◽  
Pathological Material ◽  
Simple Sentences

The effects of prior semantic context upon lexical access during sentence processing were examined for three groups of subjects; nonfluent agrammatic (Broca's) aphasic patients; fluent (Wernicke's) aphasic patients; and neurologically intact control patients. Subjects were asked to comprehend auditorily presented, structurally simple sentences containing lexical ambiguities, which were in a context strongly biased toward just one interpretation of that ambiguity. While listening to each sentence, subjects also had to perform a lexical decision task upon a visually presented letter string. For the fluent Wernicke's patients, as for the controls, lexical decisions for visual words related to each of the meanings of the ambiguity were facilitated. By contrast, agrammatic Broca's patients showed significant facilitation only for visual words related to the a priori most frequent interpretation of the ambiguity. On the basis of these data, we suggest that normal form-based word retrieval processes are crucially reliant upon the cortical tissue implicated in agrammatism, but that even the focal brain damage yielding agrammatism does not destroy the normally encapsulated form of word access. That is, we propose that in agrammatism, the modularity of word access during sentence comprehension is rendered less efficient but not lost. Additionally, we consider a number of broader issues involved in the use of pathological material to infer characteristics of the neurological organization of cognitive architecture.

When Hearing Does Not Mean Understanding: On the Neural Processing of Syntactically Complex Sentences by Listeners With Hearing Loss

2021 ◽  
pp. 1-13
Author(s):  
Margreet Vogelzang ◽  
Christiane M. Thiel ◽  
Stephanie Rosemann ◽  
Jochem W. Rieger ◽  
Esther Ruigendijk
Keyword(s):  
Hearing Loss ◽  
Sentence Processing ◽  
Secondary Task ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
Normal Hearing ◽  
Cognitive Demands ◽  
Sentence Complexity ◽  
Complex Sentences ◽  
And Task

Purpose Adults with mild-to-moderate age-related hearing loss typically exhibit issues with speech understanding, but their processing of syntactically complex sentences is not well understood. We test the hypothesis that listeners with hearing loss' difficulties with comprehension and processing of syntactically complex sentences are due to the processing of degraded input interfering with the successful processing of complex sentences. Method We performed a neuroimaging study with a sentence comprehension task, varying sentence complexity (through subject–object order and verb–arguments order) and cognitive demands (presence or absence of a secondary task) within subjects. Groups of older subjects with hearing loss ( n = 20) and age-matched normal-hearing controls ( n = 20) were tested. Results The comprehension data show effects of syntactic complexity and hearing ability, with normal-hearing controls outperforming listeners with hearing loss, seemingly more so on syntactically complex sentences. The secondary task did not influence off-line comprehension. The imaging data show effects of group, sentence complexity, and task, with listeners with hearing loss showing decreased activation in typical speech processing areas, such as the inferior frontal gyrus and superior temporal gyrus. No interactions between group, sentence complexity, and task were found in the neuroimaging data. Conclusions The results suggest that listeners with hearing loss process speech differently from their normal-hearing peers, possibly due to the increased demands of processing degraded auditory input. Increased cognitive demands by means of a secondary visual shape processing task influence neural sentence processing, but no evidence was found that it does so in a different way for listeners with hearing loss and normal-hearing listeners.

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

Sign in / Sign up

Export Citation Format

Share Document