scholarly journals Because there was a cause for concern: An investigation into a word-specific prediction account of the implicit-causality effect

2010 ◽  
Vol 63 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Cara R. Featherstone ◽  
Patrick Sturt
Keyword(s):  
Specific Prediction ◽  
Implicit Causality

Activation and Persistence of Implicit Causality Information in Spoken Language Comprehension

2010 ◽  
Vol 57 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Pirita Pyykkönen ◽  
Juhani Järvikivi
Keyword(s):  
Eye Tracking ◽  
Language Comprehension ◽  
Spoken Language ◽  
Visual World ◽  
Implicit Causality ◽  
Causality Information ◽  
Spoken Language Comprehension

A visual world eye-tracking study investigated the activation and persistence of implicit causality information in spoken language comprehension. We showed that people infer the implicit causality of verbs as soon as they encounter such verbs in discourse, as is predicted by proponents of the immediate focusing account ( Greene & McKoon, 1995 ; Koornneef & Van Berkum, 2006 ; Van Berkum, Koornneef, Otten, & Nieuwland, 2007 ). Interestingly, we observed activation of implicit causality information even before people encountered the causal conjunction. However, while implicit causality information was persistent as the discourse unfolded, it did not have a privileged role as a focusing cue immediately at the ambiguous pronoun when people were resolving its antecedent. Instead, our study indicated that implicit causality does not affect all referents to the same extent, rather it interacts with other cues in the discourse, especially when one of the referents is already prominently in focus.

Drug–target prediction utilizing heterogeneous bio-linked network embeddings

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Yue Yu ◽  
Andrew Wen ◽  
Ming Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Association Mining ◽  
Drug Target Prediction ◽  
Specific Prediction ◽  
Series Of Experiments ◽  
Inference Methods ◽  
Novel Drug ◽  
Prediction Strategy

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

scholarly journals NetMHCphosPan - Pan-specific prediction of MHC class I antigen presentation of phosphorylated ligands

2021 ◽  
pp. 100005
Author(s):  
Carina Thusgaard Refsgaard ◽  
Carolina Barra ◽  
Xu Peng ◽  
Nicola Ternette ◽  
Morten Nielsen
Keyword(s):  
Antigen Presentation ◽  
Mhc Class I ◽  
Class I ◽  
Specific Prediction ◽  
Mhc Class I Antigen

Ionization-Specific Prediction of Blood–Brain Permeability

2009 ◽  
Vol 98 (1) ◽  
pp. 122-134 ◽  
Author(s):  
Kiril Lanevskij ◽  
Pranas Japertas ◽  
Remigijus Didziapetris ◽  
Alanas Petrauskas
Keyword(s):  
Blood Brain ◽  
Specific Prediction

scholarly journals Theory of mind and preschoolers' understanding of implicit causality in verbs: A comparison between Serbian and Hungarian children

Psihologija ◽  
2010 ◽  
Vol 43 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Agota Major ◽  
Fabia Franco ◽  
Marija Zotovic
Keyword(s):  
Theory Of Mind ◽  
Native Speakers ◽  
Mother Tongue ◽  
Implicit Causality ◽  
Mind Task

This study aims to investigate the effect of theory of mind, age and mother tongue on the implicit causality effect in preschoolers from two different language backgrounds. Serbian and Hungarian native speakers aged 3-7 years participated in the study. After taking part in a Theory of Mind task, children were presented verbs in simple 'Subject verb Object' sentences describing interactions between two participants, with the interactions being based on emotional, mental or visual experiences. Children were asked 'Why does S verb O?' and their responses were categorized as containing an inference about the sentence-S or the sentence-O. The results show that Theory of Mind is a significant factor in the emergence of implicit causality, with age of participants and mother tongue being also contributing to explaining patterns of implicit causality.

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