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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 47 1 Language Models Explain Word Reading Times Better Than Empirical Predictability ... Hofmann, Markus J.; Remus, Steffen; Biemann, Chris. - : arXiv, 2022 BASE Show details 2 Language Models Explain Word Reading Times Better Than Empirical Predictability Hofmann, Markus J.; Remus, Steffen; Biemann, Chris; Radach, Ralph; Kuchinke, Lars In: Front Artif Intell (2022) Abstract: Though there is a strong consensus that word length and frequency are the most important single-word features determining visual-orthographic access to the mental lexicon, there is less agreement as how to best capture syntactic and semantic factors. The traditional approach in cognitive reading research assumes that word predictability from sentence context is best captured by cloze completion probability (CCP) derived from human performance data. We review recent research suggesting that probabilistic language models provide deeper explanations for syntactic and semantic effects than CCP. Then we compare CCP with three probabilistic language models for predicting word viewing times in an English and a German eye tracking sample: (1) Symbolic n-gram models consolidate syntactic and semantic short-range relations by computing the probability of a word to occur, given two preceding words. (2) Topic models rely on subsymbolic representations to capture long-range semantic similarity by word co-occurrence counts in documents. (3) In recurrent neural networks (RNNs), the subsymbolic units are trained to predict the next word, given all preceding words in the sentences. To examine lexical retrieval, these models were used to predict single fixation durations and gaze durations to capture rapidly successful and standard lexical access, and total viewing time to capture late semantic integration. The linear item-level analyses showed greater correlations of all language models with all eye-movement measures than CCP. Then we examined non-linear relations between the different types of predictability and the reading times using generalized additive models. N-gram and RNN probabilities of the present word more consistently predicted reading performance compared with topic models or CCP. For the effects of last-word probability on current-word viewing times, we obtained the best results with n-gram models. Such count-based models seem to best capture short-range access that is still underway when the eyes move on to the subsequent word. The prediction-trained RNN models, in contrast, better predicted early preprocessing of the next word. In sum, our results demonstrate that the different language models account for differential cognitive processes during reading. We discuss these algorithmically concrete blueprints of lexical consolidation as theoretically deep explanations for human reading. Keyword: Artificial Intelligence URL: https://doi.org/10.3389/frai.2021.730570 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847793/ BASE Hide details 3 Meditation affects word recognition of meditation novices Lusnig, Larissa; Radach, Ralph; Hofmann, Markus J. In: Psychol Res (2021) BASE Show details 4 Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words ... Lusnig, Larissa; Radach, Ralph; Mueller, Christina. - : figshare, 2020 BASE Show details 5 Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words ... Lusnig, Larissa; Radach, Ralph; Mueller, Christina. - : figshare, 2020 BASE Show details 6 Individual corpora predict fast memory retrieval during reading ... The 28th International Conference on Computational Linguistics 2020; Biemann, Chris; Hofmann, Markus J.. - : Underline Science Inc., 2020 BASE Show details 7 Individual corpora predict fast memory retrieval during reading ... Hofmann, Markus J.; Müller, Lara; Rölke, Andre. - : arXiv, 2020 BASE Show details 8 A Lexical Frequency Analysis of Irish Sign Language Hofmann, Markus; Smith, Robert G In: Other Resources (2020) BASE Show details 9 A Lexical Frequency Analysis of Irish Sign Language Smith, Robert G.; Hofmann, Markus In: Articles (2020) BASE Show details 10 Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words Lusnig, Larissa; Radach, Ralph; Mueller, Christina J.. - : Public Library of Science, 2020 BASE Show details 11 Decomposing predictability: Semantic feature overlap between words and the dynamics of reading for meaning ... Hofmann, Markus J.; Kleemann, Mareike A.; Roelke, Andre. - : arXiv, 2019 BASE Show details 12 Sampling Techniques to Overcome Class Imbalance in a Cyberbullying Context Hofmann, Markus; Colton, David. - : Universitat Politècnica de València, 2019 BASE Show details 13 A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms Goslin, Kyle; Hofmann, Markus. - : Universitat Politècnica de València, 2019 BASE Show details 14 Non-Manual Articulators in Irish Sign Language Verbs: An Analysis with Data Mining Association Rules Smith, Robert G; Hofmann, Markus In: Conference Papers (2018) BASE Show details 15 Typography and individual experience in digital reading: Do readers’ eye movements adapt to poor justification? Jarosch, Julian [Verfasser]; Schlesewsky, Matthias [Verfasser]; Füssel, Stephan [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017 DNB Subject Category Language Show details 16 Taking typography to experimental testing: On the influence of serifs, fonts and justification on eye movements in text reading Jarosch, Julian [Verfasser]; Schlesewsky, Matthias [Verfasser]; Füssel, Stephan [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017 DNB Subject Category Language Show details 17 The two sides of prediction error in reading: on the relationship between eye movements and the N400 in sentence processing Kretzschmar, Franziska [Verfasser]; Alday, Phillip M. [Verfasser]; Radach, Ralph [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017 DNB Subject Category Language Show details 18 When readers pay attention to the left: A concurrent eyetracking-fMRI investigation on the neuronal correlates of regressive eye movements during reading Weiß, Anna Fiona [Verfasser]; Kretzschmar, Franziska [Verfasser]; Nagels, Arne [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017 DNB Subject Category Language Show details 19 Sentiment Analysis: Comparative Analysis Of Multilingual Sentiment And Opinion Classification Techniques ... Sannikumar Patel; Nolan, Brian; Hofmann, Markus. - : Zenodo, 2017 BASE Show details 20 Sentiment Analysis: Comparative Analysis Of Multilingual Sentiment And Opinion Classification Techniques ... Sannikumar Patel; Nolan, Brian; Hofmann, Markus. - : Zenodo, 2017 BASE Show details Page: 1 2 3

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