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The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data
(2023)
We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: .
Cue-based retrieval theories in sentence processing predict two classes of interference effect: (i) Inhibitory interference is predicted when multiple items match a retrieval cue: cue-overloading leads to an overall slowdown in reading time; and (ii) Facilitatory interference arises when a retrieval target as well as a distractor only partially match the retrieval cues; this partial matching leads to an overall speedup in retrieval time. Inhibitory interference effects are widely observed, but facilitatory interference apparently has an exception: reflexives have been claimed to show no facilitatory interference effects. Because the claim is based on underpowered studies, we conducted a large-sample experiment that investigated both facilitatory and inhibitory interference. In contrast to previous studies, we find facilitatory interference effects in reflexives. We also present a quantitative evaluation of the cue-based retrieval model of Engelmann, Jager, and Vasishth (2019).
Previous cross-modal priming studies showed that lexical decisions to words after a pronoun were facilitated when these words were semantically related to the pronoun's antecedent. These studies suggested that semantic priming effectively measured antecedent retrieval during coreference. We examined whether these effects extended to implicit reading comprehension using the N400 response. The results of three experiments did not yield strong evidence of semantic facilitation due to coreference. Further, the comparison with two additional experiments showed that N400 facilitation effects were reduced in sentences (vs. word pair paradigms) and were modulated by the case morphology of the prime word. We propose that priming effects in cross-modal experiments may have resulted from task-related strategies. More generally, the impact of sentence context and morphological information on priming effects suggests that they may depend on the extent to which the upcoming input is predicted, rather than automatic spreading activation between semantically related words.
We report two experiments and Bayesian modelling of the data collected. In both experiments, participants performed a long-lag primed picture naming task. Black-and-white line drawings were used as targets, which were overtly named by the participants. Their naming latencies were measured. In both experiments, primes consisted of past participle verbs (er tanzt/er hat getanzt "he dances/he has danced") and the relationship between primes and targets was either morphological or unrelated. Experiment 1 additionally had phonologically and semantically related prime-target pairs as well as present tense primes. Both in Experiment 1 and 2, participants showed significantly faster naming latencies for morphologically related targets relative to the unrelated verb primes. In Experiment 1, no priming effects were observed in phonologically and semantically related control conditions. In addition, the production latencies were not influenced by verb type.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Previous cross-modal priming studies showed that lexical decisions to words after a pronoun were facilitated when these words were semantically related to the pronoun’s antecedent. These studies suggested that semantic priming effectively measured antecedent retrieval during coreference. We examined whether these effects extended to implicit reading comprehension using the N400 response. The results of three experiments did not yield strong evidence of semantic facilitation due to coreference. Further, the comparison with two additional experiments showed that N400 facilitation effects were reduced in sentences (vs. word pair paradigms) and were modulated by the case morphology of the prime word. We propose that priming effects in cross-modal experiments may have resulted from task-related strategies. More generally, the impact of sentence context and morphological information on priming effects suggests that they may depend on the extent to which the upcoming input is predicted, rather than automatic spreading activation between semantically related words.
We report two experiments and Bayesian modelling of the data collected. In both experiments, participants performed a long-lag primed picture naming task. Black-and-white line drawings were used as targets, which were overtly named by the participants. Their naming latencies were measured. In both experiments, primes consisted of past participle verbs (er tanzt/er hat getanzt “he dances/he has danced”) and the relationship between primes and targets was either morphological or unrelated. Experiment 1 additionally had phonologically and semantically related prime-target pairs as well as present tense primes. Both in Experiment 1 and 2, participants showed significantly faster naming latencies for morphologically related targets relative to the unrelated verb primes. In Experiment 1, no priming effects were observed in phonologically and semantically related control conditions. In addition, the production latencies were not influenced by verb type.
Chinese relative clauses are an important test case for pitting the predictions of expectation-based accounts against those of memory-based theories. The memory-based accounts predict that object relatives are easier to process than subject relatives because, in object relatives, the distance between the relative clause verb and the head noun is shorter. By contrast, expectation-based accounts such as surprisal predict that the less frequent object relative should be harder to process. In previous studies on Chinese relative clause comprehension, local ambiguities may have rendered a comparison between relative clause types uninterpretable. We designed experimental materials in which no local ambiguities confound the comparison. We ran two experiments (self-paced reading and eye-tracking) to compare reading difficulty in subject and object relatives which were placed either in subject or object modifying position. The evidence from our studies is consistent with the predictions of expectation-based accounts but not with those of memory-based theories. (C) 2014 Elsevier Inc. All rights reserved.
We conducted two eye-tracking experiments investigating the processing of the Mandarin reflexive ziji in order to tease apart structurally constrained accounts from standard cue-based accounts of memory retrieval. In both experiments, we tested whether structurally inaccessible distractors that fulfill the animacy requirement of ziji influence processing times at the reflexive. In Experiment 1, we manipulated animacy of the antecedent and a structurally inaccessible distractor intervening between the antecedent and the reflexive. In conditions where the accessible antecedent mismatched the animacy cue, we found inhibitory interference whereas in antecedent-match conditions, no effect of the distractor was observed. In Experiment 2, we tested only antecedent-match configurations and manipulated locality of the reflexive-antecedent binding (Mandarin allows non-local binding). Participants were asked to hold three distractors (animate vs. inanimate nouns) in memory while reading the target sentence. We found slower reading times when animate distractors were held in memory (inhibitory interference). Moreover, we replicated the locality effect reported in previous studies. These results are incompatible with structure-based accounts. However, the cue-based ACT-R model of Lewis and Vasishth (2005) cannot explain the observed pattern either. We therefore extend the original ACT-R model and show how this model not only explains the data presented in this article, but is also able to account for previously unexplained patterns in the literature on reflexive processing.