Viés de causalidade implícita para 50 predicados do Português Brasileiro / Implicity Causality Bias for 50 Predicates in Brazilian Portuguese

Resumo: Nesse trabalho, construímos um corpus com predicados de causalidade implícita para o Português Brasileiro (PB). A causalidade implícita é uma propriedade de uma classe de predicados verbais cuja causa para o evento que denotam tende a recair, para alguns verbos, no sujeito da oração (“enfurecer”, “decepcionar”) e, para outros, em seu objeto (“parabenizar”, “admirar”). Nosso objetivo foi registrar o viés de causalidade de 50 predicados. Para isso, realizamos um experimento de continuação de sentenças com 34 participantes, falantes nativos de português brasileiro. Nossas análises identificaram 24 verbos com viés de causalidade associado ao sujeito e 22 verbos com viés de causalidade associada ao objeto. Esses resultados expandem um corpus já existente em português europeu (COSTA, 2003). Que saibamos, este é o primeiro estudo normativo para a construção de um corpus de causalidade implícita que tem como alvo falantes de português brasileiro. O resultado é um conjunto de verbos que podem ser usados em futuros estudos em psicolinguística ou psicologia que lidem com relações de causalidade. Palavras-chave: psicolinguística; causalidade implícita; verbos; português brasileiro.Abstract: In this paper, we built an implicit causality corpus for Brazilian Portuguese (BP) verbal predicates. Implicit causality is a property of some verbal predicates that strongly associate their causality with their subject (for verbs such as “enrage”, “disappoint”) or their object (for verbs like “congratulate”, “admire”). Our goal was to measure the causality bias of 50 predicates. In order to do so, we carried out a sentence continuation experiment with 34 participants, all native speakers of Brazilian Portuguese. Our results identify 24 verbs with a causal bias associated with the subject of the clause, and 22 verbs with a causal bias associated with its object. These results expand a corpus that already exists in European Portuguese (Costa, 2003). To the best of our knowledge, this is the first normative study for the construction of a corpus of implicit causality that targets Brazilian Portuguese speakers. The result is a set of verbal predicates that can be used in future studies in psycholinguistics or psychology that aims at investigating causal relationships.Keywords: psycholinguistics; implicity causality; verbs; Brazilian portuguese.

Individual Differences in Print Exposure Predict Use of Implicit Causality in Pronoun Comprehension and Referential Prediction

In three experiments, we measured individual patterns of pronoun comprehension (Experiments 1 and 2) and referential prediction (Experiment 3) in implicit causality (IC) contexts and compared these with a measure of participants’ print exposure (Author Recognition Task; ART). Across all three experiments, we found that ART interacted with verb bias, such that participants with higher scores demonstrated a stronger semantic bias, i.e., they tended to select the pronoun or predict the re-mention of the character that was congruent with an implicit cause interpretation. This suggests that print exposure changes the way language is processed at the discourse level, and in particular, that it is related to implicit cause sensitivity.

Implicit causality biases and thematic roles in American Sign Language

Implicit causality (IC) biases, the tendency of certain verbs to elicit re-mention of either the first-mentioned noun phrase (NP1) or the second-mentioned noun phrase (NP2) from the previous clause, are important in psycholinguistic research. Understanding IC verbs and the source of their biases in signed as well as spoken languages helps elucidate whether these phenomena are language general or specific to the spoken modality. As the first of its kind, this study investigates IC biases in American Sign Language (ASL) and provides IC bias norms for over 200 verbs, facilitating future psycholinguistic studies of ASL and comparisons of spoken versus signed languages. We investigated whether native ASL signers continued sentences with IC verbs (e.g., ASL equivalents of ‘Lisa annoys Maya because…’) by mentioning NP1 (i.e., Lisa) or NP2 (i.e., Maya). We found a tendency towards more NP2-biased verbs. Previous work has found that a verb’s thematic roles predict bias direction: stimulus-experiencer verbs (e.g., ‘annoy’), where the first argument is the stimulus (causing annoyance) and the second argument is the experiencer (experiencing annoyance), elicit more NP1 continuations. Verbs with experiencer-stimulus thematic roles (e.g., ‘love’) elicit more NP2 continuations. We probed whether the trend towards more NP2-biased verbs was related to an existing claim that stimulus-experiencer verbs do not exist in sign languages. We found that stimulus-experiencer structure, while permitted, is infrequent, impacting the IC bias distribution in ASL. Nevertheless, thematic roles predict IC bias in ASL, suggesting that the thematic role-IC bias relationship is stable across languages as well as modalities.

Discourse expectations: explaining the implicit causality biases of verbs

This article presents a linguistic account explaining particular mechanisms underlying the generation of expectations at the discourse level. We further develop a linguistic theory – the Empty Slot Theory – explaining the phenomenon of implicit verb causality. According to our proposal, implicit causality (IC) verbs introduce lexically determined slots for causal content of specific types. If the required information is not derivable from the current or preceding context, IC verbs generate the expectation that these slots will be filled in the upcoming discourse. The cognitive mechanism underlying the bias is grounded in the general processing strategy of avoiding accommodation. Empirical evidence for the proposed theory is provided in three continuation experiments in German with comprehensive semantic annotation of the continuations provided by the participants. The reported experiments consistently show that IC bias can be manipulated in systematic ways. Experiment 1 demonstrates important ontological constraints on causal content crucial for our theory. Experiments 2 and 3 show how IC biases can be manipulated in predictable ways by filling the hypothesized slots in the prompt. Experiment 2 illustrates that stimulus-experiencer (experiencer-object) verbs in contrast to causative agent-patient verbs can be manipulated with respect to coherence and coreference by employing adverbial modification. Filling the lexically determined slot of psychological verbs resulted in predictable changes in coherence relations and types of explanations, resulting in the predicted effects on coreference. Experiment 3 extends the empirical investigations to so-called “agent-evocator” verbs. Again, filling the semantic slot as part of the prompt resulted in predictable shifts in coherence relations and explanation types with transparent effects on coreference. The reported experiments shed further light on the close correspondence between coherence and coreference as a hallmark of natural language discourse.

Implicit consequentiality bias in English: A corpus of 300+ verbs

This study provides implicit verb consequentiality norms for a corpus of 305 English verbs, for which Ferstl et al. (Behavior Research Methods, 43, 124-135, 2011) previously provided implicit causality norms. An online sentence completion study was conducted, with data analyzed from 124 respondents who completed fragments such as “John liked Mary and so…”. The resulting bias scores are presented in an Appendix, with more detail in supplementary material in the University of Sussex Research Data Repository (via 10.25377/sussex.c.5082122), where we also present lexical and semantic verb features: frequency, semantic class and emotional valence of the verbs. We compare our results with those of our study of implicit causality and with the few published studies of implicit consequentiality. As in our previous study, we also considered effects of gender and verb valence, which requires stable norms for a large number of verbs. The corpus will facilitate future studies in a range of areas, including psycholinguistics and social psychology, particularly those requiring parallel sentence completion norms for both causality and consequentiality.

PREDICTIVE PROCESSING OF IMPLICIT CAUSALITY IN A SECOND LANGUAGE

Implicit causality (IC) is a well-known phenomenon whereby certain verbs appear to create biases to remention either their subject or object in a causal dependent clause. This study investigated to what extent Korean learners of English made use of IC information for predictive processing at a discourse level, and whether L2 proficiency played a modulating role in this process. Results from a visual-world eye-tracking experiment showed early use of IC information in both L1 and L2 listeners, yet the effect was weaker and emerged later in the L2 group. None of three independent and intercorrelated proficiency measures modulated L2 listeners’ processing behavior. The findings suggest that L2 listeners are able to engage in prediction during real-time processing at a discourse level, although they did so to a more limited extent than native speakers in this study. We discuss these findings in light of similar evidence from other recent work.

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.