No abstract

Interpreting a verb-phrase ellipsis (VP ellipsis) requires accessing an antecedent in memory, and then integrating a representation of this antecedent into the local context. We investigated the online interpretation of VP ellipsis in an eye-tracking experiment and four speed-accuracy tradeoff experiments. To investigate whether the antecedent for a VP ellipsis is accessed with a search or direct-access retrieval process, Experiments 1 and 2 measured the effect of the distance between an ellipsis and its antecedent on the speed and accuracy of comprehension. Accuracy was lower with longer distances, indicating that interpolated material reduced the quality of retrieved information about the antecedent. However, contra a search process, distance did not affect the speed of interpreting ellipsis. This pattern suggests that antecedent representations are content-addressable and retrieved with a direct-access process. To determine whether interpreting ellipsis involves copying antecedent information into the ellipsis site, Experiments 3-5 manipulated the length and complexity of the antecedent. Some types of antecedent complexity lowered accuracy, notably, the number of discourse entities in the antecedent. However, neither antecedent length nor complexity affected the speed of interpreting the ellipsis. This pattern is inconsistent with a copy operation, and it suggests that ellipsis interpretation may involve a pointer to extant structures in memory.

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

Group "Language and Computation in Neural Systems" and by the Netherlands Organization for Scientific Research (grant 016.Vidi.188.029).

Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose—learning relational reasoning—processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and “cortical” oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism—using time to encode information across a layered network—generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computation.

Psychology endeavors to develop theories of human capacities and behaviors based on a variety of methodologies and dependent measures. We argue that one of the most divisive factors in our field is whether researchers choose to employ computational modeling of theories (over and above data) during the scientific inference process. Modeling is undervalued, yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us towards better science by forcing us to conceptually analyze, specify, and formalise intuitions which otherwise remain unexamined — what we dub “open theory”. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Herein, we present scientific inference in psychology as a path function, where each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability “crises” and persistent failure at coherent theory-building. This is because without formal modelling we lack open and transparent theorising. We also explain how to formalise, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

a b s t r a c tPropositional truth-value can be a defining feature of a sentence's relevance to the unfolding discourse, and establishing propositional truth-value in context can be key to successful interpretation. In the current study, we investigate its role in the comprehension of counterfactual conditionals, which describe imaginary consequences of hypothetical events, and are thought to require keeping in mind both what is true and what is false. Pre-stored real-world knowledge may therefore intrude upon and delay counterfactual comprehension, which is predicted by some accounts of discourse comprehension, and has been observed during online comprehension. The impact of propositional truth-value may thus be delayed in counterfactual conditionals, as also claimed for sentences containing other types of logical operators (e.g., negation, scalar quantifiers). In an event-related potential (ERP) experiment, we investigated the impact of propositional truth-value when described consequences are both true and predictable given the counterfactual premise. False words elicited larger N400 ERPs than true words, in negated counterfactual sentences (e.g., ''If N.A.S.A. had not developed its Apollo Project, the first country to land on the moon would have been Russia/America'') and real-world sentences (e.g., ''Because N.A.S.A. developed its Apollo Project, the first country to land on the moon was America/Russia'') alike. These indistinguishable N400 effects of propositional truth-value, elicited by opposite word pairs, argue against disruptions by real-world knowledge during counterfactual comprehension, and suggest that incoming words are mapped onto the counterfactual context without any delay. Thus, provided a sufficiently constraining context, propositional truth-value rapidly impacts ongoing semantic processing, be the proposition factual or counterfactual.

Comprehension of verb-phrase ellipsis (VPE) requires reevaluation of recently processed constituents, which often necessitates retrieval of information about the elided constituent from memory. A. E. Martin and B. McElree (2008) argued that representations formed during comprehension are content addressable and that VPE antecedents are retrieved from memory via a cue-dependent direct-access pointer rather than via a search process. This hypothesis was further tested by manipulating the location of interfering material-either before the onset of the antecedent (proactive interference; PI) or intervening between antecedent and ellipsis site (retroactive interference; RI). The speed-accuracy tradeoff procedure was used to measure the time course of VPE processing. The location of the interfering material affected VPE comprehension accuracy: RI conditions engendered lower accuracy than PI conditions. Crucially, location did not affect the speed of processing VPE, which is inconsistent with both forward and backward search mechanisms. The observed time-course profiles are consistent with the hypothesis that VPE antecedents are retrieved via a cue-dependent direct-access operation.Keywords: sentence processing, verb-phrase ellipsis, retrieval interference, speed-accuracy tradeoff, proactive interference Successful language comprehension routinely requires establishing dependencies spanning several words, phrases, or even sentences. As such, comprehenders must access products of past language processing in order to incorporate the contents of current processing into an evolving discourse. A central issue for models of comprehension thus concerns the nature of memory representations formed during comprehension and how those representations are accessed in real time.Basic memory research has identified two distinct ways that memories can be retrieved. One way involves a search operation where memory representations are sequentially sampled until the required information is recovered. This type of serial operation appears to be the primary means by which relational (temporal and spatial order) information is recovered (Gronlund, Edwards, &