Publications

Abstract

In this study, we compared the performance of a long short-term memory (LSTM) neural network to the behavior of human participants on a language task that requires hierarchically structured knowledge. We show that humans interpret ambiguous noun phrases, such as second blue ball, in line with their hierarchical constituent structure. LSTMs, instead, only do
so after unambiguous training, and they do not systematically generalize to novel items. Overall, the results of our simulations indicate that a model can behave hierarchically without relying on hierarchical constituent structure.

Abstract

How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require abstract representations of stimulus properties and relations. How we acquire such representations has central importance in an account of human cognition. We briefly describe a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured, relational representations can be learned from initially unstructured inputs. Two operations, comparing distributed representations and learning from the concomitant network dynamics in time, underpin the ability to learn these representations and to respond to invariance in the environment. Comparing analog representations of absolute magnitude produces invariant signals that carry information about similarity and relative magnitude. We describe how a system can then use this information to bootstrap learning structured (i.e., symbolic) concepts of relative magnitude from experience without assuming such representations a priori.

Abstract

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use 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 toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the 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 modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

Abstract

Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena.

Abstract

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.

Abstract

Abstract mental representation is fundamental for human cognition. Forming such representations in time, especially from dynamic and noisy perceptual input, is a challenge for any processing modality, but perhaps none so acutely as for language processing. We show that LISA (Hummel & Holyaok, 1997) and DORA (Doumas, Hummel, & Sandhofer, 2008), models built to process and to learn structured (i.e., symbolic) rep resentations of conceptual properties and relations from unstructured inputs, show oscillatory activation during processing that is highly similar to the cortical activity elicited by the linguistic stimuli from Ding et al.(2016). We argue, as Ding et al.(2016), that this activation reflects formation of hierarchical linguistic representation, and furthermore, that the kind of computational mechanisms in LISA/DORA (e.g., temporal binding by systematic asynchrony of firing) may underlie formation of abstract linguistic representations in the human brain. It may be this repurposing that allowed for the generation or mergence of hierarchical linguistic structure, and therefore, human language, from extant cognitive and neural systems. We conclude that models of thinking and reasoning and models of language processing must be integrated —not only for increased plausiblity, but in order to advance both fields towards a larger integrative model of human cognition

Abstract

We used ERPs to investigate the pre-activation of form and meaning in language comprehension. Participants read high-cloze sentence contexts (e.g., “The student is going to the library to borrow a…”), followed by a word that was predictable (book), form-related (hook) or semantically related (page) to the predictable word, or unrelated (sofa). At a 500 ms SOA (Experiment 1), semantically related words, but not form-related words, elicited a reduced N400 compared to unrelated words. At a 700 ms SOA (Experiment 2), semantically related words and form-related words elicited reduced N400 effects, but the effect for form-related words occurred in very high-cloze sentences only. At both SOAs, form-related words elicited an enhanced, post-N400 posterior positivity (Late Positive Component effect). The N400 effects suggest that readers can pre-activate meaning and form information for highly predictable words, but form pre-activation is more limited than meaning pre-activation. The post-N400 LPC effect suggests that participants detected the form similarity between expected and encountered input. Pre-activation of word forms crucially depends upon the time that readers have to make predictions, in line with production-based accounts of linguistic prediction.

Abstract

I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.

Abstract

In psycholinguistic studies using error rates as a response measure, response times (RT) are most often analyzed independently of the error rate, although it is widely recognized that they are related. In this paper we present a mixed effects logistic regression model for the error rate that uses RT as a trial-level fixed- and random-effect regression input. Production data from a translation–recall experiment are analyzed as an example. Several model comparisons reveal that RT improves the fit of the regression model for the error rate. Two simulation studies then show how the mixed effects regression model can identify individual participants for whom (a) faster responses are more accurate, (b) faster responses are less accurate, or (c) there is no relation between speed and accuracy. These results show that this type of model can serve as a useful adjunct to traditional techniques, allowing psycholinguistic researchers to examine more closely the relationship between RT and accuracy in individual subjects and better account for the variability which may be present, as well as a preliminary step to more advanced RT–accuracy modeling.

Abstract

The animacy distinction is deeply rooted in the language faculty. A key example is differential object marking, the phenomenon where animate sentential objects receive specific marking. We used event-related potentials to examine the neural processing consequences of case-marking violations on animate and inanimate direct objects in Spanish. Inanimate objects with incorrect prepositional case marker ‘a’ (‘al suelo’) elicited a P600 effect compared to unmarked objects, consistent with previous literature. However, animate objects without the required prepositional case marker (‘el obispo’) only elicited an N400 effect compared to marked objects. This novel finding, an exclusive N400 modulation by a straightforward grammatical rule violation, does not follow from extant neurocognitive models of sentence processing, and mirrors unexpected “semantic P600” effects for thematically problematic sentences. These results may reflect animacy asymmetry in competition for argument prominence: following the article, thematic interpretation difficulties are elicited only by unexpectedly animate objects.

Abstract

Language comprehension requires recovering meaning from linguistic form, even when the mapping between the two is indirect. A canonical example is ellipsis, the omission of information that is subsequently understood without being overtly pronounced. Comprehension of ellipsis requires retrieval of an antecedent from memory, without prior prediction, a property which enables the study of retrieval in situ ( Martin and McElree, 2008 and Martin and McElree, 2009). Sluicing, or inflectional-phrase ellipsis, in the presence of a conjunction, presents a test case where a competing antecedent position is syntactically licensed, in contrast with most cases of nonadjacent dependency, including verb–phrase ellipsis. We present speed–accuracy tradeoff and eye-movement data inconsistent with the hypothesis that retrieval is accomplished via a syntactically guided search, a particular variant of search not examined in past research. The observed timecourse profiles are consistent with the hypothesis that antecedents are retrieved via a cue-dependent direct-access mechanism susceptible to general memory variables.

Abstract

Two experiments examined the nature of the phonological representations used during visual word recognition. We tested whether a minimality constraint (R. Frost, 1998) limits the complexity of early representations to a simple string of phonemes. Alternatively, readers might activate elaborated representations that include prosodic syllable information before lexical access. In a modified lexical decision task (Experiment 1), words were preceded by parafoveal previews that were congruent with a target's initial syllable as well as previews that contained 1 letter more or less than the initial syllable. Lexical decision times were faster in the syllable congruent conditions than in the incongruent conditions. In Experiment 2, we recorded brain electrical potentials (electroencephalograms) during single word reading in a masked priming paradigm. The event-related potential waveform elicited in the syllable congruent condition was more positive 250-350 ms posttarget compared with the waveform elicited in the syllable incongruent condition. In combination, these experiments demonstrate that readers process prosodic syllable information early in visual word recognition in English. They offer further evidence that skilled readers routinely activate elaborated, speechlike phonological representations during silent reading. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

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.