Word Type and Frequency Effects on Lexical Decisions Are Process-dependent and Start Early.

When encountering letter strings, we rapidly determine whether they are words. The speed of such lexical decisions (LDs) is affected by word frequency. Apart from influencing late, decision-related, processing stages, frequency has also been shown to affect very early stages, and even the processing of nonwords. We developed a detailed account of the different frequency effects involved in LDs by (1) dividing LDs into processing stages using a combination of hidden semi-Markov models and multivariate pattern analysis applied to EEG data and (2) using generalized additive mixed models to investigate how the effect of continuous word and nonword frequency differs between these stages. We discovered six stages shared between word types, with the fifth stage consisting of two substages for pseudowords only. In the earliest stages, visual processing was completed faster for frequent words, but took longer for word-like nonwords. Later stages involved an orthographic familiarity assessment followed by an elaborate decision process, both affected differently by frequency. We therefore conclude that frequency indeed affects all processes involved in LDs and that the magnitude and direction of these effects differ both by process and word type.

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective.

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

Perception of spectrally degraded reflexives and pronouns by children.

Speech perception skills in cochlear-implant users are often measured with simple speech materials. In children, it is crucial to fully characterize linguistic development, and this requires linguistically more meaningful materials. The authors propose using the comprehension of reflexives and pronouns, as these specific skills are acquired at different ages. According to the literature, normal-hearing children show adult-like comprehension of reflexives at age 5, while their comprehension of pronouns only reaches adult-like levels around age 10. To provide normative data, a group of younger children (5 to 8 yrs old), older children (10 and 11 yrs old), and adults were tested under conditions without or with spectral degradation, which simulated cochlear-implant speech transmission with four and eight channels. The results without degradation confirmed the different ages of acquisition of reflexives and pronouns. Adding spectral degradation reduced overall performance; however, it did not change the general pattern observed with non-degraded speech. This finding confirms that these linguistic milestones can also be measured with cochlear-implanted children, despite the reduced quality of sound transmission. Thus, the results of the study have implications for clinical practice, as they could contribute to setting realistic expectations and therapeutic goals for children who receive a cochlear implant.

The impact of mood-induction on maladaptive thinking in the vulnerability for depression.

BACKGROUND AND OBJECTIVES: Mind-wandering, and specifically the frequency and content of mind-wandering, plays an important role in the psychological well-being of individuals. Repetitive negative thinking has been associated with a high risk to develop and maintain Major Depressive Disorder. We here combined paradigms and techniques from cognitive sciences and experimental clinical psychology to study the transdiagnostic psychiatric phenomenon of repetitive negative thinking. This allowed us to investigate the adjustability of the content and characteristics of mind-wandering in individuals varying in their susceptibility to negative affect. METHODS: Participants high (n = 42) or low (n = 40) on their vulnerability for negative affect and depression performed a Sustained Attention to Response Task (SART) after a single session of positive fantasizing and a single session of stress induction in a cross-over design. Affective states were measured before and after the interventions. RESULTS: After stress, negative affect increased, while after fantasizing both positive affect increased and negative affect decreased. Thoughts were less off-task, past-related and negative after fantasizing compared to after stress. Individuals more susceptible to negative affect showed more off-task thinking after stress than after fantasizing compared to individuals low on this. LIMITATIONS: In this cross-over design, no baseline measurement was included, limiting comparison to 'uninduced' mind-wandering. Inclusion of self-related concerns in the SART could have led to negative priming. CONCLUSIONS: Stress-induced negative thinking underlying vulnerability for depression could be partially countered by fantasizing in a non-clinical sample, which may inform the development of treatments for depression and other disorders characterized by maladaptive thinking.

A cognitive modeling approach to learning and using reference biases in language.

During real-time language processing, people rely on linguistic and non-linguistic biases to anticipate upcoming linguistic input. One of these linguistic biases is known as the implicit causality bias, wherein language users anticipate that certain entities will be rementioned in the discourse based on the entity's particular role in an expressed causal event. For example, when language users encounter a sentence like "Elizabeth congratulated Tina…" during real-time language processing, they seemingly anticipate that the discourse will continue about Tina, the object referent, rather than Elizabeth, the subject referent. However, it is often unclear how these reference biases are acquired and how exactly they get used during real-time language processing. In order to investigate these questions, we developed a reference learning model within the PRIMs cognitive architecture that simulated the process of predicting upcoming discourse referents and their linguistic forms. Crucially, across the linguistic input the model was presented with, there were asymmetries with respect to how the discourse continued. By utilizing the learning mechanisms of the PRIMs architecture, the model was able to optimize its predictions, ultimately leading to biased model behavior. More specifically, following subject-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the subject referent, whereas following object-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the object referent. In a similar fashion, the model was more likely to predict that subject referent continuations would be in the form of a pronoun, whereas object referent continuations would be in the form of a proper name. These learned biases were also shown to generalize to novel contexts in which either the verb or the subject and object referents were new. The results of the present study demonstrate that seemingly complex linguistic behavior can be explained by cognitively plausible domain-general learning mechanisms. This study has implications for psycholinguistic accounts of predictive language processing and language learning, as well as for theories of implicit causality and reference processing.

Online Processing of Temporal Agreement in a Grammatical Tone Language: An ERP Study.

Previous electrophysiological studies that have examined temporal agreement violations in (Indo-European) languages that use grammatical affixes to mark time reference, have found a Left Anterior Negativity (LAN) and/or P600 ERP components, reflecting morpho-syntactic and syntactic processing, respectively. The current study investigates the electrophysiological processing of temporal relations in an African language (Akan) that uses grammatical tone, rather than morphological inflection, for time reference. Twenty-four native speakers of Akan listened to sentences with time reference violations. Our results demonstrate that a violation of a present context by a past verb yields a P600 time-locked to the verb. There was no such effect when a past context was violated by a present verb. In conclusion, while there are similarities in both Akan and Indo-European languages, as far as the modulation of the P600 effect is concerned, the nature of this effect seems to be different for these languages.

Cognitive architectures and language acquisition: a case study in pronoun comprehension.

In this paper we discuss a computational cognitive model of children's poor performance on pronoun interpretation (the so-called Delay of Principle B Effect, or DPBE). This cognitive model is based on a theoretical account that attributes the DPBE to children's inability as hearers to also take into account the speaker's perspective. The cognitive model predicts that child hearers are unable to do so because their speed of linguistic processing is too limited to perform this second step in interpretation. We tested this hypothesis empirically in a psycholinguistic study, in which we slowed down the speech rate to give children more time for interpretation, and in a computational simulation study. The results of the two studies confirm the predictions of our model. Moreover, these studies show that embedding a theory of linguistic competence in a cognitive architecture allows for the generation of detailed and testable predictions with respect to linguistic performance.

Order Matters! Influences of Linear Order on Linguistic Category Learning.

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non-confusable. Regarding levels of abstraction, we find that premarking of super-ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation-based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre- and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system.

Analyzing the Time Course of Pupillometric Data.

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

How WM load influences linguistic processing in adults: a computational model of pronoun interpretation in discourse.

This paper presents a study of the effect of working memory load on the interpretation of pronouns in different discourse contexts: stories with and without a topic shift. We discuss a computational model (in ACT-R, Anderson, 2007) to explain how referring expressions are acquired and used. On the basis of simulations of this model, it is predicted that WM constraints only affect adults' pronoun resolution in stories with a topic shift, but not in stories without a topic shift. This latter prediction was tested in an experiment. The results of this experiment confirm that WM load reduces adults' sensitivity to discourse cues signaling a topic shift, thus influencing their interpretation of subsequent pronouns.