A Computational Evaluation of Sentence Processing Deficits in Aphasia

Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non‐canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT‐R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence‐picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees.     

Developmental Timescale of Rapid Adaptation to Conflicting Cues in Real‐Time Sentence Processing

Children and adults use established global knowledge to generate real‐time linguistic predictions, but less is known about how listeners generate predictions in circumstances that semantically conflict with long‐standing event knowledge. We explore these issues in adults and 5‐ to 10‐year‐old children using an eye‐tracked sentence comprehension task that tests real‐time activation of unexpected events that had been previously encountered in brief stories. Adults generated predictions for these previously unexpected events based on these discourse cues alone, whereas children overall did not override their established global knowledge to generate expectations for semantically conflicting material; however, they do show an increased ability to integrate discourse cues to generate appropriate predictions for sentential endings. These results indicate that the ability to rapidly integrate and deploy semantically conflicting knowledge has a long developmental trajectory, with adult‐like patterns not emerging until later in childhood.     

A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation-based model and the direct-access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self-paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k-fold cross-validation. For both IWAs and controls, the activation-based model furnishes a somewhat better quantitative fit to the data than the DA. Model comparisons using Bayes factors show that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing.     

A Computational Model of Linguistic Humor in Puns

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information‐theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine‐grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.     

A Large‐Scale Analysis of Variance in Written Language

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, 2007 ; Jones & Mewhort, 2007 ; Landauer & Dumais, 1997 ; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013 ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing.     

Association Learning with Own‐ and Other‐race Faces in three‐ and six‐month old infants – A longitudinal study

This longitudinal study assessed 133 Caucasian German infants at 3 and 6 months of age to investigate the influence of own‐race and other‐race faces as visual stimuli on association learning in the visual expectation paradigm (VExP). The study is related to the findings on the other‐race‐effect (ORE) which is said to emerge at 6 months of age. Caucasian faces were used as stimuli of a familiar ethnic category, whereas African faces were used as stimuli of an unfamiliar ethnic category. There was no significant difference between the two stimulus classes in infants' reaction time (RT) to stimulus shifts at 3 months. At 6 months of age, infants' RT decreased significantly in the Caucasian faces condition but not in the African faces condition. These results indicate that the processing of other‐race versus own‐race faces by the age of 6 months, which is also the relevant age for the onset of the ORE, has an important influence on the performance on the VExP. Copyright © 2011 John Wiley & Sons, Ltd.     

A Computational Model for the Item‐Based Induction of Construction Networks

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.     

A Decision‐Making Model for Addressing Problematic Behaviors in Counseling Students

Gatekeeping is the subject of continued discourse in the counseling profession. With revisions to the ACA Code of Ethics (American Counseling Association, 2014 ), recent litigation (Keeton v. Anderson‐Wiley, 2010 ; Ward v. Wilbanks, 2009 ), and focused attention on value conflicts in counseling (Francis & Dugger, 2014 ), counselor educators' role as gatekeepers is expanding. In addition to this expanding role, counselor educators are responsible for being culturally and developmentally sensitive in interpreting, applying, and enforcing the codes and standards with counseling students. This article proposes a decision‐making model that integrates feminist, multicultural, systems theory, and social‐constructivist ethical decision‐making principles to address problematic behaviors in counseling students.     

A Computational Model of the Self‐Teaching Hypothesis Based on the Dual‐Route Cascaded Model of Reading

The self‐teaching hypothesis describes how children progress toward skilled sight‐word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self‐teach new orthographic knowledge. We present a new computational implementation of self‐teaching within the dual‐route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self‐teaching under a variety of circumstances. The new model (ST‐DRC) uses DRC’s sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight‐word reading. ST‐DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular‐word learning. Beyond modeling orthographic learning and self‐teaching, ST‐DRC’s performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.     

Validation Study of the Orientation Model: A Dual‐Process Assessment Framework

The orientation model is a multidimensional dual‐process assessment framework composed of 4 empirically validated instruments of cognitive processing, attachment, empathy, and introspection. It lends counselors a flexible means to assess client processing patterns relevant to clinical practice. The author used 375 participant responses to determine whether selected subscales align with analytic and experiential dual processes. Results suggest that the orientation model can serve as a dispositional framework for counseling practice. Implications and directions for future research are suggested.     

Instance‐based Learning: A General Model of Repeated Binary Choice

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance‐based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance‐based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance‐based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks. Copyright © 2010 John Wiley & Sons, Ltd.     

A multi‐process model of self‐regulation: Influences of Mindfulness,Integrative Self‐Knowledge and Self‐Control in Iran

Self‐regulation presumably rests upon multiple processes that include an awareness of ongoing self‐experience, enduring self‐knowledge and self‐control. The present investigation tested this multi‐process model using the Five‐Facet Mindfulness Questionnaire (FFMQ) and the Integrative Self‐Knowledge and Brief Self‐Control Scales. Using a sample of 1162 Iranian university students, we confirmed the five‐factor structure of the FFMQ in Iran and documented its factorial invariance across males and females. Self‐regulatory variables correlated negatively with Perceived Stress, Depression, and Anxiety and positively with Self‐Esteem and Satisfaction with Life. Partial mediation effects confirmed that self‐regulatory measures ameliorated the disturbing effects of Perceived Stress. Integrative Self‐Knowledge and Self‐Control interacted to partially mediate the association of Perceived Stress with lower levels of Satisfaction with Life. Integrative Self‐Knowledge, alone or in interaction with Self‐Control, was the only self‐regulation variable to display the expected mediation of Perceived Stress associations with all other measures. Self‐Control failed to be implicated in self‐regulation only in the mediation of Anxiety. These data confirmed the need to further examine this multi‐process model of self‐regulation.     

A cognitive‐behavioral model of anxiety disorders in children and adolescents

This study investigated a cognitive‐behavioral model of anxiety disorders in Japanese children and adolescents. Participants comprised 532 children from elementary schools and 751 adolescents from junior high schools as a community group, and 41 children and adolescents who fulfilled the criteria for childhood anxiety disorders as a clinical group. All participants completed three questionnaires about anxiety symptoms, self‐statements, and cognitive errors. While the clinical group showed more anxiety symptoms, negative self‐statements, and cognitive errors than the community group, there was no significant difference in positive self‐statements. Multigroup structural equation modeling found the presence of cognitive links for the community and clinical groups. Specifically, cognitive errors generated negative self‐statements, exacerbating a higher‐order factor (childhood anxiety) affecting six anxiety symptoms corresponding to the diagnostic criteria of psychological disorders. Mediated relationships were supported in only the community sample. Clinical implications for prevention and treatment of anxiety disorders in children and adolescents are discussed.     

A parameter analysis method for the weight‐set to satisfy preference orders of alternatives in additive multi‐criteria value models

In the context of the additive multi‐criteria value model, this paper investigates how the set of criteria weights (weight‐set hereafter) can be determined according to the preference orders of alternatives given by the decision maker. A construction method is proposed for the weight‐set for different intervals of β, where β is a differential amount of value between the preference information on two alternatives. The results of this paper are important for sensitivity analysis in multi‐criteria decision making (MCDM) problems and multi‐criteria group decision analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

High‐Stakes Decision‐Making Within Complex Social Environments: A Computational Model of Belief Systems in the Arab Spring

People experiencing similar conditions may make different decisions, and their belief systems provide insight about these differences. An example of high‐stakes decision‐making within a complex social context is the Arab Spring, in which large numbers of people decided to protest and even larger numbers decided to stay at home. This study uses qualitative analyses of interview narratives and social media addressing individual decisions to develop a computational model tracing the cognitive decision‐making process. The model builds on work by Abelson and Carroll (1965), Schank and Abelson (1977), and Axelrod (1976) to systematically trace the inferences connecting beliefs to decisions. The findings show that protest decisions were often based on positive emotions such as pride, hope, courage, and solidarity, triggered by beliefs about successful protest and self‐sacrifice. By contrast, decisions to stay at home were triggered by beliefs about safety, state approval, and living conditions. As one participant said, “When I heard about the revolution in Tunisia, my heart was filled with solidarity for the people.” In the words of a non‐participant: “When people are killed, we must be careful. There are more important things than protest: safety and stability.” This study of individual explanations about events identifies the role of emotions in high‐stakes decision‐making within complex social environments.     

“Cuts in Action”: A High‐Density EEG Study Investigating the Neural Correlates of Different Editing Techniques in Film

In spite of their striking differences with real‐life perception, films are perceived and understood without effort. Cognitive film theory attributes this to the system of continuity editing, a system of editing guidelines outlining the effect of different cuts and edits on spectators. A major principle in this framework is the 180° rule, a rule recommendation that, to avoid spectators’ attention to the editing, two edited shots of the same event or action should not be filmed from angles differing in a way that expectations of spatial continuity are strongly violated. In the present study, we used high‐density EEG to explore the neural underpinnings of this rule. In particular, our analysis shows that cuts and edits in general elicit early ERP component indicating the registration of syntactic violations as known from language, music, and action processing. However, continuity edits and cuts‐across the line differ from each other regarding later components likely to be indicating the differences in spatial remapping as well as in the degree of conscious awareness of one's own perception. Interestingly, a time–frequency analysis of the occipital alpha rhythm did not support the hypothesis that such differences in processing routes are mainly linked to visual attention. On the contrary, our study found specific modulations of the central mu rhythm ERD as an indicator of sensorimotor activity, suggesting that sensorimotor networks might play an important role. We think that these findings shed new light on current discussions about the role of attention and embodied perception in film perception and should be considered when explaining spectators’ different experience of different kinds of cuts.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.     

The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans

Human control of action in routine situations involves a flexible interplay between (a) task‐dependent serial ordering constraints; (b) top‐down, or intentional, control processes; and (c) bottom‐up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top‐down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal‐directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non‐routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non‐routine goal‐based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system.