The resource-availability model of distraction and mind-wandering

This article presents a cognitive model of distraction and mind-wandering that combines and formalizes several existing theories. It assumes that task-related goals and opportunities for distraction are continuously in competition for mental resources. If the task-related goal does not need a particular resource at a particular moment, the likelihood that it is captured by a distraction is high. We applied this model to explain the results of three distraction experiments that differ from each other in a number of ways. The first experiment is a slow-paced mind-wandering study; the main result is that less mind-wandering occurs if subjects have to maintain an item in working memory. The second experiment is a working memory task in which mind-wandering is triggered by the presence of self-referential words in a secondary task; these words increase mental elaboration and reduce memory performance. The third experiment is a mental arithmetic/ memory/visual attention task, in which subjects became more distracted by a flanking (irrelevant) video as the task increased in complexity: as subjects need more time to think, they leave the visual resource vulnerable to distraction. Although these phenomena have been treated separately in the literature, we show that these phenomena can be explained by a single comprehensive model that is based on the assumption that distractions target unused cognitive resources.     

Interpretation-based processing: a unified theory of semantic sentence comprehension

We present interpretation-based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT-R [Anderson and Lebiere, The Atomic Components of Thought, Lawrence Erlbaum Associates Publishers, Mahwah, NJ, 1998].     

WOMEN'S CAREER DECISION-MAKING PROCESS

No abstract available for this article.     

And justice for all: Revisiting the Global Belief in a Just World Scale

The Global Belief in a Just World Scale (GBJWS) has been widely used in measuring the Belief in a Just World (BJW) personality trait. Despite its widespread application across the social sciences, the validity of this scale has not been sufficiently tested in the literature. In this research, the authors examine the internal and external validity of the GBJWS using both standard correlational analyses and structural equation modeling (SEM). Specifically, the authors test the concurrent validity, internal consistency, unidimensional structure, convergent validity, and both measurement and latent mean invariance of the scale across gender and culture. The results of a pilot study suggest strong concurrent validity of the GBJWS with other BJW scales, and the findings of the two main studies support the internal and external validity of GBJWS across gender and culture. The authors’ results further show an overall greater level of BJW of Chinese individuals compared to Americans. The present research provides a much needed investigation of the validity of the GBJWS, and answers calls for research examining the scale’s utility across different populations.     

Modelling the effects of semantic ambiguity in word recognition

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen-Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage for words with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high-dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.     

Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation

We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.     

Extending adaptive world modeling by identifying and handling insufficient knowledge models

Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.     

Statistical models for the induction and use of selectional preferences

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research.