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In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.

The illusion of control refers to the inference of action-outcome contingency in situations where outcomes are in fact random. The strength of this illusion has been found to be affected by whether the frequency of successes increases or decreases over repeated trials, in what can be termed a “success-slope” effect. Previous studies have generated inconsistent findings regarding the nature of this effect. In this paper we present an experiment (N = 334) that overcomes several methodological limitations within this literature, employing a wider range of dependent measures (measures of two different types of illusory control, primary (by self) and secondary (by luck), as well as measures of remembered success-frequency). Results indicate that different dependent measures lead to different effects. On measures of (primary, but not secondary) control over the task, scores were highest when the rate of success increased over time. Meanwhile, estimates of success-frequency in the task did not vary across conditions and showed trends consistent with the broader literature on human memory.

The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people’s responses are driven by the specific set of labels they see. We present an extension of Anderson’s Rational Model of Categorization that captures this effect.

Different classification systems for erroneous beliefs about gambling have been proposed, consistently alluding to ‘illusion of control’ and ‘gambler’s fallacy’ categories. None of these classification systems have, however, considered the how the illusion of control and the gambler’s fallacy might be interrelated. In this paper, we report the findings of a confirmatory factor analysis that examines the proposal that most erroneous gambling-related beliefs can be defined in terms of Rothbaum et al.’s (J Pers Soc Psychol, doi: 10.1037/0022-3514.42.1.5 , 1982) distinction between ‘primary’ and ‘secondary’ illusory control, with the former being driven to a large extent by the well-known gambler’s fallacy and the latter being driven by a complex of beliefs about supernatural forces such as God and luck. A survey consisting of 100 items derived from existing instruments was administered to 329 participants. The analysis confirmed the existence of two latent structures (beliefs in primary and secondary control), while also offering support to the idea that gambler’s fallacy-style reasoning may underlie both perceived primary control and beliefs about the cyclical nature of luck, a form of perceived secondary control. The results suggest the need for a greater focus on the role of underlying processes or belief structures as factors that foster susceptibility to specific beliefs in gambling situations. Addressing and recognising the importance of these underlying factors may also have implications for cognitive therapy treatments for problem gambling.

The use of high-throughput DNA sequencing and proteomic methods has led to an unprecedented increase in the amount of genomic and proteomic data. Application of computing technologies and development of computational tools to analyze and present these data has not kept pace with the accumulation of information. Here, we discuss the use of different database systems to store biological information and mention some of the key emerging computing technologies that are likely to have a key role in the future of bioinformatics.

Many kinds of objects and events in our world have a strongly time-dependent quality. However, most theories about concepts and categories either are insensitive to variation over time or treat it as a nuisance factor that produces irrational order effects during learning. In this article, we present two category learning experiments in which we explored peoples’ ability to learn categories whose structure is strongly time-dependent. We suggest that order effects in categorization may in part reflect a sensitivity to changing environments, and that understanding dynamically changing concepts is an important part of developing a full account of human categorization.

The idea that categorization decisions rely on subjective impressions of similarities between stimuli has been prevalent in much of the literature over the past 30 years and has led to the development of a large number of models that apply some kind of decision rule to similarity measures. A recent article by Smith (2006) has argued that these similarity-choice models of categorization have a substantial design flaw, in which the similarity and the choice components effectively cancel one another out. As a consequence of this cancellation, it is claimed, the relationship between distance and category membership probabilities is linear in these models. In this article, I discuss these claims and show mathematically that in those cases in which it is sensible to discuss the relationship between category distance and category membership at all, the function relating the two is approximately logistic. Empirical data are used to show that a logistic function can be observed in appropriate contexts.

Featural representations of similarity data assume that people represent stimuli in terms of a set of discrete properties. In this article, we consider the differences in featural representations that arise from making four different assumptions about how similarity is measured. Three of these similarity models--the common features model, the distinctive features model, and Tversky's seminal contrast model-have been considered previously. The other model is new and modifies the contrast model by assuming that each individual feature only ever acts as a common or distinctive feature. Each of the four models is tested on previously examined similarity data, relating to kinship terms, and on a new data set, relating to faces. In fitting the models, we have used the geometric complexity criterion to balance the competing demands of data-fit and model complexity. The results show that both common and distinctive features are important for stimulus representation, and we argue that the modified contrast model combines these two components in a more effective and interpretable way than Tversky's original formulation.

Smith and Minda (1998, 2002) argued that the response scaling parameter y in the exemplar-based generalized context model (GCM) makes the model unnecessarily complex and allows it to mimic the behavior of a prototype model. We evaluated this criticism in two ways. First, we estimated the complexity of the GCM with and without the yparameter and also compared its complexity to that of a prototype model. Next, we assessed the extent to which the models mimic each other, using two experimental designs (Nosofsky & Zaki, 2002, Experiment 3; Smith & Minda, 1998, Experiment 2), chosen because these designs are thought to differ in the degree to which they can discriminate the models. The results show that y can increase the complexity of the GCM, but this complexity does not necessarily allow mimicry. Furthermore, if statistical model selection methods such as minimum description length are adopted as the measure of model performance, the models will be highly discriminable, irrespective of design.

In the broad field of psycholinguistics, the modeling of language processing has evolved over the last couple of decades into a prominent subfield that now exerts substantial influence on the direction of the discipline (Christiansen & Chater, 2001). It has sparked new ways of thinking about how language is produced and perceived, most notably in the context of localist connectionist models. With these positive developments have come new challenges, such as devising tests to distinguish among competing models.