Categorization as causal reasoning⋆

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory.     

Cue integration with categories: Weighting acoustic cues in speech using unsupervised learning and distributional statistics

During speech perception, listeners make judgments about the phonological category of sounds by taking advantage of multiple acoustic cues for each phonological contrast. Perceptual experiments have shown that listeners weight these cues differently. How do listeners weight and combine acoustic cues to arrive at an overall estimate of the category for a speech sound? Here, we present several simulations using a mixture of Gaussians models that learn cue weights and combine cues on the basis of their distributional statistics. We show that a cue‐weighting metric in which cues receive weight as a function of their reliability at distinguishing phonological categories provides a good fit to the perceptual data obtained from human listeners, but only when these weights emerge through the dynamics of learning. These results suggest that cue weights can be readily extracted from the speech signal through unsupervised learning processes.     

Mindlessness Revisited: Sequential Request Techniques Foster Compliance by Draining Self-control Resources

The present research extends previous findings suggesting that sequential request techniques, such as the Foot-in-the-Door (FITD) or Door-in-the-Face (DITF) technique, are primarily effective under conditions conducive of mindlessness. We forward that this mindlessness may be the product of the influence technique itself. More specifically, based on the notion of self-control as a limited resource, we hypothesize that actively responding to the initial request-phase of a FITD-compliance gaining procedure drains the target of his/her self-regulatory resources, thus creating the mindlessness so often observed in social influence settings. This resource depletion opens the door for compliance with the target request. The results were in line with these expectations. More specifically, we observed that active responding to an initial request of a FITD technique reduced the availability of self-regulatory resources. This state of resource depletion mediated the effect of the technique on behavioral compliance. In addition, the results of this study ruled out the alternate explanation that the effects were attributable to mood or a general tendency for acquiescence.     

Continuous dynamics of color categorization

In order to assess whether color categorization is sensitive to within-category differences in hue, we monitored mouse trajectories in a modified categorization task. Participants saw color swatches from a blue-green continuum and categorized them with a computer mouse by selecting one of two colored regions at the top of a monitor. An analysis of the mouse trajectories showed that the deviation toward the competing category was a function of hue: As hues approached the category boundary, they increasingly deviated to the competitor. This work presents evidence for parallel activation on the level of hue and category processing for color, as well as simultaneous activation of perceptually adjacent categories. Thus, a dynamic process sensitive to fine-grained within-category detail best characterizes color categorization.     

Lexical effects on compensation for coarticulation: a tale of two systems?

We reply to McQueen's commentary by comparing the parsimony of his account of relevant data and the computational model he favors with the explanation and model we favor. His account requires multiple independent explanations and mechanisms. Ours requires one: lexical feedback.     

Experimental Assessment of Cheating Prevalence in the Czech Republic as an Example of a Post-Communist Country

Dishonest behavior presents a serious problem in many countries’ institutions and was found to be relatively widespread in post-communist countries. We focus on the prevalence of cheating in a sample from such country, the Czech Republic, and individual characteristics influencing dishonest behavior. We used a die rolling task where participants can cheat on their reward to determine whether anonymity conditions increase the frequency of cheating. Participants playing alone did not cheat significantly more than the control group throwing dice publicly. We did not find that gender, cognitive abilities, risk and social preferences robustly predicted the rate of cheating.     

Model for Assessing Quality of Online Health Information: A Fuzzy VIKOR Based Method

Today, tens and thousands of websites provide health‐related information on various topics to a growing number of consumers. However, the lay user is often faced with a challenge of determining the quality of information provided by one site from the other. To ensure the protection of users from sites that provide unreliable and unsafe information, there has to be a competent reviewing body that rates and ranks the quality of information provided by each site. This paper (i) proposes a new criteria framework for assessing the quality of online health information and (ii) uses a fuzzy ‘visekriterijumska optimicija i kompromisno resenje’ method to demonstrate how online health information providers could be assessed and ranked based on their quality. The fuzzy modelling uses pre‐defined linguistic variables parameterized by triangular fuzzy numbers in the assessment and subsequent ranking of providers under a particular health topic. A numerical example is demonstrated using diabetes online information providers to show how the assessment and ranking is carried out. The proposed framework provides functional basis for evaluating the quality of internet health information providers on any particular health topic. Copyright © 2015 John Wiley & Sons, Ltd.     

Classroom Collective Moral Disengagement Scale: Validation in Czech adolescents

Moral disengagement (MD) provides a fruitful framework for understanding how adolescents could attenuate moral self-regulation and behave aggressively. While measures of individual MD have a long tradition, only recently have Gini, Pozzoli, and Bussey developed an instrument measuring collective MD (CMD): Classroom Collective Moral Disengagement Scale (CCMDS). The present study examined psychometric properties of a Czech version of the scale with a sample of 662 adolescents, aged 11–15, from 20 randomly selected elementary schools. Participants completed the scale and a peer-rating measure of bullying, victimization, and defending. The analyses indicated that the CCMDS could be considered a unidimensional gender-invariant scale with very good internal consistency and criterion validity. Importantly, the scale score aggregated at the classroom level was associated with three bullying behaviors: bullying, victimization, and defending. The results support the use of the CCMDS as a promising measure of CMD.