Conceptual complexity and construct validity

Despite a century of methodological and conceptual advances in the technology of psychosocial measurement, poor correspondence between indicators and the constructs they are intended to represent remains a limiting factor to the accumulation of scientific knowledge. Longstanding conventions in measurement may contribute to the failure to develop optimal criteria. These conventions include the focus on complex over simple constructs and the use of multi-item measures of disparate content to represent those constructs. Several arguments suggest that such a measurement model compromises the potential for developing measures that accurately reflect psychosocial phenomena. The article concludes with some preliminary suggestions concerning an alternative model that may address this construct validity problem more effectively.     

Evaluative language,cognitive effort and attitude change

The present article deals with the effects of the use of evaluatively biased language on attitudes, and with the role of cognitive effort. We tested whether active information processing — formulating arguments on the basis of evaluatively biased language — was a necessary condition for attitude change. Results of the present study support the prediction that using evaluative language can infuence attitudes, but that these effects depend on the amount of cognitive effort.     

Numerical support,information processing and attitude change

In two experiments we studied the prediction that majority support induces stronger convergent processing than minority support for a persuasive message, the more so when recipients are explicitly forced to pay attention to the source's point of view; this in turn affects the amount of attitude change on related issues. Convergent processing is the systematic elaboration on the sources position, but with a stronger focus on verification and justification rather than falsification. In Experiment 1 it was found that numerical support is related to information processing as predicted: The greater the support, the more convergent the processing. Experiment 2 replicated this result, and furthermore confirmed our expectations regarding attitude change: The more convergent processing occurs, the less subjects change their attitude on related issues.     

Conceptual complexity and the bias/variance tradeoff

In this paper we propose that the conventional dichotomy between exemplar-based and prototype-based models of concept learning is helpfully viewed as an instance of what is known in the statistical learning literature as the bias/variance tradeoff. The bias/variance tradeoff can be thought of as a sliding scale that modulates how closely any learning procedure adheres to its training data. At one end of the scale (high variance), models can entertain very complex hypotheses, allowing them to fit a wide variety of data very closely—but as a result can generalize poorly, a phenomenon called overfitting. At the other end of the scale (high bias), models make relatively simple and inflexible assumptions, and as a result may fit the data poorly, called underfitting. Exemplar and prototype models of category formation are at opposite ends of this scale: prototype models are highly biased, in that they assume a simple, standard conceptual form (the prototype), while exemplar models have very little bias but high variance, allowing them to fit virtually any combination of training data. We investigated human learners’ position on this spectrum by confronting them with category structures at variable levels of intrinsic complexity, ranging from simple prototype-like categories to much more complex multimodal ones. The results show that human learners adopt an intermediate point on the bias/variance continuum, inconsistent with either of the poles occupied by most conventional approaches. We present a simple model that adjusts (regularizes) the complexity of its hypotheses in order to suit the training data, which fits the experimental data better than representative exemplar and prototype models.     

Conceptual blending of high-level features and data-driven salience computation in melodic generation

Conceptual Blending (CB) theory describes the cognitive mechanisms underlying the way humans process the emergence of new conceptual spaces by blending two input spaces. CB theory has been primarily used as a method for interpreting creative artefacts, while recently it has been utilised in the context of computational creativity for algorithmic invention of new concepts. Examples in the domain of music include the employment of CB interpretatively as a tool to explain musical semantic structures based on lyrics of songs or on the relations between body gestures and music structures. Recent work on generative applications of CB has shown that proper low-level representation of the input spaces allows the generation of consistent and sometimes surprising blends. However, blending high-level features (as discussed in the interpretative studies) of music explicitly, is hardly feasible with mere low-level representation of objects. Additionally, selecting features that are more salient in the context of two input spaces and relevant background knowledge and should, thus, be preserved and integrated in new interesting blends has not yet been tackled in a cognitively pertinent manner. The paper at hand proposes a novel approach to generating new material that allows blending high-level features by combining low-level structures, based on statistically computed salience values for each high-level feature extracted from data. The proposed framework is applied to a basic but, at the same time, complicated field of music, namely melodic generation. The examples presented herein allow an insightful examination of what the proposed approach does, revealing new possibilities and prospects.     

Impact of negation salience and cognitive resources on negation during attitude formation

Because of the increased cognitive resources required to process negations, past research has shown that explicit attitude measures are more sensitive to negations than implicit attitude measures. The current work demonstrated that the differential impact of negations on implicit and explicit attitude measures was moderated by (a) the extent to which the negation was made salient and (b) the amount of cognitive resources available during attitude formation. When negations were less visually salient, explicit but not implicit attitude measures reflected the intended valence of the negations. When negations were more visually salient, both explicit and implicit attitude measures reflected the intended valence of the negations, but only when perceivers had ample cognitive resources during encoding. Competing models of negation processing, schema-plus-tag and fusion, were examined to determine how negation salience impacts the processing of negations.     

Social categorization and attitude change

Two experiments were carried out investigating the effect of categorization on attitude change. It was predicted that the division of a number of individuals into two subgroups (categorization), in such a way that initial attitudes correlate with subgroup membership, would lead to accentuation of attitudinal differences between subgroups. It was further predicted that an identical distribution of initial attitudes without superimposed categorization would lead to convergence of attitude positions. In experiment 1, the effect of a male-female classification on attitude change was studied. It was indeed found that subjects changed their attitudes in the direction opposite to the position of the outgroup (intergroup attitude differentiation), but only for groups who were initially more extreme than the comparison group. In the control condition (no categorization), conformity effects were observed. In experiment 2, an antagonistic intergroup setting was induced. In this situation, strong intergroup attitude differentiation effects were observed, which were not affected by the magnitude of the initial intergroup discrepancy. In the control condition, subjects did not show conformity to the overall group mean, but maintained their initial noncentral attitude position.     

Aging and attitude change.

Social psychologists have proposed a variety of different models to account for people's relative openness to attitude change through the life cycle. Two of the most important models are the impressionable years model, which suggests an especially great openness to change among the young, and the lifelong openness model, which suggests that age is unrelated to openness to attitude change. Two studies were conducted to examine the openness of people of varying ages to attitude change. In both studies, the influence of personal experiences with government agencies on attitudes toward government was examined. The attitudes of older people changed as much or more in response to their personal experiences as did those of younger people. These results support the lifelong openness model of attitude change.