Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.

Abstract

This paper introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of ÒshortcutsÓ between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study. Key words: Network models, attitudes, tripartite model, connectionism, small-world consistency , recent connectionist modeling of attitudes (Monroe & Read, 2008) and recent advancements in applying network theory in psychology (e.g., Cramer, Waldorp, van der Maas, & Borsboom, 2010; for excellent discussions of the relevance of network analysis to the social sciences in general and psychology in particular see Borgatti, Mehra, Brass, & Labianca, 2009; Westaby, Pfaff, & Redding, 2014) to derive a set of requirements for a CAUSAL ATTITUDE NETWORK MODEL 6 realistic formalized measurement model of attitudes. Third, based on these requirements we develop the CAN model and discuss the proposed small-world structure of attitudes that underlies it. Fourth, we discuss the CAN modelÕs perspective on attitude formation and structure, attitude stability and change, and attitude strength. Fifth, we discuss possible extensions of the CAN model, the modelÕs implications for the assessment of attitudes, and some possible avenues for further study of the CAN model.

The development and validation of the Brief Experience of Caregiving Inventory (BECI)

“…The use of network modelling with the BECI not only visualises the clustering of dimensions but also the network of interacting evaluation of carers which can arise through direct causal influences. This is fitting with the Causal Attitude Network model [24]. …”

Network Models for Cognitive Development and Intelligence

“…A beautiful example is found in the study of attitudes [39], where the 'inverse temperature' can be equated with involvement or importance of the attitude for the person or in the society. In case of low involvement distributions of attitude scores are normal, in case of high involvement they are bimodal [43].…”

Pragmatic nihilism: how a Theory of Nothing can help health psychology progress

“…The smallest units that are often distinguished in health psychology are on the same level of psychological specificity as the 'beliefs' in attitude research. Variables on this level are still conceptually very rich, referring to many different meanings and dynamics such as causality, probability, and valence (although studies such as that of Dalege et al, 2016 are working towards establishing a finer grained perspective). The lowest possible level will likely break higher-level variables down into fundamental properties such as (aspects of) shapes, colours, and affordances.…”

“…Now, consider how the definition of a variable would manifest in such a network: it would consist of a division of all elements in the network into those that are a part of the defined variable, and those that are not. For example, the well-known social cognitive variable 'attitude' could be defined as pertaining to the interestingness, perceived harmfulness, pleasantness, goodness, and beneficiality of a behaviour, object, or other entity (see Figure 2; also see Dalege et al, 2016). This definition enables operationalisation of the construct of attitude: for example, the direct measurement of attitude consists of semantic differentials with anchors like 'harmful' versus 'beneficial', 'boring' versus 'interesting', and 'unpleasant' versus 'pleasant' (Fishbein & Ajzen, 2010).…”