Abstract

The common-sense model (Leventhal, Meyer, & Nerenz, 1980) outlines how illness representations are important for understanding adjustment to health threats. However, psychological processes giving rise to these representations are little understood. To address this, an associative-learning framework was used to model low-level process mechanics of illness representation and coping-related decision making. Associative learning was modeled within a connectionist network simulation. Two types of information were paired: Illness identities (indigestion, heart attack, cancer) were paired with illness-belief profiles (cause, timeline, consequences, control/cure), and specific illness beliefs were paired with coping procedures (family doctor, emergency services, self-treatment). To emulate past experience, the network was trained with these pairings. As an analogue of a current illness event, the trained network was exposed to partial information (illness identity or select representation beliefs) and its response recorded. The network (a) produced the appropriate representation profile (beliefs) for a given illness identity, (b) prioritized expected coping procedures, and (c) highlighted circumstances in which activated representation profiles could include self-generated or counterfactual beliefs. Encoding and activation of illness beliefs can occur spontaneously and automatically; conventional questionnaire measurement may be insensitive to these automatic representations. Furthermore, illness representations may comprise a coherent set of nonindependent beliefs (a schema) rather than a collective of independent beliefs. Incoming information may generate a "tipping point," dramatically changing the active schema as a new illness-knowledge set is invoked. Finally, automatic activation of well-learned information can lead to the erroneous interpretation of illness events, with implications for [inappropriate] coping efforts. (PsycINFO Database Record

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