LAUSR

Objective: 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. Method: 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. Results: 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. Conclusions: 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.