The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics

Unique N170 Signatures to Words and Faces in Deaf ASL Signers Reflect Experience-Specific Adaptations During Early Visual Processing

Previous studies with deaf adults reported reduced N170 waveform asymmetry to visual words, a finding attributed to reduced phonological mapping in left-hemisphere temporal regions compared to hearing adults. An open question remains whether this pattern indeed results from reduced phonological processing or from general neurobiological adaptations in visual processing of deaf individuals. Deaf ASL signers and hearing nonsigners performed a same-different discrimination task with visually presented words, faces, or cars, while scalp EEG time-locked to the onset of the first item in each pair was recorded. For word recognition, the typical left-lateralized N170 in hearing participants and reduced left-sided asymmetry in deaf participants were replicated. The groups did not differ on word discrimination but better orthographic skill was associated with larger N170 in the right hemisphere only for deaf participants. Face recognition was characterized by unique N170 signatures for both groups, and deaf individuals exhibited superior face discrimination performance. Laterality or discrimination performance effects did not generalize to the N170 responses to cars, confirming that deaf signers are not inherently less lateralized in their electrophysiological responses to words and critically, giving support to the phonological mapping hypothesis. P1 was attenuated for deaf participants compared to the hearing, but in both groups, P1 selectively discriminated between highly learned familiar objects – words and faces versus less familiar objects – cars. The distinct electrophysiological signatures to words and faces reflected experience-driven adaptations to words and faces that do not generalize to object recognition