Limits...
The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

Orr MG, Thrush R, Plaut DC - PLoS ONE (2013)

Bottom Line: 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).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.As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Columbia University, New York, New York, USA. Mo2259@columbia.edu

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

The results from Simulation I.The x-axis shows the three input set conditions: P25, P50, and P75. The y-axis represents the mean activation of each valence bank (red = positive, to intend; grey = negative, to not intend). The green dashed-horizontal lines indicate mean activation levels of 0.25, 0.50, and 0.75. The error bars show the standard deviation across 30 runs of each input set X clamping factor condition (using n = 30 in the denominator). Panel A shows the strong clamping condition; Panel B shows weak clamping.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3643950&req=5

pone-0062490-g003: The results from Simulation I.The x-axis shows the three input set conditions: P25, P50, and P75. The y-axis represents the mean activation of each valence bank (red = positive, to intend; grey = negative, to not intend). The green dashed-horizontal lines indicate mean activation levels of 0.25, 0.50, and 0.75. The error bars show the standard deviation across 30 runs of each input set X clamping factor condition (using n = 30 in the denominator). Panel A shows the strong clamping condition; Panel B shows weak clamping.

Mentions: Figure 3 shows the mean activation for each valence bank. The green dashed-horizontal lines on the y-axis demarcate mean activation levels of 0.25, 0.50, and 0.75 to capture the predicted mean activation values of the valence banks, given that the external constraints dominated the behavior of the system–e.g., for the P25 condition the mean activations should be around 0.25 and 0.75 for the intend and not intend banks, respectively, if the external constraints dominate; for the P50, 0.50 for both banks; for the P75 condition, the reverse of the P25 condition. Thus, the difference between the dashed horizontal lines and the model output illustrates the extent to which the internal constraints affected the state of the intention system.


The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

Orr MG, Thrush R, Plaut DC - PLoS ONE (2013)

The results from Simulation I.The x-axis shows the three input set conditions: P25, P50, and P75. The y-axis represents the mean activation of each valence bank (red = positive, to intend; grey = negative, to not intend). The green dashed-horizontal lines indicate mean activation levels of 0.25, 0.50, and 0.75. The error bars show the standard deviation across 30 runs of each input set X clamping factor condition (using n = 30 in the denominator). Panel A shows the strong clamping condition; Panel B shows weak clamping.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643950&req=5

pone-0062490-g003: The results from Simulation I.The x-axis shows the three input set conditions: P25, P50, and P75. The y-axis represents the mean activation of each valence bank (red = positive, to intend; grey = negative, to not intend). The green dashed-horizontal lines indicate mean activation levels of 0.25, 0.50, and 0.75. The error bars show the standard deviation across 30 runs of each input set X clamping factor condition (using n = 30 in the denominator). Panel A shows the strong clamping condition; Panel B shows weak clamping.
Mentions: Figure 3 shows the mean activation for each valence bank. The green dashed-horizontal lines on the y-axis demarcate mean activation levels of 0.25, 0.50, and 0.75 to capture the predicted mean activation values of the valence banks, given that the external constraints dominated the behavior of the system–e.g., for the P25 condition the mean activations should be around 0.25 and 0.75 for the intend and not intend banks, respectively, if the external constraints dominate; for the P50, 0.50 for both banks; for the P75 condition, the reverse of the P25 condition. Thus, the difference between the dashed horizontal lines and the model output illustrates the extent to which the internal constraints affected the state of the intention system.

Bottom Line: 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).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.As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Columbia University, New York, New York, USA. Mo2259@columbia.edu

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus