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

Species Richness and Trophic Diversity Increase Decomposition in a Co-Evolved Food Web

Ecological communities show great variation in species richness, composition and food web structure across similar and diverse ecosystems. Knowledge of how this biodiversity relates to ecosystem functioning is important for understanding the maintenance of diversity and the potential effects of species losses and gains on ecosystems. While research often focuses on how variation in species richness influences ecosystem processes, assessing species richness in a food web context can provide further insight into the relationship between diversity and ecosystem functioning and elucidate potential mechanisms underpinning this relationship. Here, we assessed how species richness and trophic diversity affect decomposition rates in a complete aquatic food web: the five trophic level web that occurs within water-filled leaves of the northern pitcher plant, Sarracenia purpurea. We identified a trophic cascade in which top-predators — larvae of the pitcher-plant mosquito — indirectly increased bacterial decomposition by preying on bactivorous protozoa. Our data also revealed a facultative relationship in which larvae of the pitcher-plant midge increased bacterial decomposition by shredding detritus. These important interactions occur only in food webs with high trophic diversity, which in turn only occur in food webs with high species richness. We show that species richness and trophic diversity underlie strong linkages between food web structure and dynamics that influence ecosystem functioning. The importance of trophic diversity and species interactions in determining how biodiversity relates to ecosystem functioning suggests that simply focusing on species richness does not give a complete picture as to how ecosystems may change with the loss or gain of species

Relationship influences on exploration in adulthood: the characteristics and function of a secure base.

This investigation advances theory and research regarding relationship influences on exploration in adulthood. This is accomplished by (a) identifying important characteristics of a secure base, (b) examining the influence of the presence or absence of these characteristics on exploration behavior in adulthood, and (c) identifying individual-difference factors that are predictive of the provision and receipt of secure base support. In 2 sessions, married couples (N = 167) provided reports of relationship dynamics involving exploration, and they participated in an exploration activity that was videotaped and coded by independent observers. Results indicated that the 3 identified characteristics of a secure base (availability, noninterference, and encouragement) are strongly predictive of exploration behavior, and that the provision and receipt of these behaviors can be predicted by individual differences in attachment. Implications of results and contributions to existing literature are discussed.</p

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

<p>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.</p

The results from Simulation II.

<p>The x-axis shows the three input set conditions: F10V, F12V, F12NV, 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.</p

Crosstabulation of the categorical neural network intention measure by the empiricial data set for the female 10th grade virgins (N = 105).

<p>Crosstabulation of the categorical neural network intention measure by the empiricial data set for the female 10th grade virgins (N = 105).</p

The valence structure in the empirical input sets used to generate the internal processing constraints for Simulation II.

<p>Each horizontal line represents the proportion of respondents (in the respective input set) that had a positive or negative valence (the proportion negative is, by definition, 1 minus the proportion positive). Black represents the F10V input set; red and green, F12V and F12NV, respectively. The proportion positive is captured from the zero-midpoint on the x-axis towards the left (see the dotted black vertical line); negative is to the right.</p

Regression of model intention score (continuous) onto Empirical Intention (dummy-coded).

<p>Notes: *reference category was intention = 1 (NO); Adj. R-sq = 0.28; omnibus <i><u>F</u></i> (3, 101) = 14.39, p<.001.</p