Time for Action: Neural Basis of the Costs and Benefits of Temporal Predictability for Competing Response Choices

The brain can anticipate the time of imminent events to optimize sensorimotor processing. Yet, there can be behavioral costs of temporal predictability under situations of response conflict. Here, we sought to identify the neural basis of these costs and benefits by examining motor control processes in a combined electroencephalography–EMG study. We recorded electrophysiological markers of response activation and inhibition over motor cortex when the onset-time of visual targets could be predicted, or not, and when responses necessitated conflict resolution, or not. If stimuli were temporally predictable but evoked conflicting responses, we observed increased intertrial consistency in the delta range over the motor cortex involved in response implementation, perhaps reflecting increased response difficulty. More importantly, temporal predictability differentially modulated motor cortex activity as a function of response conflict before the response was even initiated. This effect occurred in the hemisphere ipsilateral to the response, which is involved in inhibiting unwanted actions. If target features all triggered the same response, temporal predictability increased cortical inhibition of the incorrect response hand. Conversely, if different target features triggered two conflicting responses, temporal predictability decreased inhibition of the incorrect, yet prepotent, response. This dissociation reconciles the well-established behavioral benefits of temporal predictability for nonconflicting responses as well as its costs for conflicting ones by providing an elegant mechanism that operates selectively over the motor cortex involved in suppressing inappropriate actions just before response initiation. Taken together, our results demonstrate that temporal information differentially guides motor activity depending on response choice complexity.

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease

Background In discrete-choice experiments (DCEs), choice alternatives are described by attributes. The importance of each attribute can be quantified by analyzing respondents’ choices. Estimates are valid only if alternatives are defined comprehensively, but choice tasks can become too difficult for respondents if too many attributes are included. Several solutions for this dilemma have been proposed, but these have practical or theoretical drawbacks and cannot be applied in all settings. The objective of the current article is to demonstrate an alternative solution, the fold-in, fold-out approach (FiFo). We use a motivating example, the ABC Index for burden of disease in chronic obstructive pulmonary disease (COPD). Methods Under FiFo, all attributes are part of all choice sets, but they are grouped into domains. These are either folded in (all attributes have the same level) or folded out (levels may differ). FiFo was applied to the valuation of the ABC Index, which included 15 attributes. The data were analyzed in Bayesian mixed logit regression, with additional parameters to account for increased complexity in folded-out questionnaires and potential differences in weight due to the folding status of domains. As a comparison, a model without the additional parameters was estimated. Results Folding out domains led to increased choice complexity for respondents. It also gave domains more weight than when it was folded in. The more complex regression model had a better fit to the data than the simpler model. Not accounting for choice complexity in the models resulted in a substantially different ABC Index. Conclusion Using a combination of folded-in and folded-out attributes is a feasible approach for conducting DCEs with many attributes.

Are Choice Experiment Treatments of Outcome Uncertainty Sufficient? An Application to Climate Risk Reductions

Choice experiments addressing outcome uncertainty (OU) typically reframe continuous probability densities for each risky outcome into two discrete categories, each with a single probability of occurrence. The implications of this simplification for welfare estimation are unknown. This article evaluates the convergent validity of willingness-to-pay (WTP) estimates from a more accurate multiple-outcome treatment of OU, compared to the two-outcome approach. Results for a case study of coastal flood adaptation in Connecticut, United States, suggest that higher-resolution OU treatments increase choice complexity but can provide additional information on risk preferences and WTP. This tradeoff highlights challenges facing the valuation of uncertain outcomes.

Are More Choices Better? An Experimental Investigation of the Effects of Multiple Tax Incentives

ABSTRACT The U.S. federal income tax system includes numerous incentives intended to encourage many behaviors. However, these incentives add complexity. This study investigates how one source of complexity, the number of different incentives, affects individuals' use of tax incentives. The results from two experiments detect no evidence that having more (versus fewer) incentive choices (i.e., high choice complexity) affects individuals' decisions to engage in the targeted behavior or select an incentive. However, the results do show that individuals faced with high choice complexity are more likely to make errors and less likely to choose the optimal incentive. Further, high choice complexity leads to greater perceived complexity and difficulty, which, in turn, is related to less positive emotions and more anxiety. Thus, high choice complexity has negative consequences on individuals. This study also contributes to the choice complexity literature by examining its effect on making an optimal choice.