Self-regulation is the dynamic process by which people manage competing demands on their time and resources as they strive to achieve desired outcomes, while simultaneously preventing or avoiding undesired outcomes. In this article, we review the current state of knowledge regarding the process by which people manage these types of demands. We review studies in the organizational, cognitive, social psychology, and human factors literatures that have examined the process by which people (a) manage task demands when working on a single task or goal; (b) select which tasks or goals they work on, and the timing and order in which they work on them; and (c) make adjustments to the goals that they are pursuing. We review formal theories that have been developed to account for these phenomena and examine the prospects for an integrative account of self-regulation that can explain the broad range of empirical phenomena examined across different subdisciplines within psychology.

We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record

This article presents a theory of how people prioritize their time when pursuing goals with different deadlines. Although progress has been made in understanding the dynamics of multiple-goal pursuit, theory in this area only addresses cases where the goals have the same deadline. We rectify this issue by integrating the multiple-goal pursuit model-a formal theory of multiple goal pursuit-with theories of intertemporal motivation and choice. We examine the ability of four computational models derived from this general theory to account for participants' choices across four experiments. The models make different assumptions about how people determine the valence of prioritizing a goal (i.e., by monitoring distance to goal or time pressure), and whether the goal is subject to temporal discounting. In each experiment, participants performed a task requiring them to pursue two goals. Experiments 1 and 2 manipulated deadline and distance; Experiment 3 manipulated deadline and time pressure; Experiment 4 manipulated all three factors. Counter to the predictions of existing theory, participants generally prioritized the goal with the shorter deadline. We also observed weak, but positive effects of distance on prioritization (Experiment 2) and nonlinear effects of time pressure (Experiment 3). The model that best explained participants' decisions assumed that valence is determined by time pressure and the expected utility of a goal is subject to temporal discounting. This new model broadens the range of phenomena that can be accounted for within a single theory of multiple-goal pursuit, and improves our understanding of the interface between motivation and decision making. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

This paper examines the causes of dual-task interference in a time pressured dynamic environment. Resource sharing theories are often used as a theoretical framework to understand dual-task interference. These frameworks propose that resources from a limited pool of information-processing capacity is reallocated towards the primary task as task load increases, and as a result, secondary-task performance declines if the total demand exceeds capacity limit. However, tests of resource models have relied on behavioral results that could be due to a number of different cognitive processes, including changes in response caution, rate of information processing, non-decision processes and response biases. We applied evidence-accumulation models to quantify the cognitive processes underlying performance in a dual-task paradigm in order to examine the causes underlying dual-task interference. We fit performance in time-pressured environment on both a primary classification task and a secondary detection task using evidence-accumulation models. Under greater time pressure, the rate of information processing increased for the primary task while response caution decreased, whereas the rate of information processing for the secondary task declined with greater time pressure. Assuming the rate of evidence accumulation is proportional to available capacity these results are consistent with resource theory and highlight the value of evidence-accumulation models for understanding the complex set of processes underlying dual-task interference.

Climate change projections necessarily involve uncertainty. Analysis of the physics and mathematics of the climate system reveals that greater uncertainty about future temperature increases is nearly always associated with greater expected damages from climate change. In contrast to those normative constraints, uncertainty is frequently cited in public discourse as a reason to delay mitigative action. This failure to understand the actual implications of uncertainty may incur notable future costs. It is therefore important to communicate uncertainty in a way that improves people's understanding of climate change risks. We examined whether responses to projections were influenced by whether the projection emphasized uncertainty in the outcome or in its time of arrival. We presented participants with statements and graphs indicating projected increases in temperature, sea levels, ocean acidification and a decrease in arctic sea ice. In the uncertain-outcome condition, statements reported the upper and lower confidence bounds of the projected outcome at a fixed time point. In the uncertain time-of-arrival condition, statements reported the upper and lower confidence bounds of the projected time of arrival for a fixed outcome. Results suggested that people perceived the threat as more serious and were more likely to encourage mitigative action in the time-uncertain condition than in the outcome-uncertain condition. This finding has implications for effectively communicating the climate change risks to policy-makers and the general public.