On the pursuit of multiple goals with different deadlines.

Abstract

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).

Cognitive control and capacity for prospective memory in complex dynamic environments.

“…This suggests participants found it easier to discriminate conflicts from nonconflicts when they were given 20s to make five decisions, than when they were given 8s to make two decisions, with the lower thresholds and reduced availability possibly reflecting some degree of 'satisficing' via a reduction in the depth of processing (e.g., Donkin, Little, & Houpt, 2014). This pattern of results is consistent with research on temporal discounting (e.g., Ballard, Vancouver, & Neal, 2018), showing that individuals tend to work harder when given more immediate deadlines. Our participants likely perceived having 20s to make five decisions to be a less demanding deadline than having 8s to make two decisions, despite having equal time available per decision on average.…”

Regulatory Focus Moderates the Effects of Feedback Valence on Resource Allocation

“…Thus, it is important to reexamine these effects under the conditions in which the assessment of selfregulation success is available. Furthermore, recent research on multiple goal pursuit identified other factors such as different deadlines for active goals (Ballard, Vancouver, & Neal, 2018), and perceived power in terms of ability to control resources required for goalpursuit (Schmid, 2018). Therefore, additive effects of those factors in relation with regulatory fit effects would further our understanding on dynamic resource allocation under multiple goal-pursuit situations.…”

Translating Informal Theories Into Formal Theories: The Case of the Dynamic Computational Model of the Integrated Model of Work Motivation

“…On a more positive note, Vancouver and Scherbaum (2008) created computational models of competing theories of self-regulation and used them to develop an empirical study that pitted the theories. Computational models can also be fit to time-series data from single cases (e.g., Vancouver, Weinhardt, et al, 2010) or a set of cases simultaneously (Ballard, Vancouver, & Neal, 2018). Of course, the caveats associated with data fitting that apply to statistical modeling also apply to computational modeling.…”

“…A weakness of the IMWM computational model is that we could set parameter values to unrealistic levels to match that trajectory. To validate computational models, studies need to be designed that challenge model components (e.g., Ballard et al, 2018; Vancouver et al, 2016) or help set constraints for the parameters (Forster, 2000).…”