Effects of Deliberation Time on Multiple Goal Pursuit
Bibliographic record
Abstract
The multiple-goal pursuit model (MGPM; Vancouver et al., 2010) has provided key insight into the dynamic prioritisation of multiple goals. Since its conception, the MGPM has been adapted to account for a variety of key features such as learning (J. Vancouver et al., 2014), goal type (i.e. approach/avoidance goals) and uncertainty(Ballard et al., 2016), self-efficacy (Vancouver & Purl, 2017), and varying deadlines (Ballard et al., 2018). However so far, the MGPM has only been tested in environments where participants have had ample time to make prioritization decisions. Consequently, this represents an opportunity to determine how well the MGPM generalises to situations where deliberation time is reduced. This is essential as there are a range of circumstances where goals must be prioritized quickly, such as in emergency rooms or on the fireground. In the MGPM, preferences for each goal accumulate by analytically deliberating over goal features until the preference for one goal reaches a threshold, at which point that goal is prioritized (Ballard et al., 2018). However, when a goal must be prioritized quickly, one might not have sufficient cognitive resources to evaluate goal features so rigorously. This suggests the MGPM may not provide a complete explanation of how cognitive resources influence the deliberation process. One way to accommodate this would be to lower the decision-making threshold, meaning preferences need not be as strong to prioritize a goal, but the process of evaluating competing goals would still be the same (Palada et al., 2016; Vuckovic et al., 2013, 2014). This would simply result in spending less time accumulating evidence for each decision, although the decision would still be based off the same information. However, another way to accommodate reduced deliberation time is to rely on heuristics (Gigerenzer & Gaissmaier, 2011). This approach would accommodate reduced deliberation time by evaluating a biased set of information, such that some information from goal features is less attended to. The aim of this study is to investigate the effect of deliberation time on multiple goal pursuit. Specifically, we aim to explore whether the MGPM can account for situations where participants have less time to deliberate over which goal they will prioritize. We will test the current MGPM against three novel adaptions of the MGPM which assume a heuristic approach when deliberation time is reduced, in order to distinguish between information processing and dual-process theories of decision-making. The novel adaptions are based on three heuristics which we have identified participants might rely on when making prioritization decisions quickly: an expectancy heuristic, a valence heuristic and a deadline heuristic. The expectancy heuristic assumes participants will prioritize the goal that is easiest to achieve, the valence heuristic assumes participants will prioritize the goal with the greatest subjective urgency, and the deadline heuristic assumes participants will prioritize the goal with the nearest deadline. In the experiment, participants will complete a novel dual-goal task where participants must make prioritization decisions for two competing goals with varying amounts of time to deliberate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".