Patrick K. Belling

The effect of time constraint on anticipation, decision making, and option generation in complex and dynamic environments

Abstract Researchers interested in performance in complex and dynamic situations have focused on how individuals predict their opponent(s) potential courses of action (i.e., during assessment) and generate potential options about how to respond (i.e., during intervention). When generating predictive options, previous research supports the use of cognitive mechanisms that are consistent with long-term working memory (LTWM) theory (Ericsson and Kintsch in Phychol Rev 102(2):211–245, 1995; Ward et al. in J Cogn Eng Decis Mak 7:231–254, 2013). However, when generating options about how to respond, the extant research supports the use of the take-the-first (TTF) heuristic (Johnson and Raab in Organ Behav Hum Decis Process 91:215–229, 2003). While these models provide possible explanations about how options are generated in situ, often under time pressure, few researchers have tested the claims of these models experimentally by explicitly manipulating time pressure. The current research investigates the effect of time constraint on option-generation behavior during the assessment and intervention phases of decision making by employing a modified version of an established option-generation task in soccer. The results provide additional support for the use of LTWM mechanisms during assessment across both time conditions. During the intervention phase, option-generation behavior appeared consistent with TTF, but only in the non-time-constrained condition. Counter to our expectations, the implementation of time constraint resulted in a shift toward the use of LTWM-type mechanisms during the intervention phase. Modifications to the cognitive-process level descriptions of decision making during intervention are proposed, and implications for training during both phases of decision making are discussed.

Cognitive Processes Supporting Recognition in Complex and Dynamic Tasks

Previous research has shown that anticipation is one of the best determinants of skill in numerous complex and dynamic domains, such as law enforcement, driving, aviation, surgery, and sport (for a review see Ward, Williams, & Hancock, 2006). Likewise, recognition ability has formed the cornerstone of much of the naturalistic decision making literature for the last 3 decades (e.g., Klein, Calderwood, & Clinton-Cirocco, 1986). In this research, we examined whether skill at anticipating the outcome of a dynamic situation would predict recognition skill, over and above domain-general measures of cognitive ability. We expected that domain-general cognition would account for some of the variance in recognition skill, but that anticipation skill would explain additional, unique variance. Counter to our expectations, anticipation skill did not explain significant unique variance. Instead, only one of the domain-general cognitive measures—spatial ability—was predictive of recognition skill, suggesting that training for improvement in recognition skill should be skill-specific.

Use of Cognitive Task Analysis to Probe Option-Generation in Law Enforcement

Option-generation paradigms have been employed successfully to investigate skill-based differences in performance, particularly in complex, dynamic, and/or uncertain domains. However, although knowledge of option-generation behavior (e.g., number of options generated, frequency with which the criterion best option is selected) is informative, the underlying basis for the observed option-generation behavior is not always apparent. To address this issue, we probed option-generation behavior using cognitive task analysis. Experienced and less-experienced law enforcement officers first observed temporally-occluded video simulations, and then completed an option-generation task. The cognitive task analysis comprised elicitation of retrospective verbal reports of thinking, followed by video-stimulated recall; analysis of these data revealed information that potentially explains the observed option-generation behavior and provided information relevant to the design of decision-making training.