Cognitive style and learning: performance of adaptors and innovators in a novel dynamic task

Abstract

The in¯uence of cognitive style on performance in a novel dynamic task modelled on the task of controlling air traf®c was investigated. It was hypothesized that participants who preferred a more adaptive cognitive style would maintain better rule adherence and safe distances between aircraft in a low-®delity air traf®c control task than those who preferred a more innovative style. Problems were presented to participants using computer-based scenarios in screening, practice, and experimental trials. Performance was measured by the number of times the participant violated two types of scenario rules. Screening, practice, and experimental performance were analysed separately. Cognitive style had a signi®cant effect on performance only during experimental trials. Performance of individuals who preferred a more adaptive style improved across experimental trials while those preferring a more innovative style did not. Results suggest that the more innovative participants, although perhaps having some initial advantage, may not be able to sustain performance in this type of task.The Federal Aviation Administration (FAA) is charged with maintaining US airspaceÐa vast and increasingly complex transportation system. Highly trained air traf®c control specialists (ATCSs) form the heart of the web of radar, computers, and communication facilities that ensure the safety and ef®ciency of the system (Broach and Brecht-Clark, 1994). As part of the ongoing development of aviation systems, new automation will continue to be implemented in this century to assist ATCSs (FAA, 1999). Human interaction with technology is a vital component of these systems. Hollnagel et al. (1995) described the combination of humans and technology to accomplish particular functions as joint systems.Although both humans and technology are necessary elements of joint systems, the person in charge of controlling the system has responsibility for the system's performance (Hollnagel et al., 1995). This is of little interest in well-bounded situations where actual situations match expected conditions. In these situations, rule-based technology handles the human operator's routine tasks extremely well. However, Hollnagel et al. note that the true¯exibility of joint systems emerges from the performance of the human operator, who shapes the response of the joint system to real-time unexpected events.Despite the criticality of human performance in joint systems, research is still needed to identify desirable individual characteristics in relation to system needs beyond domain knowledge of the system itself (cf. Chidester et al., 1990;Hoc et al., 1995;Schmidt and

The evolution of growth intentions: Toward a cognition-based model

“…In the specific context of new ventures, Zacharakis et al (1999) find differences in the ways entrepreneurs attribute reasons for venture failure as compared to venture success. When individuals are compelled to engage in tasks that require them to act in ways that are inconsistent with their respective cognitive styles, coping strategies may be employed to restore "order" to these disconcerting scenarios (Pounds and Bailey, 2001). Thus, our general proposition that differences in cognitive style may influence the way intentions are updated to reflect realized competitive conditions is consistent with prior research.…”

Cognitive Style as Environmentally Sensitive Individual Differences in Cognition

“…For example, Fuller and Kaplan (2004) examined the effect of style on the performance of auditors and found that analytic auditors performed better on analytic tasks than on intuitive tasks, with intuitive auditors showing the opposite pattern. Pounds and Bailey (2001) investigated the effects of style differences on performance of air-traffic-control tasks and reported that performance improved across experimental trials for those who had a KAI adaptor style but not for those who had a KAI innovator style. Pounds and Bailey interpreted their results as indicating that the innovators' style led them to disregard rules for successful performance, such that they were unable to sustain repetitious procedures for extended periods.…”

How do we see fractures? Quantifying subjective bias in fracture data collection

“…Linear scanlines are a quick and relatively simple way of systematically collecting fracture data (Agosta et al, 2010;Bigi et al, 2015;Chesnaux et al, 2009;Guerriero et al, 2011;Ortega et al, 2006;Tóth, 2010). This method was developed in rock engineering for a quantitative description of discontinuities in rock masses (Priest, 1993) and then adopted to describe natural fracture networks (Becker and Gross, 1996;Van Dijk et al, 2000;Newman, 2005;Peacock and Sanderson, 2018). The method involves laying out a tape measure on the outcrop and measuring both the number (N) and the attributes of fractures which intersect the scanline (e.g.…”

“…The method involves laying out a tape measure on the outcrop and measuring both the number (N) and the attributes of fractures which intersect the scanline (e.g. orientation, spacing, length above and below the scanline, aperture, type of terminations, filling, or mineralisation) (Priest, 1993;Priest and Hudson, 1981). To fully sample a fracture network, multiple linear scanlines should be completed with different orientations, and the Terzaghi correction should be applied to reduce orientation bias (Mauldon and Mauldon, 1997;Terzaghi, 1965).…”

“…Bigi et al, 2013;Min et al, 2004) or for rock mechanics analysis (Harthong et al, 2012;Jing and Hudson, 2002), with applications including understanding fluid flow in tight oil and gas reservoirs (Aydin, 2000) and hydrogeology (Comerford et al, 2018), and assessing rock strength for mine engineering (Mas Ivars et al, 2011). There are four methods for characterising natural fractures in outcrops: linear scanlines (Priest, 1993;Priest and Hudson, 1981); circular scanlines (Mauldon et al, 2001;Rohrbaugh et al, 2002); topology sampling (characterising node types; Manzocchi, 2002;Nixon, 2015, 2018); and tracing out the fracture network (window sampling; Wu and Pollard, 1995). These methods handle orientation, censoring or truncation biases (Mauldon et al, 2001;Zeeb et al, 2013), and heterogeneity in the fracture network (Watkins et al, 2015) with different degrees of success.…”