Metacognitive Prompting Aids Dynamic Decision-Making

Metacognitive Prompting Aids Dynamic Decision-Making Jarrett Evans (evans@psy.fsu.edu) Florida State University, Department of Psychology, 1107 W. Call Street Tallahassee, FL 32306 USA C.Dominik Guss (dguess@unf.edu) University of North Florida, Department of Psychology, 1 UNF Drive, Jacksonville, FL 32224 USA Walter R. Boot (boot@psy.fsu.edu) Florida State University, Department of Psychology, 1107 W. Call Street Tallahassee, FL 32306 USA decision-makers slow down and process information analytically to reduce errors (Alter, Oppenheimer, Epley, & Eyre, 2007). Findings from the previous studies are somewhat limited because they focused on either incubation or metacognition and each study used a different task making it difficult to directly compare incubation and metacognition strategies. The current study addresses these limitations by assessing the performance outcomes of both metacognition and incubation strategies on the same task using the microworld approach. Abstract Recent research suggests that incubation is superior to metacognition in tasks involving many decision alternatives (Dijksterhuis et al., 2006). One explanation for these findings is the limited information processing capacity of working memory. The current study further investigates this topic by using more complex tasks as many errors in Dynamic Decision-Making (DDM) are thought to be related to limited processing. Our results indicate that metacognitive prompting results in better performance in DDM over incubation via a better strategy selection. Results are explained referring to methodological reasons and to literature on metacognition. In addition to the theoretical relevance, findings may be relevant for training programs using DDM simulations. Keywords: Dynamic Decision Making, Conscious Processing, Incubation, Unconscious Processing, Metacognition Microworlds Introduction Humans have the remarkable ability to find new strategies to solve problems. Have you ever been working on a problem and noticed a better way to do it? Have you started to work on a problem, went to lunch, and thought of a better strategy while you were eating? The first example, where you consciously monitored your performance, we will refer to as metacognition, or awareness of one’s own performance and regulation of cognitive processes (Flavell, 1981). The second example, where you arrived at a solution unconsciously, or without attention directed at performance (Dijksterhuis, Bos, Nordgren, & van Baaren, 2006), we will refer to as incubation as improvements that occur after one task is set aside and an unrelated task is started have been referred to as the ‘incubation effect’ (Vul & Pashler, 2007; Sio & Ormerod, 2007). Previous research has indicated that incubation improves the quality of choices as people attempt to select the best apartment, roommate, or automobile (Dijksterhuis, 2004; Dijksterhuis, et al., 2006) and helps decision makers to overcome misleading information (Vul & Pashler, 2007). In contrast, other researchers have found that metacognition, rather than incubation, improves the retention of lecture information (Berthold, Nuckles, & Renkl, 2007), and helps The microworld approach is a compromise between a field study and the control of the laboratory and has been used to study individual differences in complex problem solving strategies in situations that are expensive, difficult, or unethical to reproduce (e.g., Guss, Tuason, & Gerhard, 2010). Microworlds are usually computer simulations that require the same cognitive resources as the real-world situation they model (Brehmer & Dorner, 1993). There are four basic requirements for a Dynamic Decision-Making (DDM) microworld: It must be dynamic, complex, contain a hidden variable, and have dynamic complexity (Gonzalez, 2005). To preview, the present study used a simulated fire- fighting task that met these requirements as fires continue to burn in the absence of user input and change due to shifting wind direction (dynamic), there were multiple pieces of equipment and several decision alternatives (complexity), participants did not know where fires would start (hidden variable), and letting a small fire burn would have future consequences as it grew (dynamic complexity). Successful DDM required participants to use their cognitive, perceptual, and motor skills. In addition to these basic skills, they also needed to form a representation of how they believe the system operated so they could decide the appropriate level of response at the right time while maintaining a higher-order goal (Dorner, Nixon, & Rosen, 1990; Gonzalez, 2005). In sum, goals are established and updated as needed, information is collected and a mental model of the system is also established and updated as needed, from goals and collected data future system states