Learning to Decide with and without Reasoning: How Task Experience Affects Attribute Weighting and Preference Stability

Certain experiments have shown that reasoning may weaken the stability of people’s preferences, especially with regard to well-learned perceptual judgment and decision-making tasks, while learning has an opposite, consistency-enhancing effect on preferences. We examined the effects of these factors in a visual multi-attribute decision-making task where reasoning, in contrast, has been found to benefit judgments by making them more stable. The initial assumption in this study was that this benefit would be typical for novel tasks, like the one employed here, and that it would decrease when the task is thoroughly learned. This assumption was examined in three experiments by contrasting it with an alternative assumption that this previously obtained beneficial effect is caused solely by learning, not by reasoning. It was found that learning indeed makes preferences more stable by consolidating the weights of the attributes. Reasoning, however, does not benefit this task when it is completely novel but facilitates learning and stability of the preferences long run, therefore increasing the consistency of the participants in the macrolevel. © 2017 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd. key words judgment and decision making; reasoning; learning; information integration; attribute weighting; preference stability; routine Relatively little is known about the interaction between reasoning and learning in judgment and decision making (JDM). In this context, reasoning refers to the conscious thought process leading to a justified resolution to a certain decision problem. It therefore contrasts with intuition, which lacks this deliberate thought process (Kahneman, 2003). In some studies, reasoning seems to benefit relatively novel or unusual JDM tasks, and this advantage disappears with increasing routine (e.g., Berry & Broadbent, 1984, 1987; Hagafors & Brehmer, 1983; Sieck & Yates, 1997). These findings appear quite plausible: Without appropriate experience with a task, an individual can use conscious reasoning to take advantage of the explicit knowledge that has been acquired during the task or that has been acquired from other people, from the surrounding culture, or from other contexts (Baumeister & Masicampo, 2010). More familiar tasks can be executed successfully without reasoning, using existing routines or intuition, which results from learning when individual gathers experience with the task (Glöckner & Witteman, 2010; Hogarth, 2001). The themes of reasoning and learning are therefore closely associated with the current discussion about the dual process models and how deliberative cognitions gradually become intuitions. The advantage of conscious reasoning in JDM has been suggested to be in information acquisition (Dijksterhuis & Nordgren, 2006; Glöckner & Betsch, 2008). The reasoning that is evoked when there is a requirement to provide justifications for decisions may induce a more thorough information search, as the decision maker does not want to appear foolish by not considering all the available information (Leisti, Radun, Virtanen, Nyman, & Häkkinen, 2014; Lerner & Tetlock, 1999). This may decrease, for instance, the tendency towards biases evoked by the context because participants who are consciously reasoning focus more attention on less salient aspects of the problem (Hamilton, Hong, & Chernev, 2007; Sieck & Yates, 1997). In their results from a dynamic decision task, Berry and Broadbent (1984, 1987) attributed the facilitative effect of verbalization to its ability to focus attention on critical information at the right times. Thus, it may be not only the social pressure of justification, which benefits task performance, but also verbal processing in general, which strategically guides attention in a sequential manner (Baddeley, Chincotta, & Adlam, 2001; Emerson & Miyake, 2003). Also, learning has been found to influence information acquisition by gradually shifting the learner’s focus to more relevant information, usually leading to greater accuracy and speed (Haider & Frensch, 1999), which seems to apply to JDM tasks, too (Orquin, Bagger, & Mueller Loose, 2013). Learning furthermore enhances information integration, the process where acquired information is weighted and judgments are formed. Personal preferences can be quite arbitrary and unstable when there is little experience with a task (Ariely, Loewenstein, & Prelec, 2003). Preferences, in these cases, often appear to be dependent on information that is easily available for consideration (e.g., Kahneman, 2003). After making repeated choices with trade-offs, people gradually become more certain of the subjective importance of different attributes and learn to weight them accordingly, consequently making their preferences more stable (Hoeffler & Ariely, 1999). Preferences should therefore not be dependent on how easily the information is accessed. In familiar, thoroughly learned tasks that have become automatic, reasoning can even be distractive: Several studies have shown that conscious reasoning may sometimes weaken the stability of preferences and therefore lead to regret and reduced satisfaction with choices (Dijksterhuis & *Correspondence to: Tuomas Leisti, Department of Psychology and Logopedics, University of Helsinki, PO Box 9 (Siltavuorenpenger 1 A), FIN-00014, Helsinki, Finland. E-mail: tuomas.leisti@helsinki.fi © 2017 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd. Journal of Behavioral Decision Making, J. Behav. Dec. Making (2017) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/bdm.2063 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations