Chronic motivational state interacts with task reward structure in dynamic decision-makingCognitive Psychology

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Authors
Jessica A. Cooper, Darrell A. Worthy, W. Todd Maddox
Year
2015
DOI
10.1016/j.cogpsych.2015.09.001
Subject
Linguistics and Language / Experimental and Cognitive Psychology / Developmental and Educational Psychology / Artificial Intelligence / Neuropsychology and Physiological Psychology

Text

Chronic motivational sta reward structure in dyna

Jessica A. Cooper a,⇑, Darrell A. W a The University of Texas at Austin, Department of Ps b Texas A&M University, Department of Psychology, 4 a r t i c l e i n f o ocessing.

All rights re http://dx.doi.org/10.1016/j.cogpsych.2015.09.001 ⇑ Corresponding author at: Department of Psychology, The University of Texas, 108 E. Dean Keeton, Stop A8000, Austin, TX 78712, United States. Fax: +1 (512) 471 6175.

E-mail addresses: jessica.cooper@utexas.edu (J.A. Cooper), worthyda@tamu.edu (D.A. Worthy), maddox@psy.utexas.edu (W.T. Maddox).

Cognitive Psychology 83 (2015) 40–53

Contents lists available at ScienceDirect

Cognitive Psychology journal homepage: www.elsevier .com/locate/cogpsych0010-0285/ 2015 Elsevier Inc. All rights reserved.those in a mismatch engaged more habitual pr  2015 Elsevier Inc. served.goal-directed processing, and thus state-based decision-making.

Specifically, chronic promotion-focused individuals under gainmaximization and chronic prevention-focused individuals under loss-minimization both showed enhanced state-based decisionmaking. Computational modeling indicates that individuals in a match between global chronic motivational state and local task reward structure engaged more goal-directed processing, whereasArticle history:

Accepted 26 September 2015

Available online 29 October 2015

Keywords:

Motivation

Decision-making

Regulatory fit

Regulatory focus

Rewardte interacts with task mic decision-making orthy b, W. Todd Maddox a ychology, 108 E. Dean Keeton, A8000, Austin, TX 78712, United States 235 TAMU, College Station, TX 77843, United States a b s t r a c t

Research distinguishes between a habitual, model-free system motivated toward immediately rewarding actions, and a goaldirected, model-based system motivated toward actions that improve future state. We examined the balance of processing in these two systems during state-based decision-making. We tested a regulatory fit hypothesis (Maddox & Markman, 2010) that predicts that global trait motivation affects the balance of habitualvs. goal-directed processing but only through its interaction with the task framing as gain-maximization or loss-minimization. We found support for the hypothesis that a match between an individual’s chronic motivational state and the task framing enhances

J.A. Cooper et al. / Cognitive Psychology 83 (2015) 40–53 411. Introduction

Motivation is a key feature of decision-making that is often studied in terms of approaching positive states and avoiding negative states (e.g. Atkinson, 1964; Bandura, 1986; Roseman, Spindel, & Jose, 1990). We, along with others (e.g., Braver et al., 2014; Maddox & Markman, 2010), argue that the most common definition of motivation as a simple increase in effortful cognitive processing (i.e., trying harder) is outdated, and that a deeper understanding of the complex motivation–cognition interface is crucial to theorizing about motivation as well as cognition (see Braver et al., 2014, for a review).

Under more recent views, motivation is thought to operate at multiple levels, and the effects of motivation on behavior derive from the interactions between these levels. The interactive nature of motivation on behavior is captured by the notion of ‘‘regulatory fit” (Higgins, 2000; Maddox &

Markman, 2010), which is achieved when the individual’s global motivational state (chronic or situational) aligns with the local motivational task framing. Importantly, approach or avoidance motivation at one level can have vastly different effects on behavior depending upon the valence of motivation at another level. To date, little work has explored these multi-level motivational effects on the balance of cognitive processing. This is the focus of the present report.

Regulatory fit effects have been shown in a variety of domains including judgments of morality (Camacho, Higgins, & Luger, 2003), communication effectiveness (Aaker & Lee, 2001; Cesario, Grant, & Higgins, 2004), and generation of anagram solutions (Shah, Higgins, & Friedman, 1998). Unfortunately, no strong mechanistic explanations for these regulatory fit effects have been offered, mainly because these tasks are ones for which no unique optimal strategy can be defined. This shortcoming has been addressed by examining tasks for which the optimal strategy is uniquely identifiable, and importantly is mediated by a specific cognitive process. This work tests the hypothesis that a ‘‘fit” between the global and local motivational state enhances effortful cognitive processing at the expense of automatic habitual processing (see Maddox & Markman, 2010, for a review). Critically, whether this enhanced effortful processing leads to better performance depends upon whether optimal task performance is mediated by effortful processing. Thus, this work argues that the motivation–cognition relationship involves a three-way interaction between the global motivational state, the local motivational state, and the cognitive processing system that optimally mediates task performance. When the task is one for which optimal performance requires effortful processing, a regulatory fit is advantageous. However, when the task is one for which optimal performance requires automatic habitual processing, such as implicit category learning (Grimm, Markman, Maddox, & Baldwin, 2008), a regulatory mismatch is advantageous. Tests of this three-way interaction find support in studies that examine category learning (e.g. Grimm et al., 2008; Maddox, Baldwin, & Markman, 2006) and decision-making (Otto,

Markman, Gureckis, & Love, 2010; Worthy, Maddox, & Markman, 2007).

Regulatory fit effects in decision-making have shown that decision-makers in a regulatory fit more often choose to systematically explore their environment, while those in a regulatory mismatch more often exploit the highest-valued option (Otto et al., 2010; Worthy et al., 2007). Worthy et al. (2007) and Otto et al. (2010), like much work on regulatory fit, focused on the effect of situational (or induced) regulatory focus. Situational or experimentally-induced motivational focus, obtained by making individuals temporarily experience either a subjective history of promotion success or prevention success (Higgins et al., 2001), is extremely helpful in providing methods for boosting overall task performance, but does little to identify performance advantages related to stable traits of the individual.