The response dynamics of preferential choice case study

Cognitive Psychology 67 (2013) 151–185 Contents lists available at ScienceDirect Cognitive Psychology journal homepage: www.elsevier.com/locate/cogpsych The response dynamics of preferential choice Gregory J Koop a,b,⇑, Joseph G Johnson a a b Department of Psychology, Miami University, United States Department of Psychology, Syracuse University, United States a r t i c l e i n f o Article history: Accepted 20 September 2013 Keywords: Decision making Computational model Methodology Process models Preference reversals Risky decision making a b s t r a c t The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system This decidedly ‘‘embodied’’ view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice The current work develops a method for collecting and analyzing such data in the domain of decision making We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one ‘‘online’’ preference reversal, and can thus begin to discriminate amongst the candidate models Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods Ó 2013 Elsevier Inc All rights reserved ⇑ Corresponding author Address: Department of Psychology, 430 Huntington Hall, Syracuse, NY 13244, United States E-mail address: gjkoop@syr.edu (G.J Koop) 0010-0285/$ - see front matter Ó 2013 Elsevier Inc All rights reserved http://dx.doi.org/10.1016/j.cogpsych.2013.09.001 152 G.J Koop, J.G Johnson / Cognitive Psychology 67 (2013) 151–185 Introduction A hallmark of recent theoretical work in cognitive psychology (and judgment and decision making in particular) is an increased emphasis on the underlying mental processes that result in behavior That is, rather than simply trying to predict or describe the overt choices people make, researchers are increasingly interested in forming specific models about the latent cognitive and emotional processes that produce those decisions Broadly, we might classify these as computational or process models, which consist specifically of production rule systems (Payne, Bettman, & Johnson, 1992, 1993), heuristic ‘‘toolboxes’’ (Gigerenzer, Todd, & The ABC Research Group, 1999), neural network models (Glöckner & Betsch, 2008; Simon, Krawczyk, & Holyoak, 2004; Usher & McClelland, 2001), sampling models (Busemeyer & Townsend, 1993; Diederich, 1997; Roe, Busemeyer, & Townsend, 2001; Stewart, Chater, & Brown, 2006), and more To many, including the present authors, this is a welcome and exciting evolution of theorizing in our field With an increase in the explanatory scope of these process models comes the need for advancement in the methodological tools and analytic techniques by which we evaluate them (Johnson, Schulte-Mecklenbeck, & Willemsen, 2008) Traditional algebraic models, such as Savage’s (1954) instantiation of expected utility, were assumed to be paramorphic representations, not necessarily describing the exact underlying mental process of how individuals make choices, but rather what choices people make Therefore, researchers were content—and it was theoretically sufficient—to only examine choice outcomes and the maintenance (or not) of principles such as transitivity and independence (e.g., Rieskamp, Busemeyer, & Mellers, 2006) However, contemporary emphasis on process modeling requires more sophisticated means of model evaluation In the past few decades, process-tracing techniques such as mouse- and eye-tracking have become popular for drawing inferences about the information acquisition process in decision making (FrancoWatkins & Johnson, 2011; Payne, 1976; Payne et al., 1993; Wedel & Pieters, 2008; Wedell & Senter, 1997; and many more) This large body of work seeks to verify the patterns of information acquisition that decision makers employ, and compare these to the predictions of various process models This represents a boon in the ability to critically assess and compare different theoretical processing accounts Granted, there are some strong assumptions that need to be made when using this paradigm, and some limitations in the resulting inferences (Bröder & Schiffer, 2003, and the references therein; Franco-Watkins & Johnson, 2011) Still, this paradigm has proven valuable in acknowledging the importance of bringing multiple dependent variables to bear on scientific inquiry in decision research In the current work, we are not disparaging the contribution of process-tracing techniques to our understanding of decision processes However, the process-tracing paradigm is focused on patterns of information acquisition, but not necessarily the direct impact this information has en route to making a decision That is, even though this approach is able to monitor the dynamics of information collection, it does not dynamically assess how this information influences preferences or ‘‘online’’ behavioral intentions In fact, it cannot so: the only indication of preference in these tasks remains discrete, in the form of a single button press or mouse click to indicate selection of a preferred option at the conclusion of each trial At best, then, process-tracing paradigms can only draw inferences about how aggregate measures (such as number of acquisitions or time per acquisition) relate to the ultimately chosen option, or the strategy assumed to produce that option In response to this general shortcoming, we simply propose to dynamically monitor the response selection action as well Just as processtracing has been used as a proxy for dynamic attention in decision tasks, we propose that responsetracing can be used as a dynamic indicator of preference We begin with some theoretical context and a brief survey of this paradigm’s success in cognitive science before presenting a validation, extension, and application of this approach to preferential choice 1.1 Embodied cognition Our basic premise rests on the assumption that cognitive processes can be revealed in the motor system responsible for producing relevant actions This proposition can be cast as an element of embodied cognition, which is already theoretically popular in behavioral research (for overviews, G.J Koop, J.G Johnson / Cognitive Psychology 67 (2013) 151–185 153 see Clark, 1999; Wilson, 2002) For example, recent work on the hot topics of ‘‘embodied’’ and ‘‘situated’’ cognition—even now ‘‘embodied economics’’ (Oullier & Basso, 2010)—suggests that our cognitive, conceptual frameworks are driven by metaphorical relations (at least) to our perceptual and motoric structures Indeed, the recent trend in social sciences has been away from classical theories and towards embodiment theories (Gallagher, 2005) Whereas classical theories separate the body from mental operations, theories of embodiment maintain the importance of the body and its movements for cognitive processes The theoretical perspective of embodied cognition can take several forms (see Goldman & de Vignemont, 2009; and Wilson, 2002, for two possible classifications) One strong interpretation assumes that the neural machinery of thought and action are singular and inseparable, whereas a milder assumption, adopted here, is that cognitive operations produce systematic and reliable physical manifestations In general this approach appreciates the close interaction between cognition and the motor system, and questions the reductionistic tendency to study either in isolation (see Raab, Johnson, & Heekeren, 2009, for a collection of papers in the context of decision making) Embodiment theories have been spreading within and beyond cognitive sciences—they have been applied to the fields of learning, development, and education and have found their way into specialized domains such as sports, robotics and virtual environments Contemporary decision models, in contrast, still explicitly (Glimcher, 2009, p 506) or implicitly assume that the motor component of the decision is the final consequence of cognition; at best, they are silent on this relationship This is problematic as it ignores a number of empirical phenomena such as cognitive tuning (or motor congruence) that suggest the potential for motoric inputs to cognitive processing (Friedman & Förster, 2002; Förster & Strack, 1997; Raab & Green, 2005; Strack, Martin, & Stepper, 1988) For instance, Strack, Martin, and Stepper (1988) showed how inducing facial muscles to perform the action required of smiling or frowning affected the assessment of a stimulus’ valence accordingly (e.g., cartoons rated as funnier when facial muscles were in a position related to smiling) Förster and Strack (1997) and Raab and Green (2005) found similar effects for gross motor movements such as the flexion or extension of the arm on categorization and association tasks Proprioceptive and motor information may also be directly relevant for decision making in other ways, such as by constraining the set of available options, or altering the perception of available options or their attributes (see Johnson, 2009, for elaboration within the context of a computational model) Some of the processtracing work in decision research is also beginning to acknowledge these connections, such as work that shows the influence of visual attention (measured via eye-tracking) on preference (Shimojo, Simion, Shimojo, & Scheier, 2008) and problem solving (Thomas & Lleras, 2007) Just as the existing work has identified a robust connection from the motor system to cognitive processes, the current work introduces evidence for the reciprocal connection of cognitive processes to the motor system It does so by capitalizing on a recent development in other fields that have employed continuous response tracking paradigms 1.2 Mental operations revealed in response dynamics Most recently, continuous online response tracking has been used in cognitive science as evidence for the ‘‘continuity of mind’’ (Spivey, 2008) This work, here referred to as the study of response dynamics, simply involves spatial separation of response options for simple tasks to allow for continuous recording of the motor trajectory required to produce a response Substantial evidence suggests this trajectory reveals approach tendencies for the associated response options (see Dale, Kehoe, & Spivey, 2007; Spivey, Grosjean, & Knoblich, 2005; Duran, Dale, & McNamara, 2010, for methodological details) Such recordings have been successfully applied to gross motor movements, such as lifting the arm to point a response device at a large screen (Koop & Johnson, 2011; Duran et al., 2010), as well as the fine motor movements associated with using a computer mouse (Spivey et al., 2005, among others) Essentially, the major innovation is to monitor the online formation of a response, rather than simply the discrete or ballistic production of a response that is typically collected in experimental settings (a single button press, or mouse click) The validity of this research paradigm is supported by work that correlates the neural activity across the cognitive and motor brain regions for several tasks (Cisek & Kalaska, 2005; Freeman, Ambady, Midgley, & Holcomb, 2011), including perceptual decision 154 G.J Koop, J.G Johnson / Cognitive Psychology 67 (2013) 151–185 making (see Schall, 2004, for a review) Response dynamics research has revealed new insights about behaviors such as categorization (Dale et al., 2007), evaluation of information (McKinstry, Dale, & Spivey, 2008), speech perception (Spivey et al., 2005), deceptive intentions (Duran et al., 2010), stereotyping (Freeman & Ambady, 2009), and learning (Dale, Roche, Snyder, & McCall, 2008; Koop & Johnson, 2011) Additional related work has been conducted within the ‘‘rapid reach’’ paradigm (see Song & Nakayama, 2009, for an overview) A concrete example may help to illustrate the basic paradigm Spivey et al (2005) asked participants to simply click with a computer mouse the image of an object (e.g., ‘‘candle’’) that was identified through headphones The correct object was paired either with a phonologically similar distractor (e.g., ‘‘candy’’), or with a dissimilar control object (e.g., ‘‘jacket’’) Their results showed that the curvature of the response trajectories was affected by the similarity of the paired object—the similar distractor produced an increase in curvature, suggesting a competitive ‘‘pull’’ during the response movement caused by an implicit desire to select the phonologically similar distractor The current work presents one of the first true extensions of this body of research to decisions involving preferential choice (see also Dshemuchadse, Scherbaum, & Goschke, 2013, for an application to intertemporal choice) Previous research using this paradigm has largely focused on tasks such as identification and categorization where objectively correct responses could be determined a priori In contrast, the current work will seek to validate the method to situations where preferences are more subjective, and extend it to a traditional risky decision making task among gambles Our work therefore makes contributions not only from a methodological perspective to the response dynamics paradigm, but also theoretically to the study of human decision making behavior Anecdotal support (e.g., your finger’s movements when selecting a cut of meat in the grocer’s display case) and informal applications (e.g., the online tracking of focus groups’ perceptions during presidential debates) to preferential choice may abound Here, however, we hope to establish the scientific use of this paradigm for decisions in a controlled experimental design We present three experiments using this paradigm that establish its validity, ability to address theoretical predictions, and efficacy for formal computational modeling We also provide enough detail for researchers to consult as a primer in applying these methods and metrics in their own research Experiment Because this is the first extension of the response dynamics method to preferential choice, our first task is to demonstrate the validity of the method within this domain In order to so, we utilized an extremely well-studied set of stimuli, the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2008) The IAPS consists of over 1000 photographs that have been well normed (by approximately 100 participants for each picture) on three dimensions of emotion: affective valence (or pleasantness), arousal, and dominance We focused on the dimensions of pleasantness and arousal under the assumption that preference would be roughly analogous to ratings of pleasantness, given equal levels of arousal Thus we were able to directly test the claim that measures of response dynamics can accurately represent the development of preference 2.1 Methods 2.1.1 General paradigm The general paradigm simply involves participants making choices on a screen as depicted in Fig Participants began each trial by clicking on a box at the bottom-center of the screen Once they did so, this box disappeared and the picture stimuli (described in Stimuli) appeared in boxes at the upper-left and upper-right of the screen In this way, it was possible to achieve a considerable distance between the initiation and termination of the response, as well as sufficient distance between the two response options Clicking in the box of their preferred picture recorded their choice, removed the picture response boxes from the display, and began the next trial Immediate, complete, and unadulterated preference for one option would suggest that the response trajectory proceeds in a straight line from the point of initiation to the point of response Deviation from this direct path is interpreted as an G.J Koop, J.G Johnson / Cognitive Psychology 67 (2013) 151–185 155 Fig The general response dynamics paradigm Participants are initially presented with a ‘‘Start’’ button and two empty response boxes, which are then populated with response options once the ‘‘Start’’ button has been clicked attraction to the competing (non-chosen) response option (e.g., Spivey & Dale, 2006) In our case, this would suggest that even if a participant selects Picture A, the degree of curvature in the associated response trajectory serves as an indication of implicit and concurrent attraction towards Picture B during the formation of the response—an online measure of relative preference 2.1.2 Participants We recruited 98 employees at a corporate business park to complete the experiment (59 female; age, M = 40.03 years, SD = 11.97; 13 left-handed) Between-subjects analyses did not reveal any effects of handedness Participants signed-up for the experiment at a table in a common area, where other experiment options were also present For their participation, participants received product vouchers worth approximately $10 for use at a company store 2.1.3 Stimuli All stimuli were drawn from the IAPS based off of their previous ratings of average pleasantness and arousal on nine-point scales (Lang et al., 2008) We selected 140 pictures that ranged from very unpleasant (pleasantness = 1.66) to very pleasant (pleasantness = 8.34), and paired pictures based on their similarity in pleasantness ratings to create 70 trials Arousal rating was held constant (difference < 0.45) within trial pairs These 70 trials were further divided into trial classes (10 pairs per class), representing seven levels of our independent variable, Difference Specifically, we manipulated the similarity in pleasantness ratings between the pictures, ranging from similar (Difference % 0) to dissimilar (Difference % 6) The experiment was conducted in a professional setting, which resulted in 10 picture pairs being removed at the behest of the employer due to their graphic content The removed trials were more likely to have come from more dissimilar classes because these classes required more strongly negative pictures to achieve such large differences in pleasantness This left slightly unequal numbers of trials in each trial class (see Table 1) Thus, we were left with a total of 60 picture pairs that varied in pleasantness ratings but were each roughly matched for arousal 156 G.J Koop, J.G Johnson / Cognitive Psychology 67 (2013) 151–185 Table Trial classes and choice proportions for Experiment Difference # Trials Pr(Pleasant) p 10 10 9 N/A 0.54 0.68 0.83 0.93 0.94 0.96 N/A 0.01