Michael Schulte-Mecklenbeck

Framing decisions: Hypothetical and real

This paper addresses the general issue of whether the practice of investigating human decision making in hypothetical choice situations is at all warranted, or under what conditions. A particularly relevant factor that affects the match between real decisions and hypothetical decisions is the importance of a decisions consequences. In the literature experimental gambles tend to confound the reality of the decision situation with the size of the payoffs: hypothetical decisions tend to offer large payoffs, and real decisions tend to offer only small payoffs. Using the well-known framing effect (a tendency of risk-aversion for gains and of risk-seeking for losses) we find that the framing effect depends on payoff size but hypothetical choices match real choices for small as well as large payoffs. These results appear paradoxical unless size of incentive is clearly distinguished from the reality status of decision (real versus hypothetical). Since the field lacks a general theory of when hypothetical decisions match real decisions, the discussion presents an outline for developing such a theory. 2002 Elsevier Science (USA). All rights reserved.

Process models deserve process data : comment on Brandstätter, Gigerenzer, and Hertwig (2006)

Comments on the article by E. Brandstätter, G. Gigerenzer, and R. Hertwig. Resolution of debates in cognition usually comes from the introduction of constraints in the form of new data about either the process or representation. Decision research, in contrast, has relied predominantly on testing models by examining their fit to choices. The authors examine a recently proposed choice strategy, the priority heuristic, which provides a novel account of how people make risky choices. The authors identify a number of properties that the priority heuristic should have as a process model and illustrate how they may be tested. The results, along with prior research, suggest that although the priority heuristic captures some variability in the attention paid to outcomes, it fails to account for major characteristics of the data, particularly the frequent transitions between outcomes and their probabilities. The article concludes with a discussion of the properties that should be captured by process models of risky choice and the role of process data in theory development.

A lack of appetite for information and computation: Simple heuristics in food choice

The predominant, but largely untested, assumption in research on food choice is that people obey the classic commandments of rational behavior: they carefully look up every piece of relevant information, weight each piece according to subjective importance, and then combine them into a judgment or choice. In real world situations, however, the available time, motivation, and computational resources may simply not suffice to keep these commandments. Indeed, there is a large body of research suggesting that human choice is often better accommodated by heuristics-simple rules that enable decision making on the basis of a few, but important, pieces of information. We investigated the prevalence of such heuristics in a computerized experiment that engaged participants in a series of choices between two lunch dishes. Employing MouselabWeb, a process-tracing technique, we found that simple heuristics described an overwhelmingly large proportion of choices, whereas strategies traditionally deemed rational were barely apparent in our data. Replicating previous findings, we also observed that visual stimulus segments received a much larger proportion of attention than any nutritional values did. Our results suggest that, consistent with human behavior in other domains, people make their food choices on the basis of simple and informationally frugal heuristics.

Information search in the laboratory and on the Web: With or without an experimenter

The focus of this study is the effect of the location (laboratory vs. Web) of experiments on active information searchin decision-making tasks. In two experiments, participants were confronted with two different search method versions (list vs. keyword) for acquiring information about a task from a database. The amount and type of information gathered and the time required for task completion were measured. In Experiment 1, significantly more information was searched for in the laboratory than on the Web when the list version was employed, whereas there was no difference between locations in the keyword version. In Experiment 2, the participants were assigned randomly to the Web or the laboratory condition. The results of Experiment 1 were replicated. Whereas location (and the presence or absence of an experimenter) had an effect on the absolute amount of information gathered in both experiments, the relative distribution and type of information items did not differ.

WebDiP : A tool for information search experiments on the World-Wide Web

We describe WebDiP (Web Decision Processes)—an open-source, online tool—which enables a researcher to track participants while they search for information in a database, available through the Internet. After various instructions on setup and configuration are given, a detailed view of WebDiP explains the system’s technical features. Furthermore, other open-source tools are mentioned that helped in programming WebDiP, running it, or analyzing data gathered with it. We present new approaches of how open-source thinking can be incorporated into a research process and discuss future perspectives of WebDiP.

Process-Tracing Methods in Decision Making: On Growing Up in the 70s

Decision research has experienced a shift from simple algebraic theories of choice to an appreciation of mental processes underlying choice. A variety of process-tracing methods has helped researchers test these process explanations. Here, we provide a survey of these methods, including specific examples for subject reports, movement-based measures, peripheral psychophysiology, and neural techniques. We show how these methods can inform phenomena as varied as attention, emotion, strategy use, and understanding neural correlates. Two important future developments are identified: broadening the number of explicit tests of proposed processes through formal modeling and determining standards and best practices for data collection.

Blind Haste: As Light Decreases, Speeding Increases

Worldwide, more than one million people die on the roads each year. A third of these fatal accidents are attributed to speeding, with properties of the individual driver and the environment regarded as key contributing factors. We examine real-world speeding behavior and its interaction with illuminance, an environmental property defined as the luminous flux incident on a surface. Drawing on an analysis of 1.2 million vehicle movements, we show that reduced illuminance levels are associated with increased speeding. This relationship persists when we control for factors known to influence speeding (e.g., fluctuations in traffic volume) and consider proxies of illuminance (e.g., sight distance). Our findings add to a long-standing debate about how the quality of visual conditions affects drivers’ speed perception and driving speed. Policy makers can intervene by educating drivers about the inverse illuminance‒speeding relationship and by testing how improved vehicle headlights and smart road lighting can attenuate speeding.

Prospect theory reflects selective allocation of attention

There is a disconnect in the literature between analyses of risky choice based on cumulative prospect theory (CPT) and work on predecisional information processing. One likely reason is that for expectation models (e.g., CPT), it is often assumed that people behaved only as if they conducted the computations leading to the predicted choice and that the models are thus mute regarding information processing. We suggest that key psychological constructs in CPT, such as loss aversion and outcome and probability sensitivity, can be interpreted in terms of attention allocation. In two experiments, we tested hypotheses about specific links between CPT parameters and attentional regularities. Experiment 1 used process tracing to monitor participants’ predecisional attention allocation to outcome and probability information. As hypothesized, individual differences in CPT’s loss-aversion, outcome-sensitivity, and probability-sensitivity parameters (estimated from participants’ choices) were systematically associated with individual differences in attention allocation to outcome and probability information. For instance, loss aversion was associated with the relative attention allocated to loss and gain outcomes, and a more strongly curved weighting function was associated with less attention allocated to probabilities. Experiment 2 manipulated participants’ attention to losses or gains, causing systematic differences in CPT’s loss-aversion parameter. This result indicates that attention allocation can to some extent cause choice regularities that are captured by CPT. Our findings demonstrate an as-if model’s capacity to reflect characteristics of information processing. We suggest that the observed CPT–attention links can be harnessed to inform the development of process models of risky choice.

Facial expression analysis with AFFDEX and FACET: A validation study

The goal of this study was to validate AFFDEX and FACET, two algorithms classifying emotions from facial expressions, in iMotions’s software suite. In Study 1, pictures of standardized emotional facial expressions from three databases, the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP), the Amsterdam Dynamic Facial Expression Set (ADFES), and the Radboud Faces Database (RaFD), were classified with both modules. Accuracy (Matching Scores) was computed to assess and compare the classification quality. Results show a large variance in accuracy across emotions and databases, with a performance advantage for FACET over AFFDEX. In Study 2, 110 participants’ facial expressions were measured while being exposed to emotionally evocative pictures from the International Affective Picture System (IAPS), the Geneva Affective Picture Database (GAPED) and the Radboud Faces Database (RaFD). Accuracy again differed for distinct emotions, and FACET performed better. Overall, iMotions can achieve acceptable accuracy for standardized pictures of prototypical (vs. natural) facial expressions, but performs worse for more natural facial expressions. We discuss potential sources for limited validity and suggest research directions in the broader context of emotion research.

The pyeTribe: Simultaneous eyetracking for economic games

The recent introduction of inexpensive eyetrackers has opened up a wealth of opportunities for researchers to study attention in interactive tasks. No software package has previously been available to help researchers exploit those opportunities. We created “the pyeTribe,” a software package that offers, among others, the following features: first, a communication platform between many eyetrackers to allow for simultaneous recording of multiple participants; second, the simultaneous calibration of multiple eyetrackers without the experimenter’s supervision; third, data collection restricted to periods of interest, thus reducing the volume of data and easing analysis. We used a standard economic game (the public goods game) to examine the data quality and demonstrate the potential of our software package. Moreover, we conducted a modeling analysis, which illustrates how combining process and behavioral data can improve models of human decision-making behavior in social situations. Our software is open source.