Measuring information demand of a dynamic in-vehicle display while driving – A study evaluating the MARS (Masking Action Relevant Stimuli) method

The MARS (Masking Action Relevant Stimuli) method assesses information demand for dynamic stimuli while driving. An action relevant stimulus is masked and the driver presses a button to unmask the stimulus for a limited period. We interpreted button presses as information demand. Following our previous research (Rittger, Kiesel, Schmidt, & Maag, 2014), the current study further evaluates the method. We applied the MARS method to a dynamic in-vehicle display containing recommendations from a traffic light assistant. In a driving simulator, drivers approached intersections with different traffic light phasing. The display either presented simple or complex information. In half of the drives, the participants used the MARS method. The study had a full within subjects design and fixations were recorded in all conditions. The results showed that the information demand varied according to the information in the display and the traffic light phase. A comparison of button presses with fixations showed that one unmasking interval came along with one fixation on the display. As a conclusion, the MARS method can distinguish between conditions with high and low information demand for the display. Button presses relate to fixations on the display. Hence, the MARS method is a promising tool to assess the information demand in dynamic environments and can be applied as an extension or alternative for eye tracking.     

Comparing graphic and symbolic classification in interactive multiobjective optimization

In interactive multiobjective optimization systems, the classification of objective functions is a convenient way to direct the solution process in order to search for new, more satisfactory, solutions in the set of Pareto optimal solutions. Classification means that the decision maker assigns the objective functions into classes depending on what kind of changes in their values (in relation to the current values) are desirable. Here we study the role of user interfaces in implementing classification in multiobjective optimization software and how classification should be realized. In this way, we want to pay attention to the usability of multiobjective optimization software. Typically, this topic has not been of interest in the multiobjective optimization literature. However, usability aspect is important because in interactive classification‐based multiobjective optimization methods, the classification is the core of the solution process. We can say that the more convenient the classification is, the more efficient the system or the method is and the better it supports the work of the decision maker. We report experiments with two classification options, graphic and symbolic ones, which are used in connection with an interactive multiobjective optimization system WWW‐NIMBUS. The ideas and conclusions given are applicable for other interactive classification‐based method, as well. Copyright © 2005 John Wiley & Sons, Ltd.     

A scoping review on the extent and nature of anxiety-related research within the research domain criteria (RDoC) framework: Limited coverage using non-disorder-specific search terms

The need for research on modifiable risk factors in anxiety has been highlighted in previous reviews synthesizing literature on the etiology and maintenance of anxiety. The Research Domain Criteria (RDoC) provides a useful framework to lead this body of work. A scoping review was conducted to examine the extent and nature of research evidence on each RDoC domain as they have been investigated in anxiety via relevant correlates, with the end goal of informing future research priorities. The coverage of this review was limited to search terms bound within non-disorder-specific anxious pathology. The adopted selection criteria resulted in 170 cases (across 94 studies) where RDoC-relevant correlates of anxiety were studied. Results highlight disparate research focus across domains (limited research on processes within the Positive Valence Systems, Systems for Social Processes, and Arousal/Regulatory Systems), inconsistent findings within domains (Cognitive Systems), and a lack of research on cross-domain interactions.     

Individual differences in guilt proneness affect how people respond to moral tradeoffs between harm avoidance and obedience to authority

High guilt proneness was associated with valuing harm avoidance more than obedience to authority on questionnaires (Study 1). Results from a laboratory study suggest that people high in guilt proneness, but not those low in guilt proneness, disobeyed an experimenter to prevent a confederate from suffering through an unpleasant task (Study 2). The present research highlights the role of guilt proneness in moral decision making.     

Inter-individual differences in sport refereeing: A review of theory and practice

Most studies on sport officials ignore the variance of the data in terms of individual differences. Here we highlight the need to focus on these differences in refereeing as the main point and goal of the current review. We argue that the study of individual differences is important for referee selection, development and performance evaluation. We present the available research on differences related to referee communication styles, types of interactions, and game management. We regard expertise and experience level as a key contributor to individual differences. Given the gap in research around the in-group variance, we discuss new directions for research and further recommendations for the field. We suggest that the areas in which more research and practice will inform our selection, training, and evaluation processes are communication and personal communication style, while having more flexibility in applying different refereeing styles to facilitate performance in different contexts.     

Post choice information integration as a causal determinant of confidence: Novel data and a computational account

Confidence judgments are pivotal in the performance of daily tasks and in many domains of scientific research including the behavioral sciences, psychology and neuroscience. Positive resolution i.e., the positive correlation between choice-correctness and choice-confidence is a critical property of confidence judgments, which justifies their ubiquity. In the current paper, we study the mechanism underlying confidence judgments and their resolution by investigating the source of the inputs for the confidence-calculation. We focus on the intriguing debate between two families of confidence theories. According to single stage theories, confidence is based on the same information that underlies the decision (or on some other aspect of the decision process), whereas according to dual stage theories, confidence is affected by novel information that is collected after the decision was made. In three experiments, we support the case for dual stage theories by showing that post-choice perceptual availability manipulations exert a causal effect on confidence-resolution in the decision followed by confidence paradigm. These finding establish the role of RT2, the duration of the post-choice information-integration stage, as a prime dependent variable that theories of confidence should account for. We then present a novel list of robust empirical patterns (‘hurdles’) involving RT2 to guide further theorizing about confidence judgments. Finally, we present a unified computational dual stage model for choice, confidence and their latencies namely, the collapsing confidence boundary model (CCB). According to CCB, a diffusion-process choice is followed by a second evidence-integration stage towards a stochastic collapsing confidence boundary. Despite its simplicity, CCB clears the entire list of hurdles.     

An investigation of the role of scale values in the DS/AHP method of multi‐criteria decision making

DS/AHP is a method of multi‐criteria decision making based on the Dempster–Shafer theory of evidence and the analytic hierarchy process. Central to the utilization of DS/AHP is the composing of preference judgements on identified groups of decision alternatives (DA) across a number of criteria against all the DA present in the problem in question. This paper exposits a series of results whose objectives are to aid in the development of an effective set of preference scale values for use within DS/AHP. These results relate directly to the concomitant level of ignorance (uncertainty) with the judgements made on a single criterion. Two particular directions of investigation are undertaken, firstly in determining the necessary number of scale values available and secondly finding the necessary differences between scale values, dependent on whether an arithmetic or geometric progression is the basis for the scale values. Through an example, the implications and utilization of these results within DS/AHP are illustrated. Copyright © 2003 John Wiley & Sons, Ltd.     

The structure of choice

An unsolved fundamental problem in decision science concerns the extent to which the nature of the perceived relationships among items in a set of alternatives influences how they are chosen. More specifically, given a choice set with n items, how does human choice behavior differ as a function of the perceived relationships between the items of the set? In what follows, we study this problem empirically and theoretically from the standpoint of the dimensional structure of the choice set. In particular, we use generalized invariance structure theory (GIST; Vigo, 2013, 2014) to propose an inverse relationship between the degree of concept learning difficulty of a choice set (as determined by its degree of invariance or internal coherence) and choice response times on its members. To our knowledge, this is the first model that precisely unifies these two fundamental constructs. On average, the model, without free parameters, accounts for nearly 90% of the variance in the data from our two response-time experiments.     

Of matchers and maximizers: How competition shapes choice under risk and uncertainty

In a world of limited resources, scarcity and rivalry are central challenges for decision makers—animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually ‘irrational’ choice phenomenon can be socially adaptive under competition.