Geänderte Inhalte

Es werden verschiedene Ansatzpunkte zur Verbesserung der Assessment-Center Methode diskutiert. Die Einbeziehung von Selbstreflexionsfähigkeit und Lernpotenzial als diagnostische Kriterien wird empfohlen. Es wird vorgeschlagen, Assessment-Center stärker dynamisch zu gestalten und kognitiven Kompetenzen mehr Geltung zu verschaffen. Der Einsatz computersimulierter Problemlöseszenarien zur Erfassung von Fähigkeiten der operativen Intelligenz wird diskutiert. Die Umsetzung der vorgestellten Verbesserungsvorschläge wird am Beispiel eines bei der Thomas Cook AG durchgeführten Assessment-Centers zur Auswahl von Führungsnachwuchskräften verdeutlicht. Das Assessment-Center besteht im Kern aus einem komplexen Problemlöseszenario, das als länger andauernde Gruppenaufgabe vorgegeben wird. Ergänzend werden mehrere konventionelle Assessment Center-Aufgaben durchgeführt. Wie die Ergebnisse zeigen (auf der Basis von Daten von 38 Teilnehmern) trägt das Problemlöseszenario mehr zur Entscheidung der Beobachter bei als traditionelle Assessment-Center Übungen. Darüber hinaus erweisen sich Selbstreflexionsfähigkeit und Lernfähigkeit als diagnostisch wertvolle Prädiktoren. Wie die Befragung von Teilnehmern und Beobachtern des Assessment Centers ergab, wird dem Assessment-Center eine hohe Augenscheinvalidität sowie eine hohe soziale Validität bescheinigt.

A probabilistic causal chain A→B→C may intuitively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→C that violate the Markov condition. We hypothesized that participants would make transitive inferences in accordance with the Markov condition although they were presented with counterevidence showing intransitive data. For instance, participants were successively presented with data entailing positive dependencies A→B and B→C. At the same time, the data entailed that A and C were statistically independent. The results of two experiments show that transitive reasoning via a mediating event B influenced and distorted the induction of the indirect relation between A and C. Participantsʼ judgments were affected by an interaction of transitive, causal-model-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations into mental causal chains that obey the Markov condition and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Examined the correlation properties of temporal fluctuations in music on the timescale of rhythms and their influence on the perception of musical performances. The authors were able to established long-range fluctuations as an inevitable natural companion of both simple and complex human rhythmic performances. To test the preference of computer generated music that has been ``humanized'' compared to plain computer generated music, 39 choir singers (mean age 26 years) were asked to first rate their music expertise on a scale of 1 (amateur) to 6 (professional), yielding an average of 3.8. Then the listeners heard 2 versions (listening to each version 3 times) of a computer generated song that was either humanized, using professional audio editing software which offers a humanizing feature that artificially generates rhythmic fluctuations, or was accompanied by white noise. The results demonstrated that listeners strongly preferred long-range correlated (LRC) fluctuations in musical rhythms. The authors therefore conclude that the favorable fluctuation type for humanizing interbeat intervals coincides with the one generically inherent in human musical performances.

Choices do not merely identify one option among a set of possibilities; choosing is an intervention, an action that changes the world. As a result, good decision making generally requires a model specifying how actions are causally related to outcomes. Interventions license different inferences than observations because an event whose state has been determined by intervention is not diagnostic of the normal causes of that event. We integrate these ideas into a causal framework for decision making based on causal Bayes nets theory, and suggest that deliberate decision making is based on simplified causal models and imaginary interventions. The framework is consistent with what we know so far about how people make decisions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After the control task, we modified the causal structure of the environment and assessed decision makersʼ sensitivity to this manipulation. While purely instrumental knowledge does not support inferences given the new modified structure, causal knowledge does. The results showed that most participants learned the structure of the underlying causal system. However, participants acquired surprisingly little knowledge of the systemʼs parameters when the causal processes that governed the system were not perceptually separated (Experiment 1). Knowledge improved considerably once processes were separated and feedback was made more transparent (Experiment 2). These findings indicate that even without instruction, causal learning is a favored strategy for interacting with and controlling a dynamic causal system. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The paper sets out to reveal conditions enabling diagnostic self-deception, peopleʼs tendency to deceive themselves about the diagnostic value of their own actions. We characterize different types of self-deception in terms of the distinction between intervention and observation in causal reasoning. One type arises when people intervene but choose to view their actions as observations in order to find support for a self-serving diagnosis. We hypothesized that such self-deception depends on imprecision in the environment that allows leeway to represent oneʼs own actions as either observations or interventions. Four experiments tested this idea using a dot-tracking task. Participants were told to go as quickly as they could and that going fast indicated either above-average or below-average intelligence. Precision was manipulated by varying the vagueness in feedback about performance. As predicted, self-deception was observed only when feedback on the task used vague terms rather than precise values. The diagnosticity of the feedback did not matter. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Decision makers have been found to bias their interpretation of incoming information to support an emerging judgment (predecisional information distortion). This is a robust finding in human judgment, and was recently also established and measured in physiciansʼ diagnostic judgments (Kostopoulou et al. 2012). The two studies reported here extend this work by addressing the constituent modes of distortion in physicians. Specifically, we studied whether and to what extent physicians distort information to strengthen their leading diagnosis and/or to weaken a competing diagnosis. We used the 'stepwise evolution of preference' method with three clinical scenarios, and measured distortion on separate rating scales, one for each of the two competing diagnoses per scenario. In Study 1, distortion in an experimental group was measured against the responses of a separate control group. In Study 2, distortion in a new experimental group was measured against participantsʼ own, personal responses provided under control conditions, with the two response conditions separated by a month. The two studies produced consistent results. On average, we found considerable distortion of information to weaken the trailing diagnosis but little distortion to strengthen the leading diagnosis. We also found individual differences in the tendency to engage in either mode of distortion. Given that two recent studies found both modes of distortion in lay preference (Blanchard, Carlson & Meloy, 2014; DeKay, Miller, Schley & Erford, 2014), we suggest that predecisional information distortion is affected by participant and task characteristics. Our findings contribute to the growing research on the different modes of predecisional distortion and their stability to methodological variation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

In the first part, we will focus on the cues that people use to judge causality. In the second part, we will look at the assumptions people include in their causal model representations that allow them to go beyond the given information. In the third part, we will concentrate on implicit and explicit causal knowledge resulting from dealing with a causal system. In all parts a research paradigm will be introduced which allows to run studies. Possible modifications and extensions will be pointed out. In addition, theoretical approaches which allow modelling the underlying cognitive processes will be briefly mentioned and pointers to the relevant literature will be given. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

We propose a mixed belief model of self-deception. According to the theory, people distribute belief over two possible causal paths to an action, one where the action is freely chosen and one where it is due to factors outside of conscious control. Self-deceivers take advantage of uncertainty about the influence of each path on their behavior, and shift weight between them in a self-serving way. This allows them to change their behavior to provide positive evidence and deny doing so, enabling diagnostic inference to a desired trait. In Experiment 1, women changed their pain tolerance to provide positive evidence about the future quality of their skin, but judgments of effort claimed the opposite. This 'effort denial' suggests that participantsʼ mental representation of their behavior was dissociated from their actual behavior, facilitating self-deception. Experiment 2 replicated the pattern in a hidden picture task where search performance was purportedly linked to self-control. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The presentation of a Bayesian inference problem in terms of natural frequencies rather than probabilities has been shown to enhance performance. The effect of individual differences in cognitive processing on Bayesian reasoning has rarely been studied, despite enabling us to test process-oriented variants of the two main accounts of the facilitative effect of natural frequencies: The ecological rationality account (ERA), which postulates an evolutionarily shaped ease of natural frequency automatic processing, and the nested sets account (NSA), which posits analytical processing of nested sets. In two experiments, we found that cognitive reflection abilities predicted normative performance equally well in tasks featuring whole and arbitrarily parsed objects (Experiment 1) and that cognitive abilities and thinking dispositions (analytical vs. intuitive) predicted performance with single-event probabilities, as well as natural frequencies (Experiment 2). Since these individual differences indicate that analytical processing improves Bayesian reasoning, our findings provide stronger support for the NSA than for the ERA. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Kausale Modelle sind Repräsentationen von kausalen Strukturen und Prozessen in der Welt. In der vorliegenden Arbeit werden zwei Hauptfragen in Bezug auf kausale Modelle angegangen: ``Welches sind die Unterschiede zwischen verschiedenen Kausalmodellen?'' (normative Frage) und ``Sind Personen für diese Unterschiede sensibel?'' (psychologische Frage). Im Kapitel 1 werden qualitative, formale und numerische Aspekte verschiedener Kausalmodelle eingeführt und diskutiert. Kapitel 2 behandelt die Relevanz von Kausalmodellen für Lernen, Diagnose, Vorhersage und Hypothesen-Testen und rezensiert die aktuelle psychologische Literatur zu diesen Themen. Im Kapitel 3 werden eigene Experimente beschrieben. Diese weisen aus, dass Personen kausale Modelle beim Lernen benutzen, dass sie aber nicht fähig sind, selbst einfache kausale Modelle zu testen. Des Weiteren wird gezeigt, dass Personen eine implizite Sensibilität für strukturelle Implikationen von kausalen Modellen zeigen, dass ihnen aber ein explizites Verständnis für diese Implikationen fehlt. Das abschließende Kaptitel integriert die Befunde, und es wird argumentiert, dass es einen Unterschied gebe zwischen Denken mit Kausalmodellen und Denken über Kausalmodelle. Spekulationen über mögliche evolutionäre Beschränkungen dieser Differenzierung bilden den Abschluss., publication\\\\_type = type, publication\\\_type = type, publication\\_type = type, publication\_type = type

Causal considerations must be relevant for those making decisions. Whether to bring an umbrella or leave it at home depends on the causal consequences of these options. However, most current decision theories do not address causal reasoning. Here, the authors propose a causal model theory of choice based on causal Bayes nets. The critical ideas are (a) that people decide using causal models of the decision situation and (b) that people conceive of their own choice as an intervention. Four corroborating experiments are reported. The first 2 experiments showed that participants chose on the basis of the causal structure underlying a choice scenario rather than the statistical relation among actions and outcomes. Experiments 3 and 4 showed that participants treated choices and interventions similarly. They also suggest that decision makers use causal models to derive inferences about expected outcomes. Boundary conditions on causal decision making and examples of faulty causal inferences in choice (e.g., self-deception) are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

One important reason to opt for a specific intervention seems to be the expectation that the intervention will effectively alleviate a clientʼs problems. We investigated whether clinicians base their judgments of the effectiveness of interventions on the explanations that they construct for a clientʼs problems. Forty clinical child psychologists drew causal models and rank ordered interventions according to their expected effectiveness for two cases. We calculated the effects that the interventions would have in cliniciansʼ causal models and compared the outcomes with cliniciansʼ rankings. We found that cliniciansʼ causal models explained the clientsʼ problems differently and that clinicians had different expectations of the effectiveness of interventions. We could however predict ratings of effectiveness of interventions from cliniciansʼ individual models. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

Probabilistic models have dominated judgment and decision making (JDM) research, both in terms of the normative theories that people should conform to, and the mental models that people use to reason and decide under uncertainty. This is perfectly natural—what else could (or should) lie at the center of our capacity to reason about uncertainty? In this chapter, however, the authors argue that this focus on probabilistic models has obscured and sidelined an equally fundamental concept—causality. Moreover, shifting the focus onto causal models gives us a better understanding of how people make judgments and decisions under uncertainty. This thesis is not entirely new, but recent work in causal inference, both theoretical and empirical, has paved the way for a more formal and in-depth exposition. The authors will present a sampling of this work, and link this with questions traditionally addressed by JDM research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

An important reason to choose an intervention to treat psychological problems of clients is the expectation that the intervention will be effective in alleviating the problems. The authors investigated whether clinicians base their ratings of the effectiveness of interventions on models that they construct representing the factors causing and maintaining a client's problems. Forty clinical child psychologists drew causal models and rank ordered interventions according to their expected effectiveness for 2 cases. The authors found that different clinicians constructed different causal models for the same client. Also, the authors found low to moderate agreement about the effectiveness of different interventions. Nevertheless, the authors could predict clinicians' ratings of effectiveness from their individual causal models. (PsycINFO Database Record (c) 2016 APA, all rights reserved)