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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)

Objective: The AWMF-Guidelines for Hyperkinetic Disorders (ADHD) provide psychotherapists and physicians with guidance concerning diagnostics and treatment for one of the most common disorders in children and adolescents. To date, however, it is unclear how these guidelines are being applied by practicing therapists (both physicians and psychotherapists) and what they consider to be its pros and cons. This study proposes (1) to analyze the differences between the estimation of ADHD-guidelines by users and nonusers, their corresponding attitudes, experiences, and evaluations of context factors; and (2) to analyze whether users and nonusers differ in their therapeutic practice. Methods: 71 therapists participated in a nonrepresentative online survey. Results: The hypothesis was confirmed that, on average, users had a more positive attitude toward and experience with guideline-driven treatment than did nonusers. The results also show a small positive effect of guideline use on treatment quality. However, the methods employed by users and nonusers only moderately corresponded with the recommendations of the guidelines. Conclusions: It was shown that the ADHD guideline is only rarely being used, even under advantageous conditions, and that a practice-oriented form of the guideline does not exist until now. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Objectives: Improving the informational quality of referrals from primary to secondary care and appropriately re-directing referrals is an important goal of clinical commissioning groups in England. Based on the available empirical evidence, a referral management and booking service that combined referral guidelines, online referral templates and administrative and clinical triage, was developed by a primary care trust in southeast London. Methods: A pilot study of 13 out of 46 practices in the trust was conducted using a mixed methods approach. Referral numbers were investigated by analysing changes in practicesʼ rates of first outpatient attendances in secondary care. Informational referral quality was assessed by analysing triage outcomes. Semi-structured interviews were used to inquire about practicesʼ evaluation of the new system. Structured telephone interviews were conducted to assess patientsʼ satisfaction. Results: Overall rates of first outpatient attendances declined more strongly for pilot practices than controls. The number of referrals challenged for being incomplete or having insufficient clinical information decreased. The rate of referrals challenged by clinical triage for not conforming to referral guidelines was well below the rate of inappropriate referrals published in the literature. Interviews with practices revealed a number of themes and a broad range of attitudes. Patients were highly satisfied. Discussion: Findings provided favourable evidence for the effectiveness of the new referral management system. They were, however, preliminary. If referrals into secondary care continued to be reduced on a long-term basis, the system would be cost effective despite the time and effort required for clinical triage. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

In deterministic causal chains the relations ``A causes B'' and ``B causes C'' imply that ``A causes C''. However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause C, but A does not probabilistically cause C, but rather C. The normal transitive inference is only valid when the Markov condition holds, a key feature of the Bayes net formalism. However, it has been objected that the Markov assumption does not need to hold in the real world. In our studies we examined how people reason about causal chains that do not obey the Markov condition. Three experiments involving causal reasoning within causal chains provide evidence that transitive reasoning seems to hold psychologically, even when it is objectively not valid. Whereas related research has shown that learners assume the Markov condition in causal chains in the absence of contradictory data, we here demonstrate the use of this assumption for situations in which participants were directly confronted with evidence contradicting the Markov condition. The results suggest a causal transitivity heuristic resulting from chaining individual causal links into mental causal models that obey the Markov condition.

A dominant current framework to model everyday causal knowledge are causal Bayes nets, which represent causal knowledge as directed acyclic graphs. One central assumption of this approach is the Markov constraint. According to the Markov constraint, each variable is independent of all non-descending variables conditional upon its direct causes. Recent research, however, has questioned the Markov condition as part of a psychological theory of causal reasoning. In a common-cause structure, judgments about the presence of a target effect given the presence or absence of its cause depend strongly upon the states of collateral effects of this cause, which violates the Markov condition. In this thesis it is shown that causal inferences are influenced by additional knowledge, particularly knowledge about underlying causal processes; this is the reason for apparent Markov violations. A computational model is presented which extends classical causal Bayes nets by adding a preventive noise source which is attached to each cause. The reasoning process, then, is modeled as adaptive error attribution. This model is empirically tested in three contexts. Furthermore it is shown that inferences are influenced by the properties of the involved objects and therefore are dependent on possible categorizations. Based on these findings an extension of the model is developed which computes target inferences across all possible partitioning of the effects, and integrates over the uncertainty of cluster assignments. Finally, possible consequences of the findings are discussed and a broader computational model of causal reasoning is drafted, which separates the level of causal background knowledge from the processing of statistical events., publication\\\\_type = type, publication\\\_type = type, publication\\_type = type, publication\_type = type

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine peopleʼs inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., 'X occasionally causes A'). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causesʼ prior probabilities and the effectsʼ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

Es wird deutlich gemacht, dass aktuelle psychologische Theorien kausalen Denkens und Lernens grundlegende Mechanismen aus einer strukturellen bzw. inhaltlichen Perspektive betrachten. Ansätze, die einer strukturalistischen Konzeption folgen, gehen davon aus, dass kausale Mechanismen unabhängig von der jeweiligen Domäne als abstrakte, gerichtete Relationen einer bestimmten Stärke zwischen Ereignisvariablen repräsentiert werden. Die resultierenden Kausalmodelle gehorchen bestimmten, aber dennoch allgemeinen Prinzipien und bilden die Grundlage für Urteile und weitergehende Schlüsse. Als Beispiele werden das ``Gemeinsame-Ursache-Modell'', das ``Kausale-Ketten-Modell'' und das ``Gemeinsamer-Effekt-Modell'' diskutiert. Dagegen postuliert die inhaltliche Sichtweise von Mechanismen, dass Mechanismen domänen- und teilweise sogar phänomenspezifisch sind, deshalb einer reicher ausgestalteten Repräsentation von Kausalbeziehungen bedürfen und je nach Inhaltsbereich unterschiedliche Schlüsse unterstützen. Beide Ansätze werden zunächst theoretisch eingeführt und Grundannahmen herausgearbeitet. Danach werden empirische Vorhersagen aus diesen abgeleitet und exemplarisch empirische Studien referiert, welche diese Vorhersagen einem Test unterzogen haben. Dabei wird besonders die Rolle von Interventionen und Handlungen beachtet. Insgesamt zeigt sich, dass es für beide Positionen positive empirische Befunde gibt. In der abschließenden Diskussion wird dargestellt, wie theoriebasierte Bayes-Netze versuchen, beide Sichtweisen von Mechanismen in einem Ansatz zu integrieren.

Im Rahmen eines Literaturüberblicks wird der aktuelle Forschungsstand zum Zusammenhang zwischen Wissen und Lernen dargestellt. Anhand von entwicklungspsychologischen und kognitionspsychologischen Theorien und Erkenntnissen wird dokumentiert, dass Lernen fast immer unter Nutzung von bereichsspezifischen und abstraktem Weltwissen stattfindet. Im entwicklungspsychologischen Teil werden Befunde thematisiert, die die Wirksamkeit von angeborenem Wissen über grundlegende physikalische Phänomene belegt. Im kognitionspsychologischen Teil werden Untersuchungen aus der Konditionierungsforschung, zum Begriffserwerb, zum Erwerb von Kausalwissen und zur Nutzung von Analogien behandelt.

Presents some research issues in developmental psychology that involve the contents of knowledge and cognitive processes, and uses selected empirical findings to describe important features of age-typical knowledge development. Processes of probabilistic, nonanalytical, and dual concept formation are used to discuss the acquisition of concepts as one of the most fundamental achievements of the cognitive system. Fundamental concepts are illustrated with the example of causality. Taxonomic, schematic, and intuitive forms of concepts as well as ideas on metaknowledge are discussed in the development of complex knowledge organizations. Regarding structural constraints in the acquisition of knowledge, examples are used to describe area- and task-specific constraints as well as constraints due to prior knowledge and the maturation process.

Learning is typically modelled as a domain-general, data driven, associative process. Even though the potential influence of top-down knowledge is often acknowledged, the typical theoretical approach postulates two separate modules for knowledge and learning. According to this view, knowledge may influence the initial defaults and the output of the learning process but not the structure of the learning mechanism itself. In contrast to this modular approach, this article defends the position that learning and prior knowledge interact. Theoretical analyses and empirical studies are presented that indicate that specific and abstract domain knowledge influence the structure of the learning processes.

In three experiments we investigated whether two procedures of acquiring knowledge about the same causal structure, predictive learning (from causes to effects) versus diagnostic learning (from effects to causes), would lead to different base-rate use in diagnostic judgments. Results showed that learners are capable of incorporating base-rate information in their judgments regardless of the direction in which the causal structure is learned. However, this only holds true for relatively simple scenarios. When complexity was increased, base rates were only used after diagnostic learning, but were largely neglected after predictive learning. It could be shown that this asymmetry is not due to a failure of encoding base rates in predictive learning because participants in all conditions were fairly good at reporting them. The findings present challenges for all theories of causal learning. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Die Entwicklung und Bedeutung sogenannter schwacher Wissensschemata (im Sinne von Abelson) für die Gedächtnisleistungen von jungen Kindern werden untersucht. 200 vierjährigen Kindern wurden drei Geschichten vorgegeben, die die Kinder wiedererzählen mussten; nach ein sowie zwei Jahren wurden Messwiederholungen vorgenommen. Es zeigte sich unter anderem, dass - unabhängig von der Art der Geschichte - die Gedächtnisleistung mit zunehmendem Alter zunahm, dass die Kinder in allen Altersgruppen sich eher an zentrale als an periphere Aspekte der Geschichte erinnerten und dass die drei präsentierten Typen von Geschichten unterschiedlich gut behalten wurden. Die Ergebnisse werden als Beleg dafür gewertet, dass die Kategorie ``Schwache Wissensschemata'' einer weiteren Differenzierung bedarf.