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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.

Knowledge about cause and effect relationships (e.g., virus- epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in acquiring causal knowledge, it is unclear whether prediction error circuitry remains involved in the mental representation and evaluation of causal knowledge already stored in semantic memory. In an fMRI study, participants assessed whether pairs of words were causally related (e.g., virus-epidemic) or noncausally associated (e.g., emerald-ring). In a second fMRI study, a task cue prompted the participants to evaluate either the causal or the noncausal associative relationship between pairs of words. Causally related pairs elicited higher activity in OFC, amygdala, striatum, and substantia nigra/ventral tegmental area than noncausally associated pairs. These regions were alsomore activated by the causal than by the associative task cue. This network overlaps with the mesolimbic and mesocortical dopaminergic network known to code prediction errors, suggesting that prediction error processing might participate in assessments of causality even under conditions when it is not explicitly required to make predictions. [ABSTRACT FROM AUTHOR]

[Correction Notice: An Erratum for this article was reported in Vol 17(1) of Animal Cognition (see record [rid]2014-00148-002[/rid]). In the original article, there are some errors the corrections are present in the erratum.] Diagnostic reasoning, defined as the ability to infer unobserved causes based on the observation of their effects, is a central cognitive competency of humans. Yet, little is known about diagnostic reasoning in non-human primates, and what we know is largely restricted to the Great Apes. To track the evolutionary history of these skills within primates, we investigated long-tailed macaquesʼ understanding of the significance of inclinations of covers of hidden food as diagnostic indicators for the presence of an object located underneath. Subjects were confronted with choices between different objects that might cover food items. Based on their physical characteristics, the shape and orientation of the covers did or did not reveal the location of a hidden reward. For instance, hiding the reward under a solid board led to its inclination, whereas a hollow cup remained unaltered. Thus, the type of cover and the occurrence or absence of a change in their appearance could potentially be used to reason diagnostically about the location of the reward. In several experiments, the macaques were confronted with a varying number of covers and their performance was dependent on the level of complexity and on the type of change of the coversʼ orientation. The macaques could use a boardʼs inclination to detect the reward, but failed to do so if the lack of inclination was indicative of an alternative hiding place. We suggest that the monkeysʼ performance is based on a rudimentary understanding of causality, but find no good evidence for sophisticated diagnostic reasoning in this particular domain. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Evaluations of analogous situations are an important source for our moral intuitions. A puzzling recent set of findings in experiments exploring transfer effects between intuitions about moral dilemmas has demonstrated a striking asymmetry. Transfer often occurred with a specific ordering of moral dilemmas, but not when the sequence was reversed. In this article we present a new theory of transfer between moral intuitions that focuses on two components of moral dilemmas, namely their causal structure and their default evaluations. According to this theory, transfer effects are expected when the causal models underlying the considered dilemmas allow for a mapping of the highlighted aspect of the first scenario onto the causal structure of the second dilemma, and when the default evaluations of the two dilemmas substantially differ. The theoryʼs key predictions for the occurrence and the direction of transfer effects between two moral dilemmas are tested in five experiments with various variants of moral dilemmas from different domains. A sixth experiment tests the predictions of the theory for how the target action in the moral dilemmas is represented. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Most people consider it morally acceptable to redirect a trolley that is about to kill five people to a track where the trolley would kill only one person. In this situation, people seem to follow the guidelines of utilitarianism by preferring to minimize the number of victims. However, most people would not consider it moral to have a visitor in a hospital killed to save the lives of five patients who were otherwise going to die. We conducted two experiments in which we pinpointed a novel factor behind these conflicting intuitions. We show that moral intuitions are influenced by the locus of the intervention in the underlying causal model. In moral dilemmas, judgments conforming to the prescriptions of utilitarianism are more likely when the intervention influences the path of the agent of harm (e.g., the trolley) than when the intervention influences the path of the potential patient (i.e., victim). [ABSTRACT FROM AUTHOR]

The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

A. P. Blaisdell, K. Sawa, K. J. Leising, and M. R. Waldmann (2006) reported evidence for causal reasoning in rats. After learning through Pavlovian observation that Event A (a light) was a common cause of Events X (an auditory stimulus) and F (food), rats predicted F in the test phase when they observed Event X as a cue but not when they generated X by a lever press. Whereas associative accounts predict associations between X and F regardless of whether X is observed or generated by an action, causal-model theory predicts that the intervention at test should lead to discounting of A, the regular cause of X. The authors report further tests of causal-model theory. One key prediction is that full discounting should be observed only when the alternative cause is viewed as deterministic and independent of other events, 2 hallmark features of actions but not necessarily of arbitrary events. Consequently, the authors observed discounting with only interventions but not other observable events (Experiments 1 and 2). Moreover, rats were capable of flexibly switching between observational and interventional predictions (Experiment 3). Finally, discounting occurred on the very first test trial (Meta-Analysis). These results confirm causal-model theory but refute associative accounts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Explored the cognitive foundations and the ontogenetic origins of the side-effect effect. Adults' intentionality judgments regarding an action are influenced by their moral evaluation of this action. This is clearly indicated in the so-called side-effect effect: when told about an action (for example, implementing a business plan) with an intended primary effect (for example, raise profits) and a foreseen side effect (for example, harming/helping the environment), subjects tend to interpret the bringing about of the side effect more often as intentional when it is negative (harming the environment) than when it is positive (helping the environment). From a cognitive point of view, it is unclear whether the side-effect effect is driven by the moral status of the side effects specifically, or rather more generally by its normative status. And from a developmental point of view, little is known about the ontogenetic origins of the effect. In this study, 54 four- to five-year-old children were tested with scenarios in which a side effect was in accordance with/violated a norm. Crucially, the status of the norm was varied to be conventional or moral. Results show that children rated the bringing about of side-effects as more intentional when it broke a norm than when it accorded with a norm irrespective of the type of norm. It is concluded that the side-effect effect is thus an early-developing, more general and pervasive phenomenon, not restricted to morally relevant side effects.

Recent studies have shown that people have the capacity to derive interventional predictions for previously unseen actions from observational knowledge, a finding that challenges associative theories of causal learning and reasoning (e.g., Meder, Hagmayer, & Waldmann, 2008). Although some researchers have claimed that such inferences are based mainly on qualitative reasoning about the structure of a causal system (e.g., Sloman, 2005), we propose that people use both the causal structure and its parameters for their inferences. We here employ an observational trial-by-trial learning paradigm to test this prediction. In Experiment 1, the causal strength of the links within a given causal model was varied, whereas in Experiment 2, base rate information was manipulated while keeping the structure of the model constant. The results show that learnersʼ causal judgments were strongly affected by the observed learning data despite being presented with identical hypotheses about causal structure. The findings show furthermore that participants correctly distinguished between observations and hypothetical interventions. However, they did not adequately differentiate between hypothetical and counterfactual interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Scientists as well as nonscientists generate and test hypotheses about causal relations. There are two kinds of causal hypotheses, simple ones that refer to single causal relations and complex ones that refer to causal structures. Research on simple hypotheses has shown that people use statistical covariation information for their judgments in a normative fashion. Little is known, however, about how complex causal hypotheses are evaluated. According to normative theories, hypotheses about causal models require the evaluation of the strength of the individually hypothesized causal links along with tests that address the adequacy of the assumed causal structure. In 3 experiments it was investigated how participants tested complex causal hypotheses. The results showed that they tended to evaluate the individual causal links but appeared not to have any explicit knowledge about how hypotheses on the structure of causal models should be tested.