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People seamlessly generate moral intuitions about a wide range of events they observe, but to date the cognitive processes underlying this competency are poorly understood. We propose that our moral intuitions are grounded in force-dynamic intuitions. We show how the evaluation of entities engaged in schematized interactions can be predicted from specific force-dynamic properties of those interactions, and we point out how these evaluative tendencies relate to our moral norm of not interfering with others' interests.
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Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the richget- richer principle. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Nach Hinweisen auf die Notwendigkeit des Einsatzes formaler Forschungsmethoden in der Psychologie wird der Frage nachgegangen, wie durch experimentelle Methoden psychologische Theorien überprüft werden können. Grundthese ist dabei, dass es in der Psychologie in der Regel um die Prüfung kausaler Theorien geht. Es wird gezeigt, dass statistische Methoden in systematischer Weise mit theoretisch begründeten experimentellen Designs kombiniert werden müssen, um Schlussfolgerungen auf kausale Vorgänge zuzulassen. Darüber hinaus wird diskutiert, wie man durch gezielte Überprüfung der Generalität von Theorien zu verbesserten Theorien mit erweiterem Geltungsbereich kommen kann.
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The strength of causal relations typically must be inferred on the basis of statistical relations between observable events. This article focuses on the problem that there are multiple ways of extracting statistical information from a set of events. In causal structures involving a potential cause, an effect and a third related event, the assumed causal role of this third event crucially determines whether it is appropriate to control for this event when making causal assessments between the potential cause and the effect. Three experiments show that prior assumptions about the causal roles of the learning events affect the way contingencies are assessed with otherwise identical learning input. However, prior assumptions about causal roles is only one factor influencing contingency estimation. The experiments also demonstrate that processing effort affects the way statistical information is processed. These findings provide further evidence for the interaction between bottom-up and top-down influences in the acquisition of causal knowledge. They show that, apart from covariation information or knowledge about mechanisms, abstract assumptions about causal structures also may affect the learning process. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Studied the role of schematic knowledge in preschoolers' memory of familiar events. Focus was on determining preschool children's access to different types of event knowledge. Human Ss: 80 normal male and female West German preschool children (aged 4 yrs) (nursery school students); 80 normal male and female West German preschool children (aged 6 yrs) (nursery school students). Ss were presented with 4 picture stories involving different types of event knowledge. Free recall and visual recognition were used to assess memory of various aspects of the stories. Age differences were determined. (English abstract) (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Philosophische Annahmen im Zusammenhang mit der kognitiven Konstruktion von Kausalität in der Kognitiven Psychologie werden im Überblick erörtert. Dabei wird auf die folgenden Aspekte eingegangen: (1) assoziationistische Theorien (der Einfluss von D. Hume, psychologische Assoziationsthorien), (2) Kausallernen als kognitiver Prozess bzw. die Theorie der Kausalmodelle (kausale Priorität, die Einschätzung der Stärke von Kausalbeziehungen, die Wahl der Beschreibungsebene). Es wird unter Heranziehung von ausgewählten empirischen Befunden deutlich gemacht, dass die klassischen Theorien des kausalen Denkens, die davon ausgehen, dass Kausalwissen eine mentale Repräsentation objektiv in der Welt vorhandener Kausalzusammenhänge ist, nicht haltbar sind. Vielmehr bestimmen Vorannahmen, die vor jeglicher Beobachtung gemacht werden, entscheidend darüber, welchen Kausaleindruck Menschen in einer Situation erwerben.
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Studied the role of knowledge in the performance of memory tasks by young children, and assessed the hypothesis that a developmental shift in knowledge utilization occurs between 4 yrs and 6 yrs. Human subjects: 240 preschool and schoolage children (aged 4–6 yrs) (kindergartners). Ss were presented with 3 series of photographs representing events (Christmas, being sick, washing oneself, or the swimming pool), objects in categories (fruit, vehicles, animals, or buildings), or events and categories of unrelated objects. Ss were asked to sort the photographs into categories under 20 different sets of conditions. The organization and relationships of objects under free, structured, and instructional reproduction conditions were examined according to age group. An analysis of variance (ANOVA) and other statistical tests were used. (English abstract) (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Argues that a study reported by D. Shanks and F. Lopez (Memory & Cognition, 1996, Vol 24, 511-522), concluding that causal order does not influence cue selection, fails to meet four essential methodological criteria for testing whether and how causal order influences learning: (1) consistent interpretation by the participants of the learning situation in terms of directed cause-effect relations, (2) measurement of acquired causal knowledge, (3) manipulation of causal order, and (4) control of the statistical relations between cause and effect. It is suggested that several aspects of the reported results are explained by causal model theory but not by associative accounts, and that this study adds to evidence indicating that human contingency learning can indeed be guided by causal interpretation.
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A heavily disputed question of moral philosophy is whether spatial distance between agent and victim is normatively relevant for the degree of obligation to help strangers in need. In this research, we focus on the associated descriptive question whether increased distance does in fact reduce individuals' sense of helping obligation. One problem with empirically answering this question is that physical proximity is typically confounded with other factors, such as informational directness, shared group membership, or increased efficaciousness. In a series of 5 experiments, we show that distance per se does not influence people's moral intuitions when it is isolated from such confounds. We support our claims with both frequentist and Bayesian statistics. We relate these findings to philosophical arguments concerning the normative relevance of distance and to psychological theories linking distance cues to higher level social cognition. The effects of joint versus separate evaluation paradigms on moral judgments are also discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Die Beziehungen zwischen Begabung, Wissen und Lernen werden herausgearbeitet. Zunächst werden die drei Komponenten einzeln betrachtet. In Bezug auf das Lernen wird dessen Bereichsspezifität betont und der strikten Trennung basaler und akademischer Lernprozesse widersprochen. Auch Wissen, dem eine große Bedeutung für die Erklärung kognitiver Kompetenzen zugewiesen wird, wird als bereichsspezifisch ausgewiesen. Hinsichtlich der Rolle der Begabung ist eine Klärung der Beziehungen zwischen genetischen Anlagen und komplexen Kompetenzen bislang nicht möglich, da letztere vielfach trainierbar oder adaptiv modifizierbar sind. Aus den Beziehungen von Begabung, Wissen und Lernen werden Konsequenzen für die pädagogische Praxis in Form von Thesen abgeleitet, die sich auf die Fragen beziehen, ob Begabungsunterschiede durch entsprechenden Unterricht aufgehoben werden sollten und wie mit dem Spannungsfeld von spezifischem Wissenserwerb und der Förderung allgemeinen Lernen-Könnens umzugehen ist.
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Discusses the findings of three converging research domains that promise to provide a better understanding of exceptional intellectual talent and performance. The cognitive correlates and components approach is concerned with the specification of the cognitive processes that enable the individual to solve difficult test items. Expert-novice research studies the cognitive characteristics of experts and their high level performance in complex situations. Finally, differentially oriented developmental psychology seeks to elucidate the developmental progression of cognitive competencies in relation to various levels of ability.
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D. R. Shanks (1991) reported experiments that show selective-learning effects in a categorization task, and presented simulations of his data using a connectionist network model implementing the Rescorla-Wagner (R-W) theory of animal conditioning. He concluded that his results (1) support the application of the R-W theory to account for human categorization, and (2) contradict a particular variant of contingency-based theories of categorization. These conclusions are examined. It is shown that the asymptotic weights produced by the R-W model actually predict systematic deviations from the observed human learning data. Shanks claimed that his simulations provided good qualitative fits to the observed data when the weights in the networks were allowed to reach their asymptote. However, analytic derivations of the asymptotic weights reveal that the final weights obtained in Shanks's Simulations 1 and 2 do not correspond to the actual asymptotic weights, apparently because the networks were not in fact run to asymptote. It is also shown that a contingency-based theory that incorporates the notion of focal sets can provide a more adequate explanation of cue competition than does the R-W model. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Proposes and evaluates the flexible deontic logic theory, a domain-specific theory for testing prescriptive rules in the Wason selection task (WST). The theory combines older ideas of a deontic logic of prescriptive rules with that of a flexible focus on different cells of an ought table. After discussing the differences between descriptive and prescriptive rules, it is argued that the checking of prescriptive rules is based on deontic logic combined with a flexible focus on conforming cases (cooperator detection) or deviating cases (cheater detection). An experimental study involving 80 college students tested these assumptions by varying the conditional rule (obligation vs. prohibition rule) and the pragmatic focus (cheater vs. cooperator focus) in a WST. Results provided evidence for the interaction of different conditionals based on deontic logic and focus effects as proposed by the flexible deontic logic theory of the WST. It is concluded that the results favor the proposed theory over other current theories of deontic WSTs.
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The dominant theoretical approach to causal learning postulates the acquisition of associative weights between cues and outcomes. This reduction of causal induction to associative learning implies that learners are insensitive to important characteristics of causality, such as the inherent directionality between causes and effects. An ongoing debate centers on the question of whether causal learning is sensitive to causal directionality (as is postulated by causal-model theory) or whether it neglects this important feature of the physical world (as implied by associationist theories). Three experiments using different cue competition paradigms are reported that demonstrate the competence of human learners to differentiate between predictive and diagnostic learning. However, the experiments also show that this competence displays itself best in learning situations with few processing demands and with convincingly conveyed causal structures. The study provides evidence for the necessity to distinguish between competence and performance in causal learning. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Presents the causal-model approach to causal reasoning and learning. The causal-model approach is introduced as a theory which states that humans have the tendency to assume the existence of deep causal relations behind the surface, and is contrasted with traditional associationist theories. Several causal models are introduced which differ in structural aspects. Empirical studies conducted with humans and nonhuman animals testing the causal-model theory are presented which demonstrate that individuals do not just rely on covariational information in causal learning and reasoning, but instead infer a deeper causal structure. Furthermore, the studies also show that people are sensitive to the structural aspects of causal models, and coordination of identical learning input with different causal structures was observed. Moreover, it is stated that the strengths of individual links within causal models need to be learned and that causal strength does not necessarily correspond to observed covariation. Findings on limitations of causal reasoning suggest that causal models may overestimate the abilities of humans and nonhumans. Suggestions for further research are discussed.
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Causal reasoning belongs to our most central cognitive competencies. Causal knowledge is used as the basis of predictions and diagnoses, categorization, action planning, decision making, and problem solving. Whereas philosophers have analyzed causal reasoning for many centuries, psychologists have for a long time preferred to view causal reasoning and learning as special cases of domain-general competencies, such as logical reasoning or associative learning. The present chapter gives an overview of recent research about causal reasoning. It discusses competing theories, and it contrasts domain-general accounts with theories that model causal reasoning and learning as attempts to make inferences about stable hidden causal processes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Empirical research with nonhuman primates appears to support the view that causal reasoning is a key cognitive faculty that divides humans from animals. The claim is that animals approximate causal learning using associative processes. The present results cast doubt on that conclusion. Rats made causal inferences in a basic task that taps into core features of causal reasoning without requiring complex physical knowledge. They derived predictions of the outcomes of interventions after passive observational learning of different kinds of causal models. These competencies cannot be explained by current associative theories but are consistent with causal Bayes net theories. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Explored a basic claim of causal model theory which postulates that the interpretation of the learning input is directed by prior causal assumptions. An example of this is Simpson's paradox which describes the fact that a given contingency between two events which holds in a given population can disappear or be reversed in all subpopulations when the population is partitioned in certain ways. 84 college students participated in two studies examining their assessment of a contingency between a potential cause and an effect. The task in Experiment 1 (36 subjects) assessed the strength of causal relation between the irradiation of tropical fruit and the quality of the fruit. The organization of the list reflected a variant of Simpson's paradox in order to assess whether subjects' contingency judgments reflected their prior assumptions about the additional grouping variables. Experiment 2 (48 subjects) replicated the results of Experiment 1 with a grouping variable that was kept constant across the two conditions. Subjects assessed the causal efficacy of a new watering technique applied to two types of plants. It was shown that subjects' assessment between a cause and an effect is moderated by their background assumptions about the causal relevance of additional variables and the mode of presentation of the learning items. Subjects' assumptions of the relevance of an additional grouping variable led to the view that the cause enables the effect or the view that it deterred the effect. It is concluded that the acquisition of new causal knowledge is based on old causal knowledge which is already accessible at the beginning of the induction process.
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Most theories of moral judgments distinguish between acts and outcomes. According to these theories, moral judgments are either primarily based on the evaluation of the acts or the outcomes with multi-system theories allowing for both possibilities. Here we argue that it is not only the acts and outcomes that determine moral evaluations but also the causal relations linking the acts with their outcomes. Causal relations influence moral judgments by shifting attention to aspects of inter-victim relations. We report three projects that demonstrate the usefulness of this framework in tasks that range from moral judgments about trolley problems to basic force-dynamic interpretations of simple perceptual and linguistic scenes. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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The authors bring together human and animal studies, with a particular focus on causal learning. Whereas the traditional associative approach to learning views learning contingencies as basic, and the learning of causality (if it is considered at all) to be secondary, they take the goal of the agent to infer the 'causal powers' of aspects of the world. Contingencies are primarily of interest to the degree that they provide evidence for such causal relationships. The degree to which the same rational model may be applied to learning, from rat to human, puts a new complexion on the behaviourist's project of building general principles of learning across species.
