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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)

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.

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)

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.

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)

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)

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.

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)

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.

In the Michotte task, a ball (X) moves toward a resting ball (Y). In the moment of contact, X stops und Y starts moving. Previous studies have shown that subjects tend to view X as the causal agent ('X launches Y') rather than Y ('Y stops X'). Moreover, X tends to be attributed more force than Y (force asymmetry), which contradicts the laws of Newtonian mechanics. Recent theories of force asymmetry try to explain these findings as the result of an asymmetrical identification with either the (stronger) agent or the (weaker) patient of the causal interaction. We directly tested this assumption by manipulating attributions of causal agency while holding the properties of the causal interaction constant across conditions. In contrast to previous accounts, we found that force judgments stayed invariant across conditions in which assignments of causal agency shifted from X to Y and that even those subjects who chose Y as the causal agent gave invariantly higher force ratings to X. These results suggest that causal agency and the perception of force are conceptually independent of each other. Different possible explanations are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The philosopher David Hume's conclusion that causal induction is solely based on observed associations still presents a puzzle to psychology. If we only acquired knowledge about statistical covariations between observed events without accessing deeper information about causality, we would be unable to understand the differences between causal and spurious relations, between prediction and diagnosis, and between observational and interventional inferences. All these distinctions require a deep understanding of causality that goes beyond the information given. We report a number of recent studies that demonstrate that people and rats do not stick to the superficial level of event covariations but reason and learn on the basis of deeper causal representations. Causal-model theory provides a unified account of this remarkable competence. [ABSTRACT FROM AUTHOR]

This chapter argues for several interconnected theses. First, the fundamental way that people represent causal knowledge is qualitative in terms of causal structure. Second, people use a variety of cues to infer structure aside from statistical data (e.g., temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm or refute the model and (possibly) to parameterize it. The structure of a posited model influences how the statistical data are processed. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning. What is needed is a heuristic-based model that shares the strengths and weaknesses of a human learner and can take advantage of the rich causal information that the natural environment provides. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The capacity to evaluate causal relations is fundamental to human cognition, and yet little is known of its neurocognitive underpinnings. A functional magnetic resonance imaging study was performed to investigate an hypothesized dissociation between the use of semantic knowledge to evaluate specifically causal relations in contrast to general associative relations. Identical pairs of words were judged for causal or associative relations in different blocks of trials. Causal judgments, beyond associative judgments, generated distinct activation in left dorsolateral prefrontal cortex and right precuneus. These findings indicate that the evaluation of causal relations in semantic memory involves additional neural mechanisms relative to those required to evaluate associative relations. [ABSTRACT FROM AUTHOR]

Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Most studies investigating semantic memory have focused on taxonomic or associative relations. Little is known about how other relations, such as causal relations, are represented and accessed. In three experiments, we presented participants with pairs of words one after another, describing events that referred to either a cause (e.g., spark) or an effect (e.g., fire). We manipulated the temporal order of word presentation and the question participants had to respond to. The results revealed that questions referring to the existence of a causal relation are answered faster when the first word refers to a cause and the second word refers to its effect than vice versa. However, no such asymmetry was observed with questions referring to the associative relation. People appear to distinguish the roles of cause and effect when queried specifically about a causal relation, but not when the same information is evaluated for the presence of an associative relation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The article reviews the book ``Moral Minds: How Nature Designed Our Universal Sense of Right and Wrong,'' by Marc D. Hauser.

Presents a Bayesian network model to explain causal learning. Its key feature is the decoupling between the temporal order of incoming information and the represented temporal order of events. The 4 steps of the model are (1) setting up an initial causal model, (2) estimating the causal power of each cause, (3) integrating causal power estimates, and (4) subsequent reviews of the causal model. Empirical evidence on estimating causal power is reported, and the model is used to explain asymmetries in cue competition and base-rate use as well as differences in learning linearly separable vs nonlinearly separable category structures. It is concluded that causal models effectively reduce the potential computational complexity of causal learning tasks.

An efficient auditory system contributes to cognitive and psychosocial development. A right ear advantage in hearing thresholds (HTs) has been described in adults and atypical patterns of left/right hearing threshold asymmetry (HTA) have been described for psychiatric and neurodevelopmental conditions. Previous genome-wide association studies (GWASs) on HT have mainly been conducted in elderly participants whose hearing is more likely to be affected by external environmental factors. Here, we investigated HT and HTA in a children population cohort (ALSPAC, n = 6,743). Better hearing was associated with better cognitive performance and higher socioeconomic status. At the group level, HTA suggested a left ear advantage (mean = -0.28 dB) that was mainly driven by females. SNP heritability for HT and HTA was 0.13 and 0.02, respectively (n = 4,989). We found a modest negative genetic correlation between HT and reading ability. GWAS for HT (n = 5,344) did not yield significant hits but polygenic risk scores for higher educational attainment (EA, ß = -1,564.72, p = .008) and schizophrenia (ß = -241.14, p = .004) were associated with lower HT, that is, better hearing. In summary, we report new data supporting associations between hearing measures and cognitive abilities at the behavioral level. Genetic analysis suggests shared biological pathways between cognitive and sensory systems and provides evidence for a positive outcome of genetic risk for schizophrenia.

Alterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. For depression, however, this relationship is largely unclear. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. Here, we collected data from 87 studies totaling 35,501 individuals to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples and computed odds ratios (ORs) between these groups. Here, an OR > 1 signifies higher rates of atypical handedness in depressed compared to healthy samples. We found no differences in left- (OR = 1.04, 95% CI = [0.95, 1.15], p = .384), mixed- (OR = 1.64, 95% CI = [0.98, 2.74], p = .060) or non-right-handedness (OR = 1.05, 95% CI = [0.96, 1.15], p = .309) between the two groups. We could thus find no link between handedness and depression on the meta-analytical level.