Causal Learning Research Papers - Academia.edu (original) (raw)

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3-and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.

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- Psychology, Cognitive Psychology, Cognitive Science, Social Psychology

Studies on members of the crow family using the “Aesop’s Fable” paradigm have revealed remarkable abilities in these birds, and suggested a mechanism by which associative learning and folk physics may interact when learning new problems. In the present study, children between 4 and 10 years of age were tested on the same tasks as the birds. Overall the performance of the children between 5-7-years was similar to that of the birds, while children from 8-years were able to succeed in all tasks from the first trial. However the pattern of performance across tasks suggested that different learning mechanisms might be being employed by children than by adult birds. Specifically, it is possible that in children, unlike corvids, performance is not affected by counter-intuitive mechanism cues.

- by Lucy Cheke
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- Developmental Psychology, Corvids, Causal Learning, Physical cognition

Crows are passerine birds of genus corvus in family Corvidae. Current study was carried out on three species of crow, jungle crow (Corvus macrorhynchos), house crow (C. splendens) and jackdaw (C. monedula) present in district Mansehra, Pakistan. Crows were trapped for blood sampling. The total genomic DNA was isolated from the blood of each species. The RAPD -PCR analysis of isolated DNA was performed for genetic diversity estimation. All the amplification profiles were observed and genetic distances were estimated. Results of RAPD analysis revealed high level of genetic polymorphism among the three species. The average genetic distance estimates ranged from 50-90%. Phylogenetic relationship was elaborated through dendrogram which supports the genetics distances. The dendrogram showed that house crow and jungle crow share much genetic affinities to each other than to jackdaw. The results also revealed the RAPD markers as effective for such types of studies where an overall picture of genome is required.

- by Sawar Khan
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- Botany, Zoology, Developmental Psychology, Environmental Education

Philosophical works on actual causation make wide use of thought experiments. The principal aim of this paper is to show how thought experiments are used in the contemporary debate over actual causation and to discuss their role in relation to formal approaches in terms of causal models. I claim that a recourse to thought experiments is not something old fashioned or superseded by abstract models, but it is useful to interpret abstract models themselves and to use our intuitions to judge the results of the model. Recent research on actual causation has stressed the importance of integrating formal models with some notion of normality; I suggest that thought experiments can be useful in eliciting intuitions where normality is not intended in a statistical sense. The first expository part (1–3) gives a short presentation of the notion of actual causation, summarising some typical problems of counterfactual approaches and how they are treated in causal and structural models. The second part (4–7) works on the problems of model isomorphism and criticises some radical ideas opposing the role of mental experiments, claiming that they may also be of use in evaluating formal models.

- by Margherita Benzi
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- Causal reasoning, Intuition, Causation, Thought Experiments

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

- by Laura Schulz
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- Psychology, Cognitive Science, Cognitive development, Child Development

A pesar del vasto volumen de investigación científica sobre el aprendizaje y la enseñanza de la producción escrita, a menudo puede atestiguarse una falta de correspondencia entre la teoría y la praxis en las aulas de secundaria. Este trabajo final de máster pretende ofrecer un marco de referencia y unas pautas de mejora de la actuación docente en la enseñanza de la producción escrita en la asignatura de Lengua Castellana y Literatura de 4º de Educación Secundaria Obligatoria. Primero que todo, se identifican los problemas metodológicos y de contenido principales que la enseñanza de la producción escrita presenta en las aulas de secundaria. En segundo lugar, se indaga en los fundamentos teóricos sobre el desarrollo y la enseñanza de la producción escrita. A este respecto, se atiende a los factores sociales y cognitivos implicados en el desarrollo de la composición escrita, así como a su enseñanza como un proceso compuesto por distintas fases que exigen estrategias específicas. Además, se examina la relación de la producción textual con el conocimiento gramatical, abogando por las propiedades textuales de coherencia y cohesión como terreno de encuentro entre estos dos aspectos. En particular, el trabajo se centra en la enseñanza de la composición de un texto expositivo de análisis de causas en vinculación con el conocimiento gramatical de la oración subordinada adverbial causal y aporta unas orientaciones de actuación docente en la forma de una secuencia didáctica para ese nivel.

- by Remei González Manzanero
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- Causal reasoning, Secondary Education, Teaching Spanish, Subordination

Discussions on counterfactual thinking (CT) have been focused on whether it is a language skill or it emerges spontaneously before language acquisition. This paper surveys the most compelling arguments in favourabout these of both frameworks: (1) the approach 'CT as a language skill'; (2) those who claim that pretending shows that children have CT; (3) those who consider pretend play is a rehearsal for cognitive dispositions. I shall point out that the three approaches on CT are incomplete: (1) neglects pretend play (which prelinguistic children perform) as an instantiation of the CT; (2) puts too much emphasis on the linguistic dimension of pretense; (3) is highly demanding on the cognitive architecture that we ought to have by nature— the so-called 'children as scientists'.

- by Alberto Murcia
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- Cognitive Science, Bayesian Models, Counterfactual Thinking, Causal Learning

The standard approach guiding research on the relationship between categories and causality views categories as reXecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may inXuence subsequent causal learning. In three experiments we show that identical causal learning input yields diVerent attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new eVect, and when they were motivated by goals that diVered from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal eVect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal eVects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal eVect learners abandoned the categories, and used diVerent categories to predict the causal eVect.

- by Michael Waldmann
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- Psychology, Cognitive Psychology, Cognitive Science, Cognition

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.

- by Kosuke Sawa and +2
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- Cognition, Causal reasoning, Science, Forecasting

To mimic human tutors and provide optimal training, a cognitive tutoring agent should be able to continuously learn from its interactions with learners. An important element that helps a tutor better understand learner's mistake is finding the causes of the learners' mistakes. In this paper, we explain how we have designed and integrated a causal learning mechanism in a cognitive agent named CELTS (Conscious Emotional Learning Tutoring System) that assists learners during learning activities. Unlike other works in cognitive agents that used Bayesian Networks to deal with causality, CELTS's causal learning mechanism is implemented using data mining algorithms that can be used with large amount of data. The integration of a causal learning mechanism within CELTS allows it to predict learners' mistakes. Experiments showed that the causal learning mechanism help CELTS improve learners' performance.

- by Philippe Fournier-Viger
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- Data Mining, Knowledge Based Systems, Causal Learning, Cognitive Agents
- by Tom Beckers
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- Psychology, Cognitive Science, Experimental Psychology, Vocabulary

- by Alison GOPNIK
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- Psychology, Cognitive Psychology, Cognitive Science, Social Psychology

The main aim of this work was to show the impact of preexisting causal beliefs on causal induction from cause-effect co-occurrence information, when several cues compete with each other for predicting the same effect. Two different causal scenarios -one social (a), the other medical (b) -were used to check the generality of the effects. In Experiments 1a and 1b, participants were provided information on the co-occurrence of a two-cause compound and an effect, but not about the potential relationship between each cause by its own and the effect. As expected, prior beliefs -induced by means of instructionsstrongly modulated the causal strength assigned to each element of the compound. In Experiments 2a and 2b, covariation evidence was provided, not only about the predictive value of the two-cause compound, but also about one of the elements of the compound. When this evidence was available, prior beliefs had less impact on judgments, and these were mostly guided by the relative predictive value of the cue. These results demonstrate the involvement of inferential integrative mechanisms in the generation of causal knowledge and show that single covariation detection mechanisms -either rule-based or associative -are insufficient to account for human causal judgment. At the same time, the fact that the power of new covariational evidence to change prior beliefs depended on the availability of information on the relative (conditional) predictive value of the target candidate cause suggests that causal knowledge derived from information on causal mechanisms and from covariation probably share a common representational basis.

- by Antonio Maldonado
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- Psychology, Cognitive Science, Cognition, Culture

Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the ''cement of the universe'' [Hume (1739[Hume ( /2000. Causal reasoning is required for making predictions, drawing implications and inferences, and explaining phenomena. Causal relations are usually more complex than learners understand. In order to be able to understand and apply causal relationships, learners must be able to articulate numerous covariational attributes of causal relationships, including direction, valency, probability, duration, responsiveness, as well as mechanistic attributes, including process, conjunctions/disjunctions, and necessity/sufficiency. We describe different methods for supporting causal learning, including influence diagrams, simulations, questions, and different causal modeling tools, including expert systems, systems dynamics tools, and causal modeling tools. Extensive research is needed to validate and contrast these methods for supporting causal reasoning.

Previous research (e.g., Gelman & Markman, 1986; Gopnik & Sobel, 2000) suggests that children can use category labels to make inductive inferences about non-obvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when A) the property being projected varies within the category; B) the category is arbitrary (e.g., things smaller than a bread box), or C) the property being projected is due to an exogenous intervention rather than intrinsic to the object kind. In four studies, we show that preschoolers (mean: 48 months; range: 42-57 months) are sensitive to these constraints on induction and selectively engage in exploration when evidence about objects' causal properties conflicts with inductive generalizations from the objects' kind to their causal powers. This suggests that the exploratory actions children generate in free play could support causal learning.

- by Laura Schulz
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- Psychology, Cognitive Science, Developmental Psychology, Perception

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.

- by Ulf-Dietrich Reips
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- Psychology, Cognitive Psychology, Cognitive Science, Causal reasoning

10 Beyond Covariation Cues to Causal Structure David A. Lagnado, Michael R. Waldmann, York Hagmayer, & Steven A. Sloman Introduction Imagine a person with no causal knowledge or concept of cause and effect. That person would be like one... more

- by York Hagmayer
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- Causal Learning

Time series novelty or anomaly detection refers to automatic identification of novel or abnormal events embedded in normal time series points. In the case of water demand, these anomalies may be originated by external influences (such as climate factors, for example) or by internal causes (bad telemetry lectures, pipe bursts, etc.). This paper will focus on the development of markers of different possible types of anomalies in water demand time series. The goal is to obtain early warning methods to identify, prevent, and mitigate likely damages in the water supply network, and to improve the current prediction model through adaptive processes. Besides, these methods may be used to explain the effects of different dysfunctions of the water network elements and to identify zones especially sensitive to leakage and other problematic areas, with the aim to include them in reliability plans. In this paper, we use a classical Support Vector Machine (SVM) algorithm to discriminate between nominal and anomalous data. SVM algorithms for classification project low-dimensional training data into a higher dimensional feature space, where data separation is easier. Next, we adapt a causal learning algorithm, based on the reproduction of kernel Hilbert spaces (RKHS), to look for possible causes of the detected anomalies. This last algorithm and the SVM's projection are achieved by using kernel functions, which are necessarily symmetric and positive definite functions.

- by Manuel Herrera
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- Computer Science, Time Series, Anomaly Detection, Early Warning

6 Causal Reasoning Through Intervention York Hagmayer, Steven Sloman, David Lagnado, & Michael R. Waldmann Introduction Causal knowledge enables us to predict future events, to choose the right actions to achieve our goals, and to... more

- by Michael Waldmann
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- Causal reasoning, Causal Learning

Causal directionality belongs to one of the most fundamental aspects of causality that cannot be reduced to mere covariation. This paper is part of a debate between proponents of associative theories, which claim that learners are insensitive to the causal status of cues and outcomes, and proponents of causal-model theory, which postulates an interaction of assumptions about causal directionality and learning. Some researchers endorsing the associationist view have argued that evidence for the interaction between cue competition and causal directionality may be restricted to two-phase blocking designs. Furthermore, from the viewpoint of causal-model theory, blocking designs carry the potential problem that the predicted asymmetries of cue competition are partly dependent on asymmetries of retrospective inferences. The present experiments use a one-phase overshadowing paradigm that does not allow for retrospective inferences and therefore represents a more unambiguous test of sensiti...

- by Michael Waldmann
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- Psychology, Cognitive Science, Semantics, Causality

This study examines preschoolers' causal assumptions about spatial contiguity, and how these assumptions interact with new evidence in the form of conditional probabilities. Preschool children saw a novel toy that activated in the presence of certain objects by lighting up and playing music. Children were shown evidence for the toy's "activation rule" in the form of patterns of probability: The toy was either more likely to activate when objects made contact with its surface (ON condition) or when objects were held several inches above its surface (OVER condition). In experiment 1, 61 three-and four-year-olds (Mean age = 3 years, 6 months) saw a deterministic activation rule. In experiments 2 and 3, 48 four-year-olds (Mean age = 4 years, 3 months) saw an activation rule that was probabilistic. In experiment 4, 30 four-year-olds (Mean age = 4 years, 7 months) saw a more complex "screening off" pattern of activation. In all four experiments, children were able to use new evidence in the form of patterns of probability to make accurate causal inferences, even in the face of conflicting prior beliefs about spatial contiguity. However, children were more likely to make correct inferences when causes were spatially contiguous, particularly when faced with ambiguous evidence.

- by Alison GOPNIK
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- Psychology, Cognitive Science, Developmental Psychology, Causal reasoning

The main aim of this work was to show the impact of preexisting causal beliefs on causal induction from cause-effect co-occurrence information, when several cues compete with each other for predicting the same effect. Two different causal scenarios-one social (a), the other medical (b)-were used to check the generality of the effects. In Experiments 1a and 1b, participants were provided information on the co-occurrence of a two-cause compound and an effect, but not about the potential relationship between each cause by its own and the effect. As expected, prior beliefs-induced by means of instructionsstrongly modulated the causal strength assigned to each element of the compound. In Experiments 2a and 2b, covariation evidence was provided, not only about the predictive value of the two-cause compound, but also about one of the elements of the compound. When this evidence was available, prior beliefs had less impact on judgments, and these were mostly guided by the relative predictive value of the cue. These results demonstrate the involvement of inferential integrative mechanisms in the generation of causal knowledge and show that single covariation detection mechanisms-either rule-based or associative-are insufficient to account for human causal judgment. At the same time, the fact that the power of new covariational evidence to change prior beliefs depended on the availability of information on the relative (conditional) predictive value of the target candidate cause suggests that causal knowledge derived from information on causal mechanisms and from covariation probably share a common representational basis.

- by Andres Catena
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- Psychology, Cognitive Science, Cognition, Culture

1. The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect ...

- by Keith Holyoak
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- Cognitive Science, Cognition, Adolescent, Causality

Associationist theories of causal induction model learning as the acquisition of associative weights between cues and out-comes. An important deficit of this class of models is its in-sensitivity to the causal role of cues. A number of... more

- by laura martignon
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- Causal Learning, Bayesian Network
- by Anna Waismeyer
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- Psychology, Cognitive Science, Developmental Psychology, Child Development

Two experiments were conducted with the aim of exploring reinstatement after extinction using a causality judgment task in human beings. In Experiment 1, participants learned first that a fictitious medicine produced a side-effect. The medicine was then presented in extinction. Re-exposure to the side-effect by itself before the test reinstated acquisition performance. Reinstatement was greater when exposure took place in the test context than when it took place in a different context. Experiment 2 replicated reinstatement in a situation that ensured equivalent extinction for the different groups before the test.

- by Javier Vila and +1
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- Psychology, Cognitive Science, Extinction, Causal Learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4-to 5-year-olds and 6-to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects.

- by Victoria Simms
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- Psychology, Cognitive Science, Cognitive development, Child Development

In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context. For generative causes this would be a context with a low base rate of effects generated by other causes and for preventive causes a context with a high base rate. In the following experiments, we used probabilistic and/or deterministic target causes and contexts. In each experiment, participants observed several contexts in which the effect occurred with different probabilities. After this training, the participants were presented with different target causes whose causal status was unknown. In order to discover the influence of each cause, participants were allowed, on each trial, to choose the context in which the cause would be tested. As expected by inferential theories, the participants preferred t...

- by Joan Sansa
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- Psychology, Cognitive Science, Theory, Causality
- by Jessecae Marsh
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- Causal Learning
- by Tom Beckers
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- Psychology, Cognitive Science, Experimental Psychology, Cognition

People use information about the covariation between a putative cause and an outcome to determine whether a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than is the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two experiments, we adapted paradigms for studying causal learning in order to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d′) or whether people change the standard by which they assess causality (measured by β). Our results indicate that the effect is due to bias.

- by Kelly Goedert
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- Psychology, Cognitive Science, Cognition, Judgment

- by York Hagmayer
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- Psychology, Cognitive Psychology, Cognitive Science, Cognition

In a two-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK -BEANS → REACTION) and then the causal efficacy of one food from these compounds was revalued (e.g., BEANS → NO REACTION). In Experiment 1, unrelated food pairs were used and although there were no age differences in compound or single cue -outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g. STEAK). However, they had weaker within -compound associations for the unrelated foods and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK -POTATO) and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single cue revaluation. These findings suggest that age -related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes.

- by Leslie Plumlee and +1
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- Psychology, Cognitive Science, Aging, Face recognition (Psychology)

The article presents a Bayesian model of causal learning that incorporates generic priors-systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes-causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.

- by HONGJING LU
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- Cognitive Science, Cognition, Adolescent, Causality

- by HONGJING LU
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- Cognitive Science, Cognition, Adolescent, Causality

Considerable research has examined the contrasting predictions of the elemental and configural association theories proposed by Rescorla and Wagner (1972) and Pearce (1987), respectively. One simple method to distinguish between these approaches is the summation test, in which the associative strength attributed to a novel compound of two separately trained cues is examined. Under common assumptions, the configural view predicts that the strength of the compound will approximate to the average strength of its components, whereas the elemental approach predicts that the strength of the compound will be greater than the strength of either component. Different studies have produced mixed outcomes. In studies of human causal learning, Collins and Shanks (2006) suggested that the observation of summation is encouraged by training, in which different stimuli are associated with different submaximal outcomes, and by testing, in which the alternative outcomes can be scaled. The reported exp...

- by Castillo Ramón
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- Psychology, Cognitive Science, Decision Making, Cognition

- by Keith Holyoak
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- Cognitive Science, Cognition, Adolescent, Causality

- by Mimi Liljeholm
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- Cognitive Science, Cognition, Adolescent, Causality

Causal reasoning is crucial to people’s decision-making in probabilistic environments. It may rely directly on data about covariation between variables (correspondence) or on inferences based on reasonable constraints if larger causal models are constructed based on local relations (coherence). For causal chains an often assumed constraint is transitivity. For probabilistic causal relations, mismatches between such transitive inferences and direct empirical evidence may lead to distortions of empirical evidence. Previous work has shown that people may use the generative local causal relations
A → B and B → C to infer a positive indirect relation between events A and C, despite data showing that these events are actually independent (von Sydow et al., 2009, 2010, 2016). Here we used an economic sequential learning scenario to investigate how transitive reasoning in intransitive situations with negatively related distal events may relate to betting behavior. In three experiments participants bet as if they were influenced by a transitivity assumption, even when the data strongly contradicted transitivity.

- by Momme von Sydow
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- Bayesian Networks, Transitivity, Causal Learning, Coherence

In a typical blocking procedure, pairings of a compound consisting of 2 stimuli, A and X, with the outcome are preceded by pairings of only A with the outcome (i.e., A+ then AX+). This procedure is known to diminish responding to the target cue (X) relative to a control group that does not receive the preceding training with blocking cue A. We report 2 experiments that investigated the effect of extinguishing a blocking cue on responding to the target cue in a human causal learning paradigm (i.e., A+ and AX+ training followed by A- training). The results indicate that extinguishing a blocking cue increases conditioned responding to the target cue. Moreover, this increase appears to be context dependent, such that increased responding to the target is limited to the context in which extinction of the blocking cue took place. We discuss these findings in the light of associative and propositional learning theories.

- by Tom Beckers
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- Psychology, Cognitive Science, Animal Behavior, Adolescent

Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants' preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution.

- by James Denniston and +1
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- Psychology, Cognitive Science, Forecasting, Learning

Previous work has demonstrated the importance of both naïve theories and statistical evidence to children's causal reasoning. In particular, four-year-olds can use statistical evidence to update their beliefs. However, the story is more complex for three-year-olds. Although three-and-a-half-yearolds perform as well as four-year-olds when statistical evidence is theory-neutral, several studies suggest that they do not learn from statistical evidence when a statistically likely cause is inconsistent with their prior beliefs (e.g., . There are at least two possible explanations for younger children's failure to use statistical data to update their beliefs: one (the Information Processing account) suggests that younger children have a fragile ability to reason about statistical evidence; the other (a Prior Knowledge account) suggests that in some domains, younger children have stronger prior beliefs and thus require more evidence before belief revision is rational. To distinguish these accounts, we conducted a two-week training study with three-and-a-halfyear-olds. Children participated in an Information Processing Training condition, a Prior Belief Training condition, or a Control condition.

- by Laura Schulz
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- Causal reasoning, Information Processing, Belief Revision, Prior Knowledge

Researchers, educators, and parents have long believed that children learn cause and effect relationships through exploratory play. However, previous research suggests that children are poor at designing informative experiments; children fail to control relevant variables and tend to alter multiple variables simultaneously. Thus, little is known about how children's spontaneous exploration might support accurate causal inferences. Here we suggest that children's exploratory play is affected by the quality of the evidence they observe. Using a novel free-play paradigm, we show that preschoolers (mean age: 57 months) distinguish confounded and unconfounded evidence, preferentially explore causally confounded (but not matched unconfounded) toys rather than novel toys, and spontaneously disambiguate confounded variables in the course of free play.

- by Laura Schulz
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- Psychology, Cognitive Science, Developmental Psychology, Cognition

Young and older participants' ability to detect negative, random, and positive response-outcome contingencies was evaluated using both contingency estimation and response rate adaptation tasks. Age differences in contingency estimation were consistently greater for negative than positive contingencies, and these differences, though still present, were smaller when response rate adaptation was used as the measure of contingency learning. Detecting causal contingency apparently becomes more difficult with age, especially when an overt numerical estimate of contingency must be provided and when the relationship between a causal event and an outcome is negative. A model that incorporates features of both associative and rule-based approaches to contingency learning (e.g., P. C.

- by Sharon Mutter
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- Psychology, Cognitive Science, Cognition, Aging

Two experiments examined the outcome specificity of a learned predictiveness effect in human causal learning. Experiment 1 indicated that prior experience of a cue-outcome relation modulates learning about that cue with respect to a different outcome from the same affective class but not with respect to an outcome from a different affective class. Experiment 2 ruled out an interpretation of this effect in terms of context specificity. These results indicate that learned predictiveness effects in human causal learning index an associability that is specific to a particular class of outcomes. Moreover, they mirror demonstrations of the reinforcer specificity of analogous effects in animal conditioning, supporting the suggestion that, under some circumstances, human causal learning and animal conditioning reflect the operation of common associative mechanisms.

- by Mike Le Pelley and +1
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- Psychology, Cognitive Science, Adolescent, Learning
- by Michael Waldmann
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- Psychology, Cognitive Science, Causal Learning, Associative learning

- by Tamar Kushnir
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- Psychology, Cognitive Science, Cognitive development, Child Development

Recent research has focused on how interventions benefit causal learning. This research suggests that the main benefit of interventions is in the temporal and conditional probability information that interventions provide a learner. But when one generates interventions, one must also decide what interventions to generate. In three experiments, we investigated the importance of these decision demands to causal learning. Experiment 1 demonstrated that learners were better at learning causal models when they observed intervention data that they had generated, as opposed to observing data generated by another learner. Experiment 2 demonstrated the same effect between self-generated interventions and interventions learners were forced to make. Experiment 3 demonstrated that when learners observed a sequence of interventions such that the decision-making process that generated those interventions was more readily available, learning was less impaired. These data suggest that decision making may be an important part of causal learning from interventions.

- by Tamar Kushnir
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- Psychology, Cognitive Science, Psychophysics, Decision Making

When the temporal interval or delay separating cause and effect is consistent over repeated instances, it
becomes possible to predict when the effect will follow from the cause, hence temporal predictability
serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated
that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the
cause-effect interval constant elicits higher judgements of causality compared to conditions involving
variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence
when causal learning takes place through observation rather than intervention through instrumental
action. Four experiments demonstrated that judgements of causality were higher when the temporal
interval was constant than when it was variable, and that judgements declined with increasing variability.
We further found that this beneficial effect of predictability was stronger in situations where the
effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability
enhances impressions of causality, and that this effect is robust and general. Factors that
could mediate this effect are discussed.

- by James Greville
- •
- Causal Learning