Is causal induction based on causal power? Critique of Cheng (1997)

The authors empirically evaluate P. W. Cheng's (1997) power PC theory of causal induction. They reanalyze some published data taken to support the theory and show instead that the data are at variance with it. Then, they report 6 experiments in which participants evaluated the causal relationship between a fictitious chemical and DNA mutations. The power PC theory assumes that participants' estimates are based on the causal power p of a potential cause, where p is the contingency between the cause and the effect normalized by the base rate of the effect. Three of the experiments used a procedure in which causal information was presented trial by trial. For these experiments, the power PC theory was contrasted with the predictions of the probabilistic contrast model and the Rescorla-Wagner theory. For the remaining 3 experiments, a summary presentation format was employed to which only the probabilistic contrast model and the power PC theory are applicable. The power PC theory was unequivocally contradicted by the results obtained in these experiments, whereas the other 2 theories proved to be satisfactory.     

Los procesos de atribución causal

RESUMEN

El interés por los procesos de atribución, por las explicaciones -razones- que las personas dan para su conducta y la de los demás, es una de las características más sobresalientes de los modelos recientes en la Psicología Social. Heider, hace veinticinco años, ya se había fijado en la gran significación de los procesos de atribución causal en las relaciones interpersonales, pero hasta hace bastantes años después no se recogieron los frutos de aquel interés señalado por Heider. En este trabajo se realiza una revisión de diversos modelos de atribución y se señala, desde una posición crítica, algunas de sus inconsistencias.     

Can there be one-way causal conditionship?

I defend my attempt to explain causal priority by means of one-way causal conditionship by answering an argument by J. A. Cover about Charles' law. Then I attempt to say what makes a philosophical analysis a counterfactual analysis, so I can understand Cover's claim that my account is at its base a counterfactual one. Finally I examine Cover's discussion of my contention that necessary for in the circumstances is nontransitive.     

Whither causal models in the neuroscience of ADHD?

In this paper we examine the current status of the science of ADHD from a theoretical point of view. While the field has reached the point at which a number of causal models have been proposed, it remains some distance away from demonstrating the viability of such models empirically. We identify a number of existing barriers and make proposals as to the best way for these to be overcome in future studies. These include the need to work across multiple levels of analysis in multidisciplinary teams; the need to recognize the existence of, and then model, causal heterogeneity; the need to integrate environmental and social processes into models of genetic and neurobiological influence; and the need to model developmental processes in a dynamic fashion. Such a model of science, although difficult to achieve, has the potential to provide the sort of framework for programmatic model-based research required if the power and sophistication of new neuroscience technologies are to be effectively exploited.     

Measuring causal perception: connections to representational momentum?

In a collision between two objects, we can perceive not only low-level properties, such as color and motion, but also the seemingly high-level property of causality. It has proven difficult, however, to measure causal perception in a quantitatively rigorous way which goes beyond perceptual reports. Here we focus on the possibility of measuring perceived causality using the phenomenon of representational momentum (RM). Recent studies suggest a relationship between causal perception and RM, based on the fact that RM appears to be attenuated for causally 'launched' objects. This is explained by appeal to the visual expectation that a 'launched' object is inert and thus should eventually cease its movement after a collision, without a source of self-propulsion. We first replicated these demonstrations, and then evaluated this alleged connection by exploring RM for different types of displays, including the contrast between causal launching and non-causal 'passing'. These experiments suggest that the RM-attenuation effect is not a pure measure of causal perception, but rather may reflect lower-level spatiotemporal correlates of only some causal displays. We conclude by discussing the strengths and pitfalls of various methods of measuring causal perception.     

Who caused it? Interpersonal causal inferences in young children

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.     

How well is causal structure inferred from cooccurrence information?

Individual causal relations tend to form parts of larger causal systems. In five experiments the ability of participants to infer the structures of two systems involving five entities from patterns of cooccurrence was investigated. Although the systems were fully deterministic, there were no indirect causal relations, and participants were given guidance on how to infer causal structure from cooccurrence information, low rates of success were observed. Judgements were based more on information about temporal relations, even though no guidance on the use of temporal relation information for causal inference was provided. When no guidance was provided, no participants succeeded in inferring the structure of both systems. The results indicate that temporal relations may be preferred as cues to causal structure over patterns of cooccurrence.     

On unpredictability as a causal factor in “learned helplessness”

In two replications, two groups of dogs were exposed to a series of uncontrollable, electric shocks. For one group the shocks were preceded by a tone (i.e., Paired). For the other group the shocks were randomly related to the tones and hence unpredictable (i.e., Random). Each replication also included a third group; in the first it was exposed only to the series of tones (CS-only), while in the second, it was exposed only to a series of shocks (Shocks-only). Then, all dogs were required to learn a discriminative choice escape/avoidance task in which the required response was to lift the correct paw in the presence of each of two visual S^Ds to escape or avoid the shocks [($\text{S}{}_1{}^{\mathrm{D}}\text{?R}{}_1\text{)}\text{(S}{}_2{}^{\mathrm{D}}\text{?R}{}_2$)]. Dogs preexposed to random tones and shocks were least successful in learning the task relative to those groups which experienced either predicted shocks, only the tones, or only the shocks, which in turn did not differ from each other. These results permitted the inference that the proactive interference with choice behavior following random tone CSs and shocks was attributable to a learned irrelevance generalized with respect to CSs.     

Summation in causal learning: Elemental processing or configural generalization?

Results from human causal learning tasks that employ multiple cues are often interpreted in terms of the elemental theory of Rescorla and Wagner (1972). However, most results can also be successfully interpreted by the configural model proposed by Pearce (1987, 1994). One method of discriminating between these alternatives is through an investigation of summation and overexpectation. Indeed, demonstrations of these phenomena are fundamental to an elemental approach but are generally incompatible with an account that involves configural processing. Using a procedure in which the magnitude of the outcome varied, evidence for both summation and overexpectation was obtained in two experiments.     

Summation in causal learning: elemental processing or configural generalization?

Results from human causal learning tasks that employ multiple cues are often interpreted in terms of the elemental theory of Rescorla and Wagner (1972). However, most results can also be successfully interpreted by the configural model proposed by Pearce (1987, 1994). One method of discriminating between these alternatives is through an investigation of summation and overexpectation. Indeed, demonstrations of these phenomena are fundamental to an elemental approach but are generally incompatible with an account that involves configural processing. Using a procedure in which the magnitude of the outcome varied, evidence for both summation and overexpectation was obtained in two experiments.     

Is working memory capacity a causal factor in fluid intelligence?

Psychonomic Bulletin & Review - It is well established that measures of reasoning ability and of working memory capacity (WMC) correlate positively. However, the question of what explains this...     

Are preventive and generative causal reasoning symmetrical? Extinction and competition

We tested whether preventive and generative reasoning processes are symmetrical by keeping the training and testing of preventive (inhibitory) and generative (excitatory) causal cues as similar as possible. In Experiment 1, we extinguished excitors and inhibitors in a blocking design, in which each extinguished cause was presented in compound with a novel cause, with the same outcome occurring following the compound and following the novel cause alone. With this novel extinction procedure, the inhibitory cues seemed more likely to lose their properties than the excitatory cues. In Experiment 2, we investigated blocking of excitatory and inhibitory causes and found similar blocking effects. Taken together, these results suggest that acquisition of excitation and inhibition is similar, but that inhibition is more liable to extinguish with our extinction procedure. In addition, we used a variable outcome, and this enabled us to test the predictions of an inferential reasoning account about what happens when the outcome level is at its minimum or maximum (De Houwer, Beckers, & Glautier, 2002). We discuss the predictions of this inferential account, Rescorla and Wagner's (1972) model, and a connectionist model-the auto-associator.     

What triggers causal attributions? The impact of valence and subjective probability

Various field studies and experimental simulations demonstrated that causal reasoning increases after unexpected as well as after unpleasant events. However, unpleasant events are seen as less likely than pleasant ones in everyday life. Accordingly, the subjective probability of the event and its hedonic quality were naturally confounded in these studies. To isolate the contribution of both determinants, the subjective probability and the valence of an event were independently manipulated in a laboratory experiment. Subjects completed an ostensible ‘professional skills test’ and received either success or failure feedback in relation to a criterion set by the experimenter. The subjective probability of success was varied by informing subjects about the distribution of success and failure in a comparable population (either 23 per cent or 77 per cent were said to meet the criterion). The results indicate a pronounced valence effect: The intensity of causal reasoning and the number of possible reasons reported for the outcome was greater after negative than after positive feedback, independent of the a priori probability of the outcome. No evidence for an increase in causal explanations after unexpected, as compared to expected, events was obtained. Several mediating processes are discussed.     

Does anxiety or cardiovascular reactivity have a causal role in hypertension?

Anxiety, stress, and cardiovascular reactivity (CVR) are variously believed to play a role in sustained hypertension. Although acute anxiety or stress elicits acute pressor responses, there is little support for their significant role in sustained hypertension. Anxiety correlates poorly with CVR, and blood pressure levels and anxiolytics do not sustain blood pressure lowering in subjects with hypertension-associated anxiety. Chronic anxiety disorders tend to be characterized by relatively low blood pressure and prevalence of sustained hypertension. Blood Pressure Regulation in hypertension is normal, and normo- and hypertensives have similar ambulatory blood pressure variability. Laboratory CVR fails to predict variability in natural environments, hyperreactors do not exhibit increased variability in natural environments, and the increased variability and ambulatory reactivity that is "accounted for" by laboratory responses is small. These findings do not support the belief that hypertension is related to a summation of heightened pressor responses over time. Antihypertensives normalize elevated blood pressures but do not alter CVR in the laboratory or variability in natural environments, probably because of a dual central regulation of resting and reactive blood pressures. Psychological stress responses result from selective neuronal activation rather than from generalized sympathetic neural responses or dysregulation. Differences in blood pressure responses during various emotions are only quantitative, with no specificity of sympathoadrenal or emotional responses to stressors. It may be time to regard reactive cardiovascular responses as physiological, rather than as psychological, and to require much stronger evidence to confirm causal roles of anxiety, stress, and reactivity in sustained hypertension.     

On the logic of event-causation Jaśkowski-style systems of causal logic

Causality is a concept which is sometimes claimed to be easy to illustrate, but hard to explain. It is not quite clear whether the former part of this claim is as obvious as the latter one. I will not present any specific theory of causation. Our aim is much less ambitious; to investigate the formal counterparts of causal relations between events, i.e. to propose a formal framework which enables us to construct metamathematical counterparts of causal relations between singular events. This should be a good starting point to define formal counterparts for concepts like causal law, causal explanation and so on.Children in their simplicity keep asking why. The person of understanding has given this up; every why, he has long found out, is merely the end of a thread that vanishes into the thick snare of infinity, which no one can truly unravel, let him tug and worry at it as much as he likes.W. Busch,The Butterfly, translated by W. ArndtThe work on this paper was supported by grants from the Humboldt-Foundation, the Center for Philosophy of Science at the University of Pittsburgh and from the Fulbright-Foundation.This paper concerns a larger project on causal logic, Uwe Scheffler from Humboldt-University and I have been working on for several months. This collaboration explains the usage of plural forms wherever they occur in section 1 and 6. However, all results of this paper, if not stated explicitely otherwise, are due to its (single) author.Presented byJan Zygmunt;     

Covariation and quantifier polarity: what determines causal attribution in vignettes?

Tests of causal attribution often use verbal vignettes, with covariation information provided through statements quantified with natural language expressions. The effect of covariation information has typically been taken to show that set size information affects attribution. However, recent research shows that quantifiers provide information about discourse focus as well as covariation information. In the attribution literature, quantifiers are used to depict covariation, but they confound quantity and focus. In four experiments, we show that focus explains all (Experiment 1) or some (Experiment 2-4) of the impact of covariation information on the attributions made, confirming the importance of the confound. Attribution experiments using vignettes that present covariation information with natural language quantifiers may overestimate the impact of set size information, and ignore the impact of quantifier-induced focus.     

Interpersonal regulation of relationship partners’ security: A causal chain analysis

Security in relationship partners’ positive regard promotes high-quality close relationships, and insecurity in partners’ regard undermines relationship quality. Hence, people may be motivated to dispel their close partners’ insecurity. The current research used an experimental-causal-chain design to test a novel model of the interpersonal regulation of relationship partners’ security. Manipulated perceptions of relationship partners’ insecurity increased the activation of security regulation goals (i.e., goals to improve partners’ security and dispel their insecurity) and selective attention to threat-relevant information (Study 1). In turn, manipulated security regulation goals increased the expression of positive regard for relationship partners, particularly in negatively evaluated and important domains (Study 2). In turn, manipulated expression of positive regard increased partners’ relationship security and satisfaction, particularly when partners had chronic doubts about the extent to which they were valued (Study 3). These studies provide compelling causal evidence for a relationship-protective response to detection of partner insecurity; people try to dispel the relationship threat posed by an insecure partner by adopting security regulation goals, which motivate expressions of positive regard that increase partner security.     

Driven by power? Probe question and presentation format effects on causal judgment

It has been proposed that causal power (defined as the probability with which a candidate cause would produce an effect in the absence of any other background causes) can be intuitively computed from cause-effect covariation information. Estimation of power is assumed to require a special type of counterfactual probe question, worded to remove potential sources of ambiguity. The present study analyzes the adequacy of such questions to evoke normative causal power estimation. The authors report that judgments to counterfactual probes do not conform to causal power and that they strongly depend on both the probe question wording and the way that covariation information is presented. The data are parsimoniously accounted for by an alternative model of causal judgment, the Evidence Integration rule.