The causal structure of situations: The generation of plausible causal attributions as a function of type of event situation

1983 ◽  
Vol 19 (2) ◽  
pp. 185-203 ◽  
Author(s):  
Craig A Anderson
2008 ◽  
Vol 39 (3) ◽  
pp. 174-181 ◽  
Author(s):  
Udo Rudolph

Implicit verb causality refers to the phenomenon that even minimal descriptions of interpersonal events (e.g., A dominates B, A amuses B) elicit causal attributions. Two experiments investigated whether children of different age groups are able to (a) perceive the causality implicit in interpersonal verbs and (b) to detect patterns of cause-effect covariation (consensus and distinctiveness) presumably mediating the verb causality effect. Experiment 1 found that 5-year-old children detect the causal structure inherent in verbs describing interpersonal events and are able to indicate corresponding covariation patterns. Experiment 2 replicated these findings for 3-year-old children using a more sensitive method for assessing causal and covariation beliefs. Statistical mediation analyses supported the hypothesis that the verb causality effect is mediated by implicit beliefs about cause-effect covariation. Taken together, the results provide support for a covariation-based explanation of the verb causality effect.


2019 ◽  
Vol 30 (4) ◽  
pp. 516-525 ◽  
Author(s):  
Hayley M. Dorfman ◽  
Rahul Bhui ◽  
Brent L. Hughes ◽  
Samuel J. Gershman

People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, and this pattern should reverse when hidden causes are likely to generate good outcomes. To test this hypothesis, we conducted two experiments ( N = 80, N = 255) in which we explicitly manipulated the behavior of hidden agents. This gave rise to both kinds of learning asymmetries in the same paradigm, as predicted by a novel Bayesian model. These results provide a mechanistic framework for understanding how causal attributions contribute to biased learning.


2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.


Author(s):  
Tom Beckers ◽  
Uschi Van den Broeck ◽  
Marij Renne ◽  
Stefaan Vandorpe ◽  
Jan De Houwer ◽  
...  

Abstract. In a contingency learning task, 4-year-old and 8-year-old children had to predict the outcome displayed on the back of a card on the basis of cues presented on the front. The task was embedded in either a causal or a merely predictive scenario. Within this task, either a forward blocking or a backward blocking procedure was implemented. Blocking occurred in the causal but not in the predictive scenario. Moreover, blocking was affected by the scenario to the same extent in both age groups. The pattern of results was similar for forward and backward blocking. These results suggest that even young children are sensitive to the causal structure of a contingency learning task and that the occurrence of blocking in such a task defies an explanation in terms of associative learning theory.


2020 ◽  
Vol 56 (1) ◽  
pp. 70-80
Author(s):  
Alexander Noyes ◽  
Frank C. Keil ◽  
Yarrow Dunham

2008 ◽  
Author(s):  
C. Barrowclough ◽  
L. Gregg ◽  
N. Terrier
Keyword(s):  

2010 ◽  
Author(s):  
Blake Berry ◽  
Victor Cordova ◽  
Amy McGranahan ◽  
Camille Wheatley ◽  
Rik Jeffery ◽  
...  

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