scholarly journals Limitations of entropic inequalities for detecting nonclassicality in the postselected Bell causal structure

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
V. Vilasini ◽  
Roger Colbeck
Keyword(s):  
Causal Structure ◽  
Entropic Inequalities

scholarly journals Non-Shannon inequalities in the entropy vector approach to causal structures

Quantum ◽  
2018 ◽  
Vol 2 ◽  
pp. 57 ◽  
Author(s):  
Mirjam Weilenmann ◽  
Roger Colbeck
Keyword(s):  
Causal Structure ◽  
Conditional Entropy ◽  
Classical Case ◽  
Conditional Mutual Information ◽  
Entropic Inequalities ◽  
Starting Point ◽  
Outer Approximations ◽  
Open Question ◽  
Causal Structures ◽  
Vector Approach

A causal structure is a relationship between observed variables that in general restricts the possible correlations between them. This relationship can be mediated by unobserved systems, modelled by random variables in the classical case or joint quantum systems in the quantum case. One way to differentiate between the correlations realisable by two different causal structures is to use entropy vectors, i.e., vectors whose components correspond to the entropies of each subset of the observed variables. To date, the starting point for deriving entropic constraints within causal structures are the so-called Shannon inequalities (positivity of entropy, conditional entropy and conditional mutual information). In the present work we investigate what happens when non-Shannon entropic inequalities are included as well. We show that in general these lead to tighter outer approximations of the set of realisable entropy vectors and hence enable a sharper distinction of different causal structures. Since non-Shannon inequalities can only be applied amongst classical variables, it might be expected that their use enables an entropic distinction between classical and quantum causal structures. However, this remains an open question. We also introduce techniques for deriving inner approximations to the allowed sets of entropy vectors for a given causal structure. These are useful for proving tightness of outer approximations or for finding interesting regions of entropy space. We illustrate these techniques in several scenarios, including the triangle causal structure.

Special, radical, failure of reduction in psychiatry

2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross
Keyword(s):  
Mental Disorder ◽  
Brain Structure ◽  
Causal Structure ◽  
Network Models ◽  
Gambling Addiction ◽  
Brain Disorder ◽  
Mental Processes ◽  
Special Radical ◽  
Intentional Relationships

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.

Blocking Is Sensitive to Causal Structure in 4-Year-Old and 8-Year-Old Children

2005 ◽  
Vol 52 (4) ◽  
pp. 264-271 ◽  
Author(s):  
Tom Beckers ◽  
Uschi Van den Broeck ◽  
Marij Renne ◽  
Stefaan Vandorpe ◽  
Jan De Houwer ◽  
...  
Keyword(s):  
Young Children ◽  
Associative Learning ◽  
Learning Theory ◽  
Causal Structure ◽  
Age Groups ◽  
Learning Task ◽  
Contingency Learning

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.

Institutional actors: Children’s emerging beliefs about the causal structure of social roles.

2020 ◽  
Vol 56 (1) ◽  
pp. 70-80
Author(s):  
Alexander Noyes ◽  
Frank C. Keil ◽  
Yarrow Dunham
Keyword(s):  
Social Roles ◽  
Causal Structure ◽  
Institutional Actors

Institutional actors: Children’s emerging beliefs about the causal structure of social roles

2019 ◽  
Author(s):  
Alexander Noyes ◽  
Frank Keil ◽  
Yarrow Dunham
Keyword(s):  
Soccer Player ◽  
Social Roles ◽  
Social Role ◽  
Causal Structure ◽  
Executive Orders ◽  
The Social ◽  
The World ◽  
Children's Beliefs ◽  
Institutional Actors ◽  
The U.S

Institutions make new forms of acting possible: Signing executive orders, scoring goals, and officiating weddings are only possible because of the U.S. government, the rules of soccer, and the institution of marriage. Thus, when an individual occupies a particular social role (President, soccer player, and officiator) they acquire new ways of acting on the world. The present studies investigated children’s beliefs about institutional actions, and in particular whether children understand that individuals can only perform institutional actions when their community recognizes them as occupying the appropriate social role. Two studies (Study 1, N = 120 children, 4-11; Study 2, N = 90 children, 4-9) compared institutional actions to standard actions that do not depend on institutional recognition. In both studies, 4- to 5-year-old children believed all actions were possible regardless of whether an individual was recognized as occupying the social role. In contrast, 8- to 9-year-old children robustly distinguished between institutional and standard actions; they understood that institutional actions depend on collective recognition by a community.

scholarly journals Causal imprinting in causal structure learning

2012 ◽  
Vol 65 (3) ◽  
pp. 381-413 ◽  
Author(s):  
Eric G. Taylor ◽  
Woo-kyoung Ahn
Keyword(s):  
Structure Learning ◽  
Causal Structure
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