Ockham’s razor as inductive bias in preschooler’s causal explanations

Author(s):  
Elizabeth Baraff Bonawitz ◽  
Isabel Y. Chang ◽  
Catherine Clark ◽  
Tania Lombrozo
2001 ◽  
Vol 60 (4) ◽  
pp. 215-230 ◽  
Author(s):  
Jean-Léon Beauvois

After having been told they were free to accept or refuse, pupils aged 6–7 and 10–11 (tested individually) were led to agree to taste a soup that looked disgusting (phase 1: initial counter-motivational obligation). Before tasting the soup, they had to state what they thought about it. A week later, they were asked whether they wanted to try out some new needles that had supposedly been invented to make vaccinations less painful. Agreement or refusal to try was noted, along with the size of the needle chosen in case of agreement (phase 2: act generalization). The main findings included (1) a strong dissonance reduction effect in phase 1, especially for the younger children (rationalization), (2) a generalization effect in phase 2 (foot-in-the-door effect), and (3) a facilitatory effect on generalization of internal causal explanations about the initial agreement. The results are discussed in relation to the distinction between rationalization and internalization.


2010 ◽  
Vol 69 (3) ◽  
pp. 173-179 ◽  
Author(s):  
Samantha Perrin ◽  
Benoît Testé

Research into the norm of internality ( Beauvois & Dubois, 1988 ) has shown that the expression of internal causal explanations is socially valued in social judgment. However, the value attributed to different types of internal explanations (e.g., efforts vs. traits) is far from homogeneous. This study used the Weiner (1979 ) tridimensional model to clarify the factors explaining the social utility attached to internal versus external explanations. Three dimensions were manipulated: locus of causality, controllability, and stability. Participants (N = 180 students) read the explanations expressed by appliants during a job interview. They then described the applicants on the French version of the revised causal dimension scale and rated their future professional success. Results indicated that internal-controllable explanations were the most valued. In addition, perceived internal and external control of explanations were significant predictors of judgments.


Explanations are very important to us in many contexts: in science, mathematics, philosophy, and also in everyday and juridical contexts. But what is an explanation? In the philosophical study of explanation, there is long-standing, influential tradition that links explanation intimately to causation: we often explain by providing accurate information about the causes of the phenomenon to be explained. Such causal accounts have been the received view of the nature of explanation, particularly in philosophy of science, since the 1980s. However, philosophers have recently begun to break with this causal tradition by shifting their focus to kinds of explanation that do not turn on causal information. The increasing recognition of the importance of such non-causal explanations in the sciences and elsewhere raises pressing questions for philosophers of explanation. What is the nature of non-causal explanations—and which theory best captures it? How do non-causal explanations relate to causal ones? How are non-causal explanations in the sciences related to those in mathematics and metaphysics? This volume of new essays explores answers to these and other questions at the heart of contemporary philosophy of explanation. The essays address these questions from a variety of perspectives, including general accounts of non-causal and causal explanations, as well as a wide range of detailed case studies of non-causal explanations from the sciences, mathematics and metaphysics.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.


1980 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Robert Wyllie

1965 ◽  
Vol 62 (23) ◽  
pp. 695 ◽  
Author(s):  
Samuel Gorovitz

2021 ◽  
Vol 11 (4) ◽  
pp. 456
Author(s):  
Wenpeng Neng ◽  
Jun Lu ◽  
Lei Xu

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.


2021 ◽  
pp. 1-16
Author(s):  
Hiromi Nakagawa ◽  
Yusuke Iwasawa ◽  
Yutaka Matsuo

Recent advancements in computer-assisted learning systems have caused an increase in the research in knowledge tracing, wherein student performance is predicted over time. Student coursework can potentially be structured as a graph. Incorporating this graph-structured nature into a knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of graph neural networks (GNNs), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information.


Infertility ◽  
1991 ◽  
pp. 109-131 ◽  
Author(s):  
Howard Tennen ◽  
Glenn Affleck ◽  
Richard Mendola
Keyword(s):  

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