scholarly journals Causal reasoning in rats' behaviour systems

2018 ◽  
Vol 5 (7) ◽  
pp. 171448 ◽  
Author(s):  
Robert Ian Bowers ◽  
William Timberlake
Keyword(s):  
Bayesian Networks ◽  
Causal Reasoning ◽  
Causal Model ◽  
Response Competition ◽  
Process Approach ◽  
Mental Maps ◽  
Causal Relations ◽  
Specific Process ◽  
Self Production ◽  
Representational Models

Conceiving of stimuli and responses as causes and effects, and assuming that rats acquire representational models of causal relations from Pavlovian procedures, previous work by causal model theory proponents attempted to train rat subjects to represent stimulus A as a cause of both stimulus B and food. By these assumptions, with formal help from Bayesian networks, self-production of stimulus B should reduce expectation of alternative causes, including stimulus A, and their effects, including food. Reduced feeder-directed responding to stimulus B when self-produced has been taken as evidence for a general causal reasoning capacity among rats involving mental maps of causal relations. Critics have rejoined that response competition can explain these effects. The present research replicates the key effect, but uses continuous and finer-grained measurement of a broader range of behaviours. Behaviours not recorded in previous studies contradict both prior explanations. Even results cited in support of these explanations, when measured in finer detail and continuously over longer periods, show patterns not expected by either view, but supportive of a specific-process approach with attention to motivational factors. Still, the abstract prediction from Bayesian networks holds, providing a potentially complementary normative analysis. Behaviour systems theory provides firmer framing for such theories than representational-map alternatives.

Causality and Causal Reasoning in Natural Language

2017 ◽  
Author(s):  
Torgrim Solstad ◽  
Oliver Bott
Keyword(s):  
Natural Language ◽  
Cognitive Processing ◽  
Causal Reasoning ◽  
Causal Model ◽  
Discourse Markers ◽  
Causal Relations ◽  
Counterfactual Conditionals ◽  
Adequate Evaluation ◽  
Implicit Causality

This chapter provides a combined overview of theoretical and psycholinguistic approaches to causality in language. The chapter’s main phenomenological focus is on causal relations as expressed intra-clausally by verbs (e.g., break, open) and between sentences by discourse markers (e.g., because, therefore). Special attention is given to implicit causality verbs that are argued to trigger expectations of explanations to occur in subsequent discourse. The chapter also discusses linguistic expressions that do not encode causation as such, but that seem to be dependent on a causal model for their adequate evaluation, such as counterfactual conditionals. The discussion of the phenomena is complemented by an overview of important aspects of their cognitive processing as revealed by psycholinguistic experimentation.

Causation in Legal and Moral Reasoning

2017 ◽  
Author(s):  
David A. Lagnado ◽  
Tobias Gerstenberg
Keyword(s):  
Moral Reasoning ◽  
Causal Reasoning ◽  
Causal Model ◽  
Physical World ◽  
Causal Models ◽  
Responsibility Attribution ◽  
Model Framework ◽  
The Social ◽  
Legal Concepts ◽  
Legal Judgments

Causation looms large in legal and moral reasoning. People construct causal models of the social and physical world to understand what has happened, how and why, and to allocate responsibility and blame. This chapter explores people’s common-sense notion of causation, and shows how it underpins moral and legal judgments. As a guiding framework it uses the causal model framework (Pearl, 2000) rooted in structural models and counterfactuals, and shows how it can resolve many of the problems that beset standard but-for analyses. It argues that legal concepts of causation are closely related to everyday causal reasoning, and both are tailored to the practical concerns of responsibility attribution. Causal models are also critical when people evaluate evidence, both in terms of the stories they tell to make sense of evidence, and the methods they use to assess its credibility and reliability.

Inferring Causal Relations by Analogy

2017 ◽  
Author(s):  
Keith J. Holyoak ◽  
Hee Seung Lee
Keyword(s):  
Analogical Reasoning ◽  
Causal Model ◽  
Causal Knowledge ◽  
Common Pattern ◽  
Causal Relations ◽  
Analogical Inference ◽  
Psychological Evidence ◽  
Inductive Inferences ◽  
Novel Target ◽  
Constituent Elements

When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

Developing a causal model to evaluate the critical issues in reverse supply chain implementation

2018 ◽  
Vol 25 (7) ◽  
pp. 1992-2017 ◽  
Author(s):  
Kaustov Chakraborty ◽  
Sandeep Mondal ◽  
Kampan Mukherjee
Keyword(s):  
Supply Chain ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Causal Model ◽  
Market Competition ◽  
Reverse Supply Chain ◽  
Causal Relations ◽  
Content Type ◽  
Critical Issues ◽  
Practical Implications

Purpose Approximately, 800m tons of e-waste is generated per year in India. Reverse supply chain (RSC) is the probable strategy to cope up with the issue. Setting up a RSC process is not popular in the Indian sector. There are several factors that basically control the profitability of such kind of business. Hence, the purpose of this paper is to develop a causal model among the identified issues and sub-issues for setting up a RSC in an Indian semiconductor manufacturing industry and then evaluate the critical issues based on the causal relations. Design/methodology/approach Decision-making trial and evaluation laboratory (DEMATEL) method along fuzzy set theory is used to develop the causal framework among the identified strategical and tactical issues. According to the causal relations from DEMATEL, analytical network process is then used to identify the weights of the sub-issues. Findings The cause–effect interactions among the main issues show that legislations and regulations, market-related issues and organizational issue are the most significant strategic issues. Uncertainty in the acquisition time is the most significant tactical issue because it has a crucial impact on the quality and quantity of the used products. Based on the obtained causal relations of the main issues, it is identified that the reduction of waste, creation of new opportunity, market competition, cost reduction, change in technology and location, capacity and number of recovery facility are the major sub-issues in RSC implementation. Practical implications This study is conducted on the basis of the experts’ opinion from a semiconductor manufacturing industry, situated in the southern part of India. Therefore, this proves its practical implications. Originality/value The paper provides the detail illustration of the issues in the RSC process, and the prioritization of the issues based on the cause–effect relationships also provides some meaningful managerial insights.

Casual Modeling to Improve Customs Services in Iran

2018 ◽  
pp. 156-166
Author(s):  
Alireza Hassanzadeh ◽  
Hoda Eskandari
Keyword(s):  
Decision Making ◽  
System Dynamics ◽  
Causal Model ◽  
Influential Factors ◽  
Causal Relations ◽  
The World

In its reality, the world is more complex than we expect it to be predicated and controlled; so, by minor view to issues and suppose many phenomena to be independent, it would be impossible to understand their totality and complexity. Utilizing appropriate scientific tools can provide us with useful information to manage decision making to improve methods to carry out operations and apply resources; one of these scientific tools is system dynamics approach. Therefore, based on stages in the methodology of system dynamics and by taking experts comments, in the present study, causal relations between influential factors in improvement of customs service are identified. So, by knowing these causal relations, one can allocate resources and prioritize activities leading to influence on other factors following with the best results. Finally, after validation and having the confirmation of the experts, the researcher has presented a causal model to improve customs services.

scholarly journals Can Agents with Causal Misperceptions be Systematically Fooled?

2019 ◽  
Vol 18 (2) ◽  
pp. 583-617 ◽  
Author(s):  
Ran Spiegler
Keyword(s):  
Monetary Policy ◽  
Bayesian Networks ◽  
Directed Acyclic Graph ◽  
Phillips Curve ◽  
Causal Model ◽  
Expected Value ◽  
Prediction Errors ◽  
Acyclic Graph ◽  
Long Run ◽  
Individual Variables

Abstract An agent forms estimates (or forecasts) of individual variables conditional on some observed signal. His estimates are based on fitting a subjective causal model—formalized as a directed acyclic graph, following the “Bayesian networks” literature—to objective long-run data. I show that the agent’s average estimates coincide with the variables’ true expected value (for any underlying objective distribution) if and only if the agent’s graph is perfect—that is, it directly links every pair of variables that it perceives as causes of some third variable. This result identifies neglect of direct correlation between perceived causes as the kind of causal misperception that can generate systematic prediction errors. I demonstrate the relevance of this result for economic applications: speculative trade, manipulation of a firm’s reputation, and a stylized “monetary policy” example in which the inflation-output relation obeys an expectational Phillips Curve.

The Diagnosis of Mental Disorders Is Influenced by Automatic Causal Reasoning

2017 ◽  
Vol 6 (2) ◽  
pp. 177-188 ◽  
Author(s):  
Amanda Flores ◽  
Pedro L. Cobos ◽  
York Hagmayer
Keyword(s):  
Experimental Study ◽  
Mental Disorders ◽  
Causal Reasoning ◽  
Causal Model ◽  
Diagnostic Classification ◽  
Causal Knowledge ◽  
Intuitive Reasoning ◽  
Automatic Reasoning ◽  
Causal Coherence ◽  
Study Participants

Causal knowledge has been shown to affect diagnostic decisions. It is unclear, however, how causal knowledge affects diagnosis. We hypothesized that it influences intuitive reasoning processes. More precisely, we speculated that people automatically assess the coherence between observed symptoms and an assumed causal model of a disorder, which in turn affects diagnostic classification. Intuitive causal reasoning was investigated in an experimental study. Participants were asked to read clinical reports before deciding on a diagnosis. Intuitive processing was studied by analyzing reading times. It turned out that reading times were slower when causally expected consequences of present symptoms were missing or effects of absent causes were present. This causal incoherence effect was predictive of participants’ later explicit diagnostic judgments. These and related findings suggest that diagnostic judgments rely on automatic reasoning processes based on the computation of causal coherence. Potential implications of these results for the training of clinicians are discussed.

Behaviour Genetic Frameworks of Causal Reasoning for Personality Psychology

2018 ◽  
Vol 32 (3) ◽  
pp. 202-220 ◽  
Author(s):  
Daniel A. Briley ◽  
Jonathan Livengood ◽  
Jaime Derringer
Keyword(s):  
Genetic Variants ◽  
Causal Reasoning ◽  
Observational Studies ◽  
European Association ◽  
Random Assignment ◽  
Personality Psychology ◽  
Causal Relations ◽  
Methodological Tool ◽  
Correlational Data ◽  
Behaviour Genetics

Identifying causal relations from correlational data is a fundamental challenge in personality psychology. In most cases, random assignment is not feasible, leaving observational studies as the primary methodological tool. Here, we document several techniques from behaviour genetics that attempt to demonstrate causality. Although no one method is conclusive at ruling out all possible confounds, combining techniques can triangulate on causal relations. Behaviour genetic tools leverage information gained by sampling pairs of individuals with assumed genetic and environmental relatedness or by measuring genetic variants in unrelated individuals. These designs can find evidence consistent with causality, while simultaneously providing strong controls against common confounds. We conclude by discussing several potential problems that may limit the utility of these techniques when applied to personality. Ultimately, genetically informative designs can aid in drawing causal conclusions from correlational studies. Copyright © 2018 European Association of Personality Psychology

scholarly journals The Psychology of Causal Perception and Reasoning

2010 ◽  
Author(s):  
David Danks
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Psychological Research ◽  
Causal Learning ◽  
Causal Beliefs ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Causal Perception ◽  
The World ◽  
Causal Cognition

Causal beliefs and reasoning are deeply embedded in many parts of our cognition. We are clearly ‘causal cognizers’, as we easily and automatically (try to) learn the causal structure of the world, use causal knowledge to make decisions and predictions, generate explanations using our beliefs about the causal structure of the world, and use causal knowledge in many other ways. Because causal cognition is so ubiquitous, psychological research into it is itself an enormous topic, and literally hundreds of people have devoted entire careers to the study of it. Causal cognition can be divided into two rough categories: causal learning and causal reasoning. The former encompasses the processes by which we learn about causal relations in the world at both the type and token levels; the latter refers to the ways in which we use those causal beliefs to make further inferences, decisions, predictions, and so on.

scholarly journals Causality, Conditional Independence, and Graphical Separation in Settable Systems

2012 ◽  
Vol 24 (7) ◽  
pp. 1611-1668 ◽  
Author(s):  
Karim Chalak ◽  
Halbert White
Keyword(s):  
Conditional Independence ◽  
Causal Model ◽  
Functional Dependence ◽  
Structural Systems ◽  
Graphical Representations ◽  
Random Vectors ◽  
Common Cause ◽  
Causal Relations ◽  
Conditional Dependence ◽  
Stochastic Conditions

We study the connections between causal relations and conditional independence within the settable systems extension of the Pearl causal model (PCM). Our analysis clearly distinguishes between causal notions and probabilistic notions, and it does not formally rely on graphical representations. As a foundation, we provide definitions in terms of suitable functional dependence for direct causality and for indirect and total causality via and exclusive of a set of variables. Based on these foundations, we provide causal and stochastic conditions formally characterizing conditional dependence among random vectors of interest in structural systems by stating and proving the conditional Reichenbach principle of common cause, obtaining the classical Reichenbach principle as a corollary. We apply the conditional Reichenbach principle to show that the useful tools of d-separation and D-separation can be employed to establish conditional independence within suitably restricted settable systems analogous to Markovian PCMs.

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