Rethinking Temporal Contiguity and the Judgement of Causality: Effects of Prior Knowledge, Experience, and Reinforcement Procedure

2003 ◽  
Vol 56 (5) ◽  
pp. 865-890 ◽  
Author(s):  
Marc J. Buehner ◽  
Jon May
Keyword(s):  
Causal Relation ◽  
Causal Learning ◽  
Temporal Contiguity ◽  
Learning Theories ◽  
Causal Induction ◽  
Causal Relations ◽  
Free Operant ◽  
The Impact ◽  
Causal Cognition ◽  
Reinforcement Procedure

Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants’ expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.

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 Two Heads Are Better Than One, but How Much?

2014 ◽  
Vol 61 (5) ◽  
pp. 356-367 ◽  
Author(s):  
Miguel A. Vadillo ◽  
Nerea Ortega-Castro ◽  
Itxaso Barberia ◽  
A. G. Baker
Keyword(s):  
Causal Learning ◽  
Present Series ◽  
Integration Rule ◽  
Causal Induction ◽  
Series Of Experiments ◽  
Causal Impact ◽  
The Individual ◽  
The Impact ◽  
Better Than ◽  
Integration Rules

Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.

What Gives a Diagnostic Label Value? Common Use Over Informativeness

2019 ◽  
Author(s):  
Babak Hemmatian ◽  
Sze Yu Yu Chan ◽  
Steven A. Sloman
Keyword(s):  
Mental Health ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Causal Relation ◽  
Causal Beliefs ◽  
Diagnostic Categories ◽  
Diagnostic Labels ◽  
Diagnostic Label ◽  
The Impact ◽  
Practical Implications

A label’s entrenchment, its degree of use by members of a community, affects its perceived explanatory value even if the label provides no substantive information (Hemmatian & Sloman, 2018). In three experiments, we show that laypersons and mental health professionals see entrenched psychiatric and non-psychiatric diagnostic labels as better explanations than non-entrenched labels even if they are circular. Using scenarios involving experts who discuss unfamiliar diagnostic categories, we show that this preference is not due to violations of conversational norms, lack of reflectiveness or attentiveness, and the characters’ familiarity or unfamiliarity with the label. In Experiment 1, whether a label provided novel symptom information or not had no impact on lay responses, while its entrenchment enhanced ratings of explanation quality. The effect persisted in Experiment 2 for causally incoherent categories and regardless of direct provision of mechanistic information. The effect of entrenchment was partly related to induced causal beliefs about the category, even when participants were informed there is no causal relation. Most participants in both experiments did not report any effect of entrenchment and the effect was present for those who did not. In Experiment 3, mental health professionals showed the effect using diagnoses that were mere shorthands for symptoms, despite a tendency to rate all explanations as unsatisfactory. The data suggest that bringing experts’ attention to the manipulation eliminates the effect. We discuss practical implications for mental health disciplines and potential ways to mitigate the impact of entrenchment.

scholarly journals Extraction of causal relations based on SBEL and BERT model

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Yifan Shao ◽  
Haoru Li ◽  
Jinghang Gu ◽  
Longhua Qian ◽  
Guodong Zhou
Keyword(s):  
State Of The Art ◽  
Causal Relation ◽  
Relation Extraction ◽  
The Other ◽  
Biomedical Text ◽  
Intermediate Form ◽  
Biomedical Text Mining ◽  
Causal Relations ◽  
The One ◽  
Stage 1

Abstract Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

On the Causal-Doxastic Theory of the Basing Relation

2002 ◽  
Vol 32 (4) ◽  
pp. 543-559 ◽  
Author(s):  
Daniel M. Mittag
Keyword(s):  
Recent Article ◽  
Causal Relation ◽  
The Other ◽  
Causal Relations ◽  
Basing Relation ◽  
In Virtue Of

If one is to believe that p justifiably, then one must believe p for, or because of, one's evidence or reasons in support of p. The basing relation is exactly this relation that obtains between one's belief and one's reasons for believing. Keith Allen Korcz, in a recent article published in this Journal, has argued that two conditions are each sufficient and are jointly necessary to establish basing relations between beliefs and reasons. One condition is formulated to account for basing relations that can obtain in virtue of causal relations between one's belief and reasons, and the other condition is supposed to account for basing relations which can be established independently of the instantiation of any such causal relation.

scholarly journals Tenure in Office and Public Procurement

2017 ◽  
Vol 9 (3) ◽  
pp. 59-105 ◽  
Author(s):  
Decio Coviello ◽  
Stefano Gagliarducci
Keyword(s):  
Causal Relation ◽  
Public Procurement ◽  
Government Officials ◽  
Term Limit ◽  
Municipal Governments ◽  
Extra Term ◽  
The Impact

We study the impact of politicians' tenure in office on the outcomes of public procurement using a dataset on Italian municipal governments. To identify a causal relation, we first compare elections where the incumbent mayor barely won or barely lost another term. We then use the introduction of a two-term limit, which granted one potential extra term to mayors appointed before the reform. The main result is that an increase in tenure is associated with “worse” procurement outcomes. Our estimates are informative of the possibility that time in office progressively leads to collusion between government officials and local bidders. (JEL D72, H57, H76)

scholarly journals Exploring new learning paradigms in ODL: A reflection on the paper of Barber, Donnelly and Rizvi (2013): “An avalanche is coming: Higher education and the revolution ahead”.

2015 ◽  
Vol 16 (4) ◽  
Author(s):  
Tom H Brown
Keyword(s):  
Higher Education ◽  
Learning Strategies ◽  
Teaching And Learning ◽  
Learning Opportunities ◽  
Learning Theories ◽  
Learning Approaches ◽  
Policy Makers ◽  
Teaching And Learning Strategies ◽  
The Impact ◽  
The Revolution

<p class="Paragraph1"><span lang="EN-US">The paper of Barber, Donnelly &amp; Rizvi (2013): “An avalanche is coming: Higher education and the revolution ahead”  addresses some significant issues in higher education and poses some challenging questions to ODL (Open and Distance Learning) administrators, policy makers and of course to ODL faculty in general.  Barber et al.’s paper does not specifically address the area of teaching and learning theories, strategies and methodologies per se.  In this paper I would therefore like to reflect on the impact that the contemporary changes and challenges that Barber et al. describes, have on teaching and learning approaches and paradigms.  In doing so I draw on earlier work about future learning paradigms and navigationism (Brown, 2006).  We need a fresh approach and new skills to survive the revolution ahead.  We need to rethink our teaching and learning strategies to be able to provide meaningful learning opportunities in the future that lies ahead.</span></p>

scholarly journals Opportunities and Questions: A Short Report on Rubric Assessments in Asia and the Middle East

2019 ◽  
Author(s):  
Ronnie Goodwin
Keyword(s):  
Middle East ◽  
Assessment Tool ◽  
Relevant Literature ◽  
Alternative Methods ◽  
Learning Theories ◽  
Short Report ◽  
Rating Systems ◽  
Self Assessment ◽  
Advantages And Disadvantages ◽  
The Impact

This qualitative short report considers the viability of the use of rubrics or alternative methods to assess writing in Asia and the Middle East. The background of learning theories, assessment types, and self-assessment literature provides a foundation for further discussion of the appropriate use of rubrics, including the prioritization of criterion, the quality of scoring, the impact of organizational features on scoring, the influence of bias, and the best application of rubric assessment. Relevant points for further study are identified, such as differentiation in research between generalized analytical rating systems and rubric assessment with specific, empirical criterion. The contradictory research regarding the advantages and disadvantages of rubric assessment in comparison with holistic assessment are of particular and crucial interest for global pedagogy. Many of the reviewed Western articles excluded Asian perspectives- except for China- and thus present a limited understanding of social and educational compatibility with new assessments and rubric assessments in particular. The discussion identifies patterns and points of contention and seeks to explore viewpoints rather than limit the scope of inquiry and consideration thus noting that relevant literature suggests that with appropriate teacher training, teachers may appropriately use rubrics as a formative assessment tool for writing in Asia and the Middle East.

scholarly journals Analysis on the Effects of Quality of Financial Statements, Over GDP Forecasting Models. An Empirical Cross-Country Approach

2020 ◽  
Vol 15 (3) ◽  
pp. 236-260
Author(s):  
Burca Valentin ◽  
Mates Dorel ◽  
Bogdan Oana
Keyword(s):  
Economic Growth ◽  
Financial Reporting ◽  
Structural Changes ◽  
Causal Relation ◽  
Forecast Error ◽  
Financial Statements ◽  
Institutional Framework ◽  
Gdp Growth ◽  
The Impact

Abstract Under increasing macroeconomic uncertainty, governments base their economic policies on high-precision GDP estimates. The models considered based on building-up government budgets incorporate main drivers of economic growth, identified along a large range of empirical studies, mostly focused on economic productivity, factor accumulation, human capital, innovation and transfer of technology, structural changes, or institutional framework. However, there is little evidence related to the impact of accounting and assurance regulation on economic growth. Our study attempts to assess the significance of causal relation between forecasting error on GDP growth and quality of accounting standards, respectively quality of financial statements. The study analyzes the causal relation between country level measures of quality of financial reporting, synthetized by Isidro et. al. (2019), and the measure of GDP growth estimate mean error. Our results confirm a significant impact of quality of the output of financial reporting practice, related to disclosure quality and asymmetric timeliness. The results remain similar, even after controlling for accounting convergence influence. Checking for robustness of the model, we observe the main drivers of one year ahead GDP forecast error are related to institutional framework to issue high quality standards and enforce them properly. The results emphasize once again the role of economic development and corresponding complexity of economic activities and political framework impact on accounting regulation and subsequently on macroeconomic measures.

Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World

2017 ◽  
Author(s):  
Patricia W. Cheng ◽  
Hongjing Lu
Keyword(s):  
Search Space ◽  
Causal Learning ◽  
Causal Mechanism ◽  
Intuitive Reasoning ◽  
Causal Relations ◽  
Causal Understanding ◽  
Domain Specific ◽  
Specific Description ◽  
The World

This chapter illustrates the representational nature of causal understanding of the world and examines its implications for causal learning. The vastness of the search space of causal relations, given the representational aspect of the problem, implies that powerful constraints are essential for arriving at adaptive causal relations. The chapter reviews (1) why causal invariance—the sameness of how a causal mechanism operates across contexts—is an essential constraint for causal learning in intuitive reasoning, (2) a psychological causal-learning theory that assumes causal invariance as a defeasible default, (3) some ways in which the computational role of causal invariance in causal learning can become obscured, and (4) the roles of causal invariance as a general aspiration, a default assumption, a criterion for hypothesis revision, and a domain-specific description. The chapter also reviews a puzzling discrepancy in the human and non-human causal and associative learning literatures and offers a potential explanation.

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