From bias to sound intuiting: Boosting correct intuitive reasoning

Cognition ◽  
2021 ◽  
Vol 211 ◽  
pp. 104645
Author(s):  
Esther Boissin ◽  
Serge Caparos ◽  
Matthieu Raoelison ◽  
Wim De Neys
Keyword(s):  
Intuitive Reasoning

scholarly journals On The Contribution of Interest Expense (Income) on Total Output

2021 ◽  
Vol 4 (1) ◽  
pp. 31-56
Author(s):  
Ahmed Mehedi Nizam
Keyword(s):  
Interest Rate ◽  
Aggregate Demand ◽  
Total Output ◽  
Response Analysis ◽  
Panel Var ◽  
Intuitive Reasoning ◽  
Body Of Knowledge ◽  
Interest Income ◽  
Structural Panel ◽  
The Impact

Abstract A decrease in interest rate in traditional view of monetary policy transmission is linked to a lower cost of borrowing which eventually results into a greater spending in investment and a bigger GDP. However, a decrease in interest rate is also linked to a decrease in interest income which, in turn, affects the aggregate demand and total GDP. So far, no concerted effort has been made to investigate this positive inter-relation between interest income and GDP in the existing literature. Here in the first place we intuitively describe the inter-relation between interest income and output and then provide a micro-foundation of our intuitive reasoning in the context of a small endowment economy with finitely-lived identical households. Then we try to uncover the impact of nominal interest income on the macroeconomy using multiplier theory for a panel of some 04 (four) OECD countries. We define and calculate the corresponding multiplier values algebraically and then we empirically measure them using impulse response analysis under structural panel VAR framework. Large, consistent and positive values of the cumulative multipliers indicate a stable positive relationship between nominal interest income and output. Moreover, variance decomposition of GDP shows that a significant portion of the variance in GDP is attributed to interest income under VAR/VECM framework. Finally, we have shown how and where our analysis fits into the existing body of knowledge.

scholarly journals Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles

2011 ◽  
Vol 8 (61) ◽  
pp. 1128-1141 ◽  
Author(s):  
P. K. Vinod ◽  
Paula Freire ◽  
Ahmed Rattani ◽  
Andrea Ciliberto ◽  
Frank Uhlmann ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Budding Yeast ◽  
Cell Cycle Control ◽  
Computational Modelling ◽  
Eukaryotic Cell ◽  
Mitotic Exit ◽  
Intuitive Reasoning ◽  
Systematic Analysis ◽  
Systems View

The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20–APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

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.

12. Inverse probability: you can’t be indifferent about it!

2017 ◽  
pp. 84-91
Author(s):  
Graham Priest
Keyword(s):  
Intuitive Reasoning ◽  
Relevant Difference ◽  
Inverse Probability ◽  
Principle Of Indifference ◽  
The Relationship ◽  
The Way

People often confuse probabilities with their inverses. Many inductive arguments require us to reason about inverse probabilities. ‘Inverse probability: you can’t be indifferent about it!’ looks at the relationship between inverse probabilities, illustrating it with the Argument to Design, which asks: does not the fact that the physical cosmos is ordered in the way that it is give us reason to believe in the existence of a god of a certain kind? Logicians use the term Principle of Indifference to describe an important part of intuitive reasoning about probability: given a number of possibilities, with no relevant difference between them, they all have the same probability.

The Degree of Home Bias in the Holding of Share Portfolio

2017 ◽  
Vol 6 (3) ◽  
pp. 10-25 ◽  
Author(s):  
Mounira Chniguir ◽  
Asma Sghaier ◽  
Mohamed Soufeljil ◽  
Zouhayer Mighri
Keyword(s):  
Home Bias ◽  
Developed Countries ◽  
Capital Asset Pricing ◽  
Intuitive Reasoning ◽  
Important Conclusion ◽  
Developed Markets ◽  
Determining Factors ◽  
The Developed Countries ◽  
High Level

The objective of this paper is to measure the degree of Home Bias within the holdings of portfolio and to identify their determining factors. By following an intuitive reasoning, the authors have chosen a number of susceptible factors that have an impact on Home Bias. In fact, they have developed an international CAPM (Capital Asset Pricing Model). This model is estimated for 20 countries, with the use of cross-section econometrics. The authors' results show that all countries have recorded a high level of Home bias in their holdings of portfolio. In order to study whether the Home Bias of the newly emerging markets and that of the developed markets react differently to the determining factors or not the authors have evaluated the model so much jointly for all markets as separately for the developed and the newly emerging ones. In the case of classification of the sample, the results have permitted us to draw an important conclusion and to have cognizance that the volatility of the exchange rate is statistically significant concerning the newly emerging economies at a threshold of 1%, while it is hardly remarkable for the developed countries. This means that this variable prevents the American investors from investing in the former countries. Samely, for both variables of joint- variance and size.

Assessing analytic and intuitive reasoning using the cognitive reflection test in young patients with schizophrenia

2020 ◽  
Vol 284 ◽  
pp. 112683 ◽  
Author(s):  
Pugaliya Puveendrakumaran ◽  
Gagan Fervaha ◽  
Fernando Caravaggio ◽  
Gary Remington
Keyword(s):  
Young Patients ◽  
Intuitive Reasoning ◽  
Cognitive Reflection Test ◽  
Cognitive Reflection

scholarly journals Intuitive reasoning about abstract and familiar physics problems

1986 ◽  
Vol 14 (4) ◽  
pp. 308-312 ◽  
Author(s):  
Mary Kister Kaiser ◽  
John Jonides ◽  
Joanne Alexander
Keyword(s):  
Intuitive Reasoning ◽  
Physics Problems

Interaction between a pair of two-dimensional sails for the case of smoothly attached flow

1969 ◽  
Vol 310 (1502) ◽  
pp. 373-391 ◽  
Keyword(s):  
Load Distribution ◽  
Leading Edge ◽  
Successive Approximations ◽  
Matrix Equations ◽  
Iterative Technique ◽  
Two Dimensional ◽  
Intuitive Reasoning ◽  
Inviscid Incompressible Fluid ◽  
Attached Flow ◽  
Flexible Airfoils

Linearized thin-wing theory is applied to the problem of the flow of an inviscid, incompressible fluid past a pair of two-dimensional sails (flexible airfoils of zero thickness) which interact with one another. Attention is confined to the case where the flow is smoothly attached at the leading edge of each of the two sails. The results in general confirm expectations regarding sail behaviour obtained by intuitive reasoning, and should be of value to the theoretically minded sailor The analysis is complicated by the fact that the shapes of the sails depend on the load distribution and vice versa; it leads to a pair of coupled integro-differential equations, which cannot be solved by conventional techniques. Each sail is represented by its properties at a series of N points along its chord, thereby converting the two ‘critical equations’ to matrix form. The result is an eigenvalue problem involving 2 N equations, 2 N unknowns, and two eigenvalues. This is solved by the use of an iterative technique, successive approximations being obtained alternately from each of the two matrix equations.

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.

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