Human reasoning includes a mental logic

2009 ◽  
Vol 32 (1) ◽  
pp. 96-97 ◽  
Author(s):  
David P. O'Brien
Keyword(s):  
Probability Theory ◽  
Human Reasoning ◽  
Completeness Problem ◽  
Mental Logic

AbstractOaksford & Chater (O&C) have rejected logic in favor of probability theory for reasons that are irrelevant to mental-logic theory, because mental-logic theory differs from standard logic in significant ways. Similar to O&C, mental-logic theory rejects the use of the material conditional and deals with the completeness problem by limiting the scope of its procedures to local sets of propositions.

scholarly journals The uncertain reasoner: Bayes, logic, and rationality

2009 ◽  
Vol 32 (1) ◽  
pp. 105-120 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Probability Theory ◽  
Cognitive Science ◽  
Human Cognition ◽  
Uncertain Reasoning ◽  
Human Reasoning ◽  
Reasoning Abilities ◽  
Rational Explanation ◽  
Everyday Reasoning ◽  
The Right ◽  
Bayesian Rationality

AbstractHuman cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. InBayesian Rationalitywe argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the “rational” explanation of cognition; and re-evaluate the empirical case for Bayesian rationality.

Use of Finite Markov Chains in Business Problems Involving Decision Making and Case-Based Reasoning

2021 ◽  
pp. 321-338
Author(s):  
Michael Voskoglou
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Probability Theory ◽  
International Business ◽  
Case Based Reasoning ◽  
Human Reasoning ◽  
Global Level ◽  
Intelligent Machines ◽  
Business Applications ◽  
Case Based

Artificial intelligence (AI) is the branch of computer science focusing on the creation of intelligent machines that mimic human reasoning and behaviour. Probability theory is among the mathematical tools used in AI applications to deal with situations of uncertainty caused by randomness. In particular, the Markov chain (MC) theory is a smart combination of probability and linear algebra that offers ideal conditions for modelling such situations. International business is about the trade of goods, services, technology, capital, and knowledge at a global level, while decision making (DM) and case-based reasoning (CBR) are among the processes that are frequently used in this field. In this chapter, an absorbing and an ergodic MC model are developed on the steps of DM and CBR respectively for representing mathematically those two processes, thus providing valuable information about their evolution. The examples presented are connected to international business applications.

scholarly journals The Problems of the Mental Logic with the Double Negation: The Necessity of a Semantic Approach

2016 ◽  
Vol 46 (1) ◽  
pp. 143-153
Author(s):  
Miguel López-Astorga
Keyword(s):  
Mental Models ◽  
Human Mind ◽  
Human Reasoning ◽  
Double Negation ◽  
Semantic Approach ◽  
Cognitive Theories ◽  
Ancient Times ◽  
Syntactic Theories ◽  
Mental Logic ◽  
Mental Models Theory

Abstract The double negation has always been considered by the logical systems from ancient times to the present. In fact, that is an issue that the current syntactic theories studying human reasoning, for example, the mental logic theory, address today. However, in this paper, I claim that, in the case of some languages such as Spanish, the double negation causes problems for the cognitive theories mainly based on formal schemata and supporting the idea of a universal syntax of thought in the human mind. Thus, I propose that, given those problems, semantic frameworks such as that of the mental models theory seem to be more appropriate for explaining the human inferential activity.

On is an ought: Levels of analysis and the descriptive versus normative analysis of human reasoning

2009 ◽  
Vol 32 (1) ◽  
pp. 101-102 ◽  
Author(s):  
Walter Schroyens
Keyword(s):  
Probability Theory ◽  
Levels Of Analysis ◽  
Bayesian Probability ◽  
Human Reasoning ◽  
Rational Analysis ◽  
Psychological Theories ◽  
Deductive Logic ◽  
Adaptive Rationality ◽  
Normative Status ◽  
Computational Level

AbstractAlgorithmic-level specifications carry part of the explanatory burden in most psychological theories. It is, thus, inappropriate to limit a comparison and evaluation of theories to the computational level. A rational analysis considers people's goal-directed and environmentally adaptive rationality; it is not normative. Adaptive rationality is by definition non-absolute; hence, neither deductive logic nor Bayesian probability theory has absolute normative status.

A little too technical: The threat of intellectualising technical reasoning

2020 ◽  
Vol 43 ◽  
Author(s):  
Ian Robertson
Keyword(s):  
Social Cognition ◽  
Human Reasoning ◽  
Technological Culture ◽  
Technical Reasoning

Abstract Osiurak and Reynaud (O&R) claim that research into the origin of cumulative technological culture has been too focused on social cognition and has consequently neglected the importance of uniquely human reasoning capacities. This commentary raises two interrelated theoretical concerns about O&R's notion of technical-reasoning capacities, and suggests how these concerns might be met.

Mental Logic Versus Mental Models

1992 ◽  
Vol 37 (5) ◽  
pp. 438-439
Author(s):  
Richard A. Griggs
Keyword(s):  
Mental Models ◽  
Mental Logic

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

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