# An Heuristic Framework for Non-Conscious Reasoning

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## Abstract

**:**

## Featured Application

**This analysis of non-conscious reasoning can be applied in educational diagnostics and intervention, medical diagnostics and treatments, organizational and political decision making and design of artificial intelligence knowledge based systems, neuro-computers and similar devices for aiding people in the problem-solving process.**

## Abstract

## 1. Introduction

## 2. Precursors

## 3. Methods

#### 3.1. Psychological Heuristics

#### 3.2. Dual-Process and Dual-System Theories of Reasoning

#### 3.3. Fuzzy Sets and Approximate Reasoning

_{1}(c), A

_{2}(c), ... A

_{n}(c)} of attributes. The A’s span a semantic space with n dimensions. Every attribute has a value that can be quantitative, logical, or linguistic. The pair (attribute, value) express a concept property. One must define for each attribute A

_{i}valid ranges X

_{i}for its values. Therefore, each attribute A

_{i}is a function with domain C (the conceptual universe) and range X

_{i}. The particular instances of the concept c are then represented in the semantic space by points (if they have quantitative values) or regions (if they have logical or linguistic fuzzy values).

## 4. Results

#### 4.1. A Non-Conscious Reasoning Heuristic to Operate Preferably with Fuzzy, Non Measurable Object Properties

#### 4.2. A Non-Conscious Reasoning Heuristic to Operate Preferably with Linguistic, Non Numerical Values for Evaluating Object Properties

#### 4.3. A Non-Conscious Reasoning Heuristic Minimizing Problem Complexity by Reducing the Number of Necessary Significant Properties of the Concepts Involved

_{i}, c, a) of an attribute (property) A

_{i}of a concept c in certain context a as the degree of relevance of the attribute (property) A

_{i}of concept c in the given application a.

_{i}, c, a) of a property A

_{i}as a number in (0, 1) it can be interpreted as the degree of membership μ [A

_{i}, M (c, a)] of the property A

_{i}to the fuzzy set meaning M (c, a).

_{i}, c, a) = μ [(A

_{i})], M (c, a)

_{i}of the properties so that ∑s

_{i}= 1 we get the relative significance of the properties. These can be interpreted as the relative contribution of the attributes to the meaning. [14] developed a linear algebra method to calculate the relative significance of the attributes (properties) of a given concept in certain context, by pairwise comparison of attribute significances.

_{i}& A

_{j}, c, a) = f [s (A

_{i}, c, a), s (A

_{j}, c, a), s (A

_{i}, c, a) | s (A

_{j}, c, a)]

#### 4.4. A Non-Conscious Reasoning Heuristic to Assign Provisional Default Fuzzy Values to Uncertain Object Attributes

- (i)
- To consider our previous experience with similar cases. This is called the expert experience approach.
- (ii)
- To minimize the maximal possible lost, in case of assigning the wrong value. This is the minimax or pessimistic approach.
- (iii)
- To maximize the minimal possible gain in case of assigning the right value. This is the maximin or optimistic approach.

#### 4.5. A Non-Conscious Reasoning Heuristic to Simplify Problem Complexity by Adopting a Hierarchical Philosophical Framework

#### 4.6. A Non-Conscious Reasoning Heuristic to Extend the Truth of True Propositions to Fuzzier Predicates

#### 4.7. A Non-Conscious Reasoning Heuristic to Increase the Belief Value of a Verisimilar Proposition by Changing Its Context

## 5. Discussion

## 6. Conclusions

## Acknowledgments

## Conflicts of Interest

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Lara-Rosano, F.
An Heuristic Framework for Non-Conscious Reasoning. *Appl. Sci.* **2017**, *7*, 1161.
https://doi.org/10.3390/app7111161

**AMA Style**

Lara-Rosano F.
An Heuristic Framework for Non-Conscious Reasoning. *Applied Sciences*. 2017; 7(11):1161.
https://doi.org/10.3390/app7111161

**Chicago/Turabian Style**

Lara-Rosano, Felipe.
2017. "An Heuristic Framework for Non-Conscious Reasoning" *Applied Sciences* 7, no. 11: 1161.
https://doi.org/10.3390/app7111161