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E-grāmata: Mathematical Models of Perception and Cognition Volume I: A Festschrift for James T. Townsend [Taylor & Francis e-book]

Edited by , Edited by (Wright State University, USA)
  • Formāts: 284 pages, 2 Tables, black and white; 28 Line drawings, black and white; 1 Halftones, black and white
  • Sērija : Scientific Psychology Series
  • Izdošanas datums: 02-Jun-2016
  • Izdevniecība: Psychology Press Ltd
  • ISBN-13: 9781315647272
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  • Taylor & Francis e-book
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Mathematical Models of Perception and Cognition Volume I: A Festschrift for James T. TownsendPalielināt
  • Formāts: 284 pages, 2 Tables, black and white; 28 Line drawings, black and white; 1 Halftones, black and white
  • Sērija : Scientific Psychology Series
  • Izdošanas datums: 02-Jun-2016
  • Izdevniecība: Psychology Press Ltd
  • ISBN-13: 9781315647272
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9781315647272

In this two volume festschrift, contributors explore the theoretical developments (Volume I) and applications (Volume II) in traditional cognitive psychology domains, and model other areas of human performance that benefit from rigorous mathematical approaches. It brings together former classmates, students and colleagues of Dr. James T. Townsend, a pioneering researcher in the field since the early 1960s, to provide a current overview of mathematical modeling in psychology. Townsend’s research critically emphasized a need for rigor in the practice of cognitive modeling, and for providing mathematical definition and structure to ill-defined psychological topics. The research captured demonstrates how the interplay of theory and application, bridged by rigorous mathematics, can move cognitive modeling forward.

Figures and Tables
xi
1 Introduction
1(12)
Leslie M. Blaha
Joseph W. Houpt
1.1 A Brief Biography of James Townsend
3(10)
2 High-Probability Logic and Inheritance
13(24)
Donald Bamber
L. R. Goodman
Hung T. Nguyen
2.1 Reasoning With Imperfect Rules
13(5)
2.2 High-Probability Logics
18(8)
2.3 Weak Versus Strong Association
26(3)
2.4 Extending High-Probability Logics
29(3)
2.5 Relevance of High-Probability Logics to the Study of Human Reasoning
32(1)
2.6 Summary and Conclusion
33(4)
3 Stochastic Orders of Variability
37(10)
Hans Colonius
3.1 Introduction: Some Variability Orders
37(3)
3.2 The Quantile Spread
40(2)
3.3 The Quantile Spread Order
42(1)
3.4 The Quantile Spread Order for the Kumaraswamy Distribution
43(4)
4 Subset System: Mathematical Abstraction of Object and Context
47(18)
Jun Zhang
Yitong Sun
4.1 Introduction
47(3)
4.2 Mathematical Preliminaries
50(3)
4.3 Pre-order and Tolerance on V Induced from (V, E)
53(10)
4.4 Discussion
63(2)
5 Uniqueness of a Multinomial Processing Tree Constructed by Knowing Which Pairs of Processes Are Ordered
65(12)
Richard Schweickert
Hye Joo Han
5.1 Combining Information About Different Pairs of Processes
68(2)
5.2 When Is a Tree Possible?
70(3)
5.3 When Is Only One Tree Possible?
73(1)
5.4 Conclusion
74(3)
6 Simple Factorial Tweezers for Detecting Delicate Serial and Parallel Processes
77(30)
Mario Fine
6.1 The Theoretical Breakthrough
79(1)
6.2 Pure Stretching Method
80(1)
6.3 Single Factor Manipulation: Stretching One Process
80(1)
6.4 Double Factorial Manipulation: Stretching Two Processes
81(1)
6.5 SFT Statistical Tests for Two-Process Mental Networks (N = 2): MIC and SIC
82(5)
6.6 SFT Statistical Tests for 2-Process Mental Networks (N - 2): The Principle Limitations
87(1)
6.7 Factorial SIC for Homogeneous Systems: Advances to Higher Factorials
87(1)
6.8 Statistical Tests, the SIC General Form
88(1)
6.9 Limitations
89(1)
6.10 Simple Factorial SIC Functions for Homogenous Systems
90(2)
6.11 Limitations
92(2)
6.12 N-Factorial SIC for Non-homogenous Networks
94(1)
6.13 Statistical Tests and Subnetwork Decomposability
95(1)
6.14 Findings
96(1)
6.15 Limitations
97(1)
6.16 Putting It All Together: Homogeneous and Non-homogeneous Subnetworks N = 2
97(3)
6.17 Discussion
100(7)
7 Identifying Spatiotemporal Information
107(45)
Joseph S. Lappin
7.1 Introduction: From Stimulation to Information
108(9)
7.2 Visual Representations of Spatiotemporal Variation
117(13)
7.3 Empirical Criteria: Resolution and Invariance
130(14)
7.4 Conclusion
144(8)
8 Models of Intertemporal Choice
152(19)
Junyi Dai
Jerome R. Busemeyer
8.1 Probabilistic Models of Intertemporal Choice
154(9)
8.2 Results of Model Fitting and Comparisons
163(2)
8.3 Mental Architecture and Stopping Rules of Intertemporal Choice
165(1)
8.4 Equivalence Between Intertemporal Choice Models
166(2)
8.5 Concluding Comments
168(3)
9 Variations on the Theme of Independence: Tasks and Effects of Stroop, Garner, and Townsend
171(26)
Daniel Algom
9.1 Selective Attention and Perceptual Independence: A Bit of History
172(3)
9.2 General Recognition Theory and the Selectivity of Attention
175(6)
9.3 The Stroop Effect and Perceptual Separability
181(5)
9.4 Garnerian Separable Dimensions and GRT Perceptual Separability
186(5)
9.5 Conclusion
191(1)
9.6 Epilogue: The Marriage of Selectivity and Independence Gets Personal
192(5)
10 Modeling Interactive Dimensions in a Component Comparison Task Using General Recognition Theory
197(26)
Robin D. Thomas
Noah H. Silbert
Emily Grossman
Shawn Ell
10.1 Introduction
197(1)
10.2 GRT and the Same-Different Task
198(8)
10.3 Perceptual Interactions and Component Comparisons
206(13)
10.4 Conclusions
219(4)
11 Symmetry Provides a Turing-Type Test for 3D Vision
223(22)
Zygmunt Pizlo
11.1 Introduction
223(1)
11.2 How Physicists Explain Natural Phenomena
224(2)
11.3 Importing the Least-Action Principle Into Perception
226(2)
11.4 Bringing Symmetry into Theories of Perception
228(2)
11.5 Veridicality of 3D Shape Perception Seen as a Conservation Law
230(4)
11.6 Empirical Tests Verifying that Capek Sees as We Do
234(2)
11.7 Generality and Implications of Our Test
236(9)
12 Cognitive Psychometrics
245(22)
William H. Batchelder
22.2 Introduction
245(2)
12.2 Mathematics and Statistics in Psychology
247(1)
12.3 Behavioral Learning Theory and Cognitive Modeling
248(4)
12.4 Comparing Cognitive Modeling and Psychometric Test Theory
252(5)
12.5 Examples of Cognitive Psychometric Models
257(5)
12.6 Conclusion
262(5)
Index 267
Joseph W. Houpt is Assistant Professor of Psychology, Wright State University, USA.



Leslie M. Blaha is Engineering Research Psychologist, United States Air Force Research Laboratory, USA.
Permanent link: https://www.kriso.lv/db/9781315647272_pe.html
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