Modeling Individual Differences in Perceptual Decision Making (Record no. 72087)
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| fixed length control field | 07324naaaa2200325uu 4500 |
| 001 - CONTROL NUMBER | |
| control field | https://directory.doabooks.org/handle/20.500.12854/53694 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220220070059.0 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-2-88945-056-5 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9782889450565 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.3389/978-2-88945-056-5 |
| Terms of availability | doi |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | dc |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | James T. Townsend |
| Relationship | auth |
| 245 10 - TITLE STATEMENT | |
| Title | Modeling Individual Differences in Perceptual Decision Making |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Frontiers Media SA |
| Date of publication, distribution, etc. | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 electronic resource (140 p.) |
| 506 0# - RESTRICTIONS ON ACCESS NOTE | |
| Terms governing access | Open Access |
| Source of term | star |
| Standardized terminology for access restriction | Unrestricted online access |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain. To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain. |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE | |
| Terms governing use and reproduction | Creative Commons |
| Use and reproduction rights | https://creativecommons.org/licenses/by/4.0/ |
| Source of term | cc |
| -- | https://creativecommons.org/licenses/by/4.0/ |
| 546 ## - LANGUAGE NOTE | |
| Language note | English |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | perceptual decision making |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | processing capacity |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Response Time |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Cognitive Modeling |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | individual differences |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Cheng-Ta Yang |
| Relationship | auth |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Joseph W. Houpt |
| Relationship | auth |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Host name | www.oapen.org |
| Uniform Resource Identifier | <a href="http://journal.frontiersin.org/researchtopic/1847/modeling-individual-differences-in-perceptual-decision-making">http://journal.frontiersin.org/researchtopic/1847/modeling-individual-differences-in-perceptual-decision-making</a> |
| Access status | 0 |
| Public note | DOAB: download the publication |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Host name | www.oapen.org |
| Uniform Resource Identifier | <a href="https://directory.doabooks.org/handle/20.500.12854/53694">https://directory.doabooks.org/handle/20.500.12854/53694</a> |
| Access status | 0 |
| Public note | DOAB: description of the publication |
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