東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Discovering optical control strategi...

Weber, Romann M.

FindBook

Google Book

Amazon

博客來

Discovering optical control strategies: A data-mining approach.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Discovering optical control strategies: A data-mining approach./
作者:	Weber, Romann M.
面頁冊數:	148 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
Contained By:	Dissertation Abstracts International74-11B(E).
標題:	Psychology, Cognitive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3568304
ISBN:	9781303225925

Discovering optical control strategies: A data-mining approach.
Weber, Romann M.

Discovering optical control strategies: A data-mining approach. - 148 p.

Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2013.

A major focus of research into visually guided action (VGA) is the identification of control strategies that map optical information to actions. The traditional approach to this research has been to test the behavioral predictions of a few hypothesized strategies against subject performance in environments in which various manipulations to available information are made. While important and compelling results have been achieved with this standard approach, they are potentially limited by small sets of hypotheses and the methods used to test them. In this dissertation, I introduce a novel application of data-mining and machine-learning techniques in a comparatively "assumption-lite" analysis of experimental data that is able to both describe and model human behavior. This method identifies the signature of optical control in the information-action patterns that are embedded in subject data. I also introduce a modeling philosophy that conceptualizes continuous control as an effort to realize preferred trajectories through an optical state space. In applying this philosophy, I show how simple models of the evolution of these trajectories can be mined from subject data. I demonstrate the effectiveness of this approach in the analysis and modeling of data from a collision-avoidance task and in the development of a new model of visually guided, speed-controlled steering.

ISBN: 9781303225925Subjects--Topical Terms:

1017810
Psychology, Cognitive.

Discovering optical control strategies: A data-mining approach.
LDR:02276nam a2200289 4500 001 1967752
005 20141124124231.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303225925
035 $a (MiAaPQ)AAI3568304
035 $a AAI3568304
040 $a MiAaPQ $c MiAaPQ
100 1 $a Weber, Romann M. $3 2104819
245 1 0 $a Discovering optical control strategies: A data-mining approach.
300 $a 148 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
500 $a Adviser: Brett R. Fajen.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2013.
520 $a A major focus of research into visually guided action (VGA) is the identification of control strategies that map optical information to actions. The traditional approach to this research has been to test the behavioral predictions of a few hypothesized strategies against subject performance in environments in which various manipulations to available information are made. While important and compelling results have been achieved with this standard approach, they are potentially limited by small sets of hypotheses and the methods used to test them. In this dissertation, I introduce a novel application of data-mining and machine-learning techniques in a comparatively "assumption-lite" analysis of experimental data that is able to both describe and model human behavior. This method identifies the signature of optical control in the information-action patterns that are embedded in subject data. I also introduce a modeling philosophy that conceptualizes continuous control as an effort to realize preferred trajectories through an optical state space. In applying this philosophy, I show how simple models of the evolution of these trajectories can be mined from subject data. I demonstrate the effectiveness of this approach in the analysis and modeling of data from a collision-avoidance task and in the development of a new model of visually guided, speed-controlled steering.
590 $a School code: 0185.
650 4 $a Psychology, Cognitive. $3 1017810
650 4 $a Psychology, Experimental. $3 517106
650 4 $a Statistics. $3 517247
690 $a 0633
690 $a 0623
690 $a 0463
710 2 $a Rensselaer Polytechnic Institute. $b Cognitive Science. $3 2104820
773 0 $t Dissertation Abstracts International $g 74-11B(E).
790 $a 0185
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3568304