Response Selection Errors in Spatial Choice Reaction Tasks

1977 ◽  
Vol 29 (3) ◽  
pp. 415-423 ◽  
Author(s):  
John Duncan

In a “Consistent” spatial choice reaction task, the same spatial relationship obtains between each stimulus and its appropriate response. In an “Inconsistent” task this is not so. The present experiment concerns errors in Inconsistent tasks. Duncan (in press) has suggested that, when two spatial S-R relationships are involved in a task, the dominant type of error is a response bearing to the stimulus the wrong one of the two relationships. Duncan's results, however, may be described by a different generalization. Rabbitt and Vyas (1973) have suggested that confusions occur between responses which, when made correctly, bear similar spatial relationships to their stimuli. In the present experiment, a new Inconsistent task is studied. The results support the account of Duncan (in press) but provide no support for that of Rabbitt and Vyas (1973). Partly on the basis of error results, Duncan (in press) proposed a model of response selection in the spatial choice reaction task. Unlike previous accounts, this model is not based on a set of individual “S—R” associations; operations generating sets of S—R pairs are involved.

1978 ◽  
Vol 30 (3) ◽  
pp. 429-440 ◽  
Author(s):  
John Duncan

In a “consistent” spatial choice reaction task the same spatial relationship obtains between each stimulus and its correct response. In an “inconsistent” task this is not so. While Duncan (1977a) found both easy (spatially corresponding) and difficult (spatially opposite) responses to be slowed in inconsistent tasks, Smith (1977) found this only for the corresponding responses, the reverse holding for opposites. Reasons for this discrepancy are examined. The result of Smith (1977) depends on the use of different numbers of alternative responses in consistent and inconsistent tasks, a situation allowing no useful comparison between the two. Effects of consistency are related to others in the literature. The general conclusion is that, in these tasks, response selection is based not on a list of associations between individual stimuli and responses, but on operations or rules each of which will generate a set of stimulus–response pairs.


2011 ◽  
Vol 71 ◽  
pp. e242-e243
Author(s):  
Yoshifumi Tanaka ◽  
Kozo Funase ◽  
Hiroshi Sekiya ◽  
Joyo Sasaki ◽  
Yufu M. Tanaka

Author(s):  
Ju-Wei Chen ◽  
Suh-Yin Lee

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.


2000 ◽  
Vol 12 (5) ◽  
pp. 739-752 ◽  
Author(s):  
Brian A. Wandell ◽  
Suelika Chial ◽  
Benjamin T. Backus

Much of the human cortical surface is obscured from view by the complex pattern of folds, making the spatial relationship between different surface locations hard to interpret. Methods for viewing large portions of the brain's surface in a single flattened representation are described. The flattened representation preserves several key spatial relationships between regions on the cortical surface. The principles used in the implementations and evaluations of these implementations using artificial test surfaces are provided. Results of applying the methods to structural magnetic resonance measurements of the human brain are also shown. The implementation details are available in the source code, which is freely available on the Internet.


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