Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-23T01:49:32.207Z Has data issue: false hasContentIssue false
  • English
  • Français

The game of word skipping: Who are the competitors?

Published online by Cambridge University Press:  29 March 2004

Ralf Engbert  and
Reinhold Kliegl
Ralf Engbert*
Affiliation:
Department of Psychology and Center for Dynamics of Complex Systems, University of Potsdam, D-14415Potsdam, Germanywww.agnld.uni-potsdam.de/~ralf/
Reinhold Kliegl*
Affiliation:
Department of Psychology, University of Potsdam, D-14415Potsdam, Germanywww.psych.uni-potsdam.de/people/kliegl/index-e.html
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract:

Computational models such as E-Z Reader and SWIFT are ideal theoretical tools to test quantitatively our current understanding of eye-movement control in reading. Here we present a mathematical analysis of word skipping in the E-Z Reader model by semianalytic methods, to highlight the differences in current modeling approaches. In E-Z Reader, the word identification system must outperform the oculomotor system to induce word skipping. In SWIFT, there is competition among words to be selected as a saccade target. We conclude that it is the question of competitors in the “game” of word skipping that must be solved in eye movement research.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 26 , Issue 4 , August 2003 , pp. 481 - 482
Copyright
Copyright © Cambridge University Press 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Notes

1. The gamma distribution for saccade latencies can be written as , where τ is a time constant and n is the order of the distribution. Mean value and standard deviation are given by μ = (n + 1)τ and . For a relation of standard deviation to mean of one third (Reichle et al. 1998), we have to choose a gamma distribution of order n = 8.

2. This procedure may be interpreted as a mean field approximation, that is, using the average processing difficulty of the word left to the skipped word. To compute L1 and <L2> according to Equation 3 in the target article, we used word frequencies, predictabilities, and the parameters β1, β2, and Δ.