Correction to: Detecting distortions of peripherally presented letter stimuli under crowded conditions
Correction to: Atten Percept Psychophys
We corrected this bug and generated new stimuli (Fig. 1B). To assess to what extent this bug might have affected our results, author T.W. and a new naïve observer repeated the experiments from the original article.
Sensitivity to distortions applied to letters is subject to crowding, and sensitivity to RF distortions follows a lawful relationship with distortion frequency. In Experiment 2 we found little evidence for an effect of distortion pop-out. In effect, this stimulus bug means we have demonstrated crowding for distortion sensitivity using three types of distortions rather than only two.
Thus, the conclusions of our study remain, despite this stimulus software bug. The data, code, and stimuli pertaining to this correction can be downloaded from https://doi.org/10.5281/zenodo.3236251.
Author T.W. collected new psychophysical data on the same task as in Experiment 1 from the original article, with both the original and the bug-fixed stimuli interleaved. We fit T.W.’s data (from the original and bug-fixed images and from the data collected in the original experiment) with Weibull psychometric functions (as parameterized by Schütt, Harmeling, Macke, & Wichmann, 2016) using a Bayesian framework in the R statistical language (Bürkner, 2017, 2018; Carpenter et al., 2017; R Core Team, 2019).
The first experiment demonstrated the core result of our article: Sensitivity to two distortion types was reduced in the presence of flanking letters (crowding). In a second series of experiments, we examined the extent to which this result depended on target–flanker similarity and task, in order to probe the possibility of “distortion pop-out.” Briefly, the results in the original article provided little evidence for symmetrical distortion pop-out. In Experiment 2a, the thresholds were higher as more flanker letters were distorted. In Experiment 2b, the thresholds for detecting an undistorted letter were more similar to those for detecting a distorted target among four distorted flankers than for a distorted target among zero distorted flankers. In Experiment 2c, the thresholds for detecting a distorted letter among four highly distorted flankers were also higher than those for detecting distortions among zero distorted flankers.
Observer W.R. participated in the second experiment from the article for the RF distortion type. As above, the original and bug-fixed images were interleaved. Note that while replicating this experiment, we also noticed a minor typographical error in the methods of the original Experiment 2. On page 856 of the original article, the amplitudes of the RF distortions were reported as “0.05, 0.125, 0.2, 0.275, 0.25, 0.425, and 0.5,” but the value of 0.25 was actually 0.35 (consistent with an ascending series of amplitudes).
For the original images, W.R.’s results unequivocally replicate the original article. The thresholds increase as the number of distorted flankers increases (Exp. 2a), and the thresholds for Experiments 2b and 2c are similar to (or even higher than) the threshold in Experiment 2a with four distorted flankers.
For the images using the corrected RF distortion function, W.R.’s results are also within the scope of the data from the original article. The thresholds increase with the number of distorted flankers (replicating Exp. 2a). The thresholds for Experiment 2b and 2c are somewhat lower than those for Experiment 2a in the four-distorted-flanker condition, approaching the thresholds in the two-distorted-flanker condition. This pattern of results is similar to those of observers S.T. and T.W. in the original article (A.M. is difficult to judge, due to uncertainty on the threshold measurement for two distorted flankers). The thresholds remain higher than those in the zero-distorted-flanker condition, indicating little evidence for distortion pop-out (as in the original article).
We thank Christina Funke for noticing the problem when applying the function to new stimuli.
- Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., . . . Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1). https://doi.org/10.18637/jss.v076.i01
- R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from www.R-project.org