fix typo on paper

paper/paper.md

@@ -23,22 +23,22 @@ Visual perception combines top-down processes arising from participants individu
 
 # Statement of need
 
-One powerful way to control low-level properties of visual stimuli is to use the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB [@Willenbockel2010]. This toolbox builds on MATLAB image processing toolbox and contains a set of functions that allows the parametric specification of luminance and contrast, histogram, and Fourier amplitude spectra of static images. By doing so, it minimizes potential low-level confounds when investigating higher-level processes (e.g., cognitive effort, recognition). However, SHINE only works with greyscale images. Whereas this serves well to many research purposes [e.g., @Lawson2017; @Rodger2015], other research require colorful stimuli [e.g., @Cheng2019; @Hepach2016; @Zhang2019]. Here, we describe the SHINE_color, an adaptation of SHINE that allow users to perform all operations from SHINE toolbox on both static and dynamic (video) colorful images. Such adaptation can be useful for an array of research topics that require the precise low-level properties of colorful visual stimuli, such as memory [@Madore2020], cognitive effort [@Hepach2016; @Zhang2019; @Tsukahara2020], and social evaluation [@LEUNG2023]. 
+One powerful way to control low-level properties of visual stimuli is to use the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB [@Willenbockel2010]. This toolbox builds on MATLAB image processing toolbox and contains a set of functions that allows the parametric specification of luminance and contrast, histogram, and Fourier amplitude spectra of static images. By doing so, it minimizes potential low-level confounds when investigating higher-level processes (e.g., cognitive effort, recognition). However, SHINE only works with greyscale images. Whereas this serves well to many research purposes [e.g., @Lawson2017; @Rodger2015], other research requires colorful stimuli [e.g., @Cheng2019; @Hepach2016; @Zhang2019]. Here, we describe the SHINE_color, an adaptation of SHINE that allow users to perform all operations from SHINE toolbox on both static and dynamic (video) colorful images. Such adaptation can be useful for an array of research topics that require the precise low-level properties of colorful visual stimuli, such as memory [@Madore2020], cognitive effort [@Hepach2016; @Zhang2019; @Tsukahara2020], and social evaluation [@LEUNG2023]. 
 
 # Implementation
 
-The SHINE_color toolbox works in an intuitive way (\autoref{fig:Figure 1}; complete flowchart available at [OSF](https://osf.io/uxqtv/)). The toolbox can be called directly on the command line or on MATLAB's command window. On one hand, calling the toolbox from the command line requires an advanced understanding of MATLAB logic, with the advantage of allowing users to integrate SHINE_color on analytical pipelines. On the other hand, calling the toolbox from MATALAB's command window is a user-friendly approach that allow even first-time MATLAB users to take full advantage of SHINE_color power. When calling from the command window, a wizard guides the user through a series of questions that specify the input files characteristics (either a set of images or a video), the color space to be used (i.e., HSV, CIELab, RGB), and the SHINE operations to be performed. From the user input, the toolbox perform precise image manipulations and returns manipulated images, summary statistics, diagnostic visualizations, and a log of users' commands.
+The SHINE_color toolbox works in an intuitive way (\autoref{fig:Figure 1}; complete flowchart available at [OSF](https://osf.io/uxqtv/)). The toolbox can be called directly on the command line or on MATLAB's command window. On one hand, calling the toolbox from the command line requires an advanced understanding of MATLAB logic, with the advantage of allowing users to integrate SHINE_color on analytical pipelines. On the other hand, calling the toolbox from MATALAB's command window is a user-friendly approach that allow even first-time MATLAB users to take full advantage of SHINE_color power. When calling from the command window, a wizard guides the user through a series of questions that specify the input files characteristics (either a set of images or a video), the color space to be used (i.e., HSV, CIELab, RGB), and the SHINE operations to be performed. From the user input, the toolbox performs precise image manipulations and returns manipulated images, summary statistics, diagnostic visualizations, and a log of users' commands (\autoref{fig:Figure 1}).
 
 ![SHINE_color condensed flowchart. Functions (rounded rectangle) and decisions (diamonds) with dashed borders are introduced by SHINE_color (e.g., `video2frames`, `lum2scale`, `scale2lum`, `lum_calc`, `diag_plot`, `frames2mpeg`). They allow SHINE operations to be performed on colorful images.\label{fig:Figure 1}](fig1.png)
 
-All operations from SHINE are available on SHINE_color. The toolbox can precisely scale images' luminance and contrast, specify exact histograms, and control images' Fourier amplitude spectra, all optimized for perceptual visual quality  \autoref{fig:Figure 1}). We strongly recommend referring to Willenbockel and colleagues (2010) and to the [SHINE user manual](http://www.mapageweb.umontreal.ca/gosselif/shine/SHINEmanual.pdf) for a detailed description of each operation. 
+All operations from SHINE are available on SHINE_color. The toolbox can precisely scale images' luminance and contrast, specify exact histograms, and control images' Fourier amplitude spectra, all optimized for perceptual visual quality. We strongly recommend referring to Willenbockel and colleagues (2010) and to the [SHINE user manual](http://www.mapageweb.umontreal.ca/gosselif/shine/SHINEmanual.pdf) for a detailed description of each operation. 
 
 Critically, operations can be applied directly to RGB channels or by transforming RGB to HSV or CIELab color spaces. Performing operations directly on RGB channels is preferable when equating the Fourier amplitude. Using HSV or CIELab color spaces are preferable for matching luminance and histograms without changing images' Hue or Saturation [@Willenbockel2010]. If a video is provided, its frames are first extracted, then either RGB channels are split or RGB images are transformed to either HSV or CIELab, as per user preference. When choosing to work with RGB channels directly, operations are applied to each channel separately, one at a time (red, green, or blue) which are then combined to create the modified RGB images (see @Ruedeerat2018 for a similar approach using SHINE). When choosing to work with HSV or CIELab, RGB images are transformed to one of the color spaces. The HSV color space creates Hue, Saturation, and Value (luminance) channels. Likewise, the CIELab color space creates lightness (l\*), red and green (a\*), and blue and yellow (b\*) channels. Hue and Saturation or a\* and b\* (HSV or CIELab respectively) channels are held in memory and are not manipulated. The luminance channel (either Value or l\* channel) is rescaled (from 0-1 or 0-100, to 0-255, HSV and CIELab respectively). Then, all operations from SHINE (Table 1) can be performed in the scaled luminance channel. For instance, \autoref{fig:Figure 2} displays an example of exact histogram matching. Following, the luminance channel is rescaled back to its original range and is combined with the Hue and Saturation or a\* and b\* channels. These HSV or CIELab images are then transformed back to RGB images. For videos, either working with RGB or color spaces, there is an additional step in which frames are recombined back into a video.
 
-SHINE_color automatically calculates the mean and standard deviation of the luminance channel before and after manipulations for both images and videos. These statistics are saved in a `.txt` file in the folder `SHINE_color_OUTPUT/DIAGNOSTICS`. In addition, for images, but not for videos, users can choose to generate diagnostic plots of luminance histogram, spatial frequency, or spectra, to compare these properties before and after manipulations. When working with RGB, statistics and plots are generated for each channel. Finally, SHINE_color will automatically save a log of users' commands in a `.txt` file, allowing users to review their steps across different interactions with the toolbox. Please note that `SHINE_color` does not read transparent (alpha) channels from `.PNG` images. If you want to display images with transparent background on your experiment, upload them to `SHINE_color`, perform manipulations on background and foreground separately, then remove the background on an image manipulation software (e.g., GIMP, Photoshop). 
+SHINE_color automatically calculates the mean and standard deviation of the luminance channel before and after manipulations for both images and videos. These statistics are saved in a `.txt` file in the folder `SHINE_color_OUTPUT/DIAGNOSTICS`. In addition, for images, but not for videos, users can choose to generate diagnostic plots of luminance histogram, spatial frequency, or spectra, to compare these properties before and after manipulations (\autoref{fig:Figure 2}). When working with RGB, statistics and plots are generated for each channel. Finally, SHINE_color will automatically save a log of users' commands in a `.txt` file, allowing users to review their steps across different interactions with the toolbox. Please note that `SHINE_color` does not read transparent (alpha) channels from `.PNG` images. If you want to display images with transparent background on your experiment, upload them to `SHINE_color`, perform manipulations on background and foreground separately, then remove the background on an image manipulation software (e.g., GIMP, Photoshop). 
 
 
-![An example of the histogram matching by SHINE_color using the HSV color space. On the left there are images (from pexels), luminance histograms, and summary statistics before the operation. On the right, we have the same elements after the operation.\label{fig:Figure 2}](fig2.png)
+![An example of the histogram matching by SHINE_color using the HSV color space. On the left there are images (from Pexels), luminance histograms, and summary statistics before the operation. On the right, we have the same elements after the operation.\label{fig:Figure 2}](fig2.png)
 
 Table 1. Overview of the functions from SHINE_color. Most functions come from the SHINE toolbox, and their descriptions are also available on @Willenbockel2010. Single stars (\*) denotes functions that have been adapted from SHINE, double stars (\**) indicates new functions from SHINE_color. Functions are listed in alphabetical order.
 
@@ -77,6 +77,6 @@ The SHINE_color toolbox is openly available at [OSF](https://osf.io/auzjy/) and
 
 # Acknowledgments
 
-I am thankful for my former supervisors, Jessica Hay, Ph.D., Debora de Hollanda Souza, Ph.D., and Krista Byers-Heinlein, Ph.D., for their support. This work was partially funded by grants from FAPESP (#2015/26389-7, #2018/04226-7) and CAPES (\#001). The funders had no role in study design, data collection, analysis and interpretation of the data, decision to publish, or preparation of the manuscript.
+I am thankful for my former supervisors, Jessica Hay, Ph.D., Debora de Hollanda Souza, Ph.D., and Krista Byers-Heinlein, Ph.D., for their support. I am also thankful for the technical support provided by Pedro Anchieta, M.S. This work was partially funded by grants from FAPESP (#2015/26389-7, #2018/04226-7) and CAPES (\#001). The funders had no role in study design, data collection, analysis and interpretation of the data, decision to publish, or preparation of the manuscript.
 
 # References