## **Welcome** ## Protosc, for Prototypical Stimulus Characteristics, is a Matlab toolbox to help the user to find the features of 2 or more categories of images that separate and define the categories. Studies within the realm of cognitive neuroscience often use different categories of images to define their conditions. For example, a study might be interested in finding differential reaction times due the presentation of faces with a neutral versus an angry facial expression. Since any difference in these categories is a valid candidate to explain category related behavioral (or neural) differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors (or neural responses). However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively, and quantitatively defined. Our method does not aim at providing a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based, toolbox that performs such an analysis and aids the user in visualizing the features of relevance. To start using the toolbox, we recommand downloading the tutorial from the [files][2] page and use that as a guide. Alternatively, go to the [Getting Started page][3] text provides a guide for installing and using the toolbox and/or see the videos listed below. Note that the videos can also be downloaded from the [files][4] page. [Installing the toolbox][5] \ This video shows the downloading and installing of Protosc. [Using the app][6]\ This video shows how to start the app and walks through setting up an analysis. It also briefly discusses the data visualisations the app produces automatically. [Using the template analysis script][7]\ This video shows how to an analysis template and walks through analysis script. It also briefly discusses the data visualisations the script produces automatically. [The output of the analysis][8]\ This video the output of the analysis. It discusses what it contains and how to use it. It also shows how to generate text files containing methods and results, as well as an excel file reporting all the significant features. For an example of application of the beta version of Protosc see: [Stuit et al., 2021][9] For an application of version 1 see: [VSS talk Stuit 2021][10] For more detials and demonstrations please see: [Behavior research Methods][11] [2]: https://osf.io/f6nbu/files/ [3]: https://osf.io/f6nbu/wiki/Getting%20Started/ [4]: https://osf.io/f6nbu/files/ [5]: https://youtu.be/z23CKgpFJUU [6]: https://youtu.be/kkwvvnSzSZ0 [7]: https://youtu.be/7ZfkxZOqmOQ [8]: https://youtu.be/qz2hCvz1IYE [9]: https://www.nature.com/articles/s41598-021-87881-w#citeas [10]: https://youtu.be/bN24w16-CZA [11]: https://link.springer.com/epdf/10.3758/s13428-021-01737-9?sharing_token=eDi2CTi6-7cMgdmV9H7kbpAH0g46feNdnc402WrhzyrtW8EjgSSrZjJdqvMfJ_QAOQh2i7eSIifXvK4-fmJVqdO78HcqQ7QYgZhi5OC7wrxPlxsfgOvUA1UpdVfM_Ikz-5GF1LtgDPdXnxip0dWGPaV0H4NtlO-b0qZ5jroFu38%3D