We shuffled these stimuli based on behavioral work (Joyce Lacy) to create matched sets C and D.
The original work (e.g., Kirwan et al., 2007 Bakker et al. The latest versions have Sets 1-6 and Sets C, D, E, and F. In general, we recommend using the stand-alone one as it’s the most up to date and has several additional features (e.g., easily run independent sub-sets of each list to give even more test-retest opportunities). There’s even a version written in Kivy Python that allows the task to be run on Windows, OS X, Linux, Android, and iOS, although it’s still in development (svn checkout svn:///kivy_MST).Ī note about stimulus sets. This allows for repeat testing (a nice feature of the task is that there is little if any evidence for practice effects). The task can run in Matlab/Octave or as a stand-alone application (Mac and Windows). Within each set, one can run shorter versions. The task takes about 15 minutes to complete and there are several stimulus sets.
The main behavioral version, however, is the explicit, study-test version. But, in this implicit version, there is no behavioral assay of pattern separation performance. The same stimuli are used and one can use their mnemonic similarity to trace out tuning curves in activity as a function of delta-input. (2015, Behavioral Neuroscience) paper.Ī related purely implicit version is often used by us in fMRI studies of pattern separation (e.g., Bakker et al., 2008, Science Lacy et al., 2011, Learning and Memory). A good place to turn for some of the behavioral comparisons is the S. Typically, this has been done in a study-test variant, but we have (often while in the scanner) done a continuous version as well (Yassa et al., 2010, NeuroImage).
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Note, the behavioral task is an explicit one that asks participants to respond “Old”, “Similar”, or “New” on each trial (we have done “Old” vs. Here, however, we have developed the task since its creation (Kirwan & Stark, 2007, Learning & Memory) to create a set of well-matched stimuli that have been tested extensively both in our lab and in others. The task consists of assessing recognition memory performance for objects using not only the traditional targets and unrelated foils, but also using similar lures (that intentionally vary along several different dimensions). But, the goal is to have a task that would place strong demands on a system that performed pattern separation and, in so doing, get some measure of this. Pattern separation can really only be assessed by looking at representations of information and we clearly can’t do that behaviorally. The MST (for a while also called the SPST – yes, it’s had a few names!) is a behavioral task designed to tax pattern separation.
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For a full discussion of this name change, go here. Note: you do not need to set pos=(0,0) and mask=None as that is the default value of these parameters.Formerly known as the Behavioral Pattern Separation Task (BPS-O), we have renamed the same task to Mnemonic Similarity Task (MST). Change max to min if you want this to apply to the minimum length instead of the maximum length. Multiplying maintains aspect ratio as above and the righthand side is the multiplication factor to ensure scale.
Replace the last line above with this: faceRGB.size *= scale / max(faceRGB.size) Say you want to set the image size so that scale is the maximum length along either the x- og y-axis and scale the other axis proportionally. If you want to control size directly and not just proportionally, see this discussion on the users list. add one line after initiating the ImageStim: scale = 0.7įaceRGB = visual.ImageStim(win, 'male.jpg')įaceRGB.size *= scale # scale the image relative to initial size Scaling the image will preserve this aspect ratio, so that's an easy solution. If you don't change the size of the image, PsychoPy maintains the correct aspect ratio. You may have a situation where you (1) change the size of the image and (2) the image just happens to have the correct dimensions when presented in the default 800 x 800 pixels Window but appears stretched when you go fullscreen because your monitor has another aspect ratio. By default, PsychoPy uses 'norm' as units, which is size normalized to the window dimensions.
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