SlideStates are different instances of the same Slide object. For example, you may want to present two types of conditions, one where the subject responds to the color of a word, and another condition where the subject responds to the color of a car. Instead of creating two separate Slide objects, you can put both conditions into a single Slide, making the experiment more compact and more flexible.

The Feedback Display object displays different screens based on whether the response to a previous slide was correct or incorrect. This introduces the concept of SlideStates, which are different instances of a Slide that are activated based on the value stored in an attribute. The Feedback Display objects monitor the value of the Accuracy attribute from a previous Slide, determine whether it was correct or incorrect, and then display the corresponding SlideState.

Now we've constructed a very basic Stroop experiment with only words (red and blue) and two colors (again, red and blue). We arbitrarily determined that 'f' would be the response for blue-colored words, and that 'j' would be the response for red-colored words. So far this is a perfectly acceptable experiment.

Once we've created the basic template of our experiment we then turn our attention to flow control, or how to guide the subject to different parts of the experiment. In the previous video we created a basic Stroop experiment that went in a predetermined direction: The subject would encounter one Stroop trial, and then another, exactly in the order that we had laid out. This was enough to qualify as an experiment, but most studies require more elements than that - randomization and counterbalancing, for example.

This tutorial shows how to make a basic Stroop experiment. A new tool we will learn about is the Slide object - a versatile object that can contain several sub-objects, such as images and text boxes. The Slide object's properties are controlled by the property pages tab, while the sub-objects are controlled by the sub-object property pages tab.

The atlases in FSL can be divided into two classes: probabilistic and non-probabilistic. In a probabilistic atlas, the number in each voxel represents the probability that the voxel belongs to a structure. Take the Harvard-Oxford atlas, for example. A voxel at the MNI coordinates 18, -102, 6 has a value of 76. This number means the odds are 76% that the voxel coordinates will be part of the occipital cortex, for a subject taken at random.

This tutorial is about objects - the building blocks of an experiment. E-Prime uses object-oriented programming, which means that the user selects pre-designed objects from the E-Prime toolbox, and then arranges and modifies them to suit his experiment.

Over the next two weeks I'll be posting a series of videos on how to use E-Prime, by creating a Stroop experiment using building blocks known as objects. These objects have attributes, or properties, that determine object characteristics such as shape, duration, and color. Through arranging objects a certain way and modifying their properties, you can create a wide variety of experiments.

Normalization - the transforming of data to a standardized space - is a common preprocessing step, and for good reason: it allows us to perform group analysis; it reduces variability in the size and shape of the brain images; and it allows for comparisons across studies by putting the data into a standardized space. Five millimeters to the left and ten millimeters to the rear of the anterior commissure then becomes same location from one study to the next.

ROI analysis often means averaging over all the voxels within a mask - either the average parameter, or the average timeseries. For most researchers this is all they need.

Whether or not that applies to you as well, I would like to talk about one more type of ROI analysis: extracting data from each voxel. Most researchers use this analysis to extract the timeseries for each voxel in the brain, although you can extract whatever data you like - parameter estimates, timeseries, and anything else contained in your voxels.