In the first step, you are asked some parameters about your design/scanner:

#### Design parameters

##### General

Scanner TR (seconds) | The repetition time of your scanner. How many seconds are there between two whole brain scans? |

Stimulus types | The number of **different** stimuli in your design, often the same as the number of conditions. If you're comparing seeing faces with seeing houses, you have 3 stimulus types. |

Stimulus duration | How long does one stimulus last on the screen? We expect brain activation for the duration of the stimulus. Please exclude the duration of non-target parts of a trial (such as fixation crosses) and add this duration to the ITI. |

Total number of trials | How many trials does the experiment consist of in total? |

##### Trial structure

#####

We ask to specify the stimulus duration. However, very often your trial consists of fixation cross etc, where you don't expect activation. Therefore, you should specify the duration of the trial before the stimulus (or target) and the duration of the trial after the stimulus.

##### Contrasts

If you are comparing conditions (or a condition with baseline), you need to prepare contrasts for your analysis. The optimisation of your design also depends on these contrasts. If you have 3 conditions, and you want to compare all of those, your contrast matrix would be:

The first line compares condition 1 with condition 2. The second line compares condition 2 with condition 3 and the last line compares condition 1 with condition 3.

By checking to inclide all pairwise contrasts, all these one-by-one contrasts will be added.

If you want to specify custom contrasts, you can do so too. In an example with three conditions - seeing angry faces, seeing sad faces and seeing neutral faces - you'd want to compare the average of emotional faces with neutral faces. Your contrast would look like this:

##### Inter-trial interval (ITI)

You want to specify the time between stimuli, the inter-trial interval (ITI) or also known as the inter-stimulus interval (ISI). You can specify a fixed ITI, but also a variable ITI. We strongly suggest a variable ITI for a better estimation of the activation. For a fixed ITI, you only have to fill out the average ITI; for a variable ITI, you can either sample from a uniform or a truncated exponential distribution. You can also specify a minimum and a maximum *and*. For a truncated exponential distribution, you can additionally add the mean of the ITI's (which should be smaller than the mean between the minimum and the maximum).

##### Rest blocks

Very often, a researcher would include some rest blocks during the experiment to let the subject rest and refocus. You can choose how long these rest blocks are and how many trials there are between rest blocks.

**Note** that if you're modeling a very long experiment, it might be advisable to optimise 2 different runs rather than 1 long run with a long rest block.

#### Design optimisation parameters

On the previous page in the tutorial, we explained the different optimisation criteria. Here, you are asked to give weights to each of those criteria. Each criterium is scaled between 0 and 1, with higher being better. The total optimisation will be the weighted average of those criteria.

#### Psychological confounding factors

**Trial contingencies**

**Trial blockedness**

Imagine an experiment with 3 stimulus types. The probability to have randomly a block of 6 stimuli of the same type is 1/3^6. However, with a design of 300 trials, the probality of a random design to have a block of 6 stimuli is close to 0.4. As such, even a design that is completely random has a large chance of containing a *long* block of the same stimuli. You might want to avoid such long blocks, it might interfere with the expectance of the subject by setting a maximum number of repeated stimuli.

Watch out though. In an experiment with only 2 stimuli, there is on average a block of 6 repeated stimuli every 64 trials. With only two stimuli, it will not only be difficult to randomly find a design without such blocks, but it might also inversely impact the expectance of the stimuli. You might want to set the maximum number of repeated stimuli higher or leave this field empty.

#### Contrasts and Probabilities

##### Stimulus probabilities

Here, you are asked to set the probability of each stimulus type. You can fill this out in whatever unit, as long as they are relative to one another. If you want a design with 200 trials with 4 stimulus types with equal probabilities, you can fill out [0.25, 0.25, 0.25, 0.25], or [50,50,50,50], or even [1,1,1,1].

You can fill out integers or decimal values, but not fractions. For fractions that have infinite decimals (like 1/3), you can ignore the denominator. For example [1/3,2/3] can be written as [1,2].

##### Hard limit on the probabilities

During the design optimisation, it might (and it probably will) occur that the final probabilities are not exactly what you wanted for the sake of having an optimal design. To avoid this, you can set the probabilities as an optimality criterion, but it might still not be perfect. If you **only** want designs with the exact probabilities you wanted, you can check this box.

However, this will have a large impact on the optimisation. It will largely restrict the number of designs to search over, and the optimisation will take a lot longer and we might even end up without optimal design. If you check this box, we will automatically set the number of designs in each step of the algorithm a lot larger to avoid finishing without design, but there's nothing we can do about the duration.

##### Contrasts

We've explained in short how to construct a custom contrast above. For a clear overview and more information, see this video from Jeanette Mumford's blog.

Previous: Experimental Outcomes
Next: Settings