Each participant will be run in a single afternoon, which includes a 90-minute nap opportunity. Before the nap, participants will go through training and test sessions - and after it they will partake of final memory tests. Neural activity will be continuously monitored and recorded throughout the task using electrophysiological equipment.
This is a within-subjects study. The main manipulation is the unobtrusive presentation of sounds during sleep, a technique called targeted memory reactivation (TMR). All participants will hear these sounds, but the specific sounds each one will hear will be different. The results will then be compared within participant, not between different groups or individuals. Appropriate statistical methods for such analyses include paired t-test and repeated measures analysis of variance. The choice of which sounds will be presented to each participant will be made based on their performance in the pre-sleep test. This will be done in an attempt to balance pre-sleep scores between presented and unpresented stimuli to remove statistical noise. Both the participant and the experimenter will be blind to which sounds will be presented, and the selection will be automatically made by the computer. This technique has been extensively used and has no known risks.
There are two main reasons that using a within-subject design reduces the required sample size. First, the lack of a between-subject independent variable intuitively requires less participants. Second, the level of statistical noise due to individual differences is reduced (i.e., because each participant is compared with their own scores). Previous TMR studies, which have found significant cuing effects, commonly used 20-25 participants. I plan to include at least 30 participants in this study, after omitting participants who could not complete the task and those who were not sufficiently cued during sleep. Having 30 participants will allow the use of more powerful statistical methods (in accordance with the common rule of thumb derived from the central limit theorem, which states that means based on sample sizes of more than 30 participants can be assumed to follow a normal distribution). I expect the context-related TMR effect (see summary) to be smaller in magnitude relative to the common effect size observed in spatial learning TMR studies (Hedge's g = 0.39 based on a recent meta-analysis). This is why I included a higher sample size. It is important to note that even if this benefit will be of a smaller magnitude, as I expect, it will still be indicative of the underlying neurocognitive process and therefore extremely valuable for our mechanistic understanding of the role of context in sleep. Aiming at a sample size of at least 30 participants and assuming an omission rate of 80%, I therefore plan to have 38 participants altogether.
Here is a brief summary of the procedure:
Stimuli: 16 images of spatial scenes (e.g., a beach) will each be arbitrarily associated with a sound and with four smaller images of objects or animals. Half of the scenes will be randomly designated to the context-reactivation (CR) condition and half to the item-reactivation (IR) condition (see below). The 64 images will each have a unique 2D position on a circular grid presented on the screen.
Training: Participants will first learn to associate each scene with the paired sound up to criterion. Next, they will learn to associate the scene with its four images up to criterion. The last part of training will include two type of learning blocks that will be interspersed. During the spatial-training blocks, in each trial participants will have to place a single image in its correct location. They will then receive feedback to improve. The scene associated with the image will be presented while they learn, but crucially the sound will never be presented for the CR condition scenes. For the IR scenes, the sound will be presented while learning two of the items, but never for the other two.
An alternative design might have divorced the cued items in the CR condition from the scenes altogether; the items could have been associated with novel sounds (i.e., that were not connected to the scene) and not presented along with their scene. However, using such a design would have introduced a confounding factor. The novel sound may have still been associated, to an unknown degree, not only to the item but also to the context to which it belongs. The degree to which this novel sound would be associated with the context would therefore remain uncontrolled and may vary between participants and scenes. Sounds used for item in the IR condition are always additionally associated with the scene. By always having the sounds be associated both with context and - in the IR condition - additionally to items, I substantially reduce any interpretation issues.
During the Sound-scenes blocks, which do not include a spatial component or the smaller images at all, the scenes will be presented with the sounds only for the CR condition scenes (i.e., to balance the number of sound presentations between conditions). These blocks will repeat in an interleaved manner until each participant will reach the pre-set learning criterion on the spatial-task.
Pre-sleep test: After training, participants will be tested on their spatial-memory for all items without exposure to sounds or scenes.
Sleep: During NREM (non-rapid eye movement) sleep, the sounds associated with half of the CR condition scenes and half of the IR condition scenes will be presented unobtrusively. The choice of which sounds to present will be made in a manner that will balance pre-sleep results and therefore enhance the contrast between sleep-related effects for cued and non-cued images.
Post-sleep test: At least 10 minutes after the end of the nap, participants will undergo a test identical to the pre-sleep one. Immediately after, they will be tested on the scene-item and scene-sound associations using both a free-recall and a recognition test. Participants will then be thanked, debriefed, paid and dismissed.
