The general topic of this work was memory processes and the studies we performed assessed the formation and/or the consolidation of representations (either behaviorally or using functional Magnetic Resonance Imaging). Below are described the phenomena and paradigms we used to assess implicit and explicit memory. Then will be briefly described the fMRI technique and some considerations about sleep-related memory consolidation protocols.
1.1. Behavioral measures of implicit and explicit memory 1.1.1. Priming
Priming is the most widely studied manifestation of implicit memory (see reviews from Schacter, 1987; Schacter et al., 2004). It corresponds to the facilitation or bias in the processing of a stimulus as a function of a recent encounter with that stimulus. The theoretical framework of priming is ‘perceptual facilitation’ (Tulving & Schacter, 1990). According to this theory, on the first encounter with a stimulus, a particular representation is stored in a perceptual representation system. Upon repeated presentations of this stimulus, the stored representation is reactivated and leads to facilitated processing. This tool has been used to assess the nature of visual object representations, e.g. its invariance. Indeed, if the representation is viewpoint (or size)-invariant, a second presentation to a stimulus which had already been encountered from another viewpoint (or size) will lead to priming effects. If this representation is not tolerant to these changes, priming effects will be much smaller or absent.
Priming is usually assessed through reaction times but it can also be measured through accuracy in the recognition of fragmented-objects. Neural correlates of repetition priming can also be measured in fMRI through repetition suppression of brain activity (see section 1.2.2 of this experimental part for further details). Finally, priming can be dissociated from explicit recognition. Indeed, normal priming was found in patients who have medial temporal lobe lesions associated with severely impaired recognition memory (Squire & Zola, 1997). In this work, priming effects were specifically tested using a fragmented-picture completion task in the chapter on the modulation of memory by attention, and using reaction times in face learning paradigms.
1.1.2. Mere-Exposure Effect
Another experimental procedure used to investigate implicit memory is the mere-exposure effect. This effect corresponds to an increased positive affective judgment towards repeated
stimuli which were initially neutral and unfamiliar (Zajonc, 1968). The mere exposure effect has been described as an example of implicit memory, closely related to priming (Butler &
Berry, 2004). The mere exposure effect has been used on a wide range of stimuli (e.g., possible and impossible three-dimensional objects, faces, Chinese ideographs and line drawings) and is usually assessed through pleasantness ratings or other likeability ratings.
Finally, intact mere-exposure effect despite impaired recognition memory has also been observed in Alzheimer patients, where the hippocampus is affected by neurodegeneration (Willems et al., 2002). Like priming, the mere-exposure effect seems to rely on perceptual fluency (Bornstein & D'Agostino, 1994; Willems & Van der Linden, 2006). However, the mere exposure effect can occur with a more partial structural description of the stimuli than the one needed to support priming (Seamon et al., 1995). Here, we tested for mere-exposure effect, as another potential tool to measure any implicit memory effect, for newly learned unfamiliar faces.
1.1.3. Old-new recognition test
In order to assess explicit memory, the most commonly used paradigm is the old-new recognition test. In this test, participants first view a set of stimuli and, after a retention interval (from seconds to years) are presented again together with a set of new stimuli.
Subjects are then asked to answer whether each stimulus is ‘old’ or ‘new’ for them. It allows a direct measure of explicit memory, taking into account any individual liberal or conservative bias in saying ‘old’ or ‘new’. Indeed, discriminability indexes (e.g., dprime) can be calculated on the number of hits and false alarms, and allow to measure an unbiased recognition memory performance (Snodgrass & Corwin, 1988b). Moreover, the old-new recognition test is easier than a recall test (where subjects are not presented with the learned stimuli), and can be used with unnamable stimuli, such as unfamiliar faces. Another test often used to assess recognition is the Remember-Know paradigm, where the ‘remembered’ responses specifically assess recollection, and where the ‘known’ responses correspond to the feeling of familiarity (e.g., Yonelinas, 1999). This paradigm is very interesting but is not always applicable, in particular in tasks where recognition is difficult, due for example to a high number of items at encoding (like in our studies on face learning). In this work, we investigated explicit memory using old-new recognition tests.
1.2. Functional MRI 1.2.1. What is fMRI
Functional Magnetic Resonance Imaging (fMRI) has arisen as a powerful tool for connecting cerebral functioning to behavior. It has provided new insights in many cognitive functions, such as face perception, visual processes in general and episodic memory. This technique presents the advantage of being non-invasive. It can measure brain activity without any risk of radiation, unlike CT or PET techniques, and thus participants can be scanned many times during a scanning session (e.g. several hundreds of times). Thanks to its high spatial resolution, it provides anatomical measures with a resolution of 1 millimeter and functional measures of regional activity with a typical resolution of 2-3 millimeters.
The dependent measure in fMRI is the Blood-Oxygen Level-Dependent (BOLD) signal, corresponding to the proportion of oxygenated relative to deoxygenated hemoglobin. When a brain region is recruited for the execution of a task, its need more energy (in particular, more glucose and oxygen) and this is compensated by an increase in blood flow. The BOLD signal measured these changes and has been shown to correlate with neural activity (Logothetis et al., 2001).
1.2.2. Experimental protocols considerations
Despite its non-invasive nature, the MRI technique holds two constraints: its magnetic field and the fact that subjects are tested in a tunnel. The participant (body and clothes) has to be free of metal and should not suffer from claustrophobia. During an fMRI experiment, brain volumes images are acquired (‘scanned’) repeatedly, using the fast imaging technique of echo planar imaging (EPI) to record the BOLD signal, while the subject is performing a cognitive task. There are two main experimental designs: block and event-related. In the block-designs, stimuli from the same experimental condition are presented for a period of seconds (e.g., 30s) and alternated randomly or pseudo-randomly with blocks of other conditions over the course of the acquisition period. The blocks allow prolonged repetition and lead to an increase of the signal. However, external parameters, such as vigilance and expectation, can drive the BOLD signal more than the real experimental condition does. Thus, it is important to control these factors with a baseline condition. In the event-related design, a baseline time course is punctuated by stimulus onsets. The trials can be presented rapidly, in random or intermixed order, diminishing the risk that brain activity for trials from the same condition is biased by external parameters. This is possible thanks to hemodynamic response functions (HRF) that track brain activity on a temporal scale of seconds.
In the context of event-related designs, fMRI-repetition priming paradigms have been used to investigate the nature of visual representations (for a review, see Weigelt et al., 2008). It consists in the repetition of either identical stimuli or stimuli varying along one dimension. If the underlying neural representation is insensitive to this change, the fMRI signal will be reduced as if an identical stimulus was presented. On the contrary, if the neural representation is sensitive to this change, the signal will return to its baseline. This phenomenon is called
‘repetition suppression’ and might be the neural correlate of behavioral repetition priming (Grill-Spector et al., 2006). There are multiple potential causes of the repetition suppression but one of the most accepted one is the selective tuning of the stimulus representation (Wiggs
& Martin, 1998; Grill-Spector et al., 2006).
The first fMRI study presented in this work, on 3D-object learning, was composed of a block-design part, testing general learning processes, and an event-related one, investigating more specific processes of recognition. In the second fMRI study, on face learning, we also used an event-related design in order to track individual face recognition. In both studies, we investigated the nature of visual representations by comparing brain activity for repeated stimuli (previously studied) and novel ones.
1.2.3. Statistical analyses
During a typical fMRI run, a given number of slice images covering the whole brain is acquired every two seconds (TR=repetition time), producing a few hundreds complete images during the session. The BOLD signal is relatively weak and sources of noise (e.g. subject’s motion) have to be carefully controlled. For these reasons, the fMRI signal first undergoes a series of pre-processing steps (e.g., realigning images to the first one of a series, taking into account the movement of the subject in the scanner across 3 axis of rotation).
After pre-processing, the data consist of a time series of measurements for each voxel (the volume-unit of scan acquisition) of the brain. Statistical analysis based on the General Linear Model described the signal of each voxel given a set of regressors, corresponding to the experimental conditions (e.g., ‘old’ and ‘new’ items in a recognition task). This leads to the creation of a statistical map that can be used then to create contrast images (t or F tests) between conditions (e.g. old minus new). These contrast images are produced at the individual level and random-effect analysis, taking into account between-subjects variability.
This step allows to create contrast images at the group-level. Statistical significance was threshold at p < .001, and p <.05 with small volume correction with a sphere diameter of 8mm when needed.
These analysis are generally conducted on whole-brain images but can also be applied on predefined Regions Of Interest (ROI). A face-localizer experiment, including the presentation of stimuli from different categories (e.g. faces, houses, landscapes), can be used to identify face-responsive regions (e.g., FFA). The regions detected in this independent experiment are then used to test how such functionally-defined regions change their activity-level as a function of the main conditions in another fMRI experiment.
1.2.4. Limitations
FMRI is a powerful tool to investigate visual representations and memory processes.
However, its poor temporal resolution (on the order of seconds), in comparison with EEG and MEG, represents a major limitation. Thus, fMRI cannot identify processes occurring in the same brain areas but not exactly at the same time. Another limitation concerns its spatial resolution. Each voxel contains hundreds of thousands of neurons and for example in the case of category-selective regions, it might be that non-preferred categories responsive neurons are present in several regions but that we are not able to detect their signal.
However, despite these limitations, fMRI remains a very valuable tool for investigating brain functions in general, and visual object learning in particular.
1.3. Assessing the role of sleep in memory consolidation: sleep-deprivation vs natural sleep-wake cycles
Sleep researchers developed specific paradigms in order to assess the role of sleep in learning in humans. Some of these paradigms imply stressful conditions whereas other ones tend to be more ecological. Here is a presentation of the paradigms that can be used to assess the role of sleep in memory consolidation.
Total sleep deprivation has been used to assess sleep-dependent effects on learning without any circadian interference. Usually, learning takes place in the evening before a night of either sleep-deprivation or normal sleep and the test phase occurs 3 days later, at the same time point in the day across subjects, in order to allow participants to recover and control for circadian aspects. Thus, any difference between groups or experimental conditions (in within-subjects studies) would be attributed to the absence of sleep in the period following encoding and not to a general ‘fatigue’ or low vigilance. However, these protocols are very stressful for subjects and the cortisol secreted during the sleep-deprivation period may have deleterious effects on memory processes (Siegel, 2001).
Partial sleep deprivation has also been used in order to investigate the effects of sleep/no-sleep but with less stressful conditions. Partial sleep/no-sleep deprivation of the first half or second half of the sleep night provided evidence for different roles of SWS (explicit memory) and REM-sleep (implicit memory), as the first part of the night is principally composed of SWS and the second part of REM-sleep (Plihal & Born, 1999). But again, these protocols imply some stress-related effects due to the half-night sleep deprivation and the fact that they sleep during the other part of the night in the laboratory setting.
More ecological protocols tend to measure sleep-related effects using natural sleep–wake cycles. Different groups are tested across 12-h retention intervals, either morning-evening or evening-morning, with natural sleep in the later configuration. Using such protocols, it has been shown, for example, that motor learning and episodic memory could be improved when learning was followed by a period of sleep (Walker et al., 2003; Talamini et al., 2008). These 12-h groups are often accompanied by one or two 24-h groups in order to control for possible confounding circadian factors.
In this work, we favored the more ecological approaches, testing 12-h groups and in our last experiment a 24-h group as well. We also used questionnaires, vigilance assessment tools and actimeters (watches that continuously record wrist movement) in order to control for potential circadian effects and sleep quality.