Improvements in motor sequence performance have been observed after a delay involving sleep. This finding has been taken as evidence for an active sleep consolidation process that enhances subsequent performance. In a review of this literature, however, the authors observed 4 aspects of data analyses and experimental design that could lead to improved performance on the test in the absence of any sleep consolidation: (a) masking of learning effects in the averaged data, (b) masking of reactive inhibition effects in the averaged training data, (c) time-of-day and time-since-sleep confounds, and (d) a gradual buildup of fatigue over the course of massed (i.e., concentrated) training. In 2 experiments the authors show that when these factors are controlled for, or when their effects are substantially reduced, the sleep enhancement effect is eliminated. Whereas sleep may play a role in protection from forgetting of motor skills, it does not result in performance enhancement.

Memory suppression is investigated with the no-think paradigm, which produces forgetting following repeated practice of not thinking about a memory [Anderson MC, Green C (2001) Nature 410:366 -369]. Because the forgotten item is not retrieved even when tested with an independent, semantically related cue, it has been assumed that this forgetting is due to an inhibition process. However, this conclusion is based on a single stage to recall, whereas global memory models, which produce forgetting through a process of interference, include both a sampling and a recovery stage to recall. By assuming that interference exists during recovery, these models can explain cue-independent forgetting. We tested several predictions of this interference explanation of cue-independent forgetting by modifying the think/nothink paradigm. We added a condition where participants quickly pressed enter rather than not thinking. We also manipulated initial memory strength and tested recognition memory. Most importantly, learning to quickly press enter produced as much cueindependent forgetting as no-think instructions. Demonstrating the adequacy of two-stage recall, a simple computational model (SAM-RI) simultaneously captured the original cue, independent cue, and recognition results.cued recall ͉ inhibition ͉ recall ͉ recognition ͉ computational model

To assess the nature of top-down perceptual processes without contamination from bottom-up input, this functional MRI study investigated face detection in pure noise images. Greater activation was revealed for face versus nonface responses in the fusiform face area, but not in the occipital face area. Across participants, positive correlations were found for the degree of greater face-detection activation between the fusiform face area and bilateral inferior frontal gyri, suggesting a top-down pathway generating perceptual expectations. In contrast, the medial frontal, parietal, supplementary motor, parahippocampal, and striatal areas produced negative correlations between degrees of greater face-detection activation and behavioral responses, suggesting a possible role for these areas in selecting and executing appropriate responses that are based on the top-down expectations.

Immediate repetition priming for faces was examined across a range of prime durations in a threshold identification task. Similar to word repetition priming results, short duration face primes produced positive priming whereas long duration face primes eliminated or reversed this effect. A habituation model of such priming effects predicted that the speed of identification should relate to the prime duration needed to achieve negative priming. We used face priming to test this prediction in two ways. First, we examined the relationship between priming effects and individual differences in the target duration needed for threshold performance. Second, we compared priming of upright and inverted faces. As predicted, the transition from positive to negative priming as a function of prime duration occurred more slowly for inverted faces and for individuals with longer threshold target durations. Additional experiments ruled out alternative explanations.

Previous functional magnetic resonance imaging (fMRI) research on action observation has emphasized the role of putative mirror neuron areas such as Broca's area, ventral premotor cortex, and the inferior parietal lobule. However, recent evidence suggests action observation involves many distributed cortical regions, including dorsal premotor and superior parietal cortex. How these different regions relate to traditional mirror neuron areas, and whether traditional mirror neuron areas play a special role in action representation, is unclear. Here we use multi-voxel pattern analysis (MVPA) to show that action representations, including observation, imagery, and execution of reaching movements: (1) are distributed across both dorsal (superior) and ventral (inferior) premotor and parietal areas; (2) can be decoded from areas that are jointly activated by observation, execution, and imagery of reaching movements, even in cases of equal-amplitude blood oxygen level-dependent (BOLD) responses; and (3) can be equally accurately classified from either posterior parietal or frontal (premotor and inferior frontal) regions. These results challenge the presumed dominance of traditional mirror neuron areas such as Broca's area in action observation and action representation more generally. Unlike traditional univariate fMRI analyses, MVPA was able to discriminate between imagined and observed movements from previously indistinguishable BOLD activations in commonly activated regions, suggesting finer-grained distributed patterns of activation.

Evidence suggests that the neural system associated with face processing is a distributed cortical network containing both bottom-up and top-down mechanisms. While bottom-up face processing has been the focus of many studies, the neural areas involved in the top-down face processing have not been extensively investigated due to difficulty in isolating top-down influences from the bottomup response engendered by presentation of a face. In the present study, we used a novel experimental method to induce illusory face detection. This method allowed for directly examining the neural systems involved in top-down face processing while minimizing the influence of bottom-up perceptual input. A distributed cortical network of top-down face processing was identified by analyzing the functional connectivity patterns of the right fusiform face area (FFA). This distributed cortical network model for face processing includes both "core" and "extended" face processing areas. It also includes left anterior cingulate cortex (ACC), bilateral orbitofrontal cortex (OFC), left dorsolateral prefrontal cortex (DLPFC), left premotor cortex, and left inferior parietal cortex. These findings suggest that top-down face processing contains not only regions for analyzing the visual appearance of faces, but also those involved in processing low spatial frequency (LSF) information, decision making, and working memory.

To study top-down face processing, the present study used an experimental paradigm in which participants detected non-existent faces in pure noise images. Conventional BOLD signal analysis identified three regions involved in this illusory face detection. These regions included the left orbitofrontal cortex (OFC) in addition to the right fusiform face area (FFA) and right occipital face area (OFA), both of which were previously known to be involved in both top-down and bottom-up processing of faces. We used Dynamic Causal Modeling (DCM) and Bayesian model selection to further analyze the data, revealing both intrinsic and modulatory effective connectivities among these three cortical regions. Specifically, our results support the claim that the orbitofrontal cortex plays a crucial role in the top-down processing of faces by regulating the activities of the occipital face area, and the occipital face area in turn detects the illusory face features in the visual stimuli and then provides this information to the fusiform face area for further analysis.

Sleep is hypothesized to play a functional role in the consolidation of memory, with more robust findings for implicit, than explicit memory. Previous studies have observed improvements on an explicit motor task after a sleep period. We examined the role of massed practice and sleep on implicit and explicit learning within a motor task. Controlling for non-sleep factors (e.g. massed practice, circadian confounds) eliminated both explicit and implicit learning effects that have been attributed to sleep. Keywords SLEEP; NAPPING; FATIGUE; CONSOLIDATION; MOTOR MEMORY; PURSUIT MOTOR LEARNINGPerformance improvements following an inter-session sleep episode have been interpreted as sleep having a necessary role in consolidation [1], although some argue against the sleepmemory consolidation hypothesis [2]. Evidence of sleep benefits for explicit [3] and implicit [4] memory have been found, though enhancements in implicit memory are typically larger and more robust [5]. One explicit memory task frequently used to study the benefits of sleep is sequential motor learning. In this motor task, participants repeatedly enter a specific number sequence (e.g. 4-1-3-2-4) during training and then are tested 12 or 24 hrs later for speed and accuracy. Participants allowed to sleep between training and testing have shown improved performance, compared to awake controls [6].Recently, several studies have shed light on uncontrolled factors present in many sequential motor learning study designs. Factors, such as averaging artifacts, time-of-day confounds, and fatigue [7], may account for the observed benefits independent of sleep-related processes. Importantly, studies that controlled for these factors reported the elimination of sleep effects [7][8][9]. Furthermore, morning performance has been shown to be significantly better than evening performance on simple motor tasks [7], indicating that time-of-day confounds inherent in nocturnal sleep studies may produce an illusory sleep effect. Consistent with this observation, training and testing at the same time of day eliminates between session benefits [7,8]. Lastly, massing effects, due to long training blocks, create a