Neural activity in early visual cortex is modulated by luminance contrast. Cortical depth (i.e., laminar) contrast responses have been studied in monkey early visual cortex, but not in humans. In addition to the high spatial resolution needed and the ensuing low signal-to-noise ratio, laminar studies in humans using fMRI are hampered by the strong venous vascular weighting of the fMRI signal. In this study, we measured luminance contrast responses in human V1 and V2 with high-resolution fMRI at 7 T. To account for the effect of intracortical ascending veins, we applied a novel spatial deconvolution model to the fMRI depth profiles. Before spatial deconvolution, the contrast response in V1 showed a slight local maximum at mid cortical depth, whereas V2 exhibited a monotonic signal increase toward the cortical surface. After applying the deconvolution, both V1 and V2 showed a pronounced local maximum at mid cortical depth, with an additional peak in deep grey matter, especially in V1. Moreover, we found a difference in contrast sensitivity between V1 and V2, but no evidence for variations in contrast sensitivity as a function of cortical depth. These findings are in agreement with results obtained in nonhuman primates, but further research will be needed to validate the spatial deconvolution approach.

Fostering social and academic self‐concepts are central educational goals. During mid‐adolescence academic engagement and success seem to be devalued by peers and to be negatively associated with students' social standing. For this age group, is the development of a positive academic self‐concept compatible with the development of a positive social self‐concept? We investigated relations among academic self‐concept, social self‐concept, and academic achievement. 1282 students (47.60% female) participated in three‐waves of measurement in Grade 5, 6, and 8. Earlier social self‐concept of acceptance negatively predicted changes in academic self‐concept over time while earlier social self‐concept of assertion positively predicted changes in academic self‐concept. There were no significant relations between social self‐concepts and achievement but positive reciprocal relations between academic self‐concept and achievement. Results indicate that fostering adolescents self‐concept in social and academic domains are compatible goals. However, some students need support in managing the challenge to coordinate social and academic goals.

The specific contents of human consciousness rely on the activity of specialized neurons in cerebral cortex. We hypothesized that the conscious experience of a specific visual motion axis is reflected in response amplitudes of direction-selective clusters in the human motion complex. Using submillimeter fMRI at ultrahigh field (7 T) we identified fine-grained clusters that were tuned to either horizontal or vertical motion presented in an unambiguous motion display. We then recorded their responses while human observers reported the perceived axis of motion for an ambiguous apparent motion display. Although retinal stimulation remained constant, subjects reported recurring changes between horizontal and vertical motion percepts every 7 to 13 s. We found that these perceptual states were dissociatively reflected in the response amplitudes of the identified horizontal and vertical clusters. We also found that responses to unambiguous motion were organized in a columnar fashion such that motion preferences were stable in the direction of cortical depth and changed when moving along the cortical surface. We suggest that activity in these specialized clusters is involved in tracking the distinct conscious experience of a particular motion axis.

Across two experiments, we studied a phenomenon akin to choice blindness in the context of participants' accounts of their own history of norm-violating behaviors. In Experiment 1, N = 67 participants filled in an 18-item questionnaire about their history of norm-violating behaviors (QHNVB). Subsequently, they were questioned about four of their answers, two of which had covertly been manipulated by the experimenter. Of the 134 manipulations, 20 (14.9%) remained undetected concurrently and 13 were accepted in retrospect (9.7%). In Experiment 2 (N = 37), we inserted a one-week interval between questionnaire and interview. Twenty-seven (36.5%) of the 74 manipulations remained undetected concurrently and three were accepted in retrospect (8.1%). Data obtained in a four-week follow-up indicated that our manipulations may have long-term effects on participants' perception of their own history of norm-violating behaviors. Implications for the occurrence of false confessions during the course of an interrogation are discussed.

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

Human visual surface perception has neural correlates in early visual cortex, but the role of feedback during surface segmentation in human early visual cortex remains unknown. Feedback projections preferentially enter superficial and deep anatomical layers, which provides a hypothesis for the cortical depth distribution of fMRI activity related to feedback. Using ultra-high field fMRI, we report a depth distribution of activation in line with feedback during the (illusory) perception of surface motion. Our results fit with a signal re-entering in superficial depths of V1, followed by a feedforward sweep of the re-entered information through V2 and V3. The magnitude and sign of the BOLD response strongly depended on the presence of texture in the background, and was additionally modulated by the presence of illusory motion perception compatible with feedback. In summary, the present study demonstrates the potential of depth-resolved fMRI in tackling biomechanical questions on perception.

Motion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.Keywords visual neuroscience · position perception · population receptive fields · visual field projections 1 IntroductionAn important task of the visual system is to infer the location of objects in our environment. A wide range of psychophysical studies shows that motion signals lead to systematic localisation biases [1,2,3,4,5,6,7,8,9]. In illusions called motion-induced position shifts (MIPS), a coherent motion signal shifts the apparent location of a stimulus [1]. For example, when drifting Gabor patches are presented within a stationary aperture, the stimulus appears shifted in the direction of motion [2,6,7]. Such illusions raise the question how our visual system encodes location and how, in the case of MIPS, the apparent position shift can be explained. Furthermore, they offer a dissociation between the physical and the perceived position of a stimulus that can clarify which neuronal processes correspond to the apparent position of the stimulus. MOTION DISPLACES POPULATION RECEPTIVE FIELDSThe magnitude of MIPS is known to depend on spatial and temporal properties of the stimulus. MIPS are larger when the stimulus is shown for longer duration (tested up to 453 ms; [6]), presented at higher speed [6,9] or at higher eccentricities [10,6,9]. The magnitude of MIPS furthermore depends on spatial blurring of the presented stimulus. Blurred stimulus edges lead to larger perceptual displacements than sharp edges [4,9] and increasing the size of the Gaussian envelope of a Gabor stimulus yields larger MIPS [4]. Arnold et al. [7] have suggested that MIPS are driven by modulation of apparent contrast of the stimulus. Supporting this suggestion, they reported perceived position shifts when observers were asked to match the extremities of two contrast envelopes (low-contrast region),...

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.