Visual processing is not instantaneous, but instead our conscious perception depends on the integration of sensory input over time. In the case of Continuous Flash Suppression (CFS), masks are flashed to one eye, suppressing awareness of stimuli presented to the other eye. One potential explanation of CFS is that it depends, at least in part, on the flashing mask continually interrupting visual processing before the stimulus reaches awareness. We investigated the temporal features of masks in two ways. First, we measured the suppression effectiveness of a wide range of masking frequencies (0-32Hz), using both complex (faces/houses) and simple (closed/open geometric shapes) stimuli. Second, we varied whether the different frequencies were interleaved within blocks or separated in homogenous blocks, in order to see if suppression was stronger or weaker when the frequency remained constant across trials. We found that break-through contrast differed dramatically between masking frequencies, with mask effectiveness following a skewed-normal curve peaking around 6Hz and little or no masking for low and high temporal frequencies. Peak frequency was similar for trial-randomized and block randomized conditions. In terms of type of stimulus, we found no significant difference in peak frequency between the stimulus groups (complex/simple, face/house, closed/open). These findings suggest that temporal factors play a critical role in perceptual awareness, perhaps due to interactions between mask frequency and the time frame of visual processing.

Although sensory input is continuous, information must be combined over time to guide action and cognition, leading to the proposal of temporal sampling windows. A number of studies have suggested that a 10-Hz sampling window might be involved in the “frame rate” of visual processing. To investigate this, we tested the ability of participants to localize and enumerate 1 or 2 visual flashes presented either at near-threshold or full-contrast intensities, while recording magnetoencephalography. The inter-stimulus interval (ISI) between the 2 flashes was varied across trials. Performance in distinguishing between 1 and 2 flashes was linked to the alpha frequency, both at the individual level and trial-by-trial. Participants with a higher resting-state alpha peak frequency showed the greatest improvement in performance as a function of ISI within a 100-ms time window, while those with slower alpha improved more when ISI exceeded 100 ms. On each trial, correct enumeration (1 vs. 2) performance was paired with faster pre-stimulus instantaneous alpha frequency. Our results suggest that visual sampling/processing speed, linked to peak alpha frequency, is both an individual trait and can vary in a state-dependent manner.

The human visual system must extract reliable object information from cluttered visual scenes several times per second, and this temporal constraint has been taken as evidence that the underlying cortical processing must be strictly feedforward. Here we use a novel rapid reinforcement paradigm to probe the temporal dynamics of the neural circuit underlying rapid object shape perception and thus test this feedforward assumption. Our results show that two shape stimuli are optimally reinforcing when separated in time by ∼60 ms, suggesting an underlying recurrent circuit with a time constant (feedforward + feedback) of 60 ms. A control experiment demonstrates that this is not an attentional cueing effect. Instead, it appears to reflect the time course of feedback processing underlying the rapid perceptual organization of shape.SIGNIFICANCE STATEMENTHuman and nonhuman primates can spot an animal shape in complex natural scenes with striking speed, and this has been taken as evidence that the underlying cortical mechanisms are strictly feedforward. Using a novel paradigm to probe the dynamics of shape perception, we find that two shape stimuli are optimally reinforcing when separated in time by 60 ms, suggesting a fast but recurrent neural circuit. This work (1) introduces a novel method for probing the temporal dynamics of cortical circuits underlying perception, (2) provides direct evidence against the feedforward assumption for rapid shape perception, and (3) yields insight into the role of feedback connections in the object pathway.

Cardiovascular disease (CVD) is a disease with high mortality in modern times. The segmentation task for MRI to extract the related organs for CVD is essential for diagnosis. Currently, a large number of deep learning methods are designed for medical image segmentation tasks. However, the design of segmentation algorithms tends to have more focus on deepening the network architectures and tuning the parameters and hyperparameters manually, which not only leads to a high time and effort consumption, but also causes the problem that the architectures and setting designed for a single task only performs well in a single dataset, but have low performance in other cases. In this paper, nn-TransUNet, an automatic deep learning pipeline for MRI segmentation of the heart is proposed to combine the experiment planning of nnU-net and the network architecture of TransUNet. nn-TransUNet uses vision transformers and convolution layers in the design of the encoder and takes up convolution layers as decoder. With the adaptive preprocessing and network training plan generated by the proposed automatic experiment planning pipeline, nn-TransUNet is able to fulfill the target of medical image segmentation in heart MRI tasks. nn-TransUNet achieved state-of-the-art level in heart MRI segmentation task on Automatic Cardiac Diagnosis Challenge (ACDC) Dataset. It also saves the effort and time to manually tune the parameters and hyperparameters, which can reduce the burden on researchers.

The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

The human visual system can quickly and efficiently extract categorical information from a complex natural scene. The rapid detection of animals in a scene is one compelling example of this phenomenon, and it suggests the automatic processing of at least some types of categories with little or no attentional requirements (Li et al., 2002;2005). The aim of this study is to investigate whether the remarkable capability to categorize complex natural scenes exist in the absence of awareness, based on recent reports that “invisible” stimuli, which do not reach conscious awareness, can still be processed by the human visual system (Pasley et al., 2004;Williams et al., 2004;Fang and He, 2005;Jiang et al., 2006;Jiang et al., 2007;Kaunitz et al., 2011a). In two experiments we recorded ERPs in response to animal and non-animal/vehicle stimuli in both aware and unaware conditions in a continuous flash suppression (CFS) paradigm. Our results indicate that even in the “unseen” condition, the brain responds differently to animal and non-animal/vehicle images, consistent with rapid activation of animal-selective feature detectors prior to, or outside of, suppression by the CFS mask.