Facilitation and interference are asymmetric in holistic face processing.

A hallmark of face specificity is holistic processing. It is typically measured by paradigms such as the part-whole and composite tasks. However, these tasks show little evidence for common variance, so a comprehensive account of holistic processing remains elusive. One aspect that varies between tasks is whether they measure facilitation or interference from holistic processing. In this study, we examined facilitation and interference in a single paradigm to determine the way in which they manifest during a face perception task. Using congruent and incongruent trials in the complete composite face task, we found that these two aspects are asymmetrically influenced by the location and cueing probabilities of the target facial half, suggesting that they may operate somewhat independently. We argue that distinguishing facilitation and interference has the potential to disentangle mixed findings from different popular paradigms measuring holistic processing in one unified framework.

Ensemble coding of multiple facial expressions is not affected by attentional load.

Human observers can extract the mean emotion from multiple faces rapidly and precisely. However, whether attention is required in the ensemble coding of facial expressions remains debated. In this study, we examined the effect of attentional load on mean emotion processing with the dual-task paradigm. Individual emotion processing was also investigated as the control task. In the experiment, the letter string and a set of four happy or angry faces of various emotional intensities were shown. Participants had to complete the string task first, judging either the string color (low attention load) or the presence of the target letter (high attention load). Then a cue appeared indicating whether the secondary task was to evaluate the mean emotion of the faces or the emotion of the cued single face, and participants made their judgments on the visual analog scale. The results showed that compared with the color task, the letter task had a longer response time and lower accuracy, which verified the valid manipulation of the attention loads. More importantly, there was no significant difference in averaging performance between the low and high attention loads. By contrast, the individual face processing was impaired under the high attention load relative to the low attentional load. In addition, the advantage of extracting mean emotion over individual emotion was larger under the high attentional load. These results support the power of averaging and provide new evidence that a rather small amount of attention is needed in the ensemble coding of multiple facial expressions.

Foveal vision determines the perceived emotion of face ensembles.

People can extract summary statistical information from groups of similar objects, an ability called ensemble perception. However, not every object in a group is weighted equally. For example, in ensemble emotion perception, faces far from fixation were weighted less than faces close to fixation. Yet the contribution of foveal input in ensemble emotion perception is still unclear. In two experiments, groups of faces with varying emotions were presented for 100 ms at three different eccentricities (0°, 3°, 8°). Observers reported the perceived average emotion of the group. In two conditions, stimuli consisted of a central face flanked by eight faces (flankers) (central-present condition) and eight faces without the central face (central-absent condition). In the central-present condition, the emotion of the central face was either congruent or incongruent with that of the flankers. In Experiment 1, flanker emotions were uniform (identical flankers); in Experiment 2 they were varied. In both experiments, performance in the central-present condition was superior at 3° compared to 0° and 8°. At 0°, performance was superior in the central-absent (i.e., no foveal input) compared to the central-present condition. Poor performance in the central-present condition was driven by the incongruent condition where the foveal face strongly biased responses. At 3° and 8°, performance was comparable between central-present and central-absent conditions. Our results showed how foveal input determined the perceived emotion of face ensembles, suggesting that ensemble perception fails when salient target information is available in central vision.

Visual Mismatch Negativity Reflects Enhanced Response to the Deviant: Evidence From Event-Related Potentials and Electroencephalogram Time-Frequency Analysis.

Automatic detection of information changes in the visual environment is crucial for individual survival. Researchers use the oddball paradigm to study the brain's response to frequently presented (standard) stimuli and occasionally presented (deviant) stimuli. The component that can be observed in the difference wave is called visual mismatch negativity (vMMN), which is obtained by subtracting event-related potentials (ERPs) evoked by the deviant from ERPs evoked by the standard. There are three hypotheses to explain the vMMN. The sensory fatigue (or refractoriness) hypothesis considers that weakened neural activity caused by repetition results in decreased ERPs of the standard. The memory trace hypothesis proposes that vMMN results from increased responses to the deviant. The predictive coding hypothesis attributes the difference to enhanced responses for deviants and suppression for standards. However, when distinguishing between these effects, previous researchers did not consider the effect of low-level features on the vMMN. In this experiment, we used face sequences composed of different emotions (e.g., neutral and fearful face) and presented an oddball sequence, a reverse oddball sequence, and an equiprobable sequence to participants. The deviant of the oddball sequence was subtracted from the standard of the oddball sequence, the reverse oddball sequence, and the same type of stimulus of the equiprobable sequence to get oddball-vMMN (vMMN1), reverse oddball-vMMN (vMMN2), and equiprobable-vMMN (vMMN3), respectively. The results showed no significant difference between vMMN2 and vMMN3 in 100-350 ms following stimulus onset, while the vMMN effect was significant, indicating that the probability of the standard did not affect vMMN, which supported the memory trace hypothesis. Additionally, the fearful-related vMMN were more negative than the neutral-related vMMN within the range of 100-150 ms, suggesting a negative bias. We analyzed the source location of different vMMNs. There was no significant difference in brain regions between different vMMNs. Time-frequency analysis showed that the deviant had stronger theta-band oscillatory than the standard (visual mismatch oscillatory responses, vMORs). However, there was no difference between vMORs2 and vMORs3, indicating that vMORs reflect an enhanced response to the deviant in terms of neural oscillation, supporting the memory trace hypothesis.

Individual Faces Were Not Discarded During Extracting Mean Emotion Representations.

Individuals can perceive the mean emotion or mean identity of a group of faces. It has been considered that individual representations are discarded when extracting a mean representation; for example, the "element-independent assumption" asserts that the extraction of a mean representation does not depend on recognizing or remembering individual items. The "element-dependent assumption" proposes that the extraction of a mean representation is closely connected to the processing of individual items. The processing mechanism of mean representations and individual representations remains unclear. The present study used a classic member-identification paradigm and manipulated the exposure time and set size to investigate the effect of attentional resources allocated to individual faces on the processing of both the mean emotion representation and individual representations in a set and the relationship between the two types of representations. The results showed that while the precision of individual representations was affected by attentional resources, the precision of the mean emotion representation did not change with it. Our results indicate that two different pathways may exist for extracting a mean emotion representation and individual representations and that the extraction of a mean emotion representation may have higher priority. Moreover, we found that individual faces in a group could be processed to a certain extent even under extremely short exposure time and that the precision of individual representations was relatively poor but individual representations were not discarded.

Metacognition of average face perception.

Individuals have the ability to extract summary statistics from multiple items presented simultaneously. However, it is unclear yet whether we have insight into the process of ensemble coding. The aim of this study was to investigate metacognition about average face perception. Participants saw a group of four faces presented for 2 s or 5 s, and then they were asked to judge whether the following test face was present in the previous set (Experiment 1), or whether the test face was the average of the four member faces (Experiment 2). After each response, participants rated their confidence. Replicating previous findings, there was substantial endorsement for the average face derived from the four member faces in Experiment 1, even though it was not present in the set. When judging faces that had been presented in the set, confidence correlated positively with accuracy, providing evidence for metacognitive awareness of previously studied faces. Importantly, there was a negative confidence-accuracy relationship for judging average faces when duration was 2 s, and a near-zero relationship when duration was 5 s. By contrast, when the average face had to be identified explicitly in Experiment 2, performance was above chance level and there was a positive correlation between confidence and accuracy. These results suggest that people have metacognitive awareness about average face perception when averaging is required explicitly, but they lack insight into the averaging process when member identification is required.

Ensemble representation for multiple facial expressions: Evidence for a capacity limited perceptual process.

We tested the processing capacity of establishing ensemble representation for multiple facial expressions using the simultaneous-sequential paradigm. Each set consisted of 16 faces conveying a variable amount of happy and angry expressions. Participants judged on a continuous scale the perceived average emotion from each face set (Experiment 1). In the simultaneous condition, the 16 faces were presented concurrently; in the sequential condition, two sets, each containing eight faces, were presented successively. Results showed that judgments varied depending on the number of happy versus angry faces contained in the sets and were sensitive at the single trial level to the perceived mean emotion intensity (based on postexperiment ratings), providing evidence of a genuine mean representation rather than the mere use of a single face or enumeration. Experiments 2 and 3 replicated Experiment 1, but implemented a different response format (binary choices) and added masks following each display, respectively. Importantly, in all three experiments, performance was consistently better in the sequential than in the simultaneous condition, revealing a limited-capacity process. A set of control analyses ruled out the use of enumeration or mere subsampling by the participants to perform the task. Collectively, these results indicate that participants could readily extract mean emotion from multiple faces shown concurrently in a set, but this process is best conceived as being capacity limited.

Capacity limitations to extract the mean emotion from multiple facial expressions depend on emotion variance.

We examined the processing capacity and the role of emotion variance in ensemble representation for multiple facial expressions shown concurrently. A standard set size manipulation was used, whereby the sets consisted of 4, 8, or 16 morphed faces each uniquely varying along a happy-angry continuum (Experiment 1) or a neutral-happy/angry continuum (Experiments 2 & 3). Across the three experiments, we reduced the amount of emotion variance in the sets to explore the boundaries of this process. Participants judged the perceived average emotion from each set on a continuous scale. We computed and compared objective and subjective difference scores, using the morph units and post-experiment ratings, respectively. Results of the subjective scores were more consistent than the objective ones across the first two experiments where the variance was relatively large, and revealed each time that increasing set size led to a poorer averaging ability, suggesting capacity limitations in establishing ensemble representations for multiple facial expressions. However, when the emotion variance in the sets was reduced in Experiment 3, both subjective and objective scores remained unaffected by set size, suggesting that the emotion averaging process was unlimited in these conditions. Collectively, these results suggest that extracting mean emotion from a set composed of multiple faces depends on both structural (attentional) and stimulus-related effects.

Mean emotion from multiple facial expressions can be extracted with limited attention: Evidence from visual ERPs.

Human observers can readily extract the mean emotion from multiple faces shown briefly. However, it remains currently debated whether this ability depends on attention or not. To address this question, in this study, we recorded lateralized event-related brain potentials (i.e., N2pc and SPCN) to track covert shifts of spatial attention, while healthy adult participants discriminated the mean emotion of four faces shown in the periphery at an attended or unattended spatial location, using a cueing technique. As a control condition, they were asked to discriminate the emotional expression of a single face shown in the periphery. Analyses of saccade-free data showed that the mean emotion discrimination ability was above chance level but statistically undistinguishable between the attended and unattended location, suggesting that attention was not a pre-requisite for averaging. Interestingly, at the ERP level, covert shifts of spatial attention were captured by the N2pc and SPCN components. All together, these novel findings suggest that averaging multiple facial expressions shown in the periphery can operate with limited attention.

Processing of Individual Items during Ensemble Coding of Facial Expressions.

There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processing.

Clinical and Neuroimaging Features of Acute Ischemic Stroke in Cancer Patients.

BACKGROUND: The occurrence of acute ischemic stroke in cancer patients is not unusual. In clinical practice, acute ischemic stroke with cancer usually cannot be diagnosed promptly due to lack of specific markers. But for cancer patients, advanced prevention, accurate diagnosis and proper treatment of acute ischemic stroke are very important. The aim of the present study was to investigate the clinical and neuroimaging features of acute ischemic stroke in patients with cancer. METHODS: We conducted a retrospective review of all cancer-associated acute ischemic stroke patients (n = 46) admitted to the Affiliated Hospital of Academy of Military Medical Sciences between October 2011 and March 2015. A group of non-cancer acute ischemic stroke patients (n = 50) at the same period were selected randomly as control. The clinical and neuroimaging data were collected and compared between the 2 groups. RESULTS: Patients with cancer-associated stroke (CS) had a lower body mass index (23.26 ± 3.70 vs. 24.88 ± 2.83, p = 0.021) compared to non-cancer stroke (NC) patients. A lower proportion of CS patients suffered from hypertension (45.7 vs. 68.0%, p = 0.039) and hyperlipidemia (10.9 vs. 72.0%, p = 0.000) than the NC group. A higher proportion of CS patients had deep vein catheter (24.0 vs. 0%) before the onset of stoke than that of the NC group. Levels of hemoglobin, albumin and triglyceride were lower in CS groups compared with that of the NC group (p < 0.05). The prothrombin time, international normalized ratio, D-dimer and fibrinogen levels were significantly higher in the CS group than in the NC group (p < 0.05). As to the neuroimaging patterns, disperse lesions (OR 7.01; 95% CI 1.17-42.12; p < 0.05) was independently associated with CS. CONCLUSIONS: Cancer-associated ischemic stroke was different form conventional ischemic stroke in the aspect of clinical and neuroimaging manifestation. This phenomenon might be because of the embolic etiology of CS. These features together could become a clue to CS.

Unstable mean context causes sensitivity loss and biased estimation of variability.

A recent study has suggested that statistical representations of ensemble objects may provide contextual stability to facilitate perception. The present study investigated whether facilitating such perception occurs in the extraction of variability information and how the stability of context mean values influences variability perception. We designed two tasks in which participants directly judged the variability of stimuli. In Experiment 1, we manipulated both the stability of the mean values and the exposure time to observe the time course of stability facilitation. In Experiment 2, we decomposed the stability of the context mean values into between-trials and within-trial levels to further investigate the mechanism of such facilitation. The results revealed that stable mean contexts do facilitate variability perception. In particular, unstable long-term mean context causes loss of sensitivity to variability whereas response bias is determined by the interaction between long-term and transient mean stability.

Optimizing the Endmembers Using Volume Invariant Constrained Model.

The linear mixture model (LMM) plays a crucial role in the spectral unmixing of hyperspectral data. Under the assumption of LMM, the solution with the minimum reconstruction error is considered to be the ideal endmember. However, for practical hyperspectral data sets, endmembers that enclose all the pixels are physically meaningless due to the effect of noise. Therefore, in many cases, it is not sufficient to consider only the reconstruction error, some constraints (for instance, volume constraint) need to be added to the endmembers. The two terms can be considered as serving two forces: minimizing the reconstruction error forces the endmembers to move outward and thus enlarges the volume of the simplex while the endmember constraint acts in the opposite direction by driving the endmembers to move inward so as to constrain the volume to be smaller. Many existing methods obtain their solution just by balancing the two contradictory forces. The solution acquired in this way can not only minimize the reconstruction error but also be physically meaningful. Interestingly, we find, in this paper, that the two forces are not completely contradictory with each other, and the reconstruction error can be further reduced without changing the volume of the simplex. And more interestingly, our method can further optimize the solution provided by all the endmember extraction methods (both endmember selection methods and endmember generation methods). After optimization, the final endmembers outperform the initial solution in terms of reconstruction error as well as accuracy. The experiments on simulated and real hyperspectral data verify the validation of our method.

Joint skewness and its application in unsupervised band selection for small target detection.

Few band selection methods are specially designed for small target detection. It is well known that the information of small targets is most likely contained in non-Gaussian bands, where small targets are more easily separated from the background. On the other hand, correlation of band set also plays an important role in the small target detection. When the selected bands are highly correlated, it will be unbeneficial for the subsequent detection. However, the existing non-Gaussianity-based band selection methods have not taken the correlation of bands into account, which generally result in high correlation of obtained bands. In this paper, combining the third-order (third-order tensor) and second-order (correlation) statistics of bands, we define a new concept, named joint skewness, for multivariate data. Moreover, we also propose an easy-to-implement approach to estimate this index based on high-order singular value decomposition (HOSVD). Based on the definition of joint skewness, we present an unsupervised band selection for small target detection for hyperspectral data, named joint skewness band selection (JSBS). The evaluation results demonstrate that the bands selected by JSBS are very effective in terms of small target detection.

A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery.

Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.