Ensemble Coding Research Articles

Heterogeneous graph representation learning via mutual information estimation for fraud detection

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labelled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.

Effect of attention on ensemble perception: Comparison between exogenous attention, endogenous attention, and depth.

Ensemble perception is an important ability of human beings that allows one to extract summary information for scenes and environments that contain information that far exceeds the processing limit of the visual system. Although attention has been shown to bias ensemble perception, two important questions remain unclear: (1) whether direct manipulations on different types of spatial attention could produce similar effects on ensembles and (2) whether factors potentially influencing the attention distribution, such as depth perception, could evoke an indirect effect of attention on ensemble representation. This study aims to address these questions. In Experiments 1 and 2, two types of precues were used to evoke exogenous and endogenous attention, respectively, and the ensemble color perceptions were examined. We found that both exogenous and endogenous attention biased ensemble representation towards the attended items, and the latter produced a greater effect. In Experiments 3 and 4, we examined whether depth perception could affect color ensembles by indirectly influencing attention allocation in 3D space. The items were separated in two depth planes, and no explicit cues were applied. The results showed that color ensemble was biased to closer items when depth information was task relevant. This suggests that ensemble perception is naturally biased in 3D space, probably through the mechanism of attention. Computational modeling consistently showed that attention exerted a direct shift on the ensemble statistics rather than averaging the feature values over the cued and noncued items, providing evidence against an averaging process of individual perception.

Effects of outlier and familiar context in trend-line estimates in scatterplots.

Lately, there has been a growing fascination with blending research on visualizing data and understanding how our basic visual perception works. Taking this path, this research delved into the connection between ensemble perception, which involves quickly and accurately grasping essential information from sets of visually similar objects, and how we process scatterplots. Across two experiments, we aimed to answer a couple of connected questions. First, we investigated whether having an outlier in a scatterplot affects how people draw trend-line estimates. Second, we explored whether what we are familiar with and the presence of outliers that match the trend affect how we draw trend-line estimates in scatterplots. In both experiments, we showed participants scatterplots for a short time, manipulating whether there were outliers or not. Then, using a computer mouse, participants drew their trend-line estimates. By comparing what they drew with possible trend-line solutions, we discovered that when there is no context, the outlier and the other points in a scatterplot are seen as equally important in drawing the trend-line estimate. But when the scatterplot depicted a familiar context and the outlier fitted the trend, people tended to give more weight to those outlier points in their drawings. This suggested that what we already believe can sway how we draw trend-line estimates even from quickly shown scatterplots.

MEA2: A Lightweight Field-Sensitive Escape Analysis with Points-to Calculation for Golang

Escape analysis plays a crucial role in garbage-collected languages as it enables the allocation of non-escaping variables on the stack by identifying the dynamic lifetimes of objects and pointers. This helps in reducing heap allocations and alleviating garbage collection pressure. However, Go, as a garbage-collected language, employs a fast yet conservative escape analysis, which is field-insensitive and omits point-to-set calculation to expedite compilation. This results in more variables being allocated on the heap. Empirical statistics reveal that field access and indirect memory access are prevalent in real-world Go programs, suggesting potential opportunities for escape analysis to enhance program performance. In this paper, we propose MEA 2 , an escape analysis framework atop GoLLVM (an LLVM-based Go compiler), which combines field sensitivity and points-to analysis. Moreover, a novel generic function summary representation is designed to facilitate fast inter-procedural analysis. We evaluated it by using MEA 2 to perform stack allocation in 12 wildly-use open-source projects. The results show that, compared to Go’s escape analysis, MEA 2 can reduce heap allocation sites by 7.9

Continuous psychophysics reveals a temporal cost of real-time distractor suppression during ensemble perception

Continuous psychophysics reveals a temporal cost of real-time distractor suppression during ensemble perception

A Process Model of Ensemble Coding within Working Memory

A Process Model of Ensemble Coding within Working Memory

Automatic processing of unattended mean emotion: Evidence from visual mismatch responses

The mean emotion from multiple facial expressions can be extracted rapidly and precisely. However, it remains debated whether mean emotion processing is automatic which can occur under no attention. To address this question, we used a passive oddball paradigm and recorded event-related brain potentials when participants discriminated the changes in the central fixation while a set of four faces was presented in the periphery. The face set consisted of one happy and three angry expressions (mean negative) or one angry and three happy expressions (mean positive), and the mean negative and mean positive face sets were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or the vice versa. The cluster-based permutation analyses showed that the visual mismatch negativity started early at around 92 ms and was also observed in later time windows when the mean emotion was negative, while a mismatch positivity was observed at around 168- 266 ms when the mean emotion was positive. The results suggest that there might be different mechanisms underlying the processing of mean negative and mean positive emotions. More importantly, the brain can detect the changes in the mean emotion automatically, and ensemble coding for multiple facial expressions can occur in an automatic fashion without attention.

Other ethnicity effects in ensemble coding of facial expressions.

Cultural difference in ensemble emotion perception is an important research question, providing insights into the complexity of human cognition and social interaction. Here, we conducted two experiments to investigate how emotion perception would be affected by other ethnicity effects and ensemble coding. In Experiment 1, two groups of Asian and Caucasian participants were tasked with assessing the average emotion of faces from their ethnic group, other ethnic group, and mixed ethnicity groups. Results revealed that participants exhibited relatively accurate yet amplified emotion perception of their group faces, with a tendency to overestimate the weight of the faces from the other ethnic group. In Experiment 2, Asian participants were instructed to discern the emotion of a target face surrounded by faces from Caucasian and Asian faces. Results corroborated earlier findings, indicating that while participants accurately perceived emotions in faces of their ethnicity, their perception of Caucasian faces was noticeably influenced by the presence of surrounding Asian faces. These findings collectively support the notion that the other ethnicity effect stems from differential emotional amplification inherent in ensemble coding of emotion perception.

The combination operation of grouping and ensemble coding for structured biological motion crowds in working memory

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

Contingent magnetic variation and beta-band oscillations in sensorimotor temporal decision-making

The ability to accurately encode the temporal information of sensory events and hence to make prompt action is fundamental to humans’ prompt behavioral decision-making. Here we examined the ability of ensemble coding (averaging multiple inter-intervals in a sound sequence) and subsequent immediate reproduction of target duration at half, equal, or double that of the perceived mean interval in a sensorimotor loop. With magnetoencephalography (MEG), we found that the contingent magnetic variation (CMV) in the central scalp varied as a function of the averaging tasks, with a faster rate for buildup amplitudes and shorter peak latencies in the “half” condition as compared to the “double” condition. ERD (event-related desynchronization) -to-ERS (event-related synchronization) latency was shorter in the ”half” condition. A robust beta band (15–23 Hz) power suppression and recovery between the final tone and the action of key pressing was found for time reproduction. The beta modulation depth (i.e., the ERD-to-ERS power difference) was larger in motor areas than in primary auditory areas. Moreover, results of phase slope index (PSI) indicated that beta oscillations in the left supplementary motor area (SMA) led those in the right superior temporal gyrus (STG), showing SMA to STG directionality for the processing of sequential (temporal) auditory interval information. Our findings provide the first evidence to show that CMV and beta oscillations predict the coupling between perception and action in time averaging.

Ensemble Coding of Crowd with Cross-Category Facial Expressions.

Ensemble coding allows observers to form an average to represent a set of elements. However, it is unclear whether observers can extract an average from a cross-category set. Previous investigations on this issue using low-level stimuli yielded contradictory results. The current study addressed this issue by presenting high-level stimuli (i.e., a crowd of facial expressions) simultaneously (Experiment 1) or sequentially (Experiment 2), and asked participants to complete a member judgment task. The results showed that participants could extract average information from a group of cross-category facial expressions with a short perceptual distance. These findings demonstrate cross-category ensemble coding of high-level stimuli, contributing to the understanding of ensemble coding and providing inspiration for future research.

Adopting the visual perspective of a group member is influenced by implicit group averaging.

As social entities, individuals' perception and behaviors are susceptible to the influence of their social groups. Previous research has consistently shown that the group context in which individuals are situated significantly influences their perceptual processing. We aim to investigate whether the group context in which another individual is situated alters our understanding of their visual perception, which holds profound implications for interpersonal interactions. To address this inquiry, we conducted three experiments. In Experiment 1, participants were presented with a visual scene depicting multiple avatars seated around a table, all facing an arrow positioned at the center of the table. They were instructed to adopt the visual perspective of a specific avatar within the group to perceive the arrow's orientation, and then reproduce its orientation from their own perspectives. We found that participants exhibited a bias towards the group's average perspective when reproducing the arrow's orientation. Experiment 2 further demonstrated that reinforced group processing could elicit an earlier appearance of this bias. In Experiment 3, we investigated an alternative explanation positing that the aforementioned bias originated from visual ensemble perception rather than group influence by instructing participants to reproduce the target avatar's position relative to the arrow's orientation. If the bias indeed originated from ensemble perception, it should also manifest in this task. However, the absence of any reproduction bias refuted this possibility. Through these experiments, we demonstrate that our understanding of an individual's perceptual experiences is influenced by the social context in which they are situated, which manifests as a convergence phenomenon.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs.

Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants. We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest. ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.

Visual perception of group fatness as a precursor to social identity threat and safety

ABSTRACT Across two studies (N = 190), we evaluate how fat people perceive groups of bodies and how this visual processing predicts social identity threat and safety. We examined how fat people ensemble code – rapidly and simultaneously extract visual information from a group – body size from groups of bodies. Stimuli included sets of 3-d modeled bodies varying in body mass index (BMI; Study 1) and real photographs of groups of people with known BMI (Study 2). As hypothesized, we found that fat people were generally accurate at ensemble coding the average body size of arrays after 500 ms exposure, and that, particularly among fat people with medium and high stigma consciousness, arrays of increasing average BMI were associated with heightened feelings of identity safety. Our work suggests that the representation of fatness in one’s environment may be an important precursor to social identity threat and safety processes.

Consistent social information perceived in animated backgrounds improves ensemble perception of facial expressions.

Observers can rapidly extract the mean emotion from a set of faces with remarkable precision, known as ensemble coding. Previous studies have demonstrated that matched physical backgrounds improve the precision of ongoing ensemble tasks. However, it remains unknown whether this facilitation effect still occurs when matched social information is perceived from the backgrounds. In two experiments, participants decided whether the test face in the retrieving phase appeared more disgusted or neutral than the mean emotion of the face set in the encoding phase. Both phases were paired with task-irrelevant animated backgrounds, which included either the forward movement trajectory carrying the "cooperatively chasing" information, or the backward movement trajectory conveying no such chasing information. The backgrounds in the encoding and retrieving phases were either mismatched (i.e., forward and backward replays of the same trajectory), or matched (i.e., two identical forward movement trajectories in Experiment 1, or two different forward movement trajectories in Experiment 2). Participants in both experiments showed higher ensemble precisions and better discrimination sensitivities when backgrounds matched. The findings suggest that consistent social information perceived from memory-related context exerts a context-matching facilitation effect on ensemble coding and, more importantly, this effect is independent of consistent physical information.

Characterising the role of awareness in ensemble perception.

Ensemble representations are efficient codes that the brain generates effortlessly even under noisy conditions. However, the role of visual awareness for computing ensemble representations remains unclear. We present two psychophysical experiments (N = 15 × 2) using a bias-free paradigm to investigate the contribution of conscious and unconscious processing to ensemble perception. Here, we show that ensemble perception can unfold without awareness of the relevant features that define the ensemble. Computational modeling of the type-1 and type-2 drift-rates further suggest that awareness lags well behind the categorization processes that support ensemble perception. Additional evidence indicates that the dissociation between type-1 from type-2 sensitivity, was not driven by the type-2 inefficiency or a systematic disadvantage in type-2 decision making. The present study demonstrates the utility of robust measures for studying the role of visual consciousness and metacognition in stimuli and tasks of increasing complexity, crucially, without underestimating the contribution of unconscious processing in an otherwise visible stimulus. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ensemble perception of emotion: Incidental effects of social identity.

Research in vision science suggests that people possess a perceptual mechanism-ensemble perception-which enables them to rapidly identify the characteristics of groups (e.g., emotion, sex-ratio, race-ratio). This work examined whether ensemble perceptions of groups are driven by the characteristics of group members whose behavior is most likely to impact the perceiver. Specifically, we predicted that more self-relevant group members would be weighted more heavily in ensemble perceptions than less self-relevant group members. Study 1 (n = 83) found that young adult participants' ensemble perceptions of emotion were biased in favor of more self-relevant (younger adult) group members' emotional expressions, compared to less self-relevant (older adult) group members' emotional expressions, and that these ensemble perceptions informed judgments of belonging in the group. Study 2 recruited older (n = 94) and younger (n = 97) adult participants and again found a general pattern of bias in favor of more self-relevant (younger adult) group members' emotional expressions in ensemble perceptions of emotion and that these ensemble perceptions informed evaluations of belonging in the group. Finally, Study 3 (n = 193) directly manipulated the self-relevance of older and younger adult group members and found that the extent of bias in ensemble perceptions of emotion depended on whether younger or older adults were made more self-relevant. Results suggest that incidental cues of social identity can bias ensemble perceptions of emotion and influence downstream judgments of belonging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Temporal integration of target features across and within trials in the attentional blink.

Attentional blink research has typically investigated attentional limitations in multiple target processing. The current study investigated the temporal integration of target features in the attentional blink. Across two experiments, we demonstrated that the orientation estimations of individual target items in the attentional blink paradigm were systematically biased. Specifically, there was evidence for both within- and across-trial biases, revealing a general bias towards previously presented stimuli. Moreover, both biases were found to be more salient for targets suffering from the attentional blink. The current study is the first to demonstrate an across-trial bias in responses in the attentional blink paradigm. This set of findings is in line with the literature, suggesting that the human visual system can implicitly summarize information presented over time, which may lead to biases. By investigating temporal integration in the attentional blink, we have been able to address the modulatory role of attention on biases imposed by the implicit temporal effects in estimation tasks. Our findings may inform future research on attentional blink, serial dependence, and ensemble perception.

Supplemental Material for Ensemble Perception of Emotion: Incidental Effects of Social Identity

Supplemental Material for Ensemble Perception of Emotion: Incidental Effects of Social Identity