Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally

Different graph types might differ in group comparison due to differences in underlying graph schemas. Thus, this study examined whether graph schemas are based on perceptual features (i.e., each graph has a specific schema) or common invariant structures (i.e., graphs share several common schemas), and which graphic type (bar vs. dot vs. tally) is the best to compare discrete groups. Three experiments were conducted using the mixing-costs paradigm. Participants received graphs with quantities for three groups in randomized positions and were given the task of comparing two groups. The results suggested that graph schemas are based on a common invariant structure. Tally charts mixed either with bar graphs or with dot graphs showed mixing costs. Yet, bar and dot graphs showed no mixing costs when paired together. Tally charts were the more efficient format for group comparison compared to bar graphs. Moreover, processing time increased when the position difference of compared groups was increased.

Contraction bias in temporal estimation

When asked to compare the perceptual features of two serially presented objects, participants are often biased to over- or under-estimate the difference in magnitude between the stimuli. Overestimation occurs consistently when a) the two stimuli are relatively small in magnitude and the first stimulus is larger in magnitude than the second; or b) the two stimuli are relatively large in magnitude and the first stimulus is smaller in magnitude than the second; underestimation consistently occurs in the complementary cases. This systematic perceptual bias, known as the contraction bias, was demonstrated for a multitude of perceptual features and in various modalities, but it is yet unknown whether it also exists in the temporal domain. Here, we tested whether estimation of time-duration is affected by the contraction bias. In each trial of three experiments (n=20 each), participants compared the duration of two visually presented stimuli. Findings revealed over- and under-estimation effects as predicted by the contraction bias. In addition, we found that the bias was asymmetrical, indicating that, in some cases, the subjective center of the distribution was shifted to the left. Here, we discuss this asymmetry and describe how these findings can be explained via a Bayesian inference framework.

EXPRESS: Rethinking attentional reset: Task sets determine the boundaries of adaptive control

Adaptive control processes that minimize distraction often operate in a context-specific manner. For example, they may minimize distraction from irrelevant conversations during a lecture but not in the hallway afterwards. It remains unclear, however, whether (a) salient perceptual features or (b) task sets based on such features serve as contextual boundaries for adaptive control in standard distractor-interference tasks. To distinguish between these possibilities, we manipulated whether the structure of a standard, visual distractor-interference task allowed (Experiment 1) or did not allow (Experiment 2) participants to associate salient visual features (i.e., color patches and color words) with different task sets. We found that changing salient visual features across consecutive trials reduced a popular measure of adaptive control in distractor-interference tasks – the congruency sequence effect (CSE) – only when the task structure allowed participants to associate these visual features with different task sets. These findings extend prior support for the task set hypothesis from somewhat atypical cross-modal tasks to a standard unimodal task. Conversely, they pose a challenge to an alternative “attentional reset” hypothesis, and related views, wherein changing salient perceptual features always results in a contextual boundary for the CSE.

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features

In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources, leading to curtailing efficiency. To eliminate this drawback, we propose a new data-driven mesh-density boost model named SuperMeshingNet that strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model, which consisting of Res-UNet architecture, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the attention mechanism and the perceptual features are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data, which contains prior unseen cases. Based on the dataset of the plane stress fields in sheet metal forming, the comparative experiments are proceeded to demonstrate the high quality and superior precision of the reconstructed results generated by the proposed model. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2×, 4×, and 8×. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost. We publicly share our work with full detail of implementation at https://github.com/zhenguonie/2021_SuperMeshing_2D_Metal_Forming.

PFC: A Novel Perceptual Features-Based Framework for Time Series Classification

Time series classification (TSC) is a significant problem in data mining with several applications in different domains. Mining different distinguishing features is the primary method. One promising method is algorithms based on the morphological structure of time series, which are interpretable and accurate. However, existing structural feature-based algorithms, such as time series forest (TSF) and shapelet traverse, all features through many random combinations, which means that a lot of training time and computing resources are required to filter meaningless features, important distinguishing information will be ignored. To overcome this problem, in this paper, we propose a perceptual features-based framework for TSC. We are inspired by how humans observe time series and realize that there are usually only a few essential points that need to be remembered for a time series. Although the complex time series has a lot of details, a small number of data points is enough to describe the shape of the entire sample. First, we use the improved perceptually important points (PIPs) to extract key points and use them as the basis for time series segmentation to obtain a combination of interval-level and point-level features. Secondly, we propose a framework to explore the effects of perceptual structural features combined with decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT) on TSC. The experimental results on the UCR datasets show that our work has achieved leading accuracy, which is instructive for follow-up research.

Expertise Classification of Soccer Goalkeepers in Highly Dynamic Decision Tasks: A Deep Learning Approach for Temporal and Spatial Feature Recognition of Fixation Image Patch Sequences

The focus of expertise research moves constantly forward and includes cognitive factors, such as visual information perception and processing. In highly dynamic tasks, such as decision making in sports, these factors become more important to build a foundation for diagnostic systems and adaptive learning environments. Although most recent research focuses on behavioral features, the underlying cognitive mechanisms have been poorly understood, mainly due to a lack of adequate methods for the analysis of complex eye tracking data that goes beyond aggregated fixations and saccades. There are no consistent statements about specific perceptual features that explain expertise. However, these mechanisms are an important part of expertise, especially in decision making in sports games, as highly trained perceptual cognitive abilities can provide athletes with some advantage. We developed a deep learning approach that independently finds latent perceptual features in fixation image patches. It then derives expertise based solely on these fixation patches, which encompass the gaze behavior of athletes in an elaborately implemented virtual reality setup. We present a CNN-BiLSTM based model for expertise assessment in goalkeeper-specific decision tasks on initiating passes in build-up situations. The empirical validation demonstrated that our model has the ability to find valuable latent features that detect the expertise level of 33 athletes (novice, advanced, and expert) with 73.11% accuracy. This model is a first step in the direction of generalizable expertise recognition based on eye movements.

An event-coding account of attitudes

Attitudes (or opinions, preferences, biases, stereotypes) can be considered bindings of the perceptual features of the attitudes’ object to affective codes with positive or negative connotations, which effectively renders them “event files” in terms of the Theory of Event Coding. We tested a particularly interesting implication of this theoretical account: that affective codes might “migrate” from one event file to another (i.e., effectively function as a component of one while actually being part of another), if the two files overlap in terms of other features. We tested this feature-migration hypothesis by having participants categorize pictures of fictitious outer space characters as members of two fictitious races by pressing a left or right key, and to categorize positive and negative pictures of the International Affective Picture System (IAPS) as positive and negative by using the same two keys. When the outer space characters were later rated for likability, members of the race that was categorized by means of the same key as positive IAPS pictures were liked significantly more than members of the race that was categorized with the same key as negative IAPS pictures – suggesting that affective feature codes from the event files for the IAPS pictures effectively acted as an ingredient of event files for the outer space characters that shared the same key. These findings were fully replicated in a second experiment in which the two races were replaced by two unfamiliar fonts. These outcomes are consistent with the claim that attitudes, opinions, and preferences are represented in terms of event files and created by feature binding.

Projective Psychodiagnostics: Inkblots, Stories, and Drawings as Clinical Measures

Projective psychodiagnostics refers to the use of psychological instruments through which the subject is asked to respond to a set of ambiguous (though often suggestive) stimuli, thereby “projecting” aspects of their personality into these responses. The most prominent of these instruments includes the Rorschach Inkblot Technique, in which the subject is confronted with ten inkblots and is asked what these stimuli look like, and then what perceptual features make them look that way. Another common projective technique is the Thematic Apperception Test (TAT), a storytelling exercise in which the subject responds with a narrative to a series of ambiguous but sometimes highly charged black and white pictures depicting human interactions. Over time, new pictures have been developed for similar storytelling instruments targeted to children (the Children’s Apperception Test) or different ethnic populations. Both of these tests emerged under the influence of psychodynamic theories, and of the work of Carl Jung, whose Word Association Test served as a projective measure of psychological conflicts. Finally, there is a series of drawing tests which, while less commonly used, have had a projective history, including human figure drawings, the Bender–Gestalt Test, and the Wartegg Drawing Completion Test. Projective instruments have been used in a variety of psychiatric settings and have been criticized for being insufficiently grounded in either quantitative measures or scientific validity. The Rorschach has emerged with increasingly statistically based scoring systems addressing perceptual features, language, and content in the assessment of risk and diagnosis. The TAT is essentially a structured interview (since most scoring systems are not used by clinicians), but it nonetheless appears to be useful in gleaning information about a subject’s relationships with other people. Drawing tasks and sentence completion tests (derived from word association tests) are less commonly used, though more prevalent with children whose verbal abilities may be more limited. In general, projective tests appear to have some limited ability to define diagnosis and risk (and can be especially helpful in defining thought disorder and prognosis), but they may be most useful in helping clinicians obtain a deeper picture of conflicts and resources within the person tested.

Brain mechanisms underlying the subjective experience of remembering

The ability to remember events in vivid, multisensory detail is a significant part of human experience, allowing us to relive previous encounters and providing us with the store of memories that shape our identity. Recent research has sought to understand the subjective experience of remembering: what it feels like to have a memory. Such remembering involves reactivating sensory-perceptual features of an event, and the thoughts and feelings we had when the event occurred, integrating them into a conscious first-person experience. It allows us to reflect on the content of our memories, and to understand and make judgments about them, such as distinguishing events that actually occurred from those we might have imagined or been told about. In this review, we consider recent evidence from functional neuroimaging in healthy participants and studies of neurological and psychiatric conditions, which is shedding new light on how we subjectively experience remembering.