Novel-view scene recognition relies on identifying spatial reference directions

Five experiments investigated whether observer locomotion provides specialized information facilitating novel-view scene recognition. Participants detected a position change after briefly viewing a desktop scene when the table stayed stationary or was rotated and when the observer stayed stationary or locomoted. The results showed that 49° novel-view scene recognition was more accurate when the novel view was caused by observer locomotion than when the novel view was caused by table rotation. However such superiority of observer locomotion disappeared when the to-be-tested viewpoint was indicated during the study phase, when the study viewing direction was indicated during the test phase, and when the novel test view was 98°, and was even reversed when the study viewing direction was indicated during the test phase in the table rotation condition but not in the observer locomotion condition. These results suggest scene recognition relies on the identification of the spatial reference directions of the scene and accurately indicating the spatial reference direction can facilitate scene recognition. The facilitative effect of locomotion occurs because the spatial reference direction of the scene is tracked during locomotion and more accurately identified at test.     

Are errors differentiable from deceptive responses when feigning memory impairment? An fMRI study

Previous neuroimaging studies have suggested that the neural activity associated with truthful recall, with false memory, and with feigned memory impairment are different from one another. Here, we report a functional magnetic resonance imaging (fMRI) study that addressed an important but yet unanswered question: Is the neural activity associated with intentional faked responses and with errors differentiable? Using a word list learning recognition paradigm, the findings of this mixed event-related fMRI study clearly indicated that the brain activity associated with intentional faked responses was different to the activity associated with errors committed unintentionally. For intentional faked responses, significant activation was found in the ventrolateral prefrontal cortex, the posterior cingulate region, and the precuneus. However, no significant activation was observed for unintentional errors. The results suggest that deception, in terms of feigning memory impairment, is not only more cognitively demanding than making unintentional errors but also utilizes different cognitive processes.     

Lying about facial recognition: an fMRI study

Novel deception detection techniques have been in creation for centuries. Functional magnetic resonance imaging (fMRI) is a neuroscience technology that non-invasively measures brain activity associated with behavior and cognition. A number of investigators have explored the utilization and efficiency of fMRI in deception detection. In this study, 18 subjects were instructed during an fMRI "line-up" task to either conceal (lie) or reveal (truth) the identities of individuals seen in study sets in order to determine the neural correlates of intentionally misidentifying previously known faces (lying about recognition). A repeated measures ANOVA (lie vs. truth and familiar vs. unfamiliar) and two paired t-tests (familiar vs. unfamiliar and familiar lie vs. familiar truth) revealed areas of activation associated with deception in the right MGF, red nucleus, IFG, SMG, SFG (with ACC), DLPFC, and bilateral precuneus. The areas activated in the present study may be involved in the suppression of truth, working and visuospatial memories, and imagery when providing misleading (deceptive) responses to facial identification prompts in the form of a "line-up".     

线索对基本特征刺激加工作用机制研究

许多研究认为,基本特征的觉察过程是平行加工的过程,它的探测与注意分配到某个位置没有关系。本实验通过不同线索分配注意,探讨了基本特征的平行加工与选择性注意的作用的关系。结果发现：(1)基本特征的平行加工,可能受到注意分配影响的;(2)注意分配的作用可能通过抑制干扰项的加工,而不仅仅只是通过促进靶子的加工而实现的。作者在讨论中探讨了可能的原因。     

Moving to higher ground: The dynamic field theory and the dynamics of visual cognition

In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the ‘binding problem’ in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior.     

Implicit learning for probable changes in a visual change detection task

Previous research demonstrates that implicitly learned probability information can guide visual attention. We examined whether the probability of an object changing can be implicitly learned and then used to improve change detection performance. In a series of six experiments, participants completed 120–130 training change detection trials. In four of the experiments the object that changed color was the same shape (trained shape) on every trial. Participants were not explicitly aware of this change probability manipulation and change detection performance was not improved for the trained shape versus untrained shapes. In two of the experiments, the object that changed color was always in the same general location (trained location). Although participants were not explicitly aware of the change probability, implicit knowledge of it did improve change detection performance in the trained location. These results indicate that improved change detection performance through implicitly learned change probability occurs for location but not shape.     

Developing an Online Early Detection System for School Attendance Problems: Results From a Research-Community Partnership

School refusal and other school attendance problems are vexing problems for school-aged youth, families, school personnel, and clinicians. However, few resources exist to detect problematic attendance. The current report describes three steps of a research-community partnership to develop an early identification program to detect youth at risk for problematic attendance. First, a survey was conducted to estimate the scope and cost of school refusal across grades K–12. School administrators estimated relatively few youth exhibiting significant school refusal (missing 5 or more days per year) but estimated the costs associated with services for these youth to be very high (mean cost of in-district programs: $94,052; mean cost of out-of-district placements: $496,657). Second, elementary school counselors were tasked with tracking absenteeism among at-risk youth using an online attendance tracking prototype. Counselors identified a high number of youth who showed elevated absences, lates, or early departures (17.2% of enrolled students), and counselor ratings were significantly related to whether the student (a) had received an individualized education plan or 504 plan, (b) had a sibling with similar attendance problems, (c) was older, or (d) had divorced or separated parents. In a final step, counselor feedback was sought and revisions were incorporated in the attendance tracker. Findings reinforce the prevalence and cost of school attendance problems, provide guidance for using technology to monitor attendance and related indices (tardies, early departures), and direct attention to youth factors that may be useful in identifying youth at risk for poor attendance.     

Reliable pupil detection and iris segmentation algorithm based on SPS

Iris segmentation is one of the complex research areas, as human eyes contain intricate details and are difficult to process. Due to the advancement of technology, security based application employ biometrics to ensure the identity of an individual. Though fingerprint is the commonly utilized biometric, iris is the most promising biometric. However, extracting the iris is not a simple task and the iris recognition accuracy depends on the effectiveness of the iris segmentation. Taking this statement into account, this work proposes a reliable iris segmentation algorithm which is based on SuperPixel Segmentation (SPS). Initially, the eyelids and pupil of the eye image are detected. This is followed by the segmentation of iris. The proposed approach is applied over four benchmark datasets such as CASIA iris V1, V2, V3 and Ubiris V2 databases. The performance of the proposed iris segmentation algorithm is compared with the existing techniques. The proposed segmentation algorithm proves its stability in all the datasets with respect to segmentation accuracy, sensitivity and specificity.     

An accurate and efficient multi-category edge detection method

Edge detection plays an important role in image processing. With the development of deep learning, the accuracy of edge detection has been greatly improved, and people have more requirements for edge detection tasks. Most edge detection algorithms are binary edge detection methods, but there are usually multiple categories of edges in an image. In this paper, we present an accurate multi-category edge detection network, the richer category-aware semantic edge detection network (R-CASENet). In order to make full use of convolutional neural network’s powerful feature expression capabilities, we attempt to use more information from feature maps for edge feature extraction and classification. Using the ResNet101 network as the backbone, firstly we merge the building blocks in different composite blocks and down-sample to obtain the feature maps. Then we fuse the feature maps in different composite blocks to obtain the final fused classifier. Experimental results show that R-CASENet can achieve state-of-the-art performance on the large SBD dataset. Furthermore, to get precise one-pixel width edges, we also propose an edge refinement network (ERN) structure. The proposed scheme is an end-to-end method and the proposed ERN can reduce redundant points and improve computational efficiency, especially for further image processing.     

A test of the micro‐expressions training tool: Does it improve lie detection?

The purpose of the study was to examine the effectiveness of the micro‐expressions training tool (METT) in identifying and using micro‐expressions to improve lie detection. Participants (n = 90) were randomly assigned to receive training in micro‐expressions recognition, a bogus control training, or no training. All participants made veracity judgements of five randomly selected videos of targets providing deceptive or truthful statements. With the use of the Bayesian analyses, we found that the METT group did not outperform those in the bogus training and no training groups. Further, overall accuracy was slightly below chance. Implications of these results are discussed.