Object Recall Research Articles

Does daytime sleepiness alter visual memory in schoolchildren aged 9 to 11 at different times during the school semester?

Daytime sleepiness could reduce the memorization of children who are in school. Thus, the aim of this study was to study the effect of daytime sleepiness on the visual memory of schoolchildren at different times during the school semester. Individuals of both genders (n = 88) aged 9 to 11 years and regularly enrolled at the Mariana Amália Municipal School were selected. Data collection occurred in two moments: at the beginning and end of the academic semester. A semi-structured questionnaire was used to collect sociodemographic information, the Epworth Sleepiness Scale to assess sleepiness and the Rey-Osterrieth complex figure, object recall, scrambling figures and addition of dictated numbers for memory analysis tests. The data revealed that there is no direct relationship between sleepiness and impaired memory by the tests used in any of the analyzed moments. However, children showed lower visuospatial memory efficiency at the beginning of the school semester, indicating that they may have greater difficulty in memory retention. Lastly, there was an abnormality in the degree of sleepiness at the end of the school semester and the female gender showed efficiency in immediate and late memory.

Factoring in the human factor: experimental evidence on how public managers make sense of performance information

We conducted a randomized controlled trial with 902 public hospital department heads to investigate whether and how their perceived understanding and objective recall of performance information may depend on the way it is presented, specifically, the features of its graphical representation. Participants were randomly assigned to one of three alternative graphical displays of the same data, namely a target, a radar, or a pathway chart. We then measured both subjects’ perceived understanding—i.e., how clear and understandable they found the performance metrics—and their actual information recall—as assessed by an objective test. Our analyses demonstrate that alternative visual representations of equivalent data may cause significant differences in public managers’ ability to make sense of performance information. In the context of our experimental test, the pathway chart outperformed the other two in both perceived understanding and objective information recall, which suggests that public managers may have a good sense of which graphic information formats work best for them. We discuss how our findings can contribute to the advancement of behavioral public performance research.

Alpha oscillatory power decreases are associated with better memory for higher valued information

ABSTRACT Items associated with high value are often better remembered. Value may increase attention toward item in context associations. Alpha oscillations (8–13 Hz) are thought to underlie attention and their observation may reveal the role attention plays in value-based memory. In the current study, EEG is used to record brain activity while participants (n = 30) completed a source recognition memory task where items were associated with either high or low value backgrounds to determine whether greater attentional resources are deployed when encoding high value information. Participants demonstrated better memory for objects associated with high value backgrounds. Alpha oscillatory power in occipital/temporal brain regions exhibited greater desynchronization when encoding objects associated with high value that were later successfully recalled compared to those associated with low value. In addition, beta oscillatory power in midfrontal brain regions exhibited greater desynchronization during successful recall of high value objects compared to low value objects. Together these results suggest that more attentional resources are used to encode information that is associated with high value, which increases the likelihood of later successful memory recall.

Investigating the underlying mechanisms of the enactment effect: The role of action-object bindings in aiding immediate memory performance.

Previous research has established that enacted action-object phrases lead to superior immediate memory performance compared with purely verbal memory. In the current investigation, Experiment 1 examined how enactment separately affects immediate memory for actions and objects in 24 adults by presenting action-object phrases and asking participants to recall either the actions or the objects presented in correct serial order. The results showed that when employed at presentation, enactment led to superior recall performance compared with verbal repetition, but this effect was significant only for memory for actions and not objects. Enactment during immediate recall did not lead to better memory performance compared with verbal recall for either actions or objects. To examine whether the lack of an enactment at recall was due to the splitting of action-object phrases at retrieval, Experiment 2 (n = 24) examined memory for whole action-object phrases under enactment at recall. The results showed a typical enactment at recall benefit. Furthermore, a novel binding analysis suggested that enactment recall increased the likelihood of action features being remembered in a bound pair rather than alone. Together these findings suggest that action-object bindings play a crucial role in the manifestation of the enactment effect in immediate recall, especially when enactment is employed at the recall phase.

Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory.

Humans are highly sensitive to the statistical relationships between features and objects within visual scenes. Inconsistent objects within scenes (e.g., a mailbox in a bedroom) instantly jump out to us and are known to catch our attention. However, it is debated whether such semantic inconsistencies result in boosted memory for the scene, impaired memory, or have no influence on memory. Here, we examined the relationship of scene-object consistencies on memory representations measured through drawings made during recall. Participants (N = 30) were eye-tracked while studying 12 real-world scene images with an added object that was either semantically consistent or inconsistent. After a 6-minute distractor task, they drew the scenes from memory while pen movements were tracked electronically. Online scorers (N = 1,725) rated each drawing for diagnosticity, object detail, spatial detail, and memory errors. Inconsistent scenes were recalled more frequently, but contained less object detail. Further, inconsistent objects elicited more errors reflecting looser memory binding (e.g., migration across images). These results point to a dual effect in memory of boosted global (scene) but diminished local (object) information. Finally, we observed that participants fixate longest on inconsistent objects, but these fixations during study were not correlated with recall performance, time, or drawing order. In sum, these results show a nuanced effect of scene inconsistencies on memory detail during recall.

Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding

The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposals. In this paper, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., expression-agnostic), hoping that the proposals contain all right instances in the expression (i.e., expression-aware). Due to this mismatch, current two-stage methods suffer from a severe performance drop between detected and ground-truth proposals. To this end, we propose Ref-NMS, which is the first method to yield expression-aware proposals at the first stage. Ref-NMS regards all nouns in the expression as critical objects, and introduces a lightweight module to predict a score for aligning each box with a critical object. These scores can guide the NMS operation to filter out the boxes irrelevant to the expression, increasing the recall of critical objects, resulting in a significantly improved grounding performance. Since Ref- NMS is agnostic to the grounding step, it can be easily integrated into any state-of-the-art two-stage method. Extensive ablation studies on several backbones, benchmarks, and tasks consistently demonstrate the superiority of Ref-NMS. Codes are available at: https://github.com/ChopinSharp/ref-nms.

Stimulus novelty, task demands, and strategy use in episodic memory.

Cognitive task performance is a dynamic process that evolves over time, starting from the first encounters with a task. An important aspect of these task dynamics is the employment of strategies to support successful performance and task acquisition. Focusing on episodic memory performance, we: (1) tested two hypotheses on the effects of novelty and task difficulty on strategy use, (2) replicated our previous results regarding strategy use in a novel memory task, and (3) evaluated whether repeated open-ended strategy queries affect task performance and/or strategy use. The present pre-registered online study comprised 161 adult participants who were recruited through the Prolific crowdsourcing platform. We employed two separate 5-block list learning tasks, one with 10 pseudowords and the other with 18 common nouns, and collected recall performance and strategy reports for each block. Using Bayesian linear mixed effects models, the present findings (1) provide some support for the hypothesis that task-initial strategy development is not triggered only by task novelty, but can appear also in a familiar, moderately demanding task; (2) replicate earlier findings from an adaptive working memory task indicating strategy use from the beginning of a task, associations between strategy use and objective task performance, and only modest agreement between open-ended versus list-based strategy reports; and (3) indicate that repeated open-ended strategy reports do not affect objective recall. We conclude that strategy use is an important aspect of memory performance right from the start of a task, and it undergoes development at the initial stages depending on task characteristics. In a larger perspective, the present results concur with the views of skill learning and adaptivity in cognitive task performance.

“I Think I Can Remember” Age-Related Changes in Self-Efficacy for Short-Term Memory

Introduction: Changes in metacognitive abilities due to aging, like self-efficacy, have received less attention in cognitive research. Short-term memory (STM) declines among aging adults are well known but the age-related trends of self-efficacy linked to the same have received less attention. The present research aimed at studying age-related trends in self-efficacy linked to STM among the young-aged, middle-aged, and old-aged adults. Materials and Methods: Participants performed face recall, name recall, object recall, face-name association, first-second name association, and face-object association tasks. The self-efficacy linked to these STM tasks was measured through a pre-task prediction question and a post-task judgment question. Descriptive statistics and two-way mixed model ANOVA with post hoc Bonferroni analysis were performed to assess age related changes in self-efficacy measures. Results: The findings revealed significant overestimation of performance, during pretask prediction, by old-aged adults and middle-aged adults. While the posttask judgment was recalibrated closer to the actual performance by participants of all age groups. Conclusion: The current research findings indicate that self-efficacy for STM follows an age related decline. Therefore, inclusion of self-efficacy measures in the assessment of STM would provide a valuable insight as it describes an individual's own awareness about their STM abilities, provides realistic feedback about one's STM performance and also aids clinicians in understanding the perception-performance dynamics among the aging adults.

Automatic Defect Segmentation in X-Ray Images Based on Deep Learning

X-ray imaging has been broadly adopted as a nondestructive testing method for product quality inspection. Deep learning has demonstrated powerful image scene understanding capabilities. In this article, U-Net with resnet101 is taken as the baseline for defect segmentation. First, there exist gray inhomogeneous and low-contrast regions in X-ray images, which can hardly be segmented. Contrast-limited adaptive histogram equalization (CLAHE) could be used to improve the contrast and consistency of the X-ray image. A two-stream convolutional neural network (CNN) is proposed that takes the original image and CLAHE processed image as inputs to address this issue. And then in CNN, low-level feature maps are lacking semantic information, which may lead to worse results. A gated multilayer fusion module is proposed to adaptively fuse the high-level features into low-level features. Furthermore, loss functions (such as cross entropy) in semantic segmentation are usually pixel level, ignoring the regional information. A weighted intersection over union (IOU) loss function is proposed to introduce IOU information to guide the model to focus on the objects that are easy to mine. The experimental results prove that the three proposed methods have better performance than the baseline for our dataset, achieving 42.2 in mIoU, 59.2 in Dice, and 54.5%, 74.9%, and 86.3% in small, middle, and large object recall rate, respectively.

An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds

Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall.

Usefulness of cognitive training program using virtual reality and mixed reality

AbstractBackgroundNowadays virtual reality (VR) and mixed reality (MR) is used as a psychological and occupational therapeutic option to the patients of depression, phobia, post traumatic stress disorder and stroke patients. VR and MR have a number of advantages because it is entertaining, thus motivating the patient. Therefore VR and MR can be used for cognitive therapy to the elderly cognitive declined people because it can give attention, motivation and stimulation to them. Our previous pilot study showed that cognitive training program using VR and MR improved cognitive function, depressive mood and electrophysiologic brain activity on Mild cognitive impairment (MCI) patients. This is the extensive study to show the usefulness of VR and MR using cognitive training on MCI patients, which added more session, program and enrolled more patient in comparison with previous pilot study.MethodThis study was an 8‐week, single‐blind study of 20 patients with MCI. The cognitive training program included art therapy, aerobic exercises, memory training, including face‐name association, object recall training, and spaced retrieval; visuo‐motor organization using software; similarity and ruled based categorization; and behavior modification and sequencing (e.g., making change, paying bills) using VR and MR and computerized program. Neuropsychological function was assessed using K‐MMSE (Korean Mini‐Mental State Examination), Clinical dementia rating(CDR), Alzheimer`s Disease Assessment Scale‐Cognition(ADAS‐cog), Korean version of short form Geriatric Depression Scale(SGDS‐K) and Neuropsychiatric Inventory(NPI). Quantitative electroencephalogram (EEG) was assessed on cognitive training days before treatment and after each treatment phase. Then EEG was analyzed by using “iSyncbrain” which is cloud‐based, AI‐driven EEG analysis platform.ResultAfter two months (8 sessions) of treatment, cognitive function, neuropsychiatric symptoms and depression, which were assessed by using MMSE, NPI and SGDS respectively, showed significant improvements (p<0.05). Also there were significant increase of the EEG alpha/theta ratio on both dorsolateral prefrontal cortex and right parietal cortex, which may imply improvement of frontal and visuospatial function. However, there were no statistically significant differences except Source ROI Power and connectivity.ConclusionCognitive training program using VR and MR showed improvement of cognitive function, depressive mood and electrophysiologic brain activity.

16416 Cutaneous wounds and wound infection affect cognition and behavior in mice

Associated depression and cognitive disorders contribute to poor outcomes in patients with chronic wounds. Here we ask the question: does skin dysbiosis as evidenced by chronic infection in the wound alter cognition and behavior, just as altered microbiota of the gut does? The main goals are to test the hypothesis that wounding and wound infection contribute to cognitive impairment and depressive behaviors in mice with skin wounds infected with Pseudomonas aeruginosa, a common wound pathogen. Wounded, wounded/infected, or unwounded control C57Bl mice were examined using the forced swim test for depressive behavior, the light/dark box test for anxiety, and the novel object recall test for decreased recognition memory. Wounded animals exhibited higher level of depressive behavior, increased anxiety, and decreased cognition (recognition memory) relative to unwounded animals. Animals with infected wounds had similarly increased depressive behavior and anxiety, as well as altered cognition, relative to unwounded animals. We probed the animals’ hippocampi using qPCR for mediators to investigate a possible pathogenic mechanism for the observed behavioral changes. Expression of NOD1, NOD2, (Nod-like receptors that are intracellular sensors of pathogen-associated molecular patterns), NR3C1, NR3C2t (glucocorticoid receptors), and cytokines IL-10 and TNF-α were elevated. These changes were present in the brains of both the wounded and the wounded/infected animals, though the magnitude of change was higher in the wounded/infected animals. Understanding the contribution of the skin-microbiota-brain axis to cognitive function may provide novel approaches to improve outcomes of patients with chronic, infected wounds.

Context-Dependent Object Proposal and Recognition

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

Effects of Maternal Fish Oil and/or 5-Methyl-Tetrahydrofolate Supplementation during Pregnancy on Offspring Brain Resting-State at 10 Years Old: A Follow-Up Study from the NUHEAL Randomized Controlled Trial.

Recent studies have shown that maternal supplementation with folate and long-chain polyunsaturated fatty acids (LC-PUFAs) during pregnancy may affect children’s brain development. We aimed at examining the potential long-term effect of maternal supplementation with fish oil (FO) and/or 5-methyl-tetrahydrofolate (5-MTHF) on the brain functionality of offspring at the age of 9.5–10 years. The current study was conducted as a follow-up of the Spanish participants belonging to the Nutraceuticals for a Healthier Life (NUHEAL) project; 57 children were divided into groups according to mother’s supplementation and assessed through functional magnetic resonance imaging (fMRI) scanning and neurodevelopment testing. Independent component analysis and double regression methods were implemented to investigate plausible associations. Children born to mothers supplemented with FO (FO and FO + 5-MTHF groups, n = 33) showed weaker functional connectivity in the default mode (DM) (angular gyrus), the sensorimotor (SM) (motor and somatosensory cortices) and the fronto-parietal (FP) (angular gyrus) networks compared to the No-FO group (placebo and 5-MTHF groups, n = 24) (PFWE < 0.05). Furthermore, no differences were found regarding the neuropsychological tests, except for a trend of better results in an object recall (memory) test. Considering the No-FO group, the aforementioned networks were associated negatively with attention and speed-processing functions. Mother’s FO supplementation during pregnancy seems to be able to shape resting-state network functioning in their children at school age and appears to produce long-term effects on children´s cognitive processing.

Comparative study of 3 tests of cognitive impairment MMSE, AD-8, GPCOG in 200 geriatric cases suspected to have cognitive impairment out of 3750 screened

Background: Dementia is a progressive decline in cognition, function, behavior and activities of daily living. Many assessment scales are used in screening for cognitive impairment, making diagnosis of dementia and for follow-up. Assessment scales in the domains of cognition, function, behavior, quality of life, depression in dementia, care giver burden and dementia severity are used. There are many tools used to assess cognitive function.Methods: Authors performed the 3 object recall, 3 name recall tests, and included care-giver’s opinion about the patient’s cognitive state as the primary screening test for cognitive impairment. After this, we tested it the patient (in whom any of the above three were impaired) with the MMSE, AD8 and GPCOG instruments.Results: The result were compared of each of the preliminary screening test to each of these three standard tools by applying statistical test (chi square).Conclusions: The screening tests and care giver's opinion about patient's condition (that take 2-3 minutes) have been shown to correlate with MMSE (8 minutes), AD8 (6 minutes), GPCOG (6-9 minutes). This saves the doctor’s, patient’s and caregiver’s time. The screening can be done by trained health worker, thus sparing the geriatrician to do more advanced patient related work.

Real-time 3D object proposal generation and classification using limited processing resources

The task of detecting 3D objects is important in various robotic applications. The existing deep learning-based detection techniques have achieved impressive performances. However, these techniques are limited to being run on a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources, we propose an efficient detection method based on 3D proposal generation and classification. The proposal generation is based mainly on point segmentation, while proposal classification is performed by a lightweight convolution neural network (CNN). KITTI datasets are then used to validate our method. It takes only 0.082 s for our method to process one point block with one core of the central processing unit (CPU). In addition to efficiency, the experimental results also demonstrate the capability of the proposed method of producing a competitive performance in object recall and classification.

Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks

Manufacturing process defects or artificial operation mistakes may lead to solar cells having surface cracks, over welding, black edges, unsoldered areas, and other minor defects on their surfaces. These defects will reduce the efficiency of solar cells or even make them completely useless. In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different convolution neural networks, i.e., Faster R-CNN and R-FCN, are combined to improve detection precision and position accuracy. In addition, according to the inherent characteristics of the surface defects in solar cells, two other strategies are used to further improve the detection performance. First, the anchor points of the region proposal network (RPN) are set by adding multi-scale and multi-aspect regions to overcome the problem of high false negative rate caused by the limitation of anchor points. Second, in view of the subtle and concealed defects of solar cells, the hard negative sample mining strategy is used to solve the problem of low detection precision caused by the negative sample space being too large. The experimental results showed that the proposed method effectively reduced the false negative rate and the false positive rate of a single network, and it greatly improved the accuracy of the locations of defects while improving the object recall rate.

Tell me what you saw: The usefulness of verbal descriptions for others.

Describing what one saw to another person is common in everyday experience, such as spatial navigation and crime investigations. Past studies have examined the effects of recounting on one's own memory, neglecting an important function of memory recall in social communication. Here we report surprisingly low utility of one's verbal descriptions for others, even when visual memory for the stimuli has high capacity. Participants described photographs of common objects they had seen to enable judges to identify the target object from a foil in the same basic-level category. When describing from perception, participants were able to provide useful descriptions, allowing judges to accurately identify the target objects 87% of the time. Judges' accuracy decreased to just 57% when participants provided descriptions from memory acquired minutes ago, and to near chance (51.8%) when the verbal descriptions were based on memory acquired 24 hours ago. Comparison of participants' own identification accuracy with judges' accuracy suggests the presence of a common source of errors. This finding suggests that recall and recognition of visual objects share common memory sources. In addition, the low utility of one's verbal descriptions constrains theories about the extension of one's memory to the external world and has implications for eyewitness identification and laws governing it.

Robust Head Detection in Complex Videos Using Two-Stage Deep Convolution Framework

Pedestrian head detection plays an important role in identifying and localizing individuals in real world visual data. Head detection is a nontrivial problem due to considerable variance in camera view-points, scales, human poses, and appearances in the scene. Thanks to the translation invariance property of convolutional neural networks (CNNs) which enables large capacity CNNs to handle the problem of appearance and pose variations in the scene. However, the problem of scale invariance is still an open issue. To address this problem, this paper presents a two-stage head detection framework that utilizes fully convolutional network (FCN) to generate scale-aware proposals followed by CNN that classifies each proposal into two classes, i.e. head and background. Experiments results show that using scale-aware proposals obtained by FCN, the object recall rate and mean average precision (mAP) are improved. Additionaly, we demonstrate that our framework achieved state-of-the-art results on four challenging benchmark datasets, i.e. HollywoodHeads, Casablanca, SHOCK, and WIDERFACE.

Psychoeducation through digital video for Olympic and Paralympic athletic career transition

Objective: Prior research has noted Olympic and Paralympic athletes are often unaware and unprepared for upcoming career transitions, resulting in experiences of psychological distress. The purpose of this study was to explore the use of digital video as a delivery method for psychoeducation about an upcoming career transition. Design: Post-survey design. Method: Video development was guided by entertainment-education theory. Participants were shown the video at a centralised training location and asked to fill in questionnaires following viewing and again within 72 hours of viewing. Specifically, subjective and objective recall of the intended learning points was assessed. In addition, participants’ appreciation of the content and design was measured. Analysis was descriptive in nature. Results: Participants included 168 Olympic/Paralympic athletes ( N = 116), coaches ( N = 10) and athlete support providers ( N = 42). Immediately following viewing, participants could accurately recall between one and three learning points, but at 72 hours post-viewing, this range had fallen to one to two learning points. Participants indicated they enjoyed the video, identified with the narrators and found it informative and personally relevant. Conclusion: The results of this study suggest a digital video 2 minutes 35 seconds in length is a useful psychoeducation tool for up to three learning points. The results support the development of video psychoeducation resources using education-entertainment theory and the social validity of video as a psychoeducation tool in Olympic and Paralympic sport.