Object Recall Research Articles

Risk factor of disability as new certification of long-term care needs in older Japanese adults with diabetes mellitus: A longitudinal study.

This observational longitudinal study aimed to explore the risk factors for disabilities in older Japanese individuals with diabetes at a frailty clinic, an outpatient clinic specializing in comprehensive geriatric assessments for older patients. The outcome was the onset of disability as a new certification of long-term care needs in outpatients aged ≥65 years. Multivariable Cox regression analyses examined the independent associations between the onset of disability and the variables that reached significance in the bivariate analysis, after adjusting for age and sex, years of education, and certification of need for support. A total of 182 participants (mean age 77.8 years, 59.9% female) were included in the study. During a mean follow-up period of 1070 days, 28 patients (15.4%) developed disabilities. Multivariable analysis showed that poor glycemic control (higher levels of glycosylated albumin and HbA1c), frailty as measured by the Clinical Frailty Scale (CFS), cognitive decline as assessed by the revised Hasegawa Dementia Scale (especially "5 object recall" and verbal fluency as assessed by the number of "vegetable names"), poor adherence to medications, and reduced muscle mass were significantly associated with the development of disability. Multivariable Cox regression analyses revealed independent and significant associations between the development of disability and HbA1c ≥7.5%, CFS ≥4, and generating vegetable names≤9. In identifying older patients with diabetes who are at risk of developing disabilities, it may be useful to evaluate glycemic control, CFS, cognition, and medication adherence. Verbal fluency tests using vegetable names may serve as simple tools for assessing the risk of disability. Geriatr Gerontol Int 2024; ••: ••-••.

Enhanced feature extraction YOLO industrial small object detection algorithm based on receptive-field attention and multi-scale features

Abstract To guarantee the stability and safety of industrial production, it is necessary to regulate the behavior of employees. However, the high background complexity, low pixel count, occlusion and fuzzy appearance can result in a high leakage rate and poor detection accuracy of small objects. Considering the above problems, this paper proposes the EFE-YOLO (Enhanced feature extraction-You Only Look Once) algorithm to improve the detection of industrial small objects. To enhance the detection of fuzzy and occluded objects, the PSRFA (PixelShuffle and Receptive-Field Attention) upsampling module is designed to preserve and reconstruct more detailed information and extract the receptive-field attention weights. Furthermore, the MSE (multi-scale and efficient) downsampling module is designed to merge global and local semantic features to alleviate the problem of false and missed detection. Subsequently, the AFAF (Adaptive Feature Adjustment and Fusion) module is designed to highlight the important features and suppress background information that is not beneficial for detection. Finally, the EIoU loss function is used to improve the convergence speed and localization accuracy. All experiments are conducted on homemade dataset. The improved YOLOv5 algorithm proposed in this paper improves mAP@0.50 (mean average precision at a threshold of 0.50) by 2.8\% compared to the YOLOv5 algorithm. The average precision and recall of small objects show an improvement of 8.1\% and 7.5\%, respectively. The detection performance is still leading in comparison with other advanced algorithms.

Same Same, But Different: Brain Areas Underlying the Learning from Repetitive Episodic Prediction Errors.

Prediction errors (PEs) function as learning signals. It is yet unclear how varying compared to repetitive PEs affect episodic memory in brain and behavior. The current study investigated cerebral and behavioral effects of experiencing either multiple alternative versions ("varying") or one single alternative version ("repetitive") of a previously encoded episode. Participants encoded a set of episodes ("originals") by watching videos showing toy stories. During scanning, participants either experienced originals, one single, or multiple alternative versions of the previously encoded episodes. Participants' memory performance was tested through recall of original objects. Varying and repetitive PEs revealed typical brain responses to the detection of mismatching information including inferior frontal and posterior parietal regions, as well as hippocampus, which is further linked to memory reactivation, and the amygdala, known for modulating memory consolidation. Furthermore, experiencing varying and repetitive PEs triggered distinct brain areas as revealed by direct contrast. Among others, experiencing varying versions triggered activity in the caudate, a region that has been associated with PEs. In contrast, repetitive PEs activated brain areas that resembled more those for retrieval of originally encoded episodes. Thus, ACC and posterior cingulate cortex activation seemed to serve both reactivating old and integrating new but similar information in episodic memory. Consistent with neural findings, participants recalled original objects less accurately when only presented with the same, but not varying, PE during fMRI. The current findings suggest that repeated PEs interact more strongly with a recalled original episodic memory than varying PEs.

Modeling the Effect of Vegetation Coverage on Unmanned Aerial Vehicles-Based Object Detection: A Study in the Minefield Environment

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment.

Short- and long-delay consolidation of memory accessibility and precision across childhood and young adulthood.

Childhood is a period when memory consolidation and knowledge base undergo rapid changes. The present study examined short-delay (overnight) and long-delay (after a 2-week period) consolidation of new information either congruent or incongruent with prior knowledge in typically developing 6- to 8-year-old children (n = 32), 9- to 11-year-old children (n = 33), and 18- to 30-year-old young adults (YA; n = 39). Both memory accessibility (cued recall of objects) and precision (precision of object placement) of initially well-learned object-scene pairs were measured. Our results showed that overnight, memory accessibility declined similarly in all age groups; memory precision improved more in younger children (YC) compared to older children (OC) and even declined in YA. After a 2-week period, both memory accessibility and precision became worse. Specifically, while age groups showed similar decline in memory accessibility, precision decline was less in YC than in OC and YA. The accessibility and precision of congruent and incongruent information changed similarly with consolidation in all age groups. Taken together, our results showed that, for initially well-learned information, YC have robust memory consolidation, despite their overall lower mnemonic performance compared to OC and YA, which is potentially crucial for stable and precise knowledge accumulation early on in development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Context reinstatement requires a schema relevant virtual environment to benefit object recall.

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects.

Recalling Unknowns without Losing Precision: An Effective Solution to Large Model-Guided Open World Object Detection.

Open World Object Detection (OWOD) aims to adapt object detection to an open-world environment, so as to detect unknown objects and learn knowledge incrementally. Existing OWOD methods typically leverage training sets with a relatively small number of known objects. Due to the absence of generic object knowledge, they fail to comprehensively perceive objects beyond the scope of training sets. Recent advancements in large vision models (LVMs), trained on extensive large-scale data, offer a promising opportunity to harness rich generic knowledge for the fundamental advancement of OWOD. Motivated by Segment Anything Model (SAM), a prominent LVM lauded for its exceptional ability to segment generic objects, we first demonstrate the possibility to employ SAM for OWOD and establish the very first SAM-Guided OWOD baseline solution. Subsequently, we identify and address two fundamental challenges in SAM-Guided OWOD and propose a pioneering SAM-Guided Robust Open-world Detector (SGROD) method, which can significantly improve the recall of unknown objects without losing the precision on known objects. Specifically, the two challenges in SAM-Guided OWOD include: (1) Noisy labels caused by the class-agnostic nature of SAM; (2) Precision degradation on known objects when more unknown objects are recalled. For the first problem, we propose a dynamic label assignment (DLA) method that adaptively selects confident labels from SAM during training, evidently reducing the noise impact. For the second problem, we introduce cross-layer learning (CLL) and SAM-based negative sampling (SNS), which enable SGROD to avoid precision loss by learning robust decision boundaries of objectness and classification. Experiments on public datasets show that SGROD not only improves the recall of unknown objects by a large margin (~ 20%), but also preserves highly-competitive precision on known objects. The program codes are available at https://github.com/harrylin-hyl/SGROD.

MLP-YOLOv5: A Lightweight Multi-Scale Identification Model for Lotus Pods with Scale Variation

Lotus pods in unstructured environments often present multi-scale characteristics in the captured images. As a result, it makes their automatic identification difficult and prone to missed and false detections. This study proposed a lightweight multi-scale lotus pod identification model, MLP-YOLOv5, to deal with this difficulty. The model adjusted the multi-scale detection layer and optimized the anchor box parameters to enhance the small object detection accuracy. The C3 module with transformer encoder (C3-TR) and the shuffle attention (SA) mechanism were introduced to improve the feature extraction ability and detection quality of the model. GSConv and VoVGSCSP modules were adopted to build a lightweight neck, thereby reducing model parameters and size. In addition, SIoU was utilized as the loss function of bounding box regression to achieve better accuracy and faster convergence. The experimental results on the multi-scale lotus pod test set showed that MLP-YOLOv5 achieved a mAP of 94.9%, 3% higher than the baseline. In particular, the model’s precision and recall for small-scale objects were improved by 5.5% and 7.4%, respectively. Compared with other mainstream algorithms, MLP-YOLOv5 showed more significant advantages in detection accuracy, parameters, speed, and model size. The test results verified that MLP-YOLOv5 can quickly and accurately identify multi-scale lotus pod objects in complex environments. It could effectively support the harvesting robot by accurately and automatically picking lotus pods.

Memory, Emotion, and Quality of Life in Patients with Long COVID-19.

(1) Background: Persistent COVID is characterized by the presence of fatigue, mental fog, and sleep problems, among others. We aimed to study cognitive abilities (attention, executive functions, memory, language) and psychological and emotional factors in a group of participants of the population with persistent COVID-19 and asymptomatic or non-COVID-19-infected patients; (2) Methods: A total of 86 participants aged 18 to 66 years (X = 46.76) took part in the study, with 57 individuals (66.27%) in the experimental group and 29 (33.73%) in the control group. A comprehensive assessment included neuropsychological evaluations, evaluations of anxious and depressive symptomatology, assessments of the impact of fatigue, sleep quality, memory failures in daily life, and the perceived general health status of the participants; (3) Results: significant differences between groups were found in incidental learning within the Key Numbers task (U = 462.5; p = 0.001; p = 0.022) and in the Direct Digit Span (U = 562; p = 0.022), but not in the Inverse Digit Span (U = 632.5; p = 0.105). Differences were also observed in the prospective memory task of the Rivermead Prospective Memory Tasks (from the Rivermead Behavioural Memory Test) in the recall of quotations (U = 610; p = 0.020) as well as in the recall of objects (U = 681.5; p = 0.032). Concerning the task of verbal fluency, significant differences were found for both phonological cues (p- and s-) (t = -2.190; p = 0.031) and semantic cues (animals) (t = -2.277; p = 0.025). In terms of the psychological impact assessment, significant differences were found in the emotional impact across all variables studied (fatigue, quality of sleep, memory lapses, and the perceived general health status), except for quality of life; (4) Conclusions: Our results suggest that the sequelae derived from persistent COVID may have an impact on people's lives, with higher levels of anxiety and depression, worse sleep quality, a greater number of subjective memory complaints, and a greater feeling of fatigue and impact on quality of life. Furthermore, poorer performance was observed in memory and verbal fluency.

Pilot Study of Reticular Activating System (RAS) in Mild Dementia

AbstractBackgroundClinically, the common early symptoms of dementia include memory problems, reduced concentration, behaviour changes, apathy and withdrawal, loss of ability to do everyday tasks. These cognitive functions are mediated by the reticular activating system (RAS) that is a collection of interconnected brain nuclei that regulates wakefulness, alertness and modulate behaviors. The RAS degenerates early during Alzheimer’s disease progression in association with abnormal tau build‐up.MethodThis study aimed to explore the relationship between the RAS functioning and older people with mild grade dementia. The Kendrick Object Learning Test (KOLT) is a test of recall of everyday objects after viewing for a brief period, an immediate recall of briefly perceived visual data. The Kendrick Digit Copying Test (KDCT) is a test of speed in processing and recording information. These tests suggested cortical excitation mediated by both the RAS and the hypothalamic limbic network which deteriorate in older people with mild grade dementia.ResultIn this pilot study, 25 demented patients with mild grade dementia and 20 participants with known depression were recruited. They are matched in age (mean age 72 with sd 4.89) and shared similar demographic background. The participants were tested with the Kendrick Object Learning Test (KOLT) and the Kendrick Digit Copying Test (KDCT). To observe the short term longitudinal change of their RAS functioning, these tests were administered again 4 weeks following baseline. With regular day activity treatment, the depressive group showed improvement in KOLT (p < 0.1), but not on KDCT (p = .82). However, in the dementia group, there were no significant difference noted in both KOLT and KDCT (p < 0.5).ConclusionThe present findings echo some findings of RAS does not function properly as in the older people with dementia. The reticular system is not compromised in group of depressive participants, there would be improvement in object learning, but no significant difference on speed in processing and recording information as measured by speed‐performance tests (KDCT). Proper and targeted rehabilitation trainings and care giving techniques could be arranged in meeting the specific patients’ needs.

Human memory for real-world solid objects is not predicted by responses to image displays.

In experimental psychology and neuroscience, computerized image stimuli are typically used as artificial proxies for real-world objects to understand brain and behavior. Here, in a series of five experiments (n = 165), we studied human memory for objects presented as tangible solids versus computerized images. We found that recall for solids was superior to images, both immediately after learning, and after a 24-hr delay. A "realness advantage" was also evident relative to three-dimensional (3-D) stereoscopic images, and when solids were viewed monocularly, arguing against explanations based on the presence of binocular depth cues in the stimulus. Critically, memory for solids was modulated by physical distance, with superior recall for objects positioned within versus outside of observers' reach, whereas recall for images was unaffected by distance. We conclude that solids are processed quantitatively and qualitatively differently in episodic memory than are images, suggesting caution in assuming that artifice can always substitute for reality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Detect small object based on FCOS and adaptive feature fusion

Object identification has always been a difficult issue in the field of computer vision, where the objective is to identify the location and kind of particular objects in images. Convolutional neural networks have achieved significant advancements in accuracy and speed, and this is mostly due to their quick evolution. However, in practical applications, the detection of small objects is still an unresolved problem. Due to the sparse pixel information of small target, it is relatively difficult to extract small target features effectively. Although many methods have made significant progress, the detection rate and recall rate both have space for improvement under various conditions. This study suggests a small object detection technique that utilizes feature map weight self allocation and single-stage detection model FCOS to further increase the recall and accuracy of small object detection., which is used in aerial photography dataset and airport foreign object dataset respectively. The average detection accuracy and recall of the technique are roughly 10% higher than the baseline, as shown by the quantitative and qualitative trial findings, demonstrating the effectiveness of the adaptive feature fusion approach.

VL-NMS: Breaking Proposal Bottlenecks in Two-stage Visual-language Matching

The prevailing framework for matching multimodal inputs is based on a two-stage process: (1) detecting proposals with an object detector and (2) matching text queries with proposals. Existing two-stage solutions mostly focus on the matching step. In this article, 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., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i.e., query-aware). Due to this mismatch, chances are that proposals relevant to the text query are suppressed during the filtering process, which in turn bounds the matching performance. To this end, we propose VL-NMS, which is the first method to yield query-aware proposals at the first stage. VL-NMS regards all mentioned instances as critical objects and introduces a lightweight module to predict a score for aligning each proposal with a critical object. These scores can guide the NMS operation to filter out proposals irrelevant to the text query, increasing the recall of critical objects, and resulting in a significantly improved matching performance. Since VL-NMS is agnostic to the matching step, it can be easily integrated into any state-of-the-art two-stage matching method. We validate the effectiveness of VL-NMS on three multimodal matching tasks, namely referring expression grounding, phrase grounding, and image-text matching. Extensive ablation studies on several baselines and benchmarks consistently demonstrate the superiority of VL-NMS.

Memory consolidation during sleep involves context reinstatement in humans

New memories are not quarantined from each other when first encoded; rather, they are interlinked with memories that were encoded in temporal proximity or that share semantic features. By selectively biasing memory processing during sleep, here we test whether context influences sleep consolidation. Participants first formed 18 idiosyncratic narratives, each linking four objects together. Before sleep, they also memorized an on-screen position for each object. During sleep, 12 object-specific sounds were unobtrusively presented, thereby cuing the corresponding spatial memories and impacting spatial recall as a function of initial memory strength. As hypothesized, we find that recall for non-cued objects contextually linked with cued objects also changed. Post-cue electrophysiological responses suggest that activity in the sigma band supports context reinstatement and predicts context-related memory benefits. Concurrently, context-specific electrophysiological activity patterns emerge during sleep. We conclude that reactivation of individual memories during sleep evokes reinstatement of their context, thereby impacting consolidation of associated knowledge.

Surviving with story characters: What do we remember?

Readers simulate story characters' emotions, memories, and perceptual experiences. The current study consists of three experiments that investigated whether survival threat would amplify the mnemonic experience of a narrative. First, a replication study of Nairne et al. (Journal of Experimental Psychology: Learning, Memory, and Cognition, 33 (2), 263-273, 2007) was conducted with minor methodological alternations and yielded improved recall for participants imagining themselves in a survival scenario over a moving scenario (Experiment 1). In Experiments 2 and 3, participants read stories about a character either stranded in the grasslands or moving to a foreign land. Improved recall for objects included in the story (Experiments 2 and 3) and recognition of story details (Experiment 3) was found when the character was in a survival situation. The largest effects were observed when the reader was asked to imagine themselves as the story character (Experiment 3). Overall, readers remembered survival-relevant details as if they were experiencing the story character's plight. These results extend research showing that survival processing enhances memory for word lists (e.g., Nairne et al., Psychological Science, 19 (2), 176-180, 2008).

Investigating Search Among Physical and Virtual Objects Under Different Lighting Conditions.

By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these environments. Here, we conducted a wide-area outdoor AR user study ($n=48$) using a commercially available AR headset (Microsoft Hololens 2) to compare (1) user interactions with physical and virtual objects in the environment (2) the effects of different lighting conditions on user behavior and AR experience and (3) the impact of varying cognitive load on AR task performance. Participants engaged in a treasure hunt task where they searched for and classified virtual target items (green "gems") in an augmented outdoor courtyard scene populated with physical and virtual objects. Cognitive load was manipulated so that in half the search trials users were required to monitor an audio stream and respond to specific target sounds. Walking paths, head orientation and eye gaze information were measured, and users were queried about their memory of encountered objects and provided feedback on the experience. Key findings included (1) Participants self-reported significantly lower comfort in the ambient natural light condition, with virtual objects more visible and participants more likely to walk into physical objects at night; (2) recall for physical objects was worse than for virtual objects, (3) participants discovered more gems hidden behind virtual objects than physical objects, implying higher attention on virtual objects and (4) dual-tasking modified search behavior. These results suggest there are important technical, perceptual and cognitive factors that must be considered if the full potential of "anywhere and anytime mobile AR" is to be realized.

Do graspable objects always leave a motor signature? A study on memory traces

Several studies have reported the existence of reciprocal interactions between the type of motor activity physically performed on objects and the conceptual knowledge that is retained of them. Whether covert motor activity plays a similar effect is less clear. Certainly, objects are strong triggers for actions, and motor components can make the associated concepts more memorable. However, addition of an action-related memory trace may not always be automatic and could rather depend on ‘how’ objects are encountered. To test this hypothesis, we compared memory for objects that passive observers experienced as verbal labels (the word describing them), visual images (color photographs) and actions (pantomimes of object use). We predicted that the more direct the involvement of action-related representations the more effective would be the addition of a motor code to the experience and the more accurate would be the recall. Results showed that memory for objects presented as words i.e., a format that might only indirectly prime the sensorimotor system, was generally less accurate compared to memory for objects presented as photographs or pantomimes, which are more likely to directly elicit motor simulation processes. In addition, free recall of objects experienced as pantomimes was more accurate when these items afforded actions performed towards one’s body than actions directed away from the body. We propose that covert motor activity can contribute to objects’ memory, but the beneficial addition of a motor code to the experience is not necessarily automatic. An advantage is more likely to emerge when the observer is induced to take a first-person stance during the encoding phase, as may happen for objects affording actions directed towards the body, which obviously carry more relevance for the actor.

MTL-FFDET: A Multi-Task Learning-Based Model for Forest Fire Detection

Deep learning-based forest fire vision monitoring methods have developed rapidly and are becoming mainstream. The existing methods, however, are based on enormous amounts of data, and have issues with weak feature extraction, poor small target recognition and many missed and false detections in complex forest scenes. In order to solve these problems, we proposed a multi-task learning-based forest fire detection model (MTL-FFDet), which contains three tasks (the detection task, the segmentation task and the classification task) and shares the feature extraction module. In addition, to improve detection accuracy and decrease missed and false detections, we proposed the joint multi-task non-maximum suppression (NMS) processing algorithm that fully utilizes the advantages of each task. Furthermore, considering the objective fact that divided flame targets in an image are still flame targets, our proposed data augmentation strategy of a diagonal swap of random origin is a good remedy for the poor detection effect caused by small fire targets. Experiments showed that our model outperforms YOLOv5-s in terms of mAP (mean average precision) by 3.2%, APS (average precision for small objects) by 4.8%, ARS (average recall for small objects) by 4.0%, and other metrics by 1% to 2%. Finally, the visualization analysis showed that our multi-task model can focus on the target region better than the single-task model during feature extraction, with superior extraction ability.

The spatial layout of doorways and environmental boundaries shape the content of event memories

Physical boundaries in our environment have been observed to define separate events in episodic memory. To date, however, there is little evidence that the spatial properties of boundaries exert any control over event memories. To examine this possibility, we conducted four experiments that took manipulations involving boundaries that have been demonstrated to influence spatial representations, and adapted them for use in an episodic object memory paradigm. Here, participants were given 15 min to freely explore an environment that contained 36 objects, equally dispersed among six discriminable buildings. In a subsequent test of object-location binding, participants were required to indicate where they remembered encountering the objects. In Experiment 1 the spatial properties of the building boundaries were identical; however, in Experiment 2 the boundaries were differentiated by their geometric shape and the location of the doorways in the buildings. In the test phases of these experiments, we observed a shift from a bias towards remembering the positions of objects within a building but not the building itself (Experiment 1), to a bias towards remembering which building an object was in but not the location within the building (Experiment 2). In Experiment 3, the buildings shared the same geometry but were differentiated by the locations of doorways, and we observed no significant differences between response types. Finally, in Experiment 4, the buildings were uniquely shaped but shared the same doorway location, and we observed a bias towards remembering the positions of objects within a building. In addition, exploratory analyses of non-spatial interference revealed more correct recall for objects housed in the first building a participant visited during exploration, compared to all other buildings. Together, our data indicates that the location of doorways in boundaries and, to a lesser extent, boundary geometries influence event models, and that a primacy effect can be observed in the recall of multiple object-location bindings.

Attentional control and metacognitive monitoring of the effects of different types of task-irrelevant sound on serial recall.

The presence of task-irrelevant sound disrupts short-term memory for serial information. Recent studies found that enhanced perceptual task-encoding load (static visual noise added to target items) reduces the disruptive effect of an auditory deviant but does not affect the task-specific interference by changing-state sound, indicating that the deviation effect may be more susceptible to attentional control. This study aimed to further specify the role of attentional control in shielding against different types of auditory distraction, examining speech and nonspeech distractors presented in laboratory and Web based experiments. To further elucidate the role of controlled processes, we tested whether the detrimental effects of distractor sounds-and their modulation by attentional control-reach participants' awareness. We found that changing-state sound and auditory deviants in steady-state sound equally affected both objective recall performance and metacognitive confidence judgments but did not affect the accuracy of confidence judgments. Most importantly, across four experiments, an increase of task load (visual degradation of the to-be-remembered items) did not reduce either type of auditory distraction. A close replication of the original modulation of the deviation effect by perceptual task load (in an online environment) even revealed a stronger deviation effect at high task load, suggesting that the manipulation may have influenced cognitive load and the ability to control distractor interference in memory. In line with a unitary account of auditory distraction, the results suggest that although both types of distraction reach metacognitive awareness, they may be equally unrelated to perceptual load and the availability of attentional resources. (PsycInfo Database Record (c) 2022 APA, all rights reserved).