Contextual Information Research Articles

EFMF-pillars: 3D object detection based on enhanced features and multi-scale fusion

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPDarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm’s average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable.

Large language models and their applications in bioinformatics

Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language. Beyond traditional NLP applications, LLMs have shown significant promise in bioinformatics, transforming the field by addressing challenges associated with large and complex biological datasets. In genomics, proteomics, and personalized medicine, LLMs facilitate identifying patterns, predicting protein structures, or understanding genetic variations. This capability is crucial, e.g., for advancing drug discovery, where accurate prediction of molecular interactions is essential. This review discusses the current trends in LLMs research and their potential to revolutionize the field of bioinformatics and accelerate novel discoveries in the life sciences.

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms

Background and Objective:Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated segmentation methods can greatly improve the efficiency of doctors. At the same time, fetal brain tissue undergoes various changes throughout the pregnancy, leading to a continuous change in tissue contrast, which greatly increases the difficulty of training segmentation methods. This study aims to develop an automated segmentation model that can efficiently and accurately segment fetal brain tissue, improving the workflow for medical professionals. Methods:We propose a novel deep learning-based segmentation model that incorporates three innovative components: Firstly, a new Dual Dilated Attention Block (DDAB) is proposed in the encoder part to enhance the feature extraction of local spatial and structural contextual information. Secondly, a Multi-scale Deformable Transformer (MSDT) is integrated into the bottleneck to improve the feature extraction of global information on local spatial and structural contextual information. Thirdly, we use a novel block based on Graph Convolution Attention (GCAB) in the decoder, which effectively enhances the features at the decoder.The code is available at https://github.com/unicoco7/MG-Net/. Results:We trained and tested on the FeTA 2021 and FeTA 2022 datasets, and evaluated using seven popular metrics, including Dice, IoU, MAE, BoundaryF, PRE, SEN, and SPE. Compared to the current state-of-the-art 3D segmentation models such as nnFormer, SwinUNETR, and 3DUX-net, our proposed method has surpassed all of them in metrics like Dice, IoU, and MAE. Specifically, on the FeTA 2021 dataset, our model achieved a Dice of 0.8666, an IoU of 0.7646, and an MAE of 0.0027; on the FeTA 2022 dataset, it achieved a Dice of 0.8552, an IoU of 0.7470, and an MAE of 0.0005. Conclusion:In this paper, we propose a model for three-dimensional fetal brain tissue segmentation based on multi-scale feature fusion and graph convolution attention mechanism, and conduct experimental evaluation on the FeTA 2021 and FeTA 2022 datasets. Understanding the boundaries of fetal brain tissue is crucial for doctors’ diagnosis, so the proposed model is expected to improve the speed and accuracy of doctors’ diagnoses.

SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture

Identifying drivable areas between orchard rows is crucial for intelligent agricultural equipment. However, challenges remain in this field’s accuracy, real-time performance, and generalization of deep learning models. This study proposed the SwinLabNet model in the context of jujube orchards, an innovative network model that utilized a lightweight CNN-transformer hybrid architecture. This approach optimized feature extraction and contextual information capture, effectively addressing long-range dependencies, global information acquisition, and detailed boundary processing. After training on the jujube orchard dataset, the SwinLabNet model demonstrated significant performance advantages: training accuracy reached 97.24%, the mean Intersection over Union (IoU) was 95.73%, and the recall rate was as high as 98.36%. Furthermore, the model performed exceptionally well on vegetable datasets, highlighting its generalization capability across different crop environments. This study successfully applied the SwinLabNet model in orchard environments, providing essential support for developing intelligent agricultural equipment, advancing the identification of drivable areas between rows, and laying a solid foundation for promoting and applying intelligent agrarian technologies.

Semi-supervised action recognition with dynamic temporal information fusion

The most advanced semi-supervised models available are based on images for innovation, and the use of semi-supervised learning models augmented with temporal data for video-level action recognition still suffers from severe model mismatches, and the models are not sufficiently capable of capturing both local and global information about the action. Secondly the use of constant-threshold pseudo-labeling leads to low utilization of unlabeled data for difficult actions in the early stages of training, poor pseudo-labeling quality and affects recognition accuracy. To make the semi-supervised framework FixMatch more suitable for action recognition, we propose Time-Mixer and Dynamic Threshold, respectively. Time-Mixer explores complementary information between time sequences through the fusion of two-channel temporal context information. Dynamic Threshold utilizes a new core mapping function (Normal Distribution Function) to enhance pseudo-labeling quality. Extensive experiments were conducted on three action recognition datasets (Kinetics-400, UCF-101, and HMDB-51). Comprehensive experiments show that the performance of the semi-supervised model in action recognition improves considerably after using dynamic thresholding and temporal context information fusion, with a 14.4% improvement over the baseline and a 1.8% improvement over the TG (with a labeling rate of 10%) in UCF101, whereas an overall good performance is obtained for DTIF.

Polycrystalline silicon photovoltaic cell defects detection based on global context information and multi-scale feature fusion in electroluminescence images

In photovoltaic (PV) cell inspection, electroluminescence (EL) imaging provides high spatial resolution for detecting various types of defects. The recent integration of EL imaging with deep learning models has enhanced the recognition of defects in PV cells. However, the high surface impurity content in polycrystalline silicon PV cells presents a challenge. Defect features can be similar to complex background textures, making highlighting features for small target defects difficult and leading to potential misclassification as background or other classes. To address these challenges, we propose a novel deep convolutional neural network (CNN) model for effectively identifying small target defects in polycrystalline PV cells. We first utilize a global context information (GCI) block to improve CNN’s modeling of global information, aiding in distinguishing PV cell defects with similar local details. Moreover, we design a channel weight feature pyramid (CWFP) that dynamically adjusts the channel weights of extracted features. We further achieve multi-scale feature fusion through the pyramid structure to enhance the resolution capability for small targets. The proposed model was evaluated on a publicly available dataset of 8 defect classes in polycrystalline PV cells, achieving an accuracy of 96.36 %. The experimental results show that the proposed model outperforms existing deep learning models in detecting small target defects with higher precision.

How does visual context of urban open spaces drive soundscape pleasantness and eventfulness: a laboratory experiment using a higher order Ambisonics speaker system and immersive virtual reality

Perception of sound environments is influenced by the context we experience them in. A big portion of the contextual information comes from the visual domain, including our understanding of what a place is but also from its visual features. A laboratory study combining questionnaire responses and eye-tracking tools was designed to investigate if the soundscape outcomes and participants' behaviour inside the simulation can be explained by the perceptual outcomes defined by visual information. 360 degree videos and First Order Ambisonics audio recordings of 27 different urban open spaces taken from the International Soundscape Database were used as the stimuli delivered via a Virtual Reality Head-Mounted Display and a Higher Order Ambisonics speaker array, while a neutral grey environment without sounds being reproduced represented the baseline scenario. A questionnaire tool was deployed within the IVR simulation to collect participants' responses describing their perception of the reproduced environments corresponding to the circumplex model featured in the Method A of the ISO/TS 12913-2. The results revealed a good coverage of the two-dimensional perceptual circumplex space and significant differences between perceptual outcomes driven by sound and those driven by visual stimuli.

Improved Small Object Detection Algorithm CRL-YOLOv5.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Interactive transformer and CNN network for fusion classification of hyperspectral and LiDAR data

ABSTRACT The Transformer has become pivotal for the integrated analysis of multi-source remote-sensing (RS) data in Earth observation, particularly in applications such as the fusion classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data. However, Transformers are often employed as effective feature extractors by adopting similar processing blocks for different modalities from multi-source sensors, overlooking differences in imaging principles and data characteristics. Moreover, in the feature extraction process across different sensor data, there is a lack of necessary cross-modal information interaction, leading to insufficient utilization of complementary information between different sensors and resulting in suboptimal fusion outcomes. In this paper, we propose an interactive Transformer and CNN network for the fusion classification of HSI and LiDAR data. Specifically, a heterogeneous three-branch network architecture is designed for HSI and LiDAR data, where Transformers and CNNs encapsulate global contextual spatial and spectral information for HSI and capture geometric elevation patterns for LiDAR data, respectively. Elevation-Spatial Interaction (ESI) and Spectral-Spatial Interaction (SSI) modules are then introduced for multi-stage feature interaction. ESI enables the CNN-Transformer network to focus on essential local elevation details while simultaneously modelling global contextual spatial information. SSI facilitates the Transformer-Transformer network to cyclically intertwine spectral and spatial information for long-range spectral-spatial feature fusion. Finally, the interacted elevation, spatial, and spectral features undergo the Gated Fusion module to achieve hierarchical fusion adaptively, resulting in an elevation-spatial-spectral representation. Experiments conducted on three benchmark HSI-LiDAR datasets demonstrate the effectiveness of our proposed approach.

CoMaSa:Context Multi-aware Self-attention for emotional response generation

Dialogue generation is an important research direction in natural language generation, and generating utterances with appropriate contextual emotion is a challenging task. The previous work incorporates commonsense knowledge as an auxiliary information source by simply concatenating or adding it to the embeddings of the dialogue context. They did not consider updating the commonsense knowledge from the perspective of context semantic information or selecting context from the perspective of commonsense knowledge semantic information. At the same time, they did not extract emotional information from dialogue context and commonsense knowledge to enhance the embeddings of the dialogue text. These defects may lead to the dialogue model’s inability to judge the semantic consistency between context and generated utterance, and result in the generation of utterance unassociated with the dialogue context and the corresponding context emotion being untrue. Our goal is to build an emotional dialogue generation model that can deeply integrate commonsense knowledge with dialogue context and achieve bidirectional semantic interaction and enhancement. Therefore, in this paper, we propose a context multi-aware self-attention emotion dialogue generation model that models from multiple aspects such as the semantic information, emotional information of context and external knowledge, it effectively alleviates the problems of generating utterance unrelated to the context and disordered emotional expressions in utterance. First, we adopt the transformer encoder as the basic framework and carefully construct a context-aware encoder capable of deeply extracting semantic information. Specifically, we ingeniously integrate different levels of positional information to simulate the natural flow of conversation between speakers. Then, we effectively integrate these positional information with the basic word embedding vectors, and enrich the dialogue’s embedding representation. Lastly, by introducing the attention mechanism, the model can precisely capture and extract context information closely related to the current situation and achieve a deep understanding and accurate grasp of the dialogue content. Second, we consider that the relationship between context and commonsense knowledge is not just a unidirectional selection but a more intimate bidirectional interaction. Therefore, we designed a bidirectional interaction attention component for context and external knowledge. Specifically, we use the method of iterative similarity matrices to calculate the bidirectional similarity relationship between context and external knowledge. Then, using the attention algorithm, we calculate the connectivity from context to commonsense knowledge and from commonsense knowledge to context. Finally, by combining the semantic embeddings of both aspects, we achieve bidirectional updating of context and external knowledge. Third, we establish an emotion-aware decoder based on multi-task learning, which includes an emotion classifier and an utterance generator. In the emotion classifier, we establish an auxiliary task for emotion recognition to help the model understand the process of emotional transition. We use the output of the emotion recognition model as an additional supervisory signal to better control the generation of emotional responses. In the utterance generator, we integrate the output of the emotion classifier with the interaction vector from the bidirectional interaction attention component. This fusion allows us to generate utterance that aligns with the emotional context. Our experiments on DailyDialog and ESConv datasets show that CoMaSa outperforms the baselines in terms of Perplexity, Distinct-1, Distinct-2, and human evaluation. The experiment demonstrates the effectiveness of our Context Multi-aware Self-attention model in the task of emotion generation.

GazeGCN: Gaze-aware Graph Convolutional Network for Text Classification

Graph convolutional networks (GCNs) are capable of capturing contextual relationships in text classification. In this paper, we propose a novel Gaze-aware Graph Convolutional Network (GazeGCN) for text classification, where the gaze signals from humans are incorporated into the GCN architecture, empowering our GazeGCN to use gaze information that can be shared across all data to model the intricate relationships between words and documents. To be specific, we first build a gaze prediction model to obtain five gaze signals to form the gaze distribution of each word. Then, Wasserstein Distance is employed to derive the gaze-aware word–word weight by calculating the gaze distribution between words, so as to improve the capture of luxuriant and similar relationships between words. Furthermore, to enhance the extraction of contextual syntactic information, we introduce a Gaze-enhanced TF–IDF method integrating gaze signals and term frequency–inverse document frequency (TF–IDF) for gaze-aware word-document weight derivation, thus making up for the lack of TF–IDF that does not consider syntactic information. Afterward, the rich source of graph edge information including gaze-aware word–word weight and gaze-aware word-document weight is incorporated to construct graphs, so as to leverage the relationship of word–word and word-document. Experiments show encouraging results on seven benchmark datasets that our approach outperforms the state-of-the-art baseline methods.

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation.

In daily life, we coordinate both simultaneous and sequential bimanual movements to manipulate objects. Our ability to rapidly account for different object dynamics suggests there are neural mechanisms to quickly deal with them. Here we investigate how actions of one arm can serve as a contextual cue for the other arm, and facilitate adaptation. Specifically, we examine the temporal characteristics that underlie motor memory formation and recall, by testing the contextual effects of prior, simultaneous, and post contralateral arm movements in both male and female human participants. To do so, we measure their temporal generalization in three bimanual interference tasks. Importantly, the timing context of the learned action plays a pivotal role in the temporal generalization. While motor memories trained with post adaptation contextual movements generalize broadly, motor memories trained with prior contextual movements exhibit limited generalization, and motor memories trained with simultaneous contextual movements do not generalize to prior or post contextual timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization characteristics. Since these generalizations extend far beyond any variability in training times, we suggest that the observed differences may stem from inherent differences in the use of prior, current and post-adaptation contextual information in the generation of natural behavior. This would imply differences in the underlying neural circuitry involved in learning and executing the corresponding coordinated bimanual movements.Significance Statement This study addresses a fundamental question in the field of sensorimotor neuroscience of how multiple movements are temporally linked within a single motor memory. We examine the temporal generalization of motor memory formation by varying the timing of contextual movements associated with a learned motor memory across a range of prior, current, and post adaptation movement times using a bimanual motor learning task. We observed distinct patterns of temporal generalization based on whether the contextual movements occurred prior to, simultaneously with, or after the adaptation movement. For the first time, our findings reveal that the timing of contextual movements is crucial in the formation and generalization of motor memories.

Knowledge-guided Deep Temporal Clustering for Alzheimer's Disease Subtypes in Completed Clinical Trials.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder with varied patient progression. We aim to test the hypothesis that AD patients can be categorized into subgroups based on differences in progression. We leveraged data from three randomized clinical trials (RCTs) to develop a knowledge-guided, deep temporal clustering (KG-DTC) framework for AD subtyping. This model combined autoencoders for contextual information capture, k-means clustering for representation formation, and clinical outcome classification for clinical knowledge integration. The derived representations, encompassing demographics, APOE genotype, cognitive assessments, brain volumes, and biomarkers, were clustered using the Gaussian Mixture Model to identify AD subtypes. Our novel KG-DTC framework was developed using placebo data from 2,087 AD patients across three solanezumab clinical trials (EXPEDITION, EXPEDITION2, and EXPEDITION3), achieving high performance in outcome prediction and clustering. The KG-DTC model demonstrated superior clustering structures, especially when combined with k-means clustering loss. External validation with independent clinical trial data showed consistent clustering results, with a 0.33 silhouette score for three clusters. The model's stability was confirmed through a leave-one-out approach, with an average adjusted Rand Index around 0.945. Three distinct AD subtypes were identified, each exhibiting unique patterns of cognitive function, neurodegeneration, and amyloid beta levels. Notably, Subtype 3 (S3) showed rapid cognitive decline across multiple clinical measures (e.g., 0.64 in S1 vs. -1.06 in S2 vs. 15.09 in S3 of average ADAS total change score, p<.001). This innovative approach offers promising insights for understanding variability in treatment outcomes and personalizing AD treatment strategies.

AP-Net: Attention-fused volume and progressive aggregation for accurate stereo matching

Stereo matching plays an important role in advancing substantial applications such as 3D reconstruction, autonomous driving, depth sensing, and has recently made tremendous progress. It still poses a significant challenge, however, in the extraction of disparities from a pair of rectified images to achieve accurate stereo matching. In this paper, we propose an attention-fused cost volume construction and progressive cost aggregation network, namely AP-Net, to elevate the accuracy of stereo matching. Specifically, we introduce attention fusion to learn feature similarity between corresponding points on the left and right images while capturing long-dependent contextual information to construct attention-fused cost volumes. Moreover, we propose progressive cost aggregation, which consists of two stages to fully exploit information from different cost volumes. Furthermore, to make more accurate adjustments to the initial disparity, we implement an information entropy-guided disparity refinement module, which leverages information entropy as an explicit confidence measure of the initial disparity estimation to guide the refinement module in calculating the disparity residual more accurately and improving the overall accuracy of disparity estimation. Our implemented disparity refinement module can also be seamlessly embedded into a variety of stereo matching networks, significantly improving the model’s performance with only a small increase in computation and parameters. Experimental results across various stereo datasets confirm that our proposed AP-Net consistently delivers competitive performance. The code is available at https://github.com/zhuys-bupt/AP-Net.

Beyond Navigation: Exploring Users' Contextual Information Needs and Concerns When Interacting with Mobile Map Apps

ABSTRACTMobile map applications greatly facilitate users' everyday information practices and provide them with real‐time and accurate information in different contexts. However, few studies have explored the contextual information needs of users in their interactions with mobile maps. Semi‐structured interviews with 18 participants revealed that users present diverse and rich information needs in their everyday use of map applications, and in addition to the most common information needs for situ navigation, users' contextual information needs varied. Particularly, mobile map applications are increasingly characterized by serious leisure while highlighting their utility. In addition, with the evolution of the platformization of map applications, users are also demanding more autonomy, visibility, and security. This study contributes to the literature on human‐information interaction in mobile maps and sheds light on the user experience design of mobile map applications.

Spatial Contextual Information Modulates Affordance Processing and Early Electrophysiological Markers of Scene Perception.

Scene perception allows humans to extract information from their environment and plan navigation efficiently. The automatic extraction of potential paths in a scene, also referred to as navigational affordances, is supported by scene-selective regions (SSRs) that enable efficient human navigation. Recent evidence suggests that the activity of these SSRs can be influenced by information from adjacent spatial memory areas. However, it remains unexplored how these contextual information could influence the extraction of bottom-up information, such as navigational affordances, from a scene and the underlying neural dynamics. Therefore, we analyzed ERPs in 26 young adults performing scene and spatial memory tasks in artificially generated rooms with varying numbers and locations of available doorways. We found that increasing the number of navigational affordances only impaired performance in the spatial memory task. ERP results showed a similar pattern of activity for both tasks, but with increased P2 amplitude in the spatial memory task compared with the scene memory. Finally, we reported no modulation of the P2 component by the number of affordances in either task. This modulation of early markers of visual processing suggests that the dynamics of SSR activity are influenced by a priori knowledge, with increased amplitude when participants have more contextual information about the perceived scene. Overall, our results suggest that prior spatial knowledge about the scene, such as the location of a goal, modulates early cortical activity associated with SSRs, and that this information may interact with bottom-up processing of scene content, such as navigational affordances.

A Method for Extracting Power Entity Relationships Based on Hybrid Neural Networks

In response to the challenges of entity relationship extraction in unstructured text, the author proposes a power entity relationship extraction method based on a hybrid neural network. This method aims to overcome the limitations of existing models in accurately representing contextual environment information, thereby improving the accuracy of the extraction model to meet practical application needs. Firstly, a Bidirectional Gated Recurrent Unit (BiGRU) was designed to better capture contextual information in text sequences. This helps the model to better understand the relationships between entities. Secondly, an attention mechanism was adopted to enable the model to automatically focus on sequence features that have a significant impact on relationships. This helps the model to extract entity relationships more accurately in complex text environments. Finally, a segmented convolutional neural network (PCNN) was introduced to further improve the accuracy of relationship extraction by learning the environmental feature information in the adjusted sequence. This enables the model to better understand the contextual relationships between entities. On the publicly available English dataset SemEval2010Task8, this method achieved satisfactory results, achieving an F1 value of 85.62%. These experiments have confirmed the effectiveness of our method, providing new ideas and support for automatic extraction of entity relationships, and are expected to play an important role in the field of information extraction.

Emotional scenes as context in emotional expression recognition: The role of emotion or valence match.

Emotion recognition is influenced by contextual information such as social category cues or background scenes. However, past studies yielded mixed findings regarding whether broad valence or specific emotion matches drive context effects and how multiple sources of contextual information may influence emotion recognition. To address these questions, participants were asked to categorize expressions on male and female faces posing happiness and anger and happiness and fear on pleasant and fearful backgrounds (Experiment 1, conducted in 2019), fearful and disgusted expressions on fear and disgust eliciting backgrounds (Experiment 2, conducted in 2022), and fearful and sad expressions on fear and sadness eliciting backgrounds (Experiment 3, conducted in 2022). In Experiment 1 (where stimuli varied in valence), a broad valence match effect was observed. Faster recognition of happiness than fear and anger was more pronounced in pleasant compared to fearful scenes. In Experiments 2 and 3 (where stimuli were negative in valence), specific emotion match effects were observed. Faster recognition occurred when expression and background were emotionally congruent. In Experiments 1 and 3, poser sex independently moderated emotional expression recognition speed. These results suggest that the effect of emotional scenes on facial emotion recognition is mediated by a match in valence when broad valence is task-relevant. Specific emotion matches drive context effects when participants categorize expressions of a single valence. Looking at the influence of background contexts and poser sex together suggests that these two sources of contextual information have an independent rather than an interactive influence on emotional expression recognition speed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

M1 muscarinic receptor activation reverses age-related memory updating impairment in mice

Previously consolidated memories can become temporarily labile upon reactivation.Reactivation-based memory updating is chiefly studied in young subjects, so we aimed to assess this process across the lifespan. To do this, we developed a behavioural paradigm wherein a reactivated object memory is updated with contextual information; 3-month-old and 6-month-old male C57BL/6 mice displayed object memory updating, but 12-month-old mice did not. We found that M1 muscarinic acetylcholine receptor signaling during reactivation was necessary for object memory updating in the young mice. Next, we targeted this mechanism in an attempt to facilitate object memory updating in aging mice. Remarkably, systemic pharmacological M1 receptor activation reversed the age-related deficit. Quantification of cholinergic system markers within perirhinal cortex revealed subtle cellular changes that may contribute to differential performance across age groups. These findings suggest that natural cholinergic change across the lifespan contributes to inflexible memory in the aging brain.