Retrieval context determines whether event boundaries impair or enhance temporal order memory

Meaningful changes in context create "event boundaries", segmenting continuous experience into distinct episodes in memory. A foundational finding in this literature is that event boundaries impair memory for the temporal order of stimuli spanning a boundary compared to equally spaced stimuli within an event. This seems surprising in light of intuitions about memory in everyday life, where the order of within-event experiences (did I have coffee before the first bite of bagel?) often seems more difficult to recall than the order of events per se (did I have breakfast or do the dishes first?). Here, we aimed to resolve this discrepancy by manipulating whether stimuli carried information about their encoding context during retrieval, as they often do in everyday life (e.g., bagel-breakfast). In Experiments 1 and 2, we show that stimuli inherently associated with a unique encoding context produce a "flipped" order memory effect, whereby temporal memory was superior for cross-boundary than within-event item pairs. In Experiments 3 and 4, we added context information at retrieval to a standard laboratory event memory protocol where stimuli were encoded in the presence of arbitrary context cues (colored frames). We found that whether temporal order memory for cross-boundary stimuli was enhanced or impaired relative to within-event items depended on whether the context was present or absent during the memory test. Taken together, we demonstrate that the effect of event boundaries on temporal memory is malleable, and determined by the availability of context information at retrieval.

Effect of Environment-Related Cues on Auditory Distance Perception in the Context of Audio-Only Augmented Reality

Audio-only augmented reality consists of enhancing a real environment with virtual sound events. A seamless integration of the virtual events within the environment requires processing them with artificial spatialization and reverberation effects that simulate the acoustic properties of the room. However, in augmented reality, the visual and acoustic environment of the listener may not be fully mastered. This study aims to gain some insight into the acoustic cues (intensity and reverberation) that are used by the listener to form an auditory distance judgment, and to observe if these strategies can be influenced by the listener’s environment. To do so, we present a perceptual evaluation of two distance-rendering models informed by a measured Spatial Room Impulse Response. The choice of the rendering methods was made to design stimuli categories in which the availability and reproduction quality of acoustic cues are different. The proposed models have been evaluated in an online experiment gathering 108 participants who were asked to provide judgments of auditory distance about a stationary source. To evaluate the importance of environmental cues, participants had to describe the environment in which they were running the experiment, and more specifically the volume of the room and the distance to the wall they were facing. It could be shown that these context cues had a limited, but significant, influence on the perceived auditory distance.

Nonverbal Communication Reconstruction on Facebook

<p><em><span>Facebook provides users comfort in communicating even though they cannot see expressions or any other nonverbal signs, which have been an essential factor in supporting face-to-face communication. Therefore, this research is necessary because the absence of nonverbal communication, especially facial expression, touching, and gesture, renders the communication process between individuals ineffective and uncomfortable, as it was when people first used email to communicate via the internet. Through the study of Computer-Mediated Communication (CMC) perspectives, nonverbal communication, Social Presence Theory and Lack of Social Context Cues theory, this paper will discuss forms of nonverbal communication in the digital era. This study is based on research conducted by researchers using the netnography method and carried out through literature studies. The research was conducted on the Muslim community Bening Society on Facebook because the communication between them is very intense, as required in netnography. The loss of nonverbal communication in interpersonal communication does not, in fact, reduce netizens’ comfort in communicating and interacting. The emergence of digital emoticons and nonverbals is a substitute for nonverbal communication because digital emoticon and nonverbal functions in mediated interpersonal communication are the same as nonverbal communication.</span></em></p>

Sources of interference in item and associative recognition memory

A powerful theoretical framework for exploring recognition memory is the global matchingframework, in which a cue’s memory strength reflects the similarity of the retrieval cuesbeing matched against the contents of memory simultaneously. Contributions at retrievalcan be categorized as matches and mismatches to the item and context cues, including theself match (match on item and context), item noise (match on context, mismatch on item),context noise (match on item, mismatch on context), and background noise (mismatch onitem and context). We present a model that directly parameterizes the matches andmismatches to the item and context cues, which enables estimation of the magnitude ofeach interference contribution (item noise, context noise, and background noise). Themodel was fit within a hierarchical Bayesian framework to ten recognition memory datasetsthat employ manipulations of strength, list length, list strength, word frequency, study-testdelay, and stimulus class in item and associative recognition. Estimates of the modelparameters revealed at most a small contribution of item noise that varies by stimulusclass, with virtually no item noise for single words and scenes. Despite the unpopularity ofbackground noise in recognition memory models, background noise estimates dominated atretrieval across nearly all stimulus classes with the exception of high frequency words,which exhibited equivalent levels of context noise and background noise. These parameterestimates suggest that the majority of interference in recognition memory stems fromexperiences acquired prior to the learning episode.

Feature Enhancement Network for Object Detection in Optical Remote Sensing Images

Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.

Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies.

The effects of emotional valence and intensity on cognitive and affective empathy after insula lesions

The insula plays a central role in empathy. However, the complex structure of empathic deficits following insular damage is not fully understood. While previous lesion research has shown variable deficits in patients with insular damage on basic discrimination tasks or self-report measures, it is unclear in how far patients with insular damage are impaired in cognitive (CE) and affective empathy (AE) functions depending on valence and arousal of stimuli using an ecologically valid paradigm. In the present study, patients with insular lesions (n = 20) and demographically-matched healthy controls (n = 24) viewed 16 videos (duration: 60 sec each) that varied in terms of valence and emotional intensity. The videos showed a person (target) reporting on a personal life event. In CE conditions, subjects continuously rated the affective state of the target, while in AE conditions they continuously rated their own affect. Mean Squared Error (MSE) assessed deviations between subject and target ratings (CE: deviation between targets' and participants' ratings of targets' emotions; AE: deviation between targets' and participants' self-ratings of emotion). Patients differed from controls only in negative, low intensity AE, rating their own affective state less negative than the target rated his/her affect. This deficit was not related to trait empathy, neuropsychological or clinical parameters, or laterality of lesion. Our findings provide important insights into the profile of social cognition impairment after insular damage. Empathic functions may be widely spared after insular damage in a naturalistic, dynamic setting, potentially due to the intact interpretation of social context cues by residual networks outside the lesion. The particular role of the insula in AE for negative states may evolve specifically in situations that bear higher uncertainty, which points to a threshold role of the insula in online ratings of AE.

Educational Disparities in COVID-19 Prevention in China: The Role of Contextual Danger, Perceived Risk, and Interventional Context

Despite the social disparities in COVID-19 infection, little is known about factors influencing social disparities in preventive behaviors during the pandemic. This study examined how educational disparities in mask-wearing, handwashing, and limiting public outings might be contingent upon three factors: contextual cue of danger, perceived risk of local outbreak, and interventional context with different levels of intensity (i.e, Wuhan vs. other areas). Data were obtained from a telephone survey of 3327 adults, who were recruited through a random-digit-dial method to be representative of all cell phone users in China. Interviews were conducted from 28 April to 26 May 2020. Stratified multiple regression models showed that educational disparities in all three behaviors were only consistently observed among people exposed to context cues of danger, with an enhanced sense of risk of a local outbreak, or in areas other than Wuhan. College education seems to make a difference in handwashing regardless of contextual cues of danger or perception of risk. The findings suggested that, in the process of an epidemic, emerging threats in one’s immediate environment or raised awareness of risks are important conditions triggering educational disparities in prevention. However, effective public health interventions could potentially reduce such disparities.

Depression, Anxiety, and Stress Among Healthcare Workers During the COVID-19 Outbreak and Relationships With Expressive Flexibility and Context Sensitivity

This study aimed at investigating depression, anxiety, and stress symptoms among healthcare workers and examine the role of expressive flexibility and context sensitivity as key components of resilience in understanding reported symptoms. We hypothesized a significant and different contribution of resilience components in explaining depression, anxiety, and stress. A total sample of 218 Italian healthcare workers participated in this study through an online survey during the lockdown, consequently to the COVID-19. The Depression Anxiety Stress Scales-21 (DASS-21) was used to measure depression, anxiety, and stress; the Flexible Regulation of Emotional Expression (FREE) scale was used to measure the ability to enhance and suppress emotional expression; the Context Sensitivity Index (CSI) was used to measure the ability to accurately perceive contextual cues and determine cue absence. Demographic and work-related data were also collected. DASS-21 cut-off scores were used to verify the mental status among the respondents. Correlational analyses examined relationships between DASS-21, FREE, and CSI, followed by three regression analyses with depression, anxiety, and stress as dependent variables, controlling for age, gender, and work experience. Enhancement and suppression abilities, cue presence, and cue absence served as independent variables. The results showed a prevalence of moderate to extremely severe symptoms of 8% for depression, 9.8% for anxiety, and 8.9% for stress. Results of correlational analysis highlighted that enhance ability was inversely associated with depression and stress. Suppression ability was inversely associated with depression, anxiety, and stress. The ability to perceive contextual cues was inversely associated with depression and anxiety. The regression analysis showed that the ability to enhance emotional expression was statistically significant to explain depression among healthcare workers. In predicting anxiety, age, and the ability to accurately perceive contextual cues and determine cue absence made substantial contributions as predictors. In the last regression model, age, work experience, and the ability to suppress emotional expression were significant predictors of stress. This study’s findings can help understand the specific contributions of enhancement and suppression abilities and sensitivity to stressor context cues in predicting depression, anxiety, and stress among healthcare workers. Psychological interventions to prevent burnout should consider these relationships.