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1. Spontaneous Entry into an “Offline” State during Wakefulness: A Mechanism of Memory Consolidation?

   https://doi.org/10.1162/jocn_a_01587  

   Wamsley, Erin J. ; Summer, Theodore ( September 2020 , Journal of Cognitive Neuroscience)

   null (Ed.)

   Moments of inattention to our surroundings may be essential to optimal cognitive functioning. Here, we investigated the hypothesis that humans spontaneously switch between two opposing attentional states during wakefulness—one in which we attend to the external environment (an “online” state) and one in which we disengage from the sensory environment to focus our attention internally (an “offline” state). We created a data-driven model of this proposed alternation between “online” and “offline” attentional states in humans, on a seconds-level timescale. Participants ( n = 34) completed a sustained attention to response task while undergoing simultaneous high-density EEG and pupillometry recording and intermittently reporting on their subjective experience. “Online” and “offline” attentional states were initially defined using a cluster analysis applied to multimodal measures of (1) EEG spectral power, (2) pupil diameter, (3) RT, and (4) self-reported subjective experience. We then developed a classifier that labeled trials as belonging to the online or offline cluster with >95% accuracy, without requiring subjective experience data. This allowed us to classify all 5-sec trials in this manner, despite the fact that subjective experience was probed on only a small minority of trials. We report evidence of statistically discriminable “online” and “offline” states matching the hypothesized characteristics. Furthermore, the offline state strongly predicted memory retention for one of two verbal learning tasks encoded immediately prior. Together, these observations suggest that seconds-timescale alternation between online and offline states is a fundamental feature of wakefulness and that this may serve a memory processing function. 

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2. PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

   https://doi.org/10.3390/a13050126  

   Chen, Feiyang ; Jiang, Ying ; Zeng, Xiangrui ; Zhang, Jing ; Gao, Xin ; Xu, Min ( May 2020 , Algorithms)

   null (Ed.)

   Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method. 

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3. Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

   https://doi.org/10.1177/09637214231217989  

   Turner, Brandon M. ; Sloutsky, Vladimir M. ( January 2024 , Current Directions in Psychological Science)

   In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.

    

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4. Distributed Neural Systems Support Flexible Attention Updating during Category Learning

   https://doi.org/10.1162/jocn_a_01882  

   Weichart, Emily R. ; Evans, Daniel G. ; Galdo, Matthew ; Bahg, Giwon ; Turner, Brandon M. ( January 2022 , Journal of Cognitive Neuroscience)

   Abstract To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems. 

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5. Walking the Walk toward Increasing Access and Reducing Emissions: The Promise and Challenges of Virtual Climate Adaptation Convenings

   https://doi.org/10.3390/su141912586  

   Merritt, Eileen G. ; Brousseau, Jennifer J. ; Stern, Marc J. ; Hansen, Lara J. ( October 2022 , Sustainability)

   Joint professional and stakeholder meetings to share local, regional and national responses to the current and projected effects of climate change have become regular, recurring events over the past decade. These “climate adaptation convenings” generally include presentations, discussions, and social learning about how to effectively respond to climate-related impacts. Many of these convenings shifted to virtual formats during the COVID-19 pandemic. We conducted a study to understand how four virtual convenings in the United States compare with otherwise similar in-person events. Through surveys with attendees of four virtual climate adaptation convenings, we explored how attendees’ outcomes differed between conference formats and captured their perceptions of virtual vs. in-person events. Overall, 71% of attendees indicated that they were more likely or equally likely to attend future convenings online, and 62% reported that knowledge gain was about the same or better online than in-person. Many respondents appreciated the accessibility and more inclusive participation at virtual convenings, as well as the environmental benefits and reduced costs. However, interpersonal interactions were inferior in virtual formats, and some attendees experienced difficulties with technology and screen fatigue. Respondents shared suggestions for addressing these challenges. Altogether, findings suggest that virtual convenings have high potential if greater attention is paid to these elements. 

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