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https://www.readbyqxmd.com/read/28646363/the-many-routes-of-mental-navigation-contrasting-the-effects-of-a-detailed-and-gist-retrieval-approach-on-using-and-forming-spatial-representations
#1
Signy Sheldon, Alexa Ruel
Navigated routes can be recalled by remembering a schematic layout or with additional sensory and perceptual details, engaging episodic memory processes. In this study, we contrasted the effects of these remembering approaches on retrieving real-world navigated routes, the impact on flexibly using familiar route information and on learning new spatial representations. In a within-subjects design, participants were oriented to recall familiar routes under two remembering conditions-a detail condition that promoted episodic memory processes and a gist condition in which routes were recalled via schematic processes...
June 23, 2017: Psychological Research
https://www.readbyqxmd.com/read/28644105/time-course-analyses-of-orthographic-and-phonological-priming-effects-in-developing-readers
#2
M H T Zeguers, H M Huizenga, M W van der Molen, P Snellings
It has been assumed that fluent reading requires efficient integration of orthographic and phonological codes. However, it is thus far unclear how this integration process develops when children learn to become fluent readers. Therefore, we used masked priming to investigate time courses of orthographic and phonological code activation in children at incremental levels of reading development (2nd, 4th and 6th grade). The first study used targets with small phonological differences between phonological and orthographic primes, which are typical in transparent orthographies...
June 23, 2017: Quarterly Journal of Experimental Psychology: QJEP
https://www.readbyqxmd.com/read/28642938/correlation-weighted-sparse-group-representation-for-brain-network-construction-in-mci-classification
#3
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28641239/automatic-recognition-of-fmri-derived-functional-networks-using-3d-convolutional-neural-networks
#4
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28640883/an-artificial-emg-generation-model-based-on-signal-dependent-noise-and-related-application-to-motion-classification
#5
Akira Furui, Hideaki Hayashi, Go Nakamura, Takaaki Chin, Toshio Tsuji
This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance...
2017: PloS One
https://www.readbyqxmd.com/read/28638700/vowel-decoding-from-single-trial-speech-evoked-electrophysiological-responses-a-feature-based-machine-learning-approach
#6
Han G Yi, Zilong Xie, Rachel Reetzke, Alexandros G Dimakis, Bharath Chandrasekaran
INTRODUCTION: Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system. These responses, referred to as frequency-following responses (FFRs), have been widely utilized to index typical and atypical representation of speech signals in the auditory system. One of the major limitations in FFR is the low signal-to-noise ratio at the level of single trials. For this reason, the analysis relies on averaging across thousands of trials...
June 2017: Brain and Behavior
https://www.readbyqxmd.com/read/28635122/sketching-the-invisible-to-predict-the-visible-from-drawing-to-modeling-in-chemistry
#7
Melanie M Cooper, Mike Stieff, Dane DeSutter
Sketching as a scientific practice goes beyond the simple act of inscribing diagrams onto paper. Scientists produce a wide range of representations through sketching, as it is tightly coupled to model-based reasoning. Chemists in particular make extensive use of sketches to reason about chemical phenomena and to communicate their ideas. However, the chemical sciences have a unique problem in that chemists deal with the unseen world of the atomic-molecular level. Using sketches, chemists strive to develop causal mechanisms that emerge from the structure and behavior of molecular-level entities, to explain observations of the macroscopic visible world...
June 21, 2017: Topics in Cognitive Science
https://www.readbyqxmd.com/read/28635107/new-space-time-metaphors-foster-new-nonlinguistic-representations
#8
Rose K Hendricks, Lera Boroditsky
What is the role of language in constructing knowledge? In this article, we ask whether learning new relational language can create new ways of thinking. In Experiment 1, we taught English speakers to talk about time using new vertical linguistic metaphors, saying things like "breakfast is above dinner" or "breakfast is below dinner" (depending on condition). In Experiment 2, rather than teaching people new metaphors, we relied on the left-right representations of time that our American college student participants have already internalized through a lifetime of visuospatial experience reading and writing text from left to right...
June 21, 2017: Topics in Cognitive Science
https://www.readbyqxmd.com/read/28634444/experience-dependency-of-reliance-on-local-visual-and-idiothetic-cues-for-spatial-representations-created-in-the-absence-of-distal-information
#9
Fabian Draht, Sijie Zhang, Abdelrahman Rayan, Fabian Schönfeld, Laurenz Wiskott, Denise Manahan-Vaughan
Spatial encoding in the hippocampus is based on a range of different input sources. To generate spatial representations, reliable sensory cues from the external environment are integrated with idiothetic cues, derived from self-movement, that enable path integration and directional perception. In this study, we examined to what extent idiothetic cues significantly contribute to spatial representations and navigation: we recorded place cells while rodents navigated towards two visually identical chambers in 180° orientation via two different paths in darkness and in the absence of reliable auditory or olfactory cues...
2017: Frontiers in Behavioral Neuroscience
https://www.readbyqxmd.com/read/28634307/prefrontal-neurons-encode-a-solution-to-the-credit-assignment-problem
#10
Wael F Asaad, Peter M Lauro, János A Perge, Emad N Eskandar
To adapt successfully to our environments, we must use the outcomes of our choices to guide future behavior. Critically, we must be able to correctly assign credit for any particular outcome to the causal features which preceded it. In some cases, the causal features may be immediately evident, whereas in others they may be separated in time or intermingled with irrelevant environmental stimuli, creating a potentially nontrivial credit assignment problem. We examined the neuronal representation of information relevant for credit assignment in the dorsolateral prefrontal cortex (dPFC) of two male rhesus macaques performing a task that elicited key aspects of this problem...
June 20, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28630951/phenotyping-hypotensive-patients-in-critical-care-using-hospital-discharge-summaries
#11
Yang Dai, Sharukh Lokhandwala, William Long, Roger Mark, Li-Wei H Lehman
Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a data-driven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a nonparametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent "topic" structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries...
February 2017: IEEE-EMBS International Conference on Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28630317/correction-to-supporting-information-for-suzuki-et-al-behavioral-contagion-during-learning-about-another-agent-s-risk-preferences-acts-on-the-neural-representation-of-decision-risk
#12
(no author information available yet)
No abstract text is available yet for this article.
June 19, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28630254/reward-selectively-modulates-the-lingering-neural-representation-of-recently-attended-objects-in-natural-scenes
#13
Clayton Hickey, Marius Peelen
Theories of reinforcement learning and approach behaviour suggest that reward can increase the perceptual salience of environmental stimuli, ensuring that potential predictors of outcome are noticed in the future. But outcome commonly follows visual processing of the environment, occurring even when potential reward cues have long disappeared. How can reward feedback retroactively cause now-absent stimuli to become attention-drawing in the future? One possibility is that reward and attention interact to prime lingering visual representations of attended stimuli that sustain through the interval separating stimulus and outcome...
June 19, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28628630/bird-sound-spectrogram-decomposition-through-non-negative-matrix-factorization-for-the-acoustic-classification-of-bird-species
#14
Jimmy Ludeña-Choez, Raisa Quispe-Soncco, Ascensión Gallardo-Antolín
Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms...
2017: PloS One
https://www.readbyqxmd.com/read/28627281/mirroring-meaningful-actions-sensorimotor-learning-modulates-imitation-of-goal-directed-actions
#15
Caroline Catmur, Cecilia Heyes
Imitation is important in the development of social and technological skills throughout the lifespan. Experiments investigating the acquisition and modulation of imitation (and of its proposed neural substrate, the mirror neuron system) have produced evidence that the capacity for imitation depends on associative learning in which connections are formed between sensory and motor representations of actions. However, evidence that the development of imitation depends on associative learning has been found only for non-goal-directed actions...
June 19, 2017: Quarterly Journal of Experimental Psychology: QJEP
https://www.readbyqxmd.com/read/28624881/discriminative-self-representation-sparse-regression-for-neuroimaging-based-alzheimer-s-disease-diagnosis
#16
Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
In this paper, we propose a novel feature selection method by jointly considering (1) 'task-specific' relations between response variables (e.g., clinical labels in this work) and neuroimaging features and (2) 'self-representation' relations among neuroimaging features in a sparse regression framework. Specifically, the task-specific relation is devised to learn the relative importance of features for representation of response variables by a linear combination of the input features in a supervised manner, while the self-representation relation is used to take into account the inherent information among neuroimaging features such that any feature can be represented by a weighted sum of the other features, regardless of the label information, in an unsupervised manner...
June 17, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28624709/contracted-time-and-expanded-space-the-impact-of-circumnavigation-on-judgements-of-space-and-time
#17
Iva K Brunec, Amir-Homayoun Javadi, Fiona E L Zisch, Hugo J Spiers
The ability to estimate distance and time to spatial goals is fundamental for survival. In cases where a region of space must be navigated around to reach a location (circumnavigation), the distance along the path is greater than the straight-line Euclidean distance. To explore how such circumnavigation impacts on estimates of distance and time, we tested participants on their ability to estimate travel time and Euclidean distance to learned destinations in a virtual town. Estimates for approximately linear routes were compared with estimates for routes requiring circumnavigation...
June 15, 2017: Cognition
https://www.readbyqxmd.com/read/28623339/machine-learning-and-network-analysis-of-molecular-dynamics-trajectories-reveal-two-chains-of-red-ox-specific-residue-interactions-in-human%C3%A2-protein-disulfide-isomerase
#18
Razieh Karamzadeh, Mohammad Hossein Karimi-Jafari, Ali Sharifi-Zarchi, Hamidreza Chitsaz, Ghasem Hosseini Salekdeh, Ali Akbar Moosavi-Movahedi
The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes...
June 16, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28622668/a-bayesian-approach-to-policy-recognition-and-state-representation-learning
#19
Adrian Sosic, Abdelhak M Zoubir, Heinz Koeppl
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about the expert behavior, e.g. they assume the existence of a deterministic optimal ground truth policy or require direct monitoring of the expert's controls, which limits their practical use as part of a general system identification framework...
June 1, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28622667/netvlad-cnn-architecture-for-weakly-supervised-place-recognition
#20
Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic
We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval...
June 1, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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