scholarly journals Animacy and the prediction of behaviour

2020 ◽  
Author(s):  
Johannes Schultz ◽  
Chris D. Frith
Keyword(s):  
Visual Cues ◽  
Computational Models ◽  
Action Observation ◽  
Critical Role ◽  
Mirror Neuron ◽  
Neuron Network ◽  
Short Term ◽  
Prior Beliefs ◽  
Physical Constraints ◽  
Intentional Agents

To survive, all animals need to predict what other agents are going to do next. The first step is to detect that an object is an agent and, if so, how sophisticated it is. To this end, visual cues are especially important: the form of the agent and the nature of its movements. Once identified, the movements of an agent, however sophisticated, can be anticipated in the short term on the basis of purely physical constraints, but, in the longer term, it is useful to take account of the agent’s goals and intentions. Goal directed agents are marked by the rationality of their movements, reaching their goals by the shortest or least effortful path. Observing goal directed behaviour activates the brain’s action observation/mirror neuron network. The observer’s own action generating mechanism has an important role in predicting future movements of goal directed agents.Intentions have a critical role in determining actions when agents interact with other agents. In such interactions, movements can become communicative rather than directed to immediate goals. Also, each agent can be trying to predict the behaviour of the other, leading to a recursive arms race. It is difficult to infer intentional behaviour from movement kinematics and interpretation is much more dependent upon prior beliefs about the agent. When people believe that they are interacting with an intentional agent, the brain’s mentalising system is activated as the person tries to assess the degree of sophistication of the agent. Several biologically-constrained computational models of action recognition are available, but equivalent models for understanding intentional agents remain to be developed.

scholarly journals Using Functional Connectivity to Examine the Correlation between Mirror Neuron Network and Autistic Traits in a Typically Developing Sample: A fNIRS Study

2021 ◽  
Vol 11 (3) ◽  
pp. 397
Author(s):  
Thien Nguyen ◽  
Helga O. Miguel ◽  
Emma E. Condy ◽  
Soongho Park ◽  
Amir Gandjbakhche
Keyword(s):  
Functional Connectivity ◽  
Near Infrared ◽  
Action Observation ◽  
Pearson Correlation ◽  
Mirror Neuron ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Neuron Network ◽  
Action Execution ◽  
Functional Near Infrared Spectroscopy

Mirror neuron network (MNN) is associated with one’s ability to recognize and interpret others’ actions and emotions and has a crucial role in cognition, perception, and social interaction. MNN connectivity and its relation to social attributes, such as autistic traits have not been thoroughly examined. This study aimed to investigate functional connectivity in the MNN and assess relationship between MNN connectivity and subclinical autistic traits in neurotypical adults. Hemodynamic responses, including oxy- and deoxy-hemoglobin were measured in the central and parietal cortex of 30 healthy participants using a 24-channel functional Near-Infrared spectroscopy (fNIRS) system during a live action-observation and action-execution task. Functional connectivity was derived from oxy-hemoglobin data. Connections with significantly greater connectivity in both tasks were assigned to MNN connectivity. Correlation between connectivity and autistic traits were performed using Pearson correlation. Connections within the right precentral, right supramarginal, left inferior parietal, left postcentral, and between left supramarginal-left angular regions were identified as MNN connections. In addition, individuals with higher subclinical autistic traits present higher connectivity in both action-execution and action-observation conditions. Positive correlation between MNN connectivity and subclinical autistic traits can be used in future studies to investigate MNN in a developing population with autism spectrum disorder.

The capacity of short term memory from an evolutionary perspective

2019 ◽  
Author(s):  
Majid Manoochehri
Keyword(s):  
Short Term Memory ◽  
Memory Span ◽  
Critical Role ◽  
Experimental Studies ◽  
Cognitive System ◽  
Evolutionary Approach ◽  
Evolutionary Perspective ◽  
Short Term ◽  
Term Memory

Memory span in humans has been intensely studied for more than a century. In spite of the critical role of memory span in our cognitive system, which intensifies the importance of fundamental determinants of its evolution, few studies have investigated it by taking an evolutionary approach. Overall, we know hardly anything about the evolution of memory components. In the present study, I briefly review the experimental studies of memory span in humans and non-human animals and shortly discuss some of the relevant evolutionary hypotheses.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals The processing of visual food cues during bitter aftertaste perception in females with high vs. low disgust propensity: an fMRI study

2021 ◽  
Author(s):  
Anne Schienle ◽  
Albert Wabnegger
Keyword(s):  
Personality Trait ◽  
Visual Cues ◽  
Critical Role ◽  
Relevant Information ◽  
Anterior Cingulate ◽  
Conflict Monitoring ◽  
Food Cues ◽  
Sweet Food ◽  
Visual Food Cues ◽  
Disgust Propensity

AbstractAn extremely bitter taste can signal food spoilage, and therefore typically elicits disgust. The present cross-modal functional magnetic resonance imaging experiment investigated whether the personality trait ‘disgust propensity’ (DP; temporally stable tendency to experience disgust across different situations) has an influence on the processing of visual food cues during bitter aftertaste perception. Thirty females with high DP and 30 females with low DP viewed images depicting sweet food (e.g., cakes, ice cream) and vegetables, once in combination with an extremely bitter aftertaste (concentrated wormwood tea), and once with a neutral taste (water). Females highly prone to disgust (compared to low disgust-prone females) showed increased activity in the anterior cingulate cortex (ACC) and increased mPFC-insula connectivity when presented with the mismatch of a bitter aftertaste and visual cues of sweet food. The ACC is involved in conflict monitoring and is strongly interconnected with insular areas. This connection plays a critical role in awareness of changes in homeostatic states. Our findings indicate that the personality trait DP is associated with cross-modal integration processes of disgust-relevant information. Females high in DP were more alert to food-related sensory mismatch (pleasant visual features, aversive taste) than females low in DP.

scholarly journals Explaining semantic short-term memory deficits: Evidence for the critical role of semantic control

2011 ◽  
Vol 49 (3) ◽  
pp. 368-381 ◽  
Author(s):  
Paul Hoffman ◽  
Elizabeth Jefferies ◽  
Matthew A. Lambon Ralph
Keyword(s):  
Short Term Memory ◽  
Critical Role ◽  
Memory Deficits ◽  
Short Term ◽  
Term Memory

Cyclosporine increases ischemia-induced endothelial progenitor cell mobilization through manipulation of the CD26 system

2008 ◽  
Vol 294 (3) ◽  
pp. R811-R818 ◽  
Author(s):  
Chao-Hung Wang ◽  
Wen-Jin Cherng ◽  
Ning-I Yang ◽  
Chia-Ming Hsu ◽  
Chi-Hsiao Yeh ◽  
...  
Keyword(s):  
Critical Role ◽  
Serum Levels ◽  
Blood Levels ◽  
Short Term ◽  
Endothelial Progenitor ◽  
Beneficial Effects ◽  
Dpp Iv ◽  
Before And After ◽  
Cell Mobilization

Cyclosporin A (CsA) improves the success rate of transplantation. The CD26/dipeptidylpeptidase IV (DPP IV) system plays a critical role in mobilizing endothelial progenitor cells (EPCs) from bone marrow. This study investigated whether CsA manipulates CD26/DPP IV activity and increases EPC mobilization. C57BL/6 mice were divided into control and CsA-treated groups. Before and after hindlimb ischemia was induced, circulating EPC number and serum levels of different cytokines were measured. Compared with the controls, CsA treatment significantly increased the blood levels of stroma-derived factor-1α and stem cell factor after ischemic stress ( P < 0.001). The CsA group displayed a significant increase in the number of circulating EPCs (sca-1+KDR+ and c-kit+CD31+ EPCs, both P < 0.05). In vivo, CsA caused a significant increase in the numbers of EPCs incorporated into the Matrigel and ischemic limbs ( P < 0.05). In the peripheral blood, CsA significantly decreased CD26+ cell numbers and attenuated the plasma CD26/DPP IV activity ( P < 0.001). Furthermore, short-term CsA treatment significantly improved the perfusion of ischemic limbs and decreased the spontaneous digital amputation rate. In summary, CsA manipulates the mobilization of EPCs into the circulation via the CD26/DPP IV system. Short-term CsA treatment has beneficial effects on angiogenesis of ischemic tissues.

scholarly journals Decreased connexin43 expression in the mouse heart potentiates pacing-induced remodeling of repolarizing currents

2008 ◽  
Vol 295 (5) ◽  
pp. H1905-H1916 ◽  
Author(s):  
Andrianos Kontogeorgis ◽  
Xiaodong Li ◽  
Eunice Y. Kang ◽  
Jonathan E. Feig ◽  
Marc Ponzio ◽  
...  
Keyword(s):  
Gap Junction ◽  
Ventricular Myocytes ◽  
Critical Role ◽  
Mouse Heart ◽  
Wild Type ◽  
Short Term ◽  
Wild Type Littermate ◽  
Cx43 Expression ◽  
Significant Prolongation ◽  
Electrophysiological Effects

Gap junction redistribution and reduced expression, a phenomenon termed gap junction remodeling (GJR), is often seen in diseased hearts and may predispose toward arrhythmias. We have recently shown that short-term pacing in the mouse is associated with changes in connexin43 (Cx43) expression and localization but not with increased inducibility into sustained arrhythmias. We hypothesized that short-term pacing, if imposed on murine hearts with decreased Cx43 abundance, could serve as a model for evaluating the electrophysiological effects of GJR. We paced wild-type (normal Cx43 abundance) and heterozygous Cx43 knockout (Cx43+/−; 66% mean reduction in Cx43) mice for 6 h at 10–15% above their average sinus rate. We investigated the electrophysiological effects of pacing on the whole animal using programmed electrical stimulation and in isolated ventricular myocytes with patch-clamp studies. Cx43+/− myocytes had significantly shorter action potential durations (APD) and increased steady-state ( Iss) and inward rectifier ( IK1) potassium currents compared with those of wild-type littermate cells. In Cx43+/− hearts, pacing resulted in a significant prolongation of ventricular effective refractory period and APD and significant diminution of Iss compared with unpaced Cx43+/− hearts. However, these changes were not seen in paced wild-type mice. These data suggest that Cx43 abundance plays a critical role in regulating currents involved in myocardial repolarization and their response to pacing. Our study may aid in understanding how dyssynchronous activation of diseased, Cx43-deficient myocardial tissue can lead to electrophysiological changes, which may contribute to the worsened prognosis often associated with pacing in the failing heart.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

Analysis of acute naproxen administration on memory in young adults: A randomized, double-blind, placebo-controlled study

2017 ◽  
Vol 31 (10) ◽  
pp. 1374-1376
Author(s):  
Jack H Wilson ◽  
Amy H Criss ◽  
Sean A Spangler ◽  
Katherine Walukevich ◽  
Sandra Hewett
Keyword(s):  
Young Adults ◽  
Healthy Adult ◽  
Adult Population ◽  
Critical Role ◽  
Random Order ◽  
Controlled Study ◽  
High Dose ◽  
Short Term ◽  
Double Blind ◽  
Inflammatory Drugs

Nonsteroidal anti-inflammatory drugs work by non-selectively inhibiting cyclooxygenase enzymes. Evidence indicates that metabolites of the cyclooxygenase pathway play a critical role in the process of learning and memory. We evaluated whether acute naproxen treatment impairs short-term working memory, episodic memory, or semantic memory in a young, healthy adult population. Participants received a single dose of placebo or naproxen (750 mg) in random order separated by 7–10 days. Two hours following administration, participants completed five memory tasks. The administration of acute high-dose naproxen had no effect on memory in healthy young adults.

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