Model Of Perception Research Articles

Phantom perception as a Bayesian Inference problem - a pilot study.

Tinnitus is the perception of a sound in the absence of an external source. Although the role of the auditory system is well investigates, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see if the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioural and electrophysiological responses to the learning and test phases in (i) healthy young adults and (ii) people with continuous subjective tinnitus and matched controls. We observed that in both parts of the study, the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. In patients with tinnitus, we observe no significant difference in behavioural or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms going together with increased evoked activity during the learning and test phases in the visual paradigm. This impresses the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the current study also presents preliminary data supporting evidence that tinnitus patients maybe building stronger perceptual models.

Open source robot localization for nonplanar environments

AbstractThe operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional two‐dimensional localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline Adaptative Monte Carlo Localization (AMCL) approach when the robot is in nonplanar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 cm and 0.05 radians in indoor settings and less than 1.0 m in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to three‐dimensional simultaneous localization and mapping algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environments.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

The perceptual model: Emotions as possessed reasons

AbstractEmotions play vital roles in our psychology and our lives. They also often form the basis of our evaluative beliefs. On some views, emotions, like perceptions, justify the beliefs to which they give rise. It has, however, been claimed that, unlike perceptions, emotions are merely proxies for the genuine reasons that are constituted by their cognitive bases. In this paper, I argue that this objection arises from the failure to notice the difference between the notions of ‘reasons there are’ and ‘possessed reasons’. After developing an account of what it is to possess a reason, it will be argued that emotions do constitute genuine reasons for the evaluative beliefs that result from them. To support this claim, a distinction is made between thinner and thicker descriptions of the same event, where a thinner description may be in terms of the emotional response, whereas a thicker description may be in terms of possessing a normative reason to hold a belief or to act.

Peripheral myopic defocus signal affects the efficiency of visual information processing in myopic children.

Spectacle lenses with peripheral lenslets have shown promise for myopia control by providing peripheral myopic defocus signals. Here, we aimed to investigate the impact of prolonged exposure (>6 months) to peripheral myopic defocus on visual information processing in myopic children. The study included 30 myopic children who habitually wore spectacle lenses with highly aspherical lenslets (HAL group) and 34 children who habitually wore single-vision (SV group) spectacles. The quick contrast sensitivity function (qCSF) was used to measure contrast sensitivity (CS) under conditions of no or high noise. Both groups were tested with HAL and SV lenses. The perceptual template model was utilised to fit the contrast sensitivity function (CSF) and determine differences in information processing efficiency through internal additive noise ( ) and perceptual template gain (β). The areas under the log CSF in the SV group were significantly higher than for the HAL group in both zero-noise conditions with the SV test lens (p = 0.03) and high-noise conditions with the HAL test lens (p = 0.02). For 2 cycle per degree (cpd) stimuli, β was significantly higher in the SV group with the HAL test lens than in the HAL group (p = 0.02), while there was a trend towards a significant difference in β for 6 cpd stimuli (p = 0.07). However, there were no significant differences in between the two groups, with or without noise interference. The reduced CS observed in myopic children wearing HAL lenses for 6 months or more may be due to decreased β. This suggests that prolonged use of spectacle lenses with peripheral myopic defocus signals may compromise the central visual system's ability to process additional external noise, resulting in decreased efficiency in visual information processing.

Visual Perceptual Processing Abnormalities in Body Dysmorphic Disorder.

Phenomenological observations of individuals with body dysmorphic disorder (BDD), coupled with evidence from neuropsychological, psychophysical, and neuroimaging studies, support a model of aberrant visual perception characterized by deficient global/holistic, enhanced detail/local processing, and selective visual-attentional biases. These features may contribute to the core symptomatology of distorted perception of their appearance, in addition to misinterpretation of others' facial expressions and poor insight regarding their misperceived appearance defects. Insights from visual processing studies can contribute to the development of novel interventions, such as perceptual retraining and non-invasive neuromodulation. However, much remains to be understood about visual perception in BDD. Future research should leverage brain imaging modalities with high temporal resolutions and employ study designs that induce conflicts in multisensory integration, thereby advancing our mechanistic understanding of distorted visual perception observed in BDD.

Contributions of Basal Ganglia Circuits to Perception, Attention, and Consciousness.

Research into ascending sensory pathways and cortical networks has generated detailed models of perception. These same cortical regions are strongly connected to subcortical structures, such as the basal ganglia (BG), which have been conceptualized as playing key roles in reinforcement learning and action selection. However, because the BG amasses experiential evidence from higher and lower levels of cortical hierarchies, as well as higher-order thalamus, it is well positioned to dynamically influence perception. Here, we review anatomical, functional, and clinical evidence to demonstrate how the BG can influence perceptual processing and conscious states. This depends on the integrative relationship between cortex, BG, and thalamus, which allows contributions to sensory gating, predictive processing, selective attention, and representation of the temporal structure of events.

A Multi-Sensor-Based Terrain Perception Model for Locomotion Selection of Hybrid Mobile Robots

A Multi-Sensor-Based Terrain Perception Model for Locomotion Selection of Hybrid Mobile Robots

Accuracy and Cut-Off Values of Cepstral Measures in the Clinical Evaluation of Brazilian Portuguese Speakers

OBJECTIVETo identify the accuracy and cut-off values of the Cepstral Peak Prominence (CPP) and Cepstral Peak Prominence-Smoothed (CPPS) obtained from different speech tasks, to identify dysphonic voices in Brazilian Portuguese speakers, and to verify the correlation between these measures and the overall severity of dysphonia (OS). METHODIn a study with 376 subjects—277 with dysphonia and 99 controls—we recorded four speech tasks and assessed OS with a visual analog scale (VAS). We extracted CPP and CPPS from these recordings and analyzed them using receive operating characteristic (ROC) curves to determine cut-off values and other performance metrics (area under curve, accuracy, sensitivity, specificity, predictive values, and likelihood ratios). RESULTSCPP values below 28.15 dB (sustained vowel [Ɛ]), 28.77 dB (sustained vowel [a]), 28.58 dB (all CAPE-V sentences), and CPPS values below 16.42 dB (sustained vowel [Ɛ]), 17.02 dB (sustained vowel [a]), and 11.30 dB (all CAPE-V sentences) are indicative of the presence of dysphonia. CPPVE, CPPVA, CPPCAPE-V, CPPVS, CPPSVE, CPPSVA, CPPSCount, CPPSCAPE-V e CPPSVS, and CPPSVS can explain the variability of OS from 19% to 57.9%. CONCLUSIONThe cut-off values for CPPVE, CPPVA, CPPCAPE-V, CPPSVE, CPPSCAPE-V, and CPPSVA obtained from Praat software can be used as a reference for the clinical voice assessment of Brazilian Portuguese speakers in the respective speech tasks. Analysis of the correlation coefficients of the cepstral measures investigated provided us with a predictive model of OS perception based on each measure/speech task.

Acoustic cues to femininity and masculinity in spontaneous speech.

The perceived level of femininity and masculinity is a prominent property by which a speaker's voice is indexed, and a vocal expression incongruent with the speaker's gender identity can greatly contribute to gender dysphoria. Our understanding of the acoustic cues to the levels of masculinity and femininity perceived by listeners in voices is not well developed, and an increased understanding of them would benefit communication of therapy goals and evaluation in gender-affirming voice training. We developed a voice bank with 132 voices with a range of levels of femininity and masculinity expressed in the voice, as rated by 121 listeners in independent, individually randomized perceptual evaluations. Acoustic models were developed from measures identified as markers of femininity or masculinity in the literature using penalized regression and tenfold cross-validation procedures. The 223 most important acoustic cues explained 89% and 87% of the variance in the perceived level of femininity and masculinity in the evaluation set, respectively. The median fo was confirmed to provide the primary cue, but other acoustic properties must be considered in accurate models of femininity and masculinity perception. The developed models are proposed to afford communication and evaluation of gender-affirming voice training goals and improve voice synthesis efforts.

An Intelligent Airflow Perception Model for Metal Mines Based on CNN-LSTM Architecture

In view of the harsh underground working environment and difficulties of airflow monitoring sensors placement, implementing an intelligent ventilation strategy is crucial for ensuring ventilation safety during mining process. The key issue lies in obtaining global real-time airflow parameters for ventilation safety and intelligent control. Thus, we propose an intelligent perception approach based on artificial intelligence (AI) method for acquiring airflow parameters, which operates by leveraging partial airflow data from specific monitoring points to predict airflow at undisclosed locations. Initially, this approach is facilitated by the training of an AI database through numerical simulations of ventilation networks. Subsequently, an intelligent airflow perception model is constructed, incorporating convolution neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM architectures. Through iterative updates and enhancements, these models demonstrate average deviations between predicted and actual airflow parameters of less than 5% in both simulated scenarios and empirical applications. Furthermore, in the case study, the CNN-LSTM architecture model exhibits superior performance for intelligent airflow perception. This architecture combing with airflow monitoring system, and utilizing partial real-time data inputs to obtain perception point outputs, can effectively enhance employee productivity, reduce energy consumption, and prevent resource wastage.

Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning

Although many statistical methods and machine learning algorithms have been explored in both clinical and research settings to extract these patterns from neuroimaging data, differentiating between Alzheimer's disease and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. In order to improve images, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) technique has been used. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm.According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.

Motives For Fraudulent Behavior In Islamic Bank: Hexagon Model Of Perspective Fraud

This research explores the motives for fraudulent behaviour using the fraud hexagon model approach in Islamic Bank X, city Y. This research is qualitative research with primary data sources. Data collection was carried out through unstructured interviews and then deepened through observation. The informants for this research consisted of 7 people who had worked and were currently working in several parts of Islamic Bank X, city Y. This research used qualitative data analysis techniques with three stages in the form of data reduction, data presentation and conclusion. This research reveals the motives for fraudulent behaviour at Islamic Bank X in City Y from the perceptive fraud hexagon model. Fraudulent behaviour is based on the perpetrator's intention and is driven by motives in the form of pressure, rationalization, opportunity, ability, arrogance and collusion. Companies can use the results of this research to consider strategies and policies for controlling risks against fraud. The results of this research can also be helpful for Islamic Banks regarding the suitability of a person's position. Sharia banks must develop their human resources well so that there is a balance between employee rights and obligations.

An Analysis of the Learning Situation of "Introduction to Environmental Science", An Elective Course for Water Conservancy Majors in Agricultural University of Hebei

Tourism perception has always been the focus of scholars' research. This research takes tourism perception as the starting point, and builds a theoretical model of tourism perception, cultural identity, and cultural self-confidence on the basis of Zhou Yijun and Feng Gengyun's research on the mechanism of tourism promoting cultural self-confidence, and structural equation model verifies the relationship between tourism perception, cultural identity, and cultural self-confidence.

Communication spectrum prediction method based on convolutional gated recurrent unit network

In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

Identity in the Digital Age: An Investigation of Malaysian Perspectives on Technology and Privacy

Malaysia is in the process of developing a new identity system to provide citizens and residents with secure and efficient access to digital services. This study aimed to determine the responsiveness of the Malaysian population to new technologies and evaluate their opinions on managing their identity online. The study employed a quantitative research design and collected data through an online questionnaire. A total of 1,014 participants from Malaysia, aged 18 years and above, were included in the study, and the data were analysed using cross-tabulation analysis. The theoretical framework of the study was based on the technology acceptance model, the risk and negative consequences theory of individuals' perceptions, and the organizational trustworthiness theory. The results showed that various demographic characteristics influence individuals' attitudes towards technology adoption and online identity management. Gender, age, ethnicity/race, work status, employment sector, level of education, place of living, household income level, and residence state were all significantly associated with early adoption of emerging technologies, trying out new technologies, and enjoyment of trying out new technologies. Certain demographic factors may also have an impact on people's preferences for specific identity management tools. The study also found significant relationships between various demographic factors and trust levels when it comes to personal data. The study findings have implications for policymakers and practitioners involved in promoting the adoption of new technologies and the management of online identities. The study's findings may assist policymakers in implementing better measures for protecting Malaysians' online identity and privacy. Additionally, the results could also inform future research investigating technology adoption and acceptance among Malaysians.

A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception

Visual perception has become a prerequisite for automated jujube harvesting robot operations under complex orchard conditions. Catch-and-Shake harvesting, as the most efficient and common harvesting method, has widely been applied on various manually operated harvesters to complete large-area jujube fruit harvesting. However, the main factors restricting the development of existing harvesters are labor shortage, high labor cost, and low operating efficiency. To address the issues, we designed a catch-and-shake harvesting robot for jujube tree trunks and branches visual perception that can provide a barrier-free catch-and-shake operation area and guide the manipulator to reach the area to complete the harvesting operation. Meanwhile, a visual perception system including tree trunks and branches detection, skeleton extraction, catch-and-shake area confirmation was presented to guide robot intelligent operations. In the visual perception system, a novel salientobjectdetectionmodel called feature intersection and fusion Transformer (FIT-Transformer) network was proposed to split branches and background to provide reference for determining safe catch-and-shake areas. Moreover, we designed a diverse feature aggregation (DFA) and an attention feature fusion module (AFFM) to strengthen feature learning capabilities and obtain robust perception models. Comparative experimental results showed that our proposed FIT-Transformer model outperformed 12 state-of-the-art (SOTA) algorithms including C2FNet, RAS, BASNet, U2Net, SCRNet, PiCANet, EDRNet, EGNet, ICONR, VST, TransSOD and ABiU_Net. Specifically, the segmentation accuracy of jujube tree trunks and branches using our method showed the satisfactory result on five evaluation indexes under natural environment (the EM, SM, WF, FM and MAE reached 0.9713, 0.8991, 0.8854, 0.8905, and 0.0302, respectively). Field experiments also proved that our method could meet the requirements of operational accuracy and real-time operations.

AMU-Net: an accurate motion perception and aggregation model for action recognition

AMU-Net: an accurate motion perception and aggregation model for action recognition

Using Case Forms for Expressing Temporal Relations in Modern Turkish Language

Many languages, including modern Turkish, employ lexemes with spatial meaning to express temporal relations. However, the Turkish language stands out for its use of case forms of nouns both to describe the spatial position and movement of objects and to establish the temporal reference of actions and events. These differences are systemic, suggest that native Turkish speakers perceive spatial and temporal phenomena in a unique way, and determine the historical change in their models of time perception. They challenge the belief that spatial relations prevail over temporal ones in the perception of the surrounding world by peoples with nomadic roots but highlight the necessity of considering materials from other Turkic languages. The results obtained show that facts from different linguistic levels should be involved to analyze how temporal relations are conveyed. 

Selected models of speech perception by students 5 semester English Education Study Program Universitas Nias of english during lecturing

Understanding speech (speech perception) is an important ability that English language students must master. There are several theoretical models of speech comprehension that attempt to explain the cognitive and linguistic processes underlying this ability. As for speech perception models, 1) Motor Theory of Speech Perception, 2) Analysis-by-Synthesis, 3) Group Model, 4) Fuzzy Logic Model, 5) TRACE Model. This study aims to find out what kind of speech perception model used by students in lectures, especially in the process of teaching and learning English. Specifically, this research is classified as qualitative research. To collect data, the participants studied were the 5th semester population of the English education study program, especially at Nias University. Random samples were used and the samples were 7 people from class A. From the results that have been obtained, there are 4 (four) students classified as using the Cohort model, 2 (two) students classified as using the Motor Theory model, and 1 (one) student classified as using the Fuzzy Logical Model, so we can conclude that most students use the Cohort model they often use this model in English learning activities every day.