Object Perception Research Articles

Development of flexible tactile sensing arrays for hardness recognition

Hardness sensation is an essential function of human skin, which is significant for enhancing the objects perception ability of intelligent manipulators in various scenarios. Although progress has been made in mimicking this function of human hands, the hardness identification still remains a great challenge. In this study, inspired by the Hertz model and the static pressure response of Merkel cells (SA-I) mechanoreceptors, we develop a membrane-like piezoresistive flexible tactile sensor array (PFTSA) that consists of a flexible electrode array layer and a sensitive material layer with a micropyramidal structure, which can effectively identify the hardness values of different objects. The sensor can be a part of the manipulator’s system due to its conformal ability and large deformation, which shows high pressure response sensitivity (4.432 mA/N in the 0–1 N pressure range) and good cycling stability (2100 cycles at 5 N), enabling the consistent and precise detection of external information. As a proof-of-concept illustration, we integrate the PFTSA on a finger of the manipulator to discriminate six objects with different hardness values. The results demonstrate the great potential of PFTSA in the application of human-machine interactions, paving the way for the development of robotic diagnostics, mechanical sorting operations and virtual reality technology.

Instant Effects of Semantic Information on Visual Perception.

Does our perception of an object change once we discover what function it serves? We showed human participants (n = 48, 31 females and 17 males) pictures of unfamiliar objects either together with keywords matching their function, leading to semantically informed perception, or together with nonmatching keywords, resulting in uninformed perception. We measured event-related potentials to investigate at which stages in the visual processing hierarchy these two types of object perception differed from one another. We found that semantically informed compared with uninformed perception was associated with larger amplitudes in the N170 component (150-200 ms), reduced amplitudes in the N400 component (400-700 ms), and a late decrease in alpha/beta band power. When the same objects were presented once more without any information, the N400 and event-related power effects persisted, and we also observed enlarged amplitudes in the P1 component (100-150 ms) in response to objects for which semantically informed perception had taken place. Consistent with previous work, this suggests that obtaining semantic information about previously unfamiliar objects alters aspects of their lower-level visual perception (P1 component), higher-level visual perception (N170 component), and semantic processing (N400 component, event-related power). Our study is the first to show that such effects occur instantly after semantic information has been provided for the first time, without requiring extensive learning.SIGNIFICANCE STATEMENT There has been a long-standing debate about whether or not higher-level cognitive capacities, such as semantic knowledge, can influence lower-level perceptual processing in a top-down fashion. Here we could show, for the first time, that information about the function of previously unfamiliar objects immediately influences cortical processing within less than 200 ms. Of note, this influence does not require training or experience with the objects and related semantic information. Therefore, our study is the first to show effects of cognition on perception while ruling out the possibility that prior knowledge merely acts by preactivating or altering stored visual representations. Instead, this knowledge seems to alter perception online, thus providing a compelling case against the impenetrability of perception by cognition.

Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

This work proposes a vision-based perception algorithm that combines image-processing-based detection and tracking of aerial objects with convolutional neural networks (CNNs) integrated for classification of general aviation aircraft, multirotor small uncrewed aerial systems (SUAS), fixed-wing SUAS, and birds to enable improved onboard avoidance algorithm decision making. Furthermore, we integrate adversarial learning during the training of the CNNs and evaluate performance with class balanced and imbalanced datasets because this maximizes the utility of resource-expensive flight experiments to collect aviation datasets. We compare our proposed CNN with adversarial learning (CNN+ADVL) model with a state-of-the-art CNN as well as a you only look once (YOLO, v4) model retrained (YOLO v4 aircraft) on the same data. The CNN+ADVL trained on the imbalanced dataset achieves the highest 10-fold cross-validation classification accuracy of 76.2% for aircraft and birds for all ranges while achieving 87.0% aircraft classification accuracy, meeting proposed self-assurance separation distances derived from Federal Aviation Administration (FAA) guidelines. In comparison, the CNNs achieved 74.4% 10-fold cross-validation classification accuracy for aircraft and birds as well as 83.4% accuracy for the aircraft, meeting proposed self-assurance separation distances derived from FAA guidelines. Furthermore, we demonstrate that the integration of adversarial learning improves the classification performance for the perception of aerial objects using a class imbalanced dataset.

Regularities of the traffic lane change by the driver when interacting with car-obstacle

The paper presents the results of experimental studies of drivers` behavior when interacting with obstacles caused by parked vehicles. Today, parking cars on two-lane streets is a significant problem for drivers while driving as it creates obstacles. Drivers need to spot a parked car in time and perform a lane change maneuver. It affects the trajectories of vehicles and the functional state of the driver. The driver needs a certain amount of time to maneuver, which consists of the reaction time, the decision to change the lane, and the execution of the action. It complicates traffic conditions for the driver and creates danger for driving. If the driver does not receive information about the parking location on the street with high-speed traffic in time, the probability of danger increases significantly. In addition, drivers try to change the traffic lane, which is further occupied by parking, in advance to reduce the impact of parking on the functional state of their bodies. There is also a deviation in the cross-section of the street when the speed of movement increases relative to the parked car, which finally indicates a change in the position in the traffic lane. It was established that drivers individually choose the trajectories of changing the traffic lane by the speed of movement. In addition, each driver subjectively decides to start changing the traffic lane at his discretion when an obstacle occurs at a certain distance. Angular velocity was used as an indicator of the probability of finding an obstacle object in a dangerous state. Angular speed is the main parameter in the orientation of the driver and signals the danger. When the angular velocity was 0.015-0.03 rad/c, drivers tried to complete the maneuver and leave a certain distance from the obstacle (safety gap). It indicates some interval of angular velocity in relation to the perception of an obstacle object in space and the sense of danger. The resulting patterns of changing lanes by drivers allow for determining the safe distance to parking and ensuring traffic safety by using appropriate markings and road signs

Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream.

Decades of neuroscientific research has sought to understand medial temporal lobe (MTL) involvement in perception. Apparent inconsistencies in the literature have led to competing interpretations of the available evidence; critically, findings from human participants with naturally occurring MTL damage appear to be inconsistent with findings from monkeys with surgical lesions. Here we leverage a 'stimulus-computable' proxy for the primate ventral visual stream (VVS), which enables us to formally evaluate perceptual demands across stimulus sets, experiments, and species. With this approach, we analyze a series of experiments administered to monkeys with surgical, bilateral damage to perirhinal cortex (PRC), a MTL structure implicated in visual object perception. Across experiments, PRC-lesioned subjects showed no impairment on perceptual tasks; this originally led us (Eldridge et al., 2018) to conclude that PRC is not involved in perception. Here we find that a 'VVS-like' model predicts both PRC-intact and -lesioned choice behaviors, suggesting that a linear readout of the VVS should be sufficient for performance on these tasks. Evaluating these data alongside findings from human experiments, we suggest that results from Eldridge et al., 2018 alone can not be used as evidence against PRC involvement in perception. These data suggest that the experimental findings from human and non-human primate literature are consistent, and apparent discrepancies between species was due to reliance on informal accounts of perceptual processing.

Public perception of the myth in traditional objects at the Buka Luwur ceremony of the Sunan Kudus tomb

This study aims to determine the process of the Buka Luwur ceremony of the Holy Sunan Tomb and the public's perception of objects in the Buka Luwur ceremony of the Sunan Kudus tomb. This uses a qualitative method and describing data descriptively. The results of this study show that the procession of the Buka Luwurceremony of the tomb of Sunan Kudus is crossed on the 1st-10th of Muharam which is the recitation of the night of 1 Suro, the release of luwur, munadharah masail diniyah, Rosul prayers and papat flying, quranickhatamanbilghoib, orphan compensation, distribution of asyura porridge, recitation of qasidah Al-Barzanji, evening recitation of 10 Suro, distribution of brekat copies, the division of the shadaqahbrekat, the division of the general brekat, and the ceremony of installing the luwur of the Holy Sunan Tomb. In the procession of the Buka Luwur ceremony of the Tomb of Sunan Kudus, there is public belief in objects that are believed to have magical powers, including water used for sharpening or purifying Cinthakakris, used cloth from BukaLuwur, cicada rice, and asyura porridge, teak leaves, and blessing rice. It is believed that the community can bring blessings, facilitate rizki, cure diseases, fertilize plants, predict clothing and food in the coming year, and so on.

The effect of context congruency on fMRI repetition suppression for objects

The recognition of objects is strongly facilitated when they are presented in the context of other objects (Biederman, 1972). Such contexts facilitate perception and induce expectations of context-congruent objects (Trapp and Bar, 2015). The neural mechanisms underlying these facilitatory effects of context on object processing, however, are not yet fully understood. In the present study, we investigate how context-induced expectations affect subsequent object processing. We used functional magnetic resonance imaging and measured repetition suppression as a proxy for prediction error processing. Participants viewed pairs of alternating or repeated object images which were preceded by context-congruent, context-incongruent or neutral cues. We found a stronger repetition suppression in congruent as compared to incongruent or neutral cues in the object sensitive lateral occipital cortex. Interestingly, this stronger effect was driven by enhanced responses to alternating stimulus pairs in the congruent contexts, rather than by suppressed responses to repeated stimulus pairs, which emphasizes the contribution of surprise-related response enhancement for the context modulation on RS when expectations are violated. In addition, in the congruent condition, we discovered significant functional connectivity between object-responsive and frontal cortical regions, as well as between object-responsive regions and the fusiform gyrus. Our findings indicate that prediction errors, reflected in enhanced brain responses to violated contextual expectations, underlie the facilitating effect of context during object perception.

Electrophysiological differences in perception of animate and inanimate objects

Electrophysiological differences in perception of animate and inanimate objects

Social isolation and mental health: Evidence from adults with serious mental illness.

In the United States, loneliness is increasingly becoming a public health crisis. Individuals with serious mental illness (SMI) report loneliness at rates much higher than the general population. In this secondary data analysis, we examine how subjective and objective social isolation influence perceptions of mental health and well-being. Using a cross-sectional design, we conducted a multivariate hierarchical regression analysis to determine the unique influence both subjective and objective isolation had on perceptions of mental health and well-being in adults with SMI. Rates of reported loneliness in those with SMI was consistent with the literature. Increased perceptions of loneliness were found to be a significant predictor of decreased self-report mental health rating and increased frequency of bothersome symptoms. Increased perceptions of social support were also a significant predictor in increased self-report mental health rating and frequency of bothersome symptoms. For social workers and providers of services to individuals with SMI, it is important to understand the prevalence of loneliness in the population. It is also critical to provide those with SMI social support and inclusion interventions that aim to increase perceptions of mental well-being. Additionally, the distinction between subjective and objective social isolation provides evaluators of social support interventions a more accurate definition from which to measure. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium

Quanta image sensors (QIS) are a new type of single-photon imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements. Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space. On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS (DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium (MACE) framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise.

Lidar Object Perception Framework for Urban Autonomous Driving: Detection and State Tracking Based on Convolutional Gated Recurrent Unit and Statistical Approach

This article describes the development and implementation of a 3D lidar perception framework to guarantee the precise cognition of the surrounding environment for urban autonomous driving. The proposed framework consists of two different detection modules operating in parallel: a deep learning-based and a geometric model-free cluster-based method. The first module utilizes the convolutional gated recurrent unit (ConvGRU)-based residual network (CGRN). The module aims to repredict 3D objects based on results from a continuous single-frame detection network. A vision-fusion methodology based on 2D projection is adopted for postprocessing in the first module. The second module utilizes geometric model-free area (GMFA) cluster detection and is designed to cope with false-negative cases of unclassified objects from the prior module. For the second module, a cluster variance-based ground removal is conducted to prevent false-positive cases. A kinematic model-based particle filter (PF) is then applied to estimate the dynamic states of detection. The suggested framework has been developed with real-time operation in mind, to be implemented in autonomous vehicles equipped with automotive lidars and low-cost cameras. The test results show that the framework with CGRN and GMFA successfully improved the surrounding object detection and state estimation accuracy in urban autonomous driving.

Visual Evoked Responses in Adolescent Idiopathic Scoliosis

Objective: In electrophysiological studies conducted with adolescent idiopathic scoliosis (AIS) patients, no studies examining the visual evoked potentials of AIS patients were found in conjunction with objective vertical perception. The aim of this study was to examine the visual evoked potentials of individuals with AIS and healthy individuals in terms of their brain responses.
 Method: Twelve AIS patients (12.75±0.86 years) and 10 healthy subjects (13.80±1.68 years) participated. A 64-channel electroencephalography (EEG) recording system, Embedded Microcontroller Unit (EMISU), visual stimulation unit, EEG cap, and video recording system were used to examine brain responses after applying visual stimulus.
 Results: In AIS and control groups (CG), three positive and two negative peaks were observed after applying the stimulus. In the AIS, the first and second negative, and second positive peaks, and in the CG, the second positive and negative peaks appeared significantly earlier in the frontal region. The amplitude of the third positive peak in all regions was found to be higher in the AIS. In AIS and CG, the second positive peak was found to be significantly higher in the parieto-occipital region.
 Conclusion: It can be deduced that AIS patients use more sources for processing the vertical visual stimuli than procedures compared to healthy individuals. In light of the findings obtained, the effect of treatments applied to AIS patients with the method used in this study can be evaluated in terms of brain responsiveness. In addition to the subjective visual perception of individuals with AIS, this method can also evaluate objective vertical perception.

Human Exteroception during Object Handling with an Upper Limb Exoskeleton.

Upper limb exoskeletons may confer significant mechanical advantages across a range of tasks. The potential consequences of the exoskeleton upon the user's sensorimotor capacities however, remain poorly understood. The purpose of this study was to examine how the physical coupling of the user's arm to an upper limb exoskeleton influenced the perception of handheld objects. In the experimental protocol, participants were required to estimate the length of a series of bars held in their dominant right hand, in the absence of visual feedback. Their performance in conditions with an exoskeleton fixed to the forearm and upper arm was compared to conditions without the upper limb exoskeleton. Experiment 1 was designed to verify the effects of attaching an exoskeleton to the upper limb, with object handling limited to rotations of the wrist only. Experiment 2 was designed to verify the effects of the structure, and its mass, with combined movements of the wrist, elbow, and shoulder. Statistical analysis indicated that movements performed with the exoskeleton did not significantly affect perception of the handheld object in experiment 1 (BF01 = 2.3) or experiment 2 (BF01 = 4.3). These findings suggest that while the integration of an exoskeleton complexifies the architecture of the upper limb effector, this does not necessarily impede transmission of the mechanical information required for human exteroception.

A joint sparse representation and low rank prior regularization model for image deblocking

When the compression quality factor (QF) is low, JPEG images are usually accompanied by very severe block artifacts. The conventional image deblocking models based on low-rank regularization focus on eliminating block artifacts, but lack the capacity of recovering texture details on image edges. In this paper, an image deblocking model which combines sparse representation and low-rank regularization based on fractional norm is proposed. To avoid the abrupt increase in computational complexity, multiple orthogonal dictionary learning is proposed to enhance the ability to sparsify diverse image structures and eliminate correlations among the atoms in the overcomplete dictionary. To further improve the image deblocking effect, a quantization constraint and fractional norm are used to more effectively enforce the low-rank property. Finally, an alternating minimization strategy is applied to solve the derived optimization problem. The model proposed in this paper preserves more texture details while effectively removing image block artifacts compared with the mainstream image deblocking model utilizing low-rank regularization alone. Experimental results show that the proposed method outperforms the current state-of-the-art image deblocking models in terms of both subjective metrics and objective perception.

NEUROMARKETING RESEARCH TECHNOLOGIES: TOOLS AND METHODOLOGY

The article deals with neuromarketing research which is a new methodological area in the system of contemporary approaches in the sphere of marketing information collection and analysis. The author considers the parameters that must be studied when conducting neuromarketing research, which are used by specialists to determine the emotional reactions of the target audience to marketing stimuli. The main types of biometry that are used to measure the parameters of biological activity of respondents as well as the technologies and tools that are used to obtain marketing information in the implementation of research procedures in the direct and indirect approaches are described. The main channels of influence of neuromarketing research specialists on respondents' sensory perception of objects under study are indicated. The advantages and disadvantages of the method, as well as the areas of its possible use in marketing are considered.

Electrical Stimulation of Temporal and Limbic Circuitry Produces Distinct Responses in Human Ventral Temporal Cortex.

The human ventral temporal cortex (VTC) is highly connected to integrate visual perceptual inputs with feedback from cognitive and emotional networks. In this study, we used electrical brain stimulation to understand how different inputs from multiple brain regions drive unique electrophysiological responses in the VTC. We recorded intracranial EEG data in 5 patients (3 female) implanted with intracranial electrodes for epilepsy surgery evaluation. Pairs of electrodes were stimulated with single-pulse electrical stimulation, and corticocortical evoked potential responses were measured at electrodes in the collateral sulcus and lateral occipitotemporal sulcus of the VTC. Using a novel unsupervised machine learning method, we uncovered 2-4 distinct response shapes, termed basis profile curves (BPCs), at each measurement electrode in the 11-500 ms after stimulation interval. Corticocortical evoked potentials of unique shape and high amplitude were elicited following stimulation of several regions and classified into a set of four consensus BPCs across subjects. One of the consensus BPCs was primarily elicited by stimulation of the hippocampus; another by stimulation of the amygdala; a third by stimulation of lateral cortical sites, such as the middle temporal gyrus; and the final one by stimulation of multiple distributed sites. Stimulation also produced sustained high-frequency power decreases and low-frequency power increases that spanned multiple BPC categories. Characterizing distinct shapes in stimulation responses provides a novel description of connectivity to the VTC and reveals significant differences in input from cortical and limbic structures.SIGNIFICANCE STATEMENT Disentangling the numerous input influences on highly connected areas in the brain is a critical step toward understanding how brain networks work together to coordinate human behavior. Single-pulse electrical stimulation is an effective tool to accomplish this goal because the shapes and amplitudes of signals recorded from electrodes are informative of the synaptic physiology of the stimulation-driven inputs. We focused on targets in the ventral temporal cortex, an area strongly implicated in visual object perception. By using a data-driven clustering algorithm, we identified anatomic regions with distinct input connectivity profiles to the ventral temporal cortex. Examining high-frequency power changes revealed possible modulation of excitability at the recording site induced by electrical stimulation of connected regions.

Visuospatial attention revamps cortical processing of sound amid audiovisual uncertainty.

Selective attentional biases arising from one sensory modality manifest in others. The effects of visuospatial attention, important in visual object perception, are unclear in the auditory domain during audiovisual (AV) scene processing. We investigate temporal and spatial factors that underlie such transfer neurally. Auditory encoding of random tone pips in AV scenes was addressed via a temporal response function model (TRF) of participants' electroencephalogram (N = 30). The spatially uninformative pips were associated with spatially distributed visual contrast reversals ("flips"), through asynchronous probabilistic AV temporal onset distributions. Participants deployed visuospatial selection on these AV stimuli to perform a task. A late (~300 ms) cross-modal influence over the neural representation of pips was found in the original and a replication study (N = 21). Transfer depended on selected visual input being (i) presented during or shortly after a related sound, in relatively limited temporal distributions (<165 ms); (ii) positioned across limited (1:4) visual foreground to background ratios. Neural encoding of auditory input, as a function of visual input, was largest at visual foreground quadrant sectors and lowest at locations opposite to the target. The results indicate that ongoing neural representations of sounds incorporate visuospatial attributes for auditory stream segregation, as cross-modal transfer conveys information that specifies the identity of multisensory signals. A potential mechanism is by enhancing or recalibrating the tuning properties of the auditory populations that represent them as objects. The results account for the dynamic evolution under visual attention of multisensory integration, specifying critical latencies at which relevant cortical networks operate.

The Affordance Directive: Affordance Priming Facilitates Object Detection Similar to Semantic Priming.

The present study investigated differences in priming perceptions of target objects via affordance or semantic primes. Affordances denote possibilities for action in relation to objects (e.g., chair - sit), whereas semantic primes describe related concepts and features of objects (e.g., chair - legs). In Experiments 1A/1B the effects of affordance and semantic priming were compared via a semantic-categorization task using a normed word list of objects. In Experiments 2-4 we investigated affordance priming on object identification of pictures using a shoebox-classification task. In Experiment 1A participants were asked to respond by categorizing the presented word as concrete or abstract. Experiment 1B was similar to 1A, but with a 1000 ms response deadline. Experiment 2 presented target objects as words or photographs. Experiment 3 presented target objects as photographs degraded at three levels (clear, medium blur, extreme blur). Experiment 4 presented target objects as photographs that began degraded and slowly became clear. Experiment 1B found word priming for semantic primes, but not affordances. In contrast, Experiments 2-4 found object priming was facilitated by both affordances and semantic primes. Collectively, our results indicate that affordances facilitate object classification.

Multimodal deep neural decoding reveals highly resolved spatiotemporal profile of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. Recently, neural decoding schemes, such as machine learning in functional magnetic resonance imaging (fMRI), has been employed to elucidate the underlying neural mechanisms. However, it remains unclear as to how spatially distributed brain regions temporally represent visual object categories and sub-categories. One promising strategy to address this issue is neural decoding with concurrently obtained neural response data of high spatial and temporal resolution. In this study, we explored the spatial and temporal organization of visual object representations using concurrent fMRI and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). We hypothesized that neural decoding by multimodal neural data with DNN would show high classification performance in visual object categorization (faces or non-face objects) and sub-categorization within faces and objects. Visualization of the fMRI DNN was more sensitive than that in the univariate approach and revealed that visual categorization occurred in brain-wide regions. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization compared with fMRI DNN or EEG DNN alone. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner, and provide strong evidence of the ability of multimodal deep learning to uncover spatiotemporal neural machinery in sensory processing.

Towards imaging the infant brain at play

ABSTRACT Infants’ first-person experiences are crucial to early cognitive and neural development. To a vast extent, these early experiences involve play, which in infancy takes the form of object exploration. While at the behavioral level infant play has been studied both using specific tasks and in naturalistic scenarios, the neural correlates of object exploration have largely been studied in highly controlled task settings. These neuroimaging studies did not tap into the complexities of everyday play and what makes object exploration so important for development. Here, we review selected infant neuroimaging studies, spanning from typical, highly controlled screen-based studies on object perception to more naturalistic designs and argue for the importance of studying the neural correlates of key behaviors such as object exploration and language comprehension in naturalistic settings. We suggest that the advances in technology and analytic approaches allow measuring the infant brain at play with the use of functional near-infrared spectroscopy (fNIRS). Naturalistic fNIRS studies offer a new and exciting avenue to studying infant neurocognitive development in a way that will draw us away from our laboratory constructs and into an infant’s everyday experiences that support their development.