Limited Perception Research Articles

The Geometric Scale of an Urban Space—Neuro-perceptual Limitations for Face-to-Face Driver Behavior

For the last century, the roadway design paradigm has been grounded in the physics of point masses, adjusted for human limitations. However, this is inadequate to assure pedestrian safety in complete streets where vehicle kinematics no longer control. Instead, social and psychological factors manage behavior. Unfortunately, the appropriate design critical psychological principles have yet to be elucidated. We posit that interpersonal perception governs drivers’ behavior, attentiveness and speed in streets. This is bounded by previously delineated human neurological and perceptual propensities. Using these perceptual limitations as postulates in a geometric style proof, we derive the person perception panorama (PPP): a window of interactivity around the moving driver that is continuously monitored for human presence, roughly 60 to 90 feet wide. In this area interpersonal interaction is implicit, functional, and has an impact on driver behavior. Validating evidence, additional governing principles, and an initial speed prediction formula are also included.

A multi-institution longitudinal randomised control trial of speaking up: Implications for theory and practice.

Speaking up is an important yet challenging aspect of health professional communication. To overcome social-cognitive influences and improve speaking up, an intervention based on Kolb's experiential learning cycle was developed, which integrated Virtual Simulation, curriculum, and practice speaking up. The present study investigated if integrating Virtual Simulation influenced Respiratory Therapy students' ability to challenge a physician compared to a control condition at multiple time points during training. A multi-institutional longitudinal randomized control trial was conducted. Students from two schools completed a Virtual Simulation or No Virtual Simulation before classroom instruction on speaking up and an in-person simulation requiring speaking up. After three-to-six months and post-clinical placement, students completed a second simulation requiring speaking up. The student's ability to speak up and use CUS (Concerned, Uncomfortable, Safety Issue) was measured. No significant effects for the intervention were observed across time points, p>.05, with a small effect for using CUS, ϕ=.28. During the study, two unexpected findings emerged with theoretical and practical implications. The multi-institutional design created a natural experiment that allowed for the identification of instructor effects on speaking up and Bloom's Two-Sigma problem. Observations were also made related to perceptual limitations that diminish the ability to speak up. Single speaking-up interventions continue to appear to be ineffective. To substantially influence behaviour, consistent mentorship through a "champion" is likely necessary to train for and create a culture of speaking up. Training in situational awareness is also likely needed to counter human perceptual limitations in complex situations.

Vagus nerve stimulation recruits the central cholinergic system to enhance perceptual learning.

Perception can be refined by experience, up to certain limits. It is unclear whether perceptual limits are absolute or could be partially overcome via enhanced neuromodulation and/or plasticity. Recent studies suggest that peripheral nerve stimulation, specifically vagus nerve stimulation (VNS), can alter neural activity and augment experience-dependent plasticity, although little is known about central mechanisms recruited by VNS. Here we developed an auditory discrimination task for mice implanted with a VNS electrode. VNS applied during behavior gradually improved discrimination abilities beyond the level achieved by training alone. Two-photon imaging revealed VNS induced changes to auditory cortical responses and activated cortically projecting cholinergic axons. Anatomical and optogenetic experiments indicated that VNS can enhance task performance through activation of the central cholinergic system. These results highlight the importance of cholinergic modulation for the efficacy of VNS and may contribute to further refinement of VNS methodology for clinical conditions.

Maximum exercise capacity: Barcroft revisited in a large population

RATIONALE In 1973, the first exercise test was performed at McMaster University Medical Center to provide a standardized approach to clinical exercise testing, based on Barcroft’s maxim that the maximum capacity to exercise is “the essence of the machine,” emphasizing integration between different mechanisms. The test was incremental ergometer exercise to symptom limited maximum. Physiologic and symptom measurements were made before and during exercise. 46,288 tests were collected and entered into a dataset; unique both in terms of the large number and the physiologic variables collected (skeletal muscle strength, gas transfer capacity, symptoms). OBJECTIVES The objective of this study was to understand CO2 production in relation to oxygen uptake when exercising to maximum capacity. MEASUREMENTS AND MAIN RESULTS Physiologic factors contributing to maximum exercise capacity (Wmax) were skeletal muscle strength, FEV1 (ventilatory capacity) and DLCO (gas transfer capacity). The response of VCO2 in relation to VO2 (RER) showed a lag in CO2 production (related to CO2 storage), which was longer in subjects with higher maximum exercise capacity. This lag was followed by a progressive increase toward W max that reached a maximum value independent of W max. The perceived intensity of leg effort and dyspnea increased in a positively accelerating manner in parallel with increases in CO2 output. CONCLUSION Maximum power in incremental cycling was symptom limited, dependent on muscle (strength, oxidative capacity), together with FEV1. Previous emphasis on oxygen delivery as limiting exercise, irrespective of clinical diagnosis, may be augmented by the perceptual limitation that accompanies, remarkably symmetrically, the adaptation to CO2 removal maintaining homeostasis in muscle (muscle buffering capacity).

HEAD: High-Speed Approximate HEterogeneous ADder for Error-Resilient Applications

Media processing applications can tolerate error up to a certain limit due to the perceptual limitations of the human eye. Since adders are ubiquitous in power-hungry media processing applications, they can be approximated without much compromise in quality. An adder with better error characteristics without significant compromise in area and power is required to meet the future demands of ever-increasing computational needs. In order to achieve the same, a novel segmentation technique has been proposed. By varying the number of bits in each segment, three different designs are proposed, each with its advantage. Each proposed adder is segmented into three portions, namely Most Significant Portion (MSP), Intermediate Significant Portion (ISP) and Least Significant Portion (LSP). The sub-adder’s sum in each portion is computed concurrently to reduce the critical path delay. The proposed heterogeneous adder (HEAD) designs achieve better accuracy than the state-of-the-art designs. Synthesis results show that HEAD consumes less area up to 41.8% and consumes less power ranging from 0.8% to 51%, than existing adders. Exhaustive simulations are carried out on benchmarking image sharpening application to prove that the proposed adders obtain a better quality-effort tradeoff than the existing designs.

Consonant and vowel production in children with cochlear implants: acoustic measures and multiple factor analysis

IntroductionThe acoustic limitations of cochlear implants (CIs) can lead to perceptual limitations and consequently to imprecise phonological representations and production difficulties. The aim of the study is to document the phonological and phonetic skills of children with CIs and their typically hearing peers. Phonetically, three types of segments were targeted, each characterized by contrasting acoustic information: nasal/oral vowels, fricative segments, and voiced/voiceless stops.MethodsForty-seven typically hearing children (TH) and 23 children with CIs performed a picture-naming task. Productions were analyzed to obtain phonological measures (percentages of correct phonemes, types of errors), and various acoustic measures were collected to characterize the productions on the three types of segments investigated. Multiple factor analyses were conducted to study productive profiles on the various acoustic measures, and the dimensions were correlated with phonological measures.ResultsThe results showed lower performance in lexical (target word retrieval) and phonological (percentages of correct phonemes) skills among children with CIs (CI group), although with better performances among children exposed to CS. Acoustically, children in the CI group exhibited productions significantly different from those of the TH group in terms of the distinction of fricative consonants, marking nasalization through nasal resonance cues, and in the production of voiceless stops. However, the CI group demonstrated compensatory strategies (lengthening of VOT for voiced stops, marking of nasalization through oropharyngeal configuration cues).ConclusionsThe results indicate that children with CIs are at risk of experiencing difficulties in both phonetic and phonological domains. However, there are opportunities for compensation through the exploitation of acoustic cues better coded by the CI and/or through perceptual means (utilization of visual cues).

RegMamba: An Improved Mamba for Medical Image Registration

Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets.

Auditory localization: a comprehensive practical review.

Auditory localization is a fundamental ability that allows to perceive the spatial location of a sound source in the environment. The present work aims to provide a comprehensive overview of the mechanisms and acoustic cues used by the human perceptual system to achieve such accurate auditory localization. Acoustic cues are derived from the physical properties of sound waves, and many factors allow and influence auditory localization abilities. This review presents the monaural and binaural perceptual mechanisms involved in auditory localization in the three dimensions. Besides the main mechanisms of Interaural Time Difference, Interaural Level Difference and Head Related Transfer Function, secondary important elements such as reverberation and motion, are also analyzed. For each mechanism, the perceptual limits of localization abilities are presented. A section is specifically devoted to reference systems in space, and to the pointing methods used in experimental research. Finally, some cases of misperception and auditory illusion are described. More than a simple description of the perceptual mechanisms underlying localization, this paper is intended to provide also practical information available for experiments and work in the auditory field.

89‐3: Invited Paper: An Investigation of Quantitative Measure of See‐Through Image Quality for Transparent Displays

To provide clear see‐through image quality is one of the most important performances of a transparent display, because of its see‐through capability. Purity is one of the quantitative measure can evaluate the see‐through image quality in terms of perceived blurriness. In this paper, the characteristics of purity are investigated. Experimental results indicate that the purity values have highly correlated with perceptual blurriness of see‐through images. Furthermore, the perceptual limit of purity value is also derived.

FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design

Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce FinMem, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent's characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare FinMem with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent's perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, FinMem presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

DPCalib: Dual-Perspective View Network for LiDAR-Camera Joint Calibration

The precise calibration of a LiDAR-camera system is a crucial prerequisite for multimodal 3D information fusion in perception systems. The accuracy and robustness of existing traditional offline calibration methods are inferior to methods based on deep learning. Meanwhile, most parameter regression-based online calibration methods directly project LiDAR data onto a specific plane, leading to information loss and perceptual limitations. A novel network, DPCalib, a dual perspective view network that mitigates the aforementioned issue, is proposed in this paper. This paper proposes a novel neural network architecture to achieve the fusion and reuse of input information. We design a feature encoder that effectively extracts features from two orthogonal views using attention mechanisms. Furthermore, we propose an effective decoder that aggregates features from two views, thereby obtaining accurate extrinsic parameter estimation outputs. The experimental results demonstrate that our approach outperforms existing SOTA methods, and the ablation experiments validate the rationality and effectiveness of our work.

CastleCSF - A contrast sensitivity function of color, area, spatiotemporal frequency, luminance and eccentricity.

The contrast sensitivity function (CSF) is a fundamental visual model explaining our ability to detect small contrast patterns. CSFs found many applications in engineering, where they can be used to optimize a design for perceptual limits. To serve such a purpose, CSFs must explain possibly a complete set of stimulus parameters, such as spatial and temporal frequency, luminance, and others. Although numerous contrast sensitivity measurements can be found in the literature, none fully explains the complete space of stimulus parameters. Therefore, in this work, we first collect and consolidate contrast sensitivity measurements from 18 studies, which explain the sensitivity variation across the parameters of interest. Then, we build an analytical contrast sensitivity model that explains the data from all those studies. The proposed castleCSF model explains the sensitivity as the function of spatial and temporal frequencies, an arbitrary contrast modulation direction in the color space, mean luminance, and chromaticity of the background, eccentricity, and stimulus area. The proposed model uses the same set of parameters to explain the data from 18 studies with an error of 3.59dB. The consolidated contrast sensitivity data and the code for the model are publicly available at https://github.com/gfxdisp/castleCSF/.

Art Is Magic

Art Is Magic

Complex sound perception by laboratory mice

Although mice are known to produce vocalizations with varying spectrotemporal characteristics, exactly how they are using these vocalizations for communication is still not entirely known. Ultrasonic vocalizations (USVs) are produced by male and female mice of many strains and researchers have parsed these USVs into different categories. Whether or not these USV categories are meaningful to the mice has been the subject of many preference and playback studies, which have weaknesses. Using an operant conditioning nose poke paradigm, we have trained laboratory mice to detect, discriminate, and identify simple and complex stimuli, including USVs used for communication. We have found that USVs are easier to detect than pure tones, although noise, aging, and blast trauma disrupts this process. Like humans listening to words, the beginnings of USVs appear to hold more importance than the middle or ends of USVs. Mice can discriminate amongst USVs, and there is evidence for categorization of at least some USVs, but this ability is much worse than that of other animals tested on similar tasks. Psychophysical experiments with awake trained mice are important for determining the perceptual limitations of the mouse auditory system and using these rodents as models for human communication disorders.

Cortical temporal integration can account for limits of temporal perception: investigations in the binaural system

The auditory system has exquisite temporal coding in the periphery which is transformed into a rate-based code in central auditory structures, like auditory cortex. However, the cortex is still able to synchronize, albeit at lower modulation rates, to acoustic fluctuations. The perceptual significance of this cortical synchronization is unknown. We estimated physiological synchronization limits of cortex (in humans with electroencephalography) and brainstem neurons (in chinchillas) to dynamic binaural cues using a novel system-identification technique, along with parallel perceptual measurements. We find that cortex can synchronize to dynamic binaural cues up to approximately 10 Hz, which aligns well with our measured limits of perceiving dynamic spatial information and utilizing dynamic binaural cues for spatial unmasking, i.e. measures of binaural sluggishness. We also find that the tracking limit for frequency modulation (FM) is similar to the limit for spatial tracking, demonstrating that this sluggish tracking is a more general perceptual limit that can be accounted for by cortical temporal integration limits.

On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR.

Head tracking in head-mounted displays (HMDs) enables users to explore a 360-degree virtual scene with free head movements. However, for seated use of HMDs such as users sitting on a chair or a couch, physically turning around 360-degree is not possible. Redirection techniques decouple tracked physical motion and virtual motion, allowing users to explore virtual environments with more flexibility. In seated situations with only head movements available, the difference of stimulus might cause the detection thresholds of rotation gains to differ from that of redirected walking. Therefore we present an experiment with a two-alternative forced-choice (2AFC) design to compare the thresholds for seated and standing situations. Results indicate that users are unable to discriminate rotation gains between 0.89 and 1.28, a smaller range compared to the standing condition. We further treated head amplification as an interaction technique and found that a gain of 2.5, though not a hard threshold, was near the largest gain that users consider applicable. Overall, our work aims to better understand human perception of rotation gains in seated VR and the results provide guidance for future design choices of its applications.

:The Aesthetic Commonplace: Wordsworth, Eliot, Wittgenstein and the Language of Every Day

:<i>The Aesthetic Commonplace: Wordsworth, Eliot, Wittgenstein and the Language of Every Day</i>

Dominantne odlike sintaksičkog oblikovanja u Crvenoj planeti Gorana Korunovića

The text provides an analysis of the dominant syntactic features in the poetry book Red Planet by Goran Korunović, one of the most notable contemporary Serbian poets. The analysis of the text has revealed that in the prose fragments, sentence simplicity is achieved through the use of specific sentence types based on meaning, realized through coordinating relationships within communicative sentences, employed dependent clauses, and predicate actualization. Furthermore, the prose text lacks extensive syntagms, sentence condensers, and interpolation and inversion of sentence constituents demanding for reception. In the poetic section of the Red Planet, there is a slight change in syntactic composition: more demanding sentence constructions, an interweaving of multiple dependent clauses, and occasional verb condensers, appositives, and more complex syntagms occur. These mentioned aspects of syntactic shaping are closely related to the semantic layer of the text, suggesting the immediacy of the narrator's discourse, a sense of emotional distance, and perceptual limitations in encountering a new and different world. Additionally, subtle deviations from the uniform prevailing syntactic groundwork are derived, conveying the quality of the narrator's consciousness and the procedural nature of the events in which they participate and unfold within them. All of the above demonstrates the skilfull and artistically relevant shaping of the world of the Red Planet, one of the most remarkable works in contemporary Serbian literature.

There is no such thing as a “Just Noticeable” Difference

The Just Noticeable Difference, or JND, is defined as the smallest perceptible difference between two stimuli. Despite SDT clearly eliminating this notion of a JND, the vast majority of people still maintain this intuitive belief about discrimination (see below). We investigated whether this belief is substantiated by evaluating whether the JND construct holds in 20 different datasets from a variety of species (including humans, mice, seals, bats, and butterflies) tested on a variety of dimensions on different senses (including visual contrast, auditory velocity, rubber smell, and salt taste). We fit data from each domain with two models, a No JND model and a Weibull function, and compared fits using BIC. We found that the No JND Model provided a better fit to the data in nearly every case. Given that the data are uniformly consistent with No JND, we turned to the question of why so many still believe in the JND. We hypothesized that the belief that some differences are too small to perceive is an intuitive belief about perception. To evaluate this, we surveyed naïve undergraduate subjects to assess their intuitive beliefs about perceptual limits. The survey evaluated peoples’ beliefs about perception generally as well as about specific domains such as visual number, visual color, and felt weight. Overwhelmingly, regardless of domain, people endorsed the idea of a JND. This suggests that one reason for the prevalence and longevity of the JND in psychological theorizing may be because it aligns with our intuitive beliefs about the limits of our perceptual abilities.

Influence-aware memory architectures for deep reinforcement learning in POMDPs

AbstractDue to its perceptual limitations, an agent may have too little information about the environment to act optimally. In such cases, it is important to keep track of the action-observation history to uncover hidden state information. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize past observations. However, these models are expensive to train and have convergence difficulties, especially when dealing with high dimensional data. In this paper, we propose influence-aware memory, a theoretically inspired memory architecture that alleviates the training difficulties by restricting the input of the recurrent layers to those variables that influence the hidden state information. Moreover, as opposed to standard RNNs, in which every piece of information used for estimating Q values is inevitably fed back into the network for the next prediction, our model allows information to flow without being necessarily stored in the RNN’s internal memory. Results indicate that, by letting the recurrent layers focus on a small fraction of the observation variables while processing the rest of the information with a feedforward neural network, we can outperform standard recurrent architectures both in training speed and policy performance. This approach also reduces runtime and obtains better scores than methods that stack multiple observations to remove partial observability.