Human Perception Research Articles

The self-relevant spotlight metaphor: Self-relevant targets diminish distractor–response-binding effects

AbstractRecently, it has been proposed that self-relevance of a stimulus enhances executive control and reduces the impact of distractors on current task performance. The present study aimed to test whether the binding between a distractor and a response is influenced by self-relevance, too. We assumed that targets’ self-relevance should increase executive control processes and therefore reduce the influence of distractors on current performance. In a distractor–response-binding (DRB) task, which measures the strength of binding between distractor stimuli and responses, we varied target relevance so that participants responded to targets that either were or were not self-relevant. Our design made it possible to measure DRB effects for both relevance conditions separately. DRB effects were diminished if targets were self-relevant compared to when they were not. These results expand our understanding of the influence of self-relevance on cognitive performance. The influence of self-relevance is not purely perceptual (Sui & Humphreys, 2012, Journal of Experimental Psychology: Human Perception and Performance, 38[5], 1105–1117), but also found in higher-order processes such as executive control. Moreover, whereas for different paradigms binding advantages of self-relevance are assumed (Sui & Humphreys, 2015a, Trends in Cognitive Sciences, 19[12], 719–728; Humphreys & Sui, 2016, Cognitive Neuroscience, 7[1/4], 5–17), this study identifies an important boundary condition, in that distractor–response binding is reduced by target self-relevance.

Human perception of self-motion and orientation during galvanic vestibular stimulation and physical motion

Galvanic vestibular stimulation (GVS) is an emergent tool for stimulating the vestibular system, offering the potential to manipulate or enhance processes relying on vestibular-mediated central pathways. However, the extent of GVS’s influence on the perception of self-orientation pathways is not understood, particularly in the presence of physical motions. Here, we quantify roll tilt perception impacted by GVS during passive whole-body roll tilts in humans (N = 11). We find that GVS systematically amplifies and attenuates perceptions of roll tilt during physical tilt, dependent on the GVS waveform. Subsequently, we develop a novel computational model that predicts 6DoF self-motion and self-orientation perceptions for any GVS waveform and motion by modeling the vestibular afferent neuron dynamics modulated by GVS in conjunction with an observer central processing model. This effort provides a means to systematically alter spatial orientation perceptions using GVS during concurrent physical motion, and we find that irregular afferent dynamics alone best describe resultant perceptions.

Face cells encode object parts more than facial configuration of illusory faces.

Humans perceive illusory faces in everyday objects with a face-like configuration, an illusion known as face pareidolia. Face-selective regions in humans and monkeys, believed to underlie face perception, have been shown to respond to face pareidolia images. Here, we investigated whether pareidolia selectivity in macaque inferotemporal cortex is explained by the face-like configuration that drives the human perception of illusory faces. We found that face cells responded selectively to pareidolia images. This selectivity did not correlate with human faceness ratings and did not require the face-like configuration. Instead, it was driven primarily by the "eye" parts of the illusory face, which are simply object parts when viewed in isolation. In contrast, human perceptual pareidolia relied primarily on the global configuration and could not be explained by "eye" parts. Our results indicate that face-cells encode local, generic features of illusory faces, in misalignment with human visual perception, which requires holistic information.

EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes.

Haptics in social interaction with agents and avatars in virtual reality: a systematic review

AbstractIncorporating the sense of touch through haptics in virtual spaces enables people to communicate emotions and engage in more naturalistic and meaningful social interactions. Advances in haptics and virtual reality technologies and applications have been essential to support researchers in the exploration of mediated social touch in virtual environments. The aim of this work is to review the last decade of research on haptics and virtual reality technologies investigating social touch behavior between human avatars as well as between humans and non-human virtual agents. Our systematic review organizes the variety of the conducted research in three dimensions: the context against which mediated social touch is studied, the types of haptics and virtual reality technology used, and empirical studies including data collected and outcome measures. We discuss the results of the analysis of the three dimensions and present implications for future research. We pinpoint the importance of considering in-the-wild studies and emerging issues on social virtual reality; understanding human touch perception for people with different physical and cognitive abilities, and; creating development tools to broaden the exploration of advanced technological setups.

No-Reference Quality Assessment Based on Dual-Channel Convolutional Neural Network for Underwater Image Enhancement

Underwater images are important for underwater vision tasks, yet their quality often degrades during imaging, promoting the generation of Underwater Image Enhancement (UIE) algorithms. This paper proposes a Dual-Channel Convolutional Neural Network (DC-CNN)-based quality assessment method to evaluate the performance of different UIE algorithms. Specifically, inspired by the intrinsic image decomposition, the enhanced underwater image is decomposed into reflectance with color information and illumination with texture information based on the Retinex theory. Afterward, we design a DC-CNN with two branches to learn color and texture features from reflectance and illumination, respectively, reflecting the distortion characteristics of enhanced underwater images. To integrate the learned features, a feature fusion module and attention mechanism are conducted to align efficiently and reasonably with human visual perception characteristics. Finally, a quality regression module is used to establish the mapping relationship between the extracted features and quality scores. Experimental results on two public enhanced underwater image datasets (i.e., UIQE and SAUD) show that the proposed DC-CNN method outperforms a variety of the existing quality assessment methods.

A Taxonomy of Embodiment in the AI Era

This paper presents a taxonomy of agents’ embodiment in physical and virtual environments. It categorizes embodiment based on five entities: the agent being embodied, the possible mediator of the embodiment, the environment in which sensing and acting take place, the degree of body, and the intertwining of body, mind, and environment. The taxonomy is applied to a wide range of embodiment of humans, artifacts, and programs, including recent technological and scientific innovations related to virtual reality, augmented reality, telepresence, the metaverse, digital twins, and large language models. The presented taxonomy is a powerful tool to analyze, clarify, and compare complex cases of embodiment. For example, it makes the choice between a dualistic and non-dualistic perspective of an agent’s embodiment explicit and clear. The taxonomy also aided us to formulate the term “embodiment by proxy” to denote how seemingly non-embodied agents may affect the world by using humans as “extended arms”. We also introduce the concept “off-line embodiment” to describe large language models’ ability to create an illusion of human perception.

Differential Effect of TRPV1 Modulators on Neural and Behavioral Responses to Taste Stimuli

In our diet, we ingest a variety of compounds that are TRPV1 modulators. It is important to understand if these compounds alter neural and behavioral responses to taste stimuli representing all taste qualities. Here, we will summarize the effects of capsaicin, resiniferatoxin, cetylpyridinium chloride, ethanol, nicotine, N-geranyl cyclopropylcarboxamide, Kokumi taste peptides, pH, and temperature on neural and behavioral responses to taste stimuli in rodent models and on human taste perception. The above TRPV1 agonists produced characteristic biphasic effects on chorda tympani taste nerve responses to NaCl in the presence of amiloride, an epithelial Na+ channel blocker, at low concentrations enhancing and at high concentrations inhibiting the response. Biphasic responses were also observed with KCl, NH4Cl, and CaCl2. In the presence of multiple stimuli, the effect is additive. These responses are blocked by TRPV1 antagonists and are not observed in TRPV1 knockout mice. Some TRPV1 modulators also increase neural responses to glutamate but at concentrations much above the concentrations that enhance salt responses. These modulators also alter human salt and glutamate taste perceptions at different concentration ranges. Glutamate responses are TRPV1-independent. Sweet and bitter responses are TRPV1-independent but the off-taste of sweeteners is TRPV1-dependent. Aversive responses to acids and ethanol are absent in animals in which both the taste system and the TRPV1-trigeminal system are eliminated. Thus, TRPV1 modulators differentially alter responses to taste stimuli.

Heuristic satisficing inferential decision making in human and robot active perception

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or because they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as “treasure hunt”, this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

The Sustainability Coefficient of Urban Open Space Illumination Compliance as a Subjective Indicator of Environmental Comfort

Smart lighting of public open spaces plays a crucial role in creating sustainable cities. Investing in optimal methods for improving the energy efficiency of urban lighting systems has become strategic for the economic, technological, and social development of cities. In addition to technical considerations such as energy efficiency and photometric measurements, lighting design should also take into account the subjective aspect of visual safety. However, measuring lighting efficiency in terms of human perception can be challenging because people’s perceptions vary and depend on the urban and architectural context. To address this issue, a sustainability coefficient of outdoor lighting ambiences (Sn) is presented to quantify the compliance of urban open spaces with sustainable lighting design. This coefficient combines two subjective factors: illumination likability and perceived illuminance intensity. The study uses the SEC methodology (suitable for everyone, environmentally accepted, cost-effective), which provides insight into the factors that influence the Sn value. The results showed significant correlations between psychological and aesthetic-functional factors and the Sn, while sociological factors had little influence. The validity of the Sn in a realistic environment can be confirmed. The main reasons for certain Sn levels were identified and possible solutions were proposed, ranging from changes in lighting intensity to architectural redesigns, to improve environmental comfort in urban environments. This study paves the way for a deeper understanding of the role of urban lighting in shaping environmental comfort and human behavior.

Agenda of Early Life Experience and Its Association with Sensitivity to Human Presence and Familiarity in Wild-Born Orphaned Captive Cheetahs

It has been argued that domestication explains the ability of domestic animals to use human cues, but similar abilities exist in wild animals repeatedly exposed to humans. Little is known on the importance of the developmental stage of this exposure for developing such abilities. Orphancy and subsequent hand-rearing constitute a quasi-experimental situation for investigating this question. Cheetahs (Acinonyx jubatus) are interesting as they present a two-step development, spending their first two months of life in a den with occasional visits from the mother, and then accompanying their mother outdoors. Mother loss may occur at any of these stages, and the orphaned cheetahs are then taken under human care. We tested the importance of the stage of development on their long-term perception of humans by comparing the reactions of adult wild-born cheetahs, orphaned at “den-age” (Early-Orphaned, 0–2 months) or at a later stage (Late-Orphaned, 2–6 months), to motionless humans (familiar vs. unfamiliar). The results showed that Early-Orphaned, but not Late-Orphaned cheetahs, produced affiliative calls (purrs) towards humans and discriminated familiar and unfamiliar humans. Taken together, these results suggest that the timing in which an emotional bond is created with the primary caregiver (humans in the case of Early-Orphaned cheetahs and the mother in the case of Late-Orphaned cheetahs) is crucial in determining the reaction towards the species of the early caregiver throughout life.

Competence Perceptions at the Intersection of Gender and Race-Ethnicity

Competence perceptions represent a fundamental dimension of human perception and have wide-ranging consequences for social interaction. Therefore, it is no surprise that multiple prominent social psychological theories emphasize the role of competence perceptions. We know that both a person’s gender and race-ethnicity influence assumptions about their competence, but we have only a few studies examining the intersections of these factors. Using a large survey experiment (N = 1,219), I examine competence perceptions at the intersection of 2 gender and 13 racial-ethnic categories. Results show large effects of both gender and race-ethnicity and also highly intersectional stereotypes; that is, there is no one overarching effect of gender or race-ethnicity but instead contingent effects. By oversampling racial-ethnic minorities, I also test the assumption of consensus for competence perceptions. Results show highly consensual perceptions, suggesting we all recognize society’s stereotypes even if we do not agree with them.

Genetic and Neuronal Basis for Facial Emotion Perception in Humans and Macaques

Abstract The ability to rapidly recognize basic facial emotions (e.g., fear) is crucial for social interactions and adaptive functioning. To date, the origin of facial emotion recognition ability remains equivocal. Using a classical twin design in humans, we found a clear dissection of low and high spatial frequencies (LSF and HSF) in facial emotion perception: whereas genetic factors contributed to individual variation in LSF processing, HSF processing is largely shaped by environmental effects. Furthermore, the ability to recognize facial emotion of LSF content genetically correlated with the function of the amygdala. Crucially, single-unit recording of the amygdala in macaques further revealed the dissociation between LSF and HSF processing in facial emotion perception, indicating the existence of an evolutionarily conserved mechanism. This cross-species study enhances insights into the neurobiological dual-route model (subcortical vs. cortical) of emotion perception and illuminates the origin and the functional development of the emotional brain in primates.

Human Perception of the Emotional Expressions of Humanoid Robot Body Movements: Evidence from Survey and Eye-Tracking Measurements

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.

Human Perception of Robot Failure and Explanation During a Pick-and-Place Task

In recent years, researchers have extensively used non-verbal gestures, such as head and arm movements, to express a robot's intentions and capabilities to humans. Inspired by past research, we investigated how different explanation modalities can aid human understanding and perception of how robots communicate failures and provide explanations during block pick-and-place tasks. Through an in-person, within-subjects experiment with 24 participants, we studied four modes of explanations across four types of failures. Some of these were chosen to mimic combinations from prior work in order to both extend and replicate past findings by the community. We found that speech explanations were preferred to non-verbal and visual cues in terms of similarity to humans. Additionally, projected images had a comparable effect on explanation as other non-verbal modules. We also found consistent results with a prior online study.

Integrating Vision: From Neuroscience to Artificial Intelligence

Abstract. This review article explores the complex interplay between neuroscience and artificial intelligence, focusing on vision processing and its applications. It starts by outlining the biological basis of vision, delving into how visual information is processed and encoded in the brain. The discussion then transitions to artificial intelligence, particularly machine vision, highlighting the advancements and technologies that mimic biological processes. A critical analysis compares how visual information is utilized in both biological organisms and artificial systems, with an emphasis on cognitive functions and neural encoding. The challenges of integrating vision across various sensory modalities are examined, underscoring the technological and cognitive limitations currently faced. The review culminates by identifying potential research paths aimed at closing the gap between neuroscience and AI. This involves enhancing the understanding and functionality of vision in multisensory contexts, striving to foster a more comprehensive approach to artificial intelligence that mirrors the complexity of human perception.

Psychophysical and Rheological Investigation of Toothpaste Tube Squeezability: A People-Centric Approach.

In this work, toothpaste tube squeezability was tested by three different methods: assessment by a human panel, a tactile glove, and several purely instrumental tests. The panelists characterized squeezability in terms of the acceptability of the product. The tactile glove was utilized to determine the maximum grip forces (GF) applied by the same panelists during their assessment. The instrumental tests consisted of emulative tests by a squeezing device and rheological tests. Along with commercial pastes, a series of samples were deliberately formulated and tested, and covered a wide range of squeezability rates of 0.07-2.31 cc/s. The study showed that it is possible to predict human sensory perception using either an emulative squeezing instrument or the rheological measurements of the pastes. The study also suggests that human perception of acceptable squeezability includes not only its low limit (pastes being hard to squeeze) but also its upper limit (pastes perceived as too runny) which may also be related to the inability of the paste to retain its shape on the brush. Based on this study consumer-acceptable ranges of rheological and squeezability parameters were defined. These results are expected to be useful, especially for oral/personal care product developers.

The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-Making

Human-AI collaborative decision-making can achieve better outcomes than either party individually. The success of this collaboration can depend on whether the human decision-maker perceives the AI contribution as beneficial to the decision-making process. Beneficial AI explanations are often described as relevant, convincing, and trustworthy. Yet, we know little about the characteristics of explanations that result in these perceptions. Focusing on collaborative subjective decision-making, using the context of subtle sexism, where explanations can surface new interpretations, we conducted a user study (N=20) to explore the structural and content characteristics that affect perceptions of human and AI-generated verbal (text and audio) explanations. We find four groups of characteristics ( Tone, Grammatical Elements, Argumentative Sophistication and Relation to User ), and that the effect of these characteristics on the perception of explanations for subtle sexism depends on the perceived author. Thus, we also identify which explanation characteristics participants use to identify the author of an explanation. Demonstrating the relationship between these characteristics and explanation perceptions, we present a categorized set of characteristics that system builders can leverage to produce the appropriate perception of an explanation for various sensitive contexts. We also highlight human perception biases and associated issues resulting from these perceptions.

Friedrich Engels’s view on theoretical thinking: innovative application by the Communist Party of Vietnam in the reform process

Marxism-Leninism has become a widespread ideological foundation that is consistently and firmly pursued by the Communist Party of Vietnam (CPV) in its leadership orientation for national development. This research aims to explore and identify the distinctive issues in Friedrich Engels’ theoretical thinking regarding the formation of theoretical thinking based on practical experience, the close relationship between theoretical and scientific thinking, materialist dialectical thinking and mastering the scientific principles of philosophy. These orientations have become methodological guides for the CPV to apply to its leadership activities. This research was conducted based on the analysis and synthesis of available sources, with the main sources being the Marx-Engels Collected Works and some classical works written independently by Friedrich Engels, along with a synthesis of related studies. The results indicate that Friedrich Engels’ theoretical thinking has been studied, supplemented, developed, and creatively applied by the Communist Party of Vietnam in the renovation process in Vietnam, especially since the CPV initiated the renovation policy in December 1986. To date, Vietnamese socialist theory continues to be supplemented by Friedrich Engels’ theoretical thinking, which in his time had not been able to be experimented, contributing significantly to the development of the CPV’s theoretical understanding of political, economic, cultural, and social leadership, becoming an important guiding tool for the CPV to firmly lead the implementation of the renovation policy. Friedrich Engels’ theoretical thinking also holds important value in the struggle against erroneous, eclectic, dogmatic, and mechanical views on human perception and practical action.

Computational Power and Subjective Quality of AI-Generated Outputs: The Case of Aesthetic Judgement and Positive Emotions in AI-Generated Art

As AI technology advances, important questions arise about how the computational power behind these models affects human perception of AI-generated contents. Furthermore, the significant energy consumption of resource-heavy AI models brings growing environmental concerns as the demand for computational resources continues to rise. This research examines the influence of computational power in AI-generated art on human aesthetic perception and emotional response. A group of 100 participants evaluated several artworks either produced by humans or created by an AI operating at three distinct levels of quality, corresponding to varying computational demands. The results showed that participants preferred AI-generated artworks created with greater computational effort, indicating higher likability and more positive emotional reactions. However, the improvements in perception and emotion were modest and exhibited diminishing returns as computational power increased. The study addresses ethical concerns, including the environmental impact of the large computational resources generative AI requires and its implications for sustainable AI development.