Neural correlates of recognition memory obtained after encoding under conventional laboratory, virtual, and real-life conditions reveal modality-specific mnemonic processing.

This study investigates humans’ ability to recognize cats’ stress-related behavioral states expressed through visual body language, including facial expressions, posture, and tail position. A total of 1,950 participants evaluated 12 videos of cats displaying three behavioral states—relaxed, tense, and fearful—and reported their perceived state. We examined whether recognition accuracy was influenced by individual observer characteristics, including age, gender, and prior cat ownership. Response accuracy exceeded chance level (33%) but remained relatively low, indicating that the task was challenging. No significant main effect of behavioral state was observed. In contrast, individual observer characteristics significantly shaped performance: participants identifying as female and those with prior cat ownership showed higher accuracy. Age also had a small but reliable negative effect, with accuracy gradually decreasing across adulthood. Overall, these findings indicate that humans show a general difficulty in detecting feline stress based on visual cues alone. Instead, recognition performance appears to be driven primarily by observer-related factors. This highlights the complexity of human–cat communication and underscores the importance of individual features in interpreting feline behavior. Improving humans’ ability to detect subtle visual indicators of feline stress may help foster more positive interactions and support companion animal welfare.

SUMMARYHow does the visual system recognize objects in noisy natural environments? Several psychophysical techniques, including critical-band masking, have established that human object recognition is mediated by a narrow 1.5-octave spatial-frequency band (a “channel”). The narrow band has been linked to the robustness of human recognition. Here we investigate its physiological basis. Along the ventral stream, from V1 to V2 to V3 to V4 to ventral temporal cortex (VTC), we used fMRI to measure BOLD responses to bandpass noise and to natural images perturbed by that noise. Along this stream, the BOLD signal is sensitive to noise across an increasingly wide range of stimulus spatial frequencies, from 2 octaves in V1 to 5 octaves in VTC. However, when we assess the effect of the same noise on the accuracy of decoding scene images from the BOLD response, we obtain a different result: Recognition bandwidth is conserved along the ventral stream at about 2 octaves, close to the 1.5-octave behavioral channel. Though the recognition band is conserved, its noise tolerance increases steadily along the ventral stream, approaching behavioral levels in VTC. These findings suggest that V1 sets the bandwidth of the object-recognition channel, while downstream areas progressively denoise the signal, setting the channel’s noise tolerance.

Purpose This study aims to examine how the perceived hypotheticality of technological business ideas affects entrepreneurs' opportunity evaluations. Drawing on construal level theory (CLT), we explore how psychological distance – amplified by the abstract nature of emerging technologies – shapes beliefs about the timing, location and social context in which a business opportunity is considered viable. The aim is to advance understanding of cognitive mechanisms underlying early-stage entrepreneurial decision-making under uncertainty. Design/methodology/approach We conducted two vignette-based experimental studies using fictional but realistic technology scenarios in the textile and human recognition technology sectors. Each study employed a between-subjects design to manipulate levels of perceived hypotheticality (high vs low). Participants evaluated when, where and for whom each technological opportunity would be most viable. Data were analyzed using analysis of covariance, controlling for entrepreneurial experience and technological awareness. Findings Results from both studies show that higher hypotheticality increases psychological distance, leading entrepreneurs to associate the opportunity with later timeframes, geographically distant markets and socially dissimilar users. These effects were consistent across both technological domains, supporting the robustness of the findings and the theoretical propositions derived from CLT. Research limitations/implications The use of online samples may limit the generalizability of results, and the study focuses exclusively on technological enablers. Future research should test these mechanisms across different types of opportunities and with varied entrepreneurial populations. Longitudinal studies could also explore how hypotheticality perceptions evolve over time. Practical implications Understanding how hypotheticality shapes opportunity beliefs (OB) helps entrepreneurs and innovation managers make more grounded decisions about early-stage technologies. Tools that reduce psychological distance – such as prototyping or early market testing – can help overcome premature discounting of viable innovations. Social implications By clarifying how psychological distance influences technology adoption judgments, this research can inform policy initiatives that aim to democratize innovation and reduce perceived inaccessibility of advanced technologies in underserved regions or communities. Originality/value This study introduces hypotheticality as a cognitive antecedent of OB and applies CLT to entrepreneurship. It offers a novel framework for understanding how abstraction affects early-stage opportunity evaluations, extending entrepreneurial cognition research into emerging technology contexts.

Objective: This study primarily aims to examine the different varieties of palatal rugae in our population, focusing on gender differences and their role, generally and in forensics in particular. Palatal rugae are biometric tools that can be used in addition to other identification tools for human recognition, as they are considered unique and stable. The study aims to observe the prevalence of palatal rugae in our population, as subject specialists should encourage and pave the way, facilitate its search, research, and create avenues for future studies.Study Design: Cross-sectional study.Place and Duration of Study: This study was conducted at the Department of Oral Biology, Post Graduate Medical Institute (PGMI), Lahore, Pakistan, from 1st September 2021 to 31st August 2022.Methods: In this study, a total of 320 willing individuals were randomly selected to record their palatal rugae from Islamabad, Lahore, and the peripheries. 160 males and 160 females participated voluntarily, with informed consent, and their biometric records, primarily palatal rugae, were documented. The palatal rugae were recorded with the help of alginate impression material, which was then recorded by pouring the casts. Palatal rugae were studied and analyzed for their types, predominance, and gender variation.Results: Differences were observed in the palatal rugae of all individuals. The predominant pattern of palatal rugae was wavy and curved in males and females, respectively.Conclusion: Our study shows that males and females can exhibit different types of palatal rugae. Wavy patternsof palatal rugae are significant in males compared to curved patterns in females. How to cite this: Rasheed K, Ilyas MS, Umer S, Malik AA, Hussain M. Prevalence of Palatal Rugae as Biometric Markers in Population ofLahore and Islamabad: A Cross-Sectional Study. Life and Science. 2026; 7(1): 80-86. doi: http://doi.org/10.37185/LnS.1.1.977

As a pivotal component of smart home control systems (SHCS), the human-computer interaction (HCI) interface facilitates human-home information exchange and control. But contemporary HCI interfaces still harbor substantial security vulnerabilities due to the static and singular nature of the collected information. In this study, we formulate high-performance ZnS:Mn2+ mechanoluminescence (ML) ink featuring high-resolution printing capability (around 100µm) and achieve a stress detection threshold as low as 0.01MPa. Subsequently, we present an optical-electric dual-mode flexible sensor (OEDM-FS) crafted combining 2D and 3D printing technologies. This sensor integrates a capacitive pressure sensor (CPS) with a ML sensor. It precisely responds to the magnitude and distribution of applied stress through capacitive output and luminescent imaging. The system boasts exceptional stability in stress sensing (signal fluctuation below ±0.3% under fatigue testing involving over 10000 cycles), and it can achieve a very high-resolution stress spatial distribution up to about 200 micrometers. By amalgamating the ML signals with machine learning algorithms, we can accurately identify stress distribution with distinct characteristics, attaining an impressive accuracy rate of 98.5%. Furthermore, we demonstrate the functionality of this OEDM-FS as an HCI interface for human recognition and its potential applications within SHCS.

To analyze how the nursing image, present in public comments on YouTube videos that broadcast news, reports, or articles related to the profession in four television channels in Chile, is publicly represented and symbolically constructed. A qualitative study based on content analysis, according to the guidelines of Elo and Kyngäs. A total of 1,018 public comments from 13 YouTube videos were analyzed. Purposive sampling and inductive thematic analysis were applied using ATLAS.ti® software. The interpretation of the findings was furthered using Jean Watson's Theory of Human Caring. Ethical approval was not required, but the commentators' identity was protected. Thematic analysis identified two categories: public representations that violate the ethics of humane care in nursing and public representations that reinforce it. The findings reveal a polarization between discourses of stigmatization and those of ethical and human recognition. These tensions reflect challenges in the social positioning of nursing and in the appreciation of care as the core of practice.

This paper explores the phenomena arising from the complex interplay and multifaceted relationships between religion and the process of globalization. In comparative - descriptive manner we examines the mutual impact and relational dynamics between religion and globalization, analyzing the accompanying societal challenges: syncretism, secularization, homogenization, “world religion”, versus the genuine freedom of being, the spiritual aspiration toward Truth, and the authentic values that foster mutual human recognition of diversity, love, and cooperation. In doing so, the study offers insight into the position of religion, with the accent on Christian antropologie, within the process of globalization and how contemporary global trends influence the collective value system, shape traditional practices, form identity, and contribute to the emergence of current social anomalies and religious doctrines.

This study examines the shift in Joshua’s designation from mesharet Mosheh (servant of Moses) in Joshua 1:1 to ebed YHWH (servant of the LORD) in Joshua 24:29 as a significant theological marker. This change is not merely a linguistic variation but part of the construction of leadership identity that highlights a transition of authority—from human recognition to divine acknowledgment. The primary aim of this research is to uncover the theological meaning embedded in these titles. The study employs a qualitative approach through library research, applying a narrative-theological exegetical method. The research process involves collecting biblical texts and academic literature, reducing the data to focus on key terms, conducting exegetical analysis that includes historical, morphological, semantic, and narrative aspects, and drawing theological conclusions. The findings reveal that the title mesharet Mosheh reflects Joshua’s formative stage as Moses’ assistant, whereas the title ebed YHWH affirms his authority as Israel’s leader recognized by God. This study emphasizes the function of these titles as narrative-theological instruments rather than mere historical records.

Correctly identifying emotions of other species is central to the welfare of animals in our care; yet, the factors underlying variation in our ability to recognise animal emotions remain unclear. Here, we investigated the human ability to recognise emotional valence and arousal in zoo-living Barbary macaques, tigers, and markhors, in short video clips from which contextual information and other clues were removed. Visitors at Korkeasaari Zoo were recruited to rate valence and arousal, and their ratings were analysed for correct identification of positive vs. negative valence and low vs. high arousal. Overall, arousal was more accurately recognised than valence, low arousal more accurately than high arousal, and negative valence more accurately than positive valence. Moreover, recognition accuracy varied among species being rated. Valence was recognised most accurately in macaques, arousal most accurately in markhors, and tigers’ emotional states were recognised the least accurately, both for valence and arousal. The results suggest that while we can recognise non-domesticated species’ emotional states, accuracy varies depending on the species and the emotional state in question, which highlights the importance of training in assessing animal welfare. Overall, considering animal welfare, it is crucial that we improve education for the identification of animal emotions, both positive and negative ones.

This study aims to explain the metaphysical concept contained in the term Sabaya Diri’ in the Dayak Kanayatn community in West Kalimantan. Sabaya Diri’ is a human recognition of the existence of others as part of oneself that should not be hurt, hated and belittled. The perspective used in this study is the concept of Metaphysics of Hope from the French philosopher Gabriel Marcel. The method used in this study is a qualitative study method with a literature review. The author collected data on Gabriel Marcel’s thoughts, both physical and digital sources. Meanwhile, the idea of Sabaya Diri’ was elaborated from the author's meaning as a native Dayak Kanayatn. This study found that the term Sabaya Diri’ contains three elements of affirmation of the existence of others as the human self itself. The first, fellow Dayaks are the most solid recognition of Sabaya Diri’ because of the similarity of ethnicity. The second, non-Dayak tribes are Sabaya Diri’ because they are dignified and noble humans. The third, the natural environment is Sabaya Diri’ because of the closeness of the Dayak people's relationality to the universe.

Contextual feedback in object recognition: A biologically inspired computational model and human behavioral study.

Perceiving both external and internal object features is essential for accurate recognition and manipulation of humans and robots. However, relying on a single signal processing paradigm may hinder the extraction of specific tactile information from structurally similar signals, posing challenges in both accuracy and computational efficiency in artificial tactile perception systems. Active contact offers a means to modulate the mechanical interaction between tactile systems and objects, enabling targeted perception of specific properties. This work presents a flexible piezoelectric tactile sensing device with active perception capability. It exhibits high force sensitivity (<0.02 N), multi-axis responsiveness, a wide frequency response range (upper than 2000Hz), and high spectral resolution (<1Hz). With two distinct active exploration motions (sliding and vibration), the system extracts edge features through time-domain spike characteristics and infers internal contents via frequency-domain decay trends. The device is integrated on the fingertip of a robotic dexterous hand, demonstrating its effectiveness in tasks such as texture recognition and liquid identification. It also shows promise for handling complex interaction tasks involving multiple subtasks. This study contributes to the advancement of tactile interaction paradigms in robotics and provides a foundation for more intuitive and adaptable robotic perception in human-centered environments.

Research shows that mobile support robots are becoming increasingly valuable in various situations, such as monitoring daily activities, providing medical services, and supporting elderly people. For interpreting human conduct and intention, these robots largely depend on human activity recognition (HAR). However, previous awareness of human appearance (human recognition) and recognition of humans for monitoring (human surveillance) are necessary to enable HAR to work with assistance robots. Al-so However, multimodal human behavior recognition is constrained by costly hardware and a rigorous setting, making it challenging to effectively balance inference accuracy and system expense. Naturally, a key problem in human pose or behavior detection is the ability to extract additional purposeful interpretations from easily accessible live videos. In this paper, we employ human pose detection to address the problem and provide well-crafted assessment measures to show demonstrate the effectiveness of our approach, which utilizes deep neural networks (DNNs) This article proposes a human intention detection system that anticipates human intentions in human- and robot-centered scenarios by utilizing the incorporation of visual information as well as input features, including human positions, head orientations, and critical skeletal key points. Our goal is to aid human-robot interactions by helping mobile robots through realtime human pose prediction using the recognition of 18 distinct key points in the body's structure. The effectiveness of this strategy is demonstrated by the suggested study using Python, and the results of simulations verify the reliability and accuracy of this method.

Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising models of the ventral stream, surpassing feedforward DNNs in the ability to account for brain representations. In this study, we asked whether recurrent DNNs could also better account for human behaviour during visual recognition. We assembled a stimulus set that includes manipulations that are often associated with recurrent processing in the literature, like occlusion, partial viewing, clutter, and spatial phase scrambling. We obtained a benchmark dataset from human participants performing a categorisation task on this stimulus set. By applying a wide range of model architectures to the same task, we uncovered a nuanced relationship between recurrence, model size, and performance. First, results show that increases in performance were most strongly linked to increases in model size, with architecture seemingly not playing a role, even for more challenging manipulations. Second, we found larger models to be more consistent with humans on which manipulations they found more difficult, regardless of model architecture. Finally, we found a negative effect of size in matching human confusion matrices in recurrent but not feedforward DNNs. Contrary to previous assumptions, our findings challenge the notion that recurrent models are better models of human recognition behaviour than feedforward models, and emphasise the complexity of incorporating recurrence into computational models.

Human-cat interactions require accurate interpretation of cat behavioural cues to ensure welfare and safety for both species. Misinterpretation of cat communications during play can lead to unwanted interactions that prolong stress for cats and increase the risk of human injury. A survey investigated factors associated with human ability to recognize cat emotional valence during human-cat ‘play’ interactions and a randomized controlled trial assessed the effectiveness of an educational training video. Participants were randomized to receive either a training video on cat play cues or a control video. A total of 368 adult participants within Australia categorized cat behaviours in videos of human-cat interactions as positive or negative. Novel use of a hierarchical summary receiver operating characteristic (HSROC) framework was used to assess participant accuracy. Results showed that participants were generally accurate when recognizing overt cat behaviours but performed at levels approximating chance when recognizing subtle negative cues. Previous vocational cat experience was associated with higher accuracy in negative interactions. Training had a small but significant positive impact on overall performance but paradoxically significantly decreased subtle negative behaviour recognition. On average, one in four cats in an overtly negative state were misclassified by participants. Even when valence was correctly recognized, a concerning proportion of participants still selected that they would engage in high-risk interactions with a cat in a negative state. Brief educational interventions may be insufficient or counterproductive for teaching subtle cue recognition in cats, highlighting a need for more comprehensive training approaches that prioritize early stress signals and appropriate response strategies. When promoting human-cat play interactions, care should be taken to ensure guardians are able to recognize when their cat does not wish to play and understand how to correctly respond to cats in a negative state.

Towards safer environments: A YOLO and MediaPipe-based human fall detection system with alert automation

Covering the eyes of patients in reports or public images is often done to preserve anonymity, dignity and confidentiality. However, human recognition is a complex process involving a variety of sensory inputs that extend far beyond mere visual processing. One can often identify a person based on other non-visual cues, including auditory, tactile, social, contextual, cultural, and emotional dimensions. With regards to the face, blocking out the eyes alone does not guarantee that a person’s identity will be concealed. This is because there are many other geometric and anatomical features that the brain is attuned to recognising. In addition, modernAI programmes can often be used to re-insert blocked-out features. This paper suggests that from both an ethical and legal perspective, we need to develop more sophisticated ways of treating patient facial photographs to ensure that they are truly not recognisable to others.

PurposeWe aimed to investigate whether a deep learning model can detect eyes receiving glaucoma medications from anterior segment images and to visualize the anatomical areas prioritized during classification.MethodsThe training dataset was comprised of 20,000 augmented images of eyes receiving or not receiving glaucoma medications. The test dataset was comprised of 100 images each of eyes receiving and not receiving glaucoma medications. Diagnostic performance of the model was evaluated using the area under the receiver operating characteristic curve (AROC) and compared with human recognition. Subgroup analyses were performed based on conjunctival hyperemia, prostaglandin analog use, and illumination conditions. Gradient-Weighted Class Activation Mapping (Grad-CAM) was applied to explore anatomical areas prioritized by the model.ResultsThe deep learning model detected the eyes receiving glaucoma medications with significantly higher accuracy than human recognition (AROC, 0.90 vs. 0.75; P < 0.01). No significant AROC differences were observed in the presence or absence of conjunctival hyperemia, prostaglandin analog use, or under varying illumination. Grad-CAM analysis revealed the periocular area was significantly more frequently highlighted in eyes receiving glaucoma medication than in those not receiving medication (P < 0.01).ConclusionsThe deep learning model objectively detected glaucoma medication use based on anterior segment images. Saliency mapping suggests that the model can identify subtle periocular changes induced by treatment.Translational RelevanceThe deep learning model will contribute to assessing the severity of side-effects of glaucoma medications and facilitate the development of eye drops with improved tolerability.

The popularity of volleyball is increasing. Athletes must follow correct techniques for practical training and fair competition. However, errors frequently occur during training or matches, and traditional methods struggle to identify them accurately. This adversely affects athletes’ training effectiveness and the fairness of volleyball matches. This article presented a volleyball error technique action recognition method based on visual image technology. The camera device was used to obtain visual images of volleyball actions, and support vector machine technology was used to classify the skeletal features of athletes in volleyball action images to identify volleyball error technique actions. This article conducted a 10-day analysis identifying incorrect technical movements among 50 volleyball players. The experimental results showed that the average recognition accuracy of the traditional human recognition judgment method and the error passing technique in this article was about 72.09% and 95.59%, respectively. The average recognition accuracy of this article’s traditional human identification judgment method and the error pad technique was about 68.11% and 94.33%, respectively. Therefore, sports volleyball error technology action recognition based on visual image technology can improve the accuracy of error technology action recognition.