Differences In Perception Research Articles

Factors influencing shared decision-making on hospital wards as perceived by healthcare professionals: A qualitative study.

The extent to which healthcare professionals apply Shared Decision Making (SDM) on hospital wards is still unknown. The aim was to explore the current knowledge of SDM among healthcare professionals and the experienced factors influencing SDM on the wards of Dutch hospitals, regarding both treatment and care decisions. Twelve hospital wards in two university medical centres and one teaching hospital. Explorative qualitative design including semi-structured interviews with healthcare professionals working on hospital wards. 23 Healthcare professionals of 12 different wards were interviewed about their knowledge and the factors influencing SDM on their hospital ward. Most healthcare professionals had heard about SDM, but their understanding of the concept of SDM differed. Influencing factors were found on three levels. Organizational factors: lack of a clear vision, various approaches of ward rounds, time, and the organizational structure. Patient-related factors: the situation of the patient, participation of relatives and perceptual differences. Healthcare professional-related factors: motivation, experience, workload, continuity of care, familiarity with the patient, interprofessional communication, tools, and education. All healthcare professionals recognize the importance of SDM and involving the patient in healthcare decisions, because they feel it enhances the quality of care and prioritizes patient-centered care. Nevertheless, healthcare professionals frequently encounter barriers in this setting. To facilitate SDM on hospital wards in the Netherlands, it is beneficial to embed SDM in the inpatient setting and establish a clear structure for interprofessional SDM regarding both treatment and care decisions, in which physicians, nurses and patients play an essential role.

Socratic wisdom in the age of AI: a comparative study of ChatGPT and human tutors in enhancing critical thinking skills

IntroductionThis study investigates the perceptual differences between ChatGPT and human tutors in fostering critical thinking among students, highlighting the effectiveness of Socratic tutoring methodologies in modern educational contexts.MethodsConducted with a sample of 230 university students in Taiwan, the research employed a mixed-methods approach, combining quantitative surveys and qualitative interviews.Results and discussionResults reveal that while a significant portion of students appreciates the benefits of ChatGPT—namely its non-judgmental nature and accessibility—human tutors are acknowledged for their capability to provide tailored feedback and emotional support. Through threshold analysis conducted via a Multilayer Perceptron (MLP) model, the study identified key features affecting student preferences, emphasizing the need for a balanced integration of AI and human tutoring methods. Findings underscore the importance of developing hybrid educational models that leverage both the strengths of human facilitators and the efficiencies of AI tools to enhance student learning and critical thinking skills.

CNEV: A corpus of Chinese nonverbal emotional vocalizations with a database of emotion category, valence, arousal, and gender.

Nonverbal emotional vocalizations play a crucial role in conveying emotions during human interactions. Validated corpora of these vocalizations have facilitated emotion-related research and found wide-ranging applications. However, existing corpora have lacked representation from diverse cultural backgrounds, which may limit the generalizability of the resulting theories. The present paper introduces the Chinese Nonverbal Emotional Vocalization (CNEV) corpus, the first nonverbal emotional vocalization corpus recorded and validated entirely by Mandarin speakers from China. The CNEV corpus contains 2415 vocalizations across five emotion categories: happiness, sadness, fear, anger, and neutrality. It also includes a database containing subjective evaluation data on emotion category, valence, arousal, and speaker gender, as well as the acoustic features of the vocalizations. Key conclusions drawn from statistical analyses of perceptual evaluations and acoustic analysis include the following: (1) the CNEV corpus exhibits adequate reliability and high validity; (2) perceptual evaluations reveal a tendency for individuals to associate anger with male voices and fear with female voices; (3) acoustic analysis indicates that males are more effective at expressing anger, while females excel in expressing fear; and (4) the observed perceptual patterns align with the acoustic analysis results, suggesting that the perceptual differences may stem not only from the subjective factors of perceivers but also from objective expressive differences in the vocalizations themselves. For academic research purposes, the CNEV corpus and database are freely available for download at https://osf.io/6gy4v/ .

Quantitative investigation of a 3D bubble trapper in a high shear stress microfluidic chip using computational fluid dynamics and L*A*B* color space

Microfluidic chips often face challenges related to the formation and accumulation of air bubbles, which can hinder their performance. This study investigated a bubble trapping mechanism integrated into microfluidic chip to address this issue. Microfluidic chip design includes a high shear stress section of fluid flow that can generate up to 2.7 Pa and two strategically placed bubble traps. Commercially available magnets are used for fabrication, effectively reducing production costs. The trapping efficiency is assessed through video recordings with a phone camera and analysis of captured air volumes by injecting dye at flow rates of 50, 100, and 150 µL/min. This assessment uses L*A*B* color space with analysis of the perceptual color difference ∆E and computational fluid dynamics (CFD) simulations. The results demonstrate successful application of the bubble trap mechanism for lab-on-chip bubble detection, effectively preventing bubbles from entering microchannels and mitigating potential damage. Furthermore, the correlation between the L*A*B* color space and volume fraction from CFD simulations allows accurate assessment of trap performance. Therefore, this observation leads to the hypothesis that ∆E could be used to estimate the air volume inside the bubble trap. Future research will validate the bubble trap performance in cell cultures and develop efficient methods for long-term air bubble removal.Graphical abstract

Perceptual fluency affects recognition memory under deep encoding conditions promoting recollection: Evidence from an ERP study using letter-segregated method.

Perceptual fluency can increase familiarity of some of the items in recognition tests and enhance attributions of these items to the past. It is not clear, however, whether perceptual fluency can influence recognition under conditions promoting recollection-based memory. To this end, we performed a systematic replication of a study by Lucas and Paller (2013) using a letter-segregated method. We recorded ERPs while participants performed recognition task in letter segregated (LS) blocks, in which new words were always composed of different letters than old words, and in letter non-segregated (LNS) blocks, in which half of the new words came from the same letter pool as the studied words (new related words), and the other half came from the other pool (new unrelated words). Unlike the Lucas and Paller study, deep encoding promoted more recollection-based memory. In the LNS blocks, the comparison between old and new unrelated words revealed early (180-260ms) P200 old/new effect, showing that recognition can be supported by an early discrimination of perceptual differences between studied and unstudied test probes. The relatively large hit rates and relatively high sensitivity measures, as well as the late (500-700ms) LPC old/new effects in both blocks, indicated high levels of recollection for old words. Still, recognition memory was more accurate in the LS blocks, whereas in the LNS blocks there were more false alarms for new related than for new unrelated words. This suggests that perceptual fluency derived from low-level information may influence not only familiarity, but also recollection-based memory judgments.

PPerceptual differences between AI and human compositions: the impact of musical factors and cultural background

The issues of what Artificial Intelligence (AI) can and cannot do in the field of music are among the important topics that both music researchers and AI experts are curious about. This study offers a significant analysis within the context of the growing role of AI technologies in music composition and their impact on creative processes. It contributes to the literature by positioning AI as a complementary tool to the composer’s creativity and by enhancing the understanding of cultural adaptation processes. The study aims to identify the perceptual differences between AI and composer compositions, examine the musical and cultural foundations of these differences, and uncover the factors that influence the listener’s experience. In the research design, a mixed-method approach was adopted, combining qualitative and quantitative research methods. In the quantitative phase, a double-blind experimental design was employed to ensure that participants evaluated composer and AI works impartially. In the qualitative phase, participants’ opinions were gathered. The participants were 10 individuals aged between 19 and 25, with diverse cultural and educational backgrounds; 6 had received formal music education, while 4 were casual listeners. The data collection instruments included a structured interview form and the Assessment Scale for Perceptual Factors in Musical Works. During the research process, each participant evaluated two AI and two composer works in 20-minute standardized listening sessions. All listening sessions were conducted using professional audio equipment. The analysis revealed that composer works scored significantly higher than AI works across all categories (p

Spatial Sound, Part 1: Exploring the Impact of Listening Room on Perceptual Immersion

Room acoustics and sound reproduction systems shape the spatial sound character in a listening room, influencing sound quality. This study introduces a novel technique to assess the spatial character of room acoustics and sound reproduction systems, addressing a research gap in understanding spatial differences between listening environments. The technique was employed in a listening test where 15 assessors evaluated the impact of three listening rooms on perceptual immersion for music in 2.0 and 7.1 configurations using loudspeakers and binaural audio for headphones. The results indicate room reverberation enhances perceptual immersion, particularly with two-channel stereophonic setups. Surround speakers reduce room-induced effects when the music is mixed sufficiently enveloping, especially with distinct, nonreverberant surround elements. The results with headphones were similar but less pronounced. Assessors found the normal stereo tonally preferable to binaural conditions, but perceptions of immersiveness varied. Some described it as highly immersive, whereas others reported a lack of spatial depth, although it was perceived as supernaturally wide and effective compared with binaural sound. This highlights the challenge of transitioning from normal stereo to a spatialized mix in streaming services. Overall, the findings can enhance the immersive experience and even out perceptual differences between listening environments, including loudspeakers and headphones.

Kielten aineenopettajaopiskelijoiden eriyttämiskäsitysten kehittyminen pedagogisten opintojen aikana

This article focuses on pre-service language teachers’ (N=67) perceptions of differentiation at different stages of their pedagogical studies. The participants visualised an “ideal language learning situation” and completed several teaching-related metaphors at the start of their studies. The participants returned to reflect on these outputs in writing at the end of their studies. Moreover, seven participants collectively reflected on them in group discussions. The data were analysed using thematic content analysis. Although differentiation did not appear very strongly in the visualisations and metaphors, some elements of differentiation were evident in them. In the reflective texts and group discussions, the participants emphasised the importance of differentiation more clearly. The results suggest that the ideal of differentiation is already present at some level when pre-service teachers start their teacher education and that they are able to adopt it more accurately during their pedagogical studies, albeit still to a somewhat limited extent.

Cross-Project Software Defect Prediction Using Differential Perception Combined with Inheritance Federated Learning

Cross-project software defect prediction (CPDP) refers to the construction of defect prediction models by collecting multi-source project data, but the heterogeneity of data among projects and the modern problem of “data islands” hinder its development. In response to these challenges, we propose a CPDP algorithm based on differential perception combined with inheritance federated learning (FedDPI). Firstly, we design an efficient data preprocessing scheme, which lays a reliable data foundation for federated learning by integrating oversampling and optimal feature selection methods. Secondly, a two-stage collaborative optimization mechanism is proposed in the federated learning framework: the inheritance private model (IPM) is introduced in the local training stage, and the differential perception algorithm is used in the global aggregation stage to dynamically allocate aggregation weights, forming positive feedback for training to overcome the negative impact of data heterogeneity. In addition, we utilize the Ranger optimization algorithm to improve the convergence speed and privacy security of the model through its irreversible mixed optimization operation. The experimental results show that FedDPI significantly improves predictive performance in various defect item data combination experiments. Compared with different deep learning and federated learning algorithms, the average improvement in AUC and G-mean indicators is 0.2783 and 0.2673, respectively, verifying the practicality and effectiveness of federated learning and two-stage collaborative optimization mechanisms in the field of CPDP.

Pain is what you think: functional magnetic resonance imaging evidence toward a cognitive and affective approach for pain research

The sensory/discriminative domain of pain is often given more consideration than the cognitive and affective influences that ultimately make pain what it is: a highly subjective experience that is based on an individual's life history and experiences. While many investigations of the underlying mechanisms of pain have focused on solely noxious stimuli, few have compared somatosensory stimuli that cross the boundary from innocuous to noxious. Of those that have, there is little consensus on the similarities and differences in neural signaling across these sensory domains. The purpose of this study was to apply our established network connectivity analyses toward the goal of understanding the neural mechanisms behind sensory, cognitive, and affective responses to noxious and innocuous stimuli. Functional MRI data were collected from 19 healthy women and men that experienced warm and hot thermal stimuli across multiple trials. This is a within-subjects cross-sectional experimental study with repeated measures. Ratings of stimulus intensity and unpleasantness that were collected during each run confirmed significant perceptual differences between the two types of stimuli. Despite this finding, no group differences in network connectivity were found across conditions. When individual differences related to pain ratings were investigated, subtle differences were found in connectivity that could be attributed to sensory and association regions in the innocuous condition, and cognitive, affective, and autonomic regions in the pain condition. These results were reflected in the time-course data for each condition. Overall, signaling mechanisms for innocuous and noxious somatosensation are intricately linked, but pain-specific perception appears to be driven by our psychological and autonomic states.

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction. To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT. An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test. Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4HU/2.9HU/40.4dB/0.82 for CBCTLR, 136.6HU/-0.4HU/38.6dB/0.77 for CBCTSRpro, and 128.2HU/1.9HU/39.0dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66lp/mm, CBCTSRpro: 0.88lp/mm, CBCTSRimg: 0.95lp/mm, CBCTHR: 1.01lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06mm for translations (CBCTHR as reference). This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

Coparenting perceptions of (Grand)parents and associations with demographic characteristics and early adolescent problem behavior in China

Prior studies on coparenting have primarily focused on mothers and fathers, neglecting coparenting dynamics in multi-generational families. This study aimed to investigate coparenting perceptions among mothers, fathers and grandparents, and explore the associations between the coparenting relationship and adolescent problem behavior, as well as demographic characteristics. The sample included 1163 families with children aged 10–13 years. Mothers ( n = 997) and fathers ( n = 863) provided ratings of their coparenting relationship toward their partners and one grandparent, while grandparents ( n = 249) reported on the coparenting relationship with mothers. Results showed that compared to mothers, fathers reported more undermining of their own parenting by their partner. They were more supportive of their partner’s parenting and more positive about the division of labor. Fathers rated the overall coparenting relationship more positively than mothers, as indicated by the General Coparenting Scale. Notably, grandparents reported significantly more positive coparenting relations with mothers than mothers’ reports of coparenting with grandparents, expressing affirmation of parental competency and alignment with parenting practices. More positive coparenting was associated with less problem behaviors, which was true for coparenting across parents and grandparents. Furthermore, older adolescent age and higher socioeconomic status were significantly correlated with increased coparenting agreement and closeness. Maternal and paternal grandmothers did not differ in their ratings of the coparenting relationship with the child’s mother. These findings underscore the significance of recognizing differential perceptions among family members regarding coparenting behaviors, providing valuable insights for tailoring specific recommendations to enhance coparenting quality within diverse families.

An updated examination of gender differences in sexual harassment perception: A meta-analysis and a survey study.

Twenty years ago, Rotundo et al. (2001) meta-analyzed the gender differences in sexual harassment (SH) perception. They found an overall d of 0.30: Women are more likely than men to label certain behaviors as SH. Much has changed since then, including the increased social awareness and the prevalence of SH training. Given the prevalence of SH in the workplace and the importance of SH perception in SH research, we conducted a mixed-methods research program to explore possible changes in the gender gap. In Study 1 (k = 72, N = 27,767), we meta-analyzed the perceptual gender differences to compare with those in Rotundo et al. and examined several moderators of the differences. We found an overall mean d of 0.33, implying a similar gender gap in SH perception as 20 years ago, yet none of the moderators examined in this study showed significant results. In Study 2, we empirically examined gender differences in mean levels of SH perception using the same measurement scales used in two older studies and compared with the differences found in these two studies. We found higher levels of SH perception for both men and women, but no difference in the mean d between men and women, suggesting that no change over time in mean d does not mean no change in SH perception. The implications of our findings are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation.

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we project point clouds to the camera coordinate using perspective projection, and process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU.

Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling.

Video Coding for Machines (VCM) aims to compress visual signals for machine analysis. However, existing methods only consider a few machines, neglecting the majority. Moreover, the machine's perceptual characteristics are not leveraged effectively, resulting in suboptimal compression efficiency. To overcome these limitations, this paper introduces Satisfied Machine Ratio (SMR), a metric that statistically evaluates the perceptual quality of compressed images and videos for machines by aggregating satisfaction scores from them. Each score is derived from machine perceptual differences between original and compressed images. Targeting image classification and object detection tasks, we build two representative machine libraries for SMR annotation and create a large-scale SMR dataset to facilitate SMR studies. We then propose an SMR prediction model based on the correlation between deep feature differences and SMR. Furthermore, we introduce an auxiliary task to increase the prediction accuracy by predicting the SMR difference between two images in different quality. Extensive experiments demonstrate that SMR models significantly improve compression performance for machines and exhibit robust generalizability on unseen machines, codecs, datasets, and frame types.

Arousal and valence have dissociable effects on responses to schematic emotional faces.

Studies examining behavioural responses to emotional stimuli usually report one of two patterns of responses to negative stimuli. Some studies find faster responses to negative material. Other studies find slower responses to negative stimuli. While the attentional mechanisms proposed to explain these findings (attentional capture in the former case, delayed disengagement in the latter) are not at odds with one another, the behavioural findings do need to be reconciled. We posit that arousal, being the primary differentiator of threatening and nonthreatening stimuli, needs to be more carefully considered. To this end, two experiments were conducted evaluating the role of stimulus arousal and valence in the processing of schematic emotional faces. In Experiment 1, stimulus arousal was manipulated via the presence or absence of eyebrows in the schematic faces in a faces flanker task. Results showed faster responses to faces with eyebrows but no differences in the faces flanker asymmetry between faces with and without eyebrows. In Experiment 2, participants rated the faces on an evaluative space grid. Results showed the presence of the eyebrows had a greater impact on negative ratings for negative faces than for the other expressions. This suggests that stimulus valence and arousal were manipulated by the eyebrows and the reaction time differences could not be attributed purely to perceptual differences. Together these results suggests that both valence and arousal impact the processing of emotional schematic faces, and that these effects are dissociable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pixelator v2: A Novel Perceptual Image Comparison Method with LAB Colour Space and Sobel Edge Detection for Enhanced Security Analysis

In this paper, we introduce Pixelator v2, a novel perceptual image comparison method designed to enhance security and analysis through improved image difference detection. Unlike traditional metrics such as MSE, Q, and SSIM, which often fail to capture subtle but critical changes in images, Pixelator v2 integrates the LAB (CIE-LAB) colour space for perceptual relevance and Sobel edge detection for structural integrity. By combining these techniques, Pixelator v2 offers a more robust and nuanced approach to identifying variations in images, even in cases of minor modifications. The LAB colour space ensures that the method aligns with human visual perception, making it particularly effective at detecting differences that are less visible in RGB space. Sobel edge detection, on the other hand, emphasises structural changes, allowing Pixelator v2 to focus on the most significant areas of an image. This combination makes Pixelator v2 ideal for applications in security, where image comparison plays a vital role in tasks like tamper detection, authentication, and analysis. We evaluate Pixelator v2 against other popular methods, demonstrating its superior performance in detecting both perceptual and structural differences. Our results indicate that Pixelator v2 not only provides more accurate image comparisons but also enhances security by making it more difficult for subtle alterations to go unnoticed. This paper contributes to the growing field of image-based security systems by offering a perceptually-driven, computationally efficient method for image comparison that can be readily applied in information system security.

Bridging Perceptual Gaps: Designers vs. Non-Designers in Urban Wayfinding Signage Preferences

As urban environments become increasingly complex and the costs and challenges of infrastructure upgrades continue to rise, wayfinding signage has become an effective solution to cope with urban dynamics due to its low cost and high flexibility. Although the functionality of wayfinding signage has been extensively studied, the perceptual differences between designers and non-designers have not been adequately explored. Ignoring these differences may lead to the overlooking of users’ real and diverse needs, resulting in suboptimal signage performance in practical applications and ultimately a reduction in the overall functionality and user experience of urban spaces. This study aims to bridge this perceptual gap. For this study, we conducted a questionnaire survey in China to compare the visual preferences of designers and non-designers regarding text, shape, color coding, and patterns. The results indicate that designers prioritize functionality and clarity to ensure the effective use of signage in complex urban environments, whereas non-designers prefer wayfinding signages that reflect local cultural symbols and characteristics. Our conclusions suggest that the public’s expectations for wayfinding signage extend beyond basic navigational functions, with an emphasis on cultural expression and visual appeal. Understanding these perceptual differences is crucial in developing design strategies that balance functionality, esthetics, and sustainability, thereby facilitating the sustainable integration of signage into urban landscapes.

AUDIO-VISUAL LEARNING THROUGH ANIMATED VIDEO APPROACH FOR CHILDREN WITH INTELLECTUAL DISABILITIES

Fundamentally, children with intellectual disabilities face learning difficulties, particularly in academic memory, and thus require education that emphasizes training, guidance, and support to optimize their potential. Visual learning is related to visual attention, memory, and the ability to distinguish conceptual and perceptual differences (shape stability, form/model, and base) and its cognitive aspects. The learning method we use involves several stages. First, we present two animated educational videos about letters and numbers, as well as animated videos introducing numbers, along with various interactive approaches for the participants with intellectual disabilities. The selection of participants is also based on the criteria and categories set by the institution. The results of this animated video learning are not only engaging and enjoyable but also foster good interaction between participants, educators, parents, and their environment. With visuals that are focused and well-organized, the learning attention can be directed effectively. The characters featured in the animated videos provide a more enjoyable experience, interact with the actions of the characters, and become iconic, making them easy for the participants to follow and remember.

Cross-species Perception and Ecological Disposition in Animation

Studies on environmental animations have explored the politics of world-making and the interdependence of humans and animals. Taking as a case study the short Hungarian animation A Légy (Ferenc Rófusz, 1980), this article explores ways that animation can foster an ecological disposition in the viewer by presenting the worldview of a housefly from a first-person perspective. A Légy focuses on altersubjectivity, a term that Vinciane Despret (‘Ethology between empathy, standpoint and perspectivism’, 2010) uses to describe Jakob von Uexküll’s practice of adopting the animals’ perspectives. Because each species is attuned to its particular dwelling-world, Von Uexküll ( A Foray into the Worlds of Animals and Humans with a Theory of Meaning, 2010) suggests, we must study animals by examining the features available to them by erecting ‘a bubble’ around them. A Légy gestures toward this idea by facilitating a becoming-animal that highlights perceptual similarities and differences between the viewer and the animated insect. The animation machine enables a ‘cross-species perception’, a form of perception that extends the spectator’s perceptual awareness of the nonhuman worldview.