Human Preferences Research Articles

Constructing a combined brand identity

Identity has acquired the status of a key concept in social, cultural and economic contexts, because it affects the formation of human values, beliefs, preferences and behavior, in particular, consumer behavior. Moreover, consumption itself has long been a stable form of organizing social life, as it focuses on almost all social positions, becoming an almost ideal identifier. Because of this, the research of the target audience is considered the basis of bran­ding, because its coincidence with the brand identity significantly increases consumer loyalty to the brand. On the other hand, brands tend to become part of the consumerʼs self-concept, influencing his values and lifestyle. It highlights the different roles of identity as an influencer (consumer on the brand) and as an object of influence (brand on the consumer). The search for ways to achieve a balance between consumer identity and brand identity outlined the research hypothesis, that systemic and structural app­roach will allow taking into account all elements of the consumerʼs identity in the process of brand identity formation and bring the "brand-consumer" relationship to a new emotional level through the construction of a complex, joint identity. Based on the methods of scientific ana­lysis and synthesis, conceptualization and struc­tural modelling, the key components of consumer identity and brand identity were identified. The structure of the consumerʼs identity has been improved by supplementing it with goals and ambitions of the consumer. A categorical apparatus of brand management has been developed due to the introduction of the concept of "combined brand identity", the starting condi­tion for the construction of which is the analysis of the consumerʼs identity. The result of the proposed system-structural approach is a gene­ralized matrix that allows forming a combi­ned brand identity through a combination of diffe­rent level factors of consumer identity and constituent elements of brand identity. The appli­cation of a system-structural approach will provide a detailed analysis of various consumer segments and will allow determining which elements of the consumerʼs identity should be reproduced in the brand for each segment. This will help to transform the brandʼs partnership with the consumer into a vital priority based on emotional intelligence

Less Artificial, More Intelligent: Understanding Affinity, Trustworthiness, and Preference for Digital Humans

Practice- and policy-oriented abstract: Companies are increasingly deploying highly realistic digital human agents (DHAs) controlled by advanced AI for online customer service, tasks typically handled by chatbots. We conducted four experiments to assess users’ perceptions (trustworthiness, affinity, and willingness to work with) and behaviors while using DHAs, utilizing quantitative surveys, qualitative interviews, direct observations, and neurophysiological measurements. Our studies involved four DHAs, including two commercial products (found to be immature) and two future-focused ones (where participants believed the AI-controlled DHAs were human-controlled). In the first study, comparing perceptions of a DHA, chatbot, and human agent from descriptions revealed few differences between the DHA and chatbot. The second study, involving actual use of a commercial DHA, showed participants found it uncanny, robotic, or difficult to converse with. The third and fourth studies used a “Wizard of Oz” design, with participants believing a human-controlled DHA was AI-driven. Results showed a preference for human agents via video conferencing, but no significant differences between DHAs and human agents when visual fidelity was controlled. Current DHAs, despite communication issues, trigger more affinity than chatbots. When DHAs match human communication abilities, they are perceived similarly to human agents for simple tasks. This research also suggests DHAs may alleviate algorithm aversion.

Genome Sequencing of Lentinula edodes Revealed a Genomic Variant Block Associated with a Thermo-Tolerant Trait in Fruit Body Formation.

The formation of multicellular fruiting bodies in basidiomycete mushrooms is a crucial developmental process for sexual reproduction and subsequent spore development. Temperature is one of the most critical factors influencing the phase transition for mushroom reproduction. During the domestication of mushrooms, traits related to fruiting bodies have significantly impacted agricultural adaptation and human preferences. Recent research has demonstrated that chromosomal variations, such as structural variants (SVs) and variant blocks (VBs), play crucial roles in agronomic traits and evolutionary processes. However, the lack of high-quality genomic information and important trait data have hindered comprehensive identification and characterization in Lentinula edodes breeding processes. In this study, the genomes of two monokaryotic L. edodes strains, characterized by thermo-tolerance and thermo-sensitivity during fruiting body formation, were reassembled at the chromosomal level. Comparative genomic studies of four thermo-tolerant and thermo-sensitive monokaryotic L. edodes strains identified a 0.56 Mbp variant block on chromosome 9. Genes associated with DNA repair or cellular response to DNA damage stimulus were enriched in this variant block. Finally, we developed eight CAPS markers from the variant block to discriminate the thermo-tolerant traits in L. edodes cultivars. Our findings show that the identified variant block is highly correlated with the thermo-tolerant trait for fruiting body formation and that alleles present in this block may have been artificially selected during L. edodes domestication.

AI generates covertly racist decisions about people based on their dialect

Hundreds of millions of people now interact with language models, with uses ranging from help with writing1,2 to informing hiring decisions3. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans4–7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Depth Matters: Spatial Proximity-based Gaze Cone Generation for Gaze Following in Wild

Gaze following aims to predict where a person is looking in a scene. Existing methods tend to prioritize traditional 2D RGB visual cues or require burdensome prior knowledge and extra expensive datasets annotated in 3D coordinate systems to train specialized modules to enhance scene modeling. In this work, we introduce a novel framework deployed on a simple ResNet backbone, which exclusively uses image and depth maps to mimic human visual preferences and realize 3D-like depth perception. We first leverage depth maps to formulate spatial-based proximity information regarding the objects with the target person. This process sharpens the focus of the gaze cone on the specific region of interest pertaining to the target while diminishing the impact of surrounding distractions. To capture the diverse dependence of scene context on the saliency gaze cone, we then introduce a learnable grid-level regularized attention that anticipates coarse-grained regions of interest, thereby refining the mapping of the saliency feature to pixel-level heatmaps. This allows our model to better account for individual differences when predicting others’ gaze locations. Finally, we employ the KL-divergence loss to super the grid-level regularized attention, which combines the gaze direction, heatmap regression, and in/out classification losses, providing comprehensive supervision for model optimization. Experimental results on two publicly available datasets demonstrate the comparable performance of our model with less help of modal information. Quantitative visualization results further validate the interpretability of our method. The source code will be available at https://github.com/VUT-HFUT/DepthMatters .

Chronotype and Lifestyle Schedule in Healthy Rural Population in South-Eastern Region of Chhattisgarh

Chronotype is morningness and eveningness preference of humans. The human biological clock is primarily entrained by sunlight but can show the highly flexible phase-shifting capacity of the biological clock allows a person to adjust to new time zones, shift work, temperature changes, seasonal changes, job schedules, social obligations, and other urgent activities. The aim of the current study was to identify the chronotype of interior rural residents who have limited access to electricity. 2117 healthy subjects (aged between 6 to 75 years) willingly participated in the study. All the subjects were provided with biographical information sheets. Chronotype was subjectively assessed using Horne and Östberg questionnaire [1].Result revealed predominant morningness in the studied population. Evening-types more exposed to nighttime artificial light as well as screen time. In addition, body surface area (BSA) of evening-types was also significantly higher than that in other chronotypes. Further the sleeping and waking hours significantly delayed in evening-type as compared to morning-type and intermediate-type, irrespective of work days and free days. Present study provides insights to the chronotype of rural populations living under semi-modernized conditions. Absence of light at nights, limited screen time, and less facility of electricity could be a reason for predominant morningness among the population. Further eveningtypes with higher BSA are seems to be more susceptible to disturbed sleep-wake pattern.

Using a location-based game to collect preference information for urban and rural forest planning

Location-based games provide an opportunity to collect crowdsourced, spatially explicit data that can be used in decision-making processes in forest planning. While these games have been more widely used in collecting mapping and modelling data, there is limited research on their suitability for collecting location-based data on human preferences, opinions, and emotions in these contexts. Collecting human-centred data can be resource-intensive and costly, hence exploring the potential of location-based games is important. This study investigates the use of geocaching in gathering meaningful data in urban and rural settings. A methodology was developed wherein surveys were embedded in geocaches on trails located in urban and rural forests in Finland. These surveys collected quantitative, qualitative, and photographic data relating to human-forest relationships and landscape preferences. The findings indicate that trails closer to urban areas with higher population density showed increased cache log volume and survey response rates. Moreover, trails tended to collect most information in the first summer after establishment. It is recommended that future studies employing this methodology create shorter, easily accessible, circular trails and that survey questions should be clear and incorporate a method to verify that submitted responses reflect experiences at the chosen location. Furthermore, forest planning professionals and researchers seeking to utilise location-based games in data collection are advised to engage with the communities active in playing them to ensure success..

Children’s risk preferences vary across sexes, social contexts, and cultures

People exhibit more risk-prone behaviors when together with peers than when in private. The interplay of social context effects and other variables that alter human risk preferences (i.e., age, sex, or culture) remains poorly understood. Here, we explored risk preferences among Namibian Hai||om and Ovambo children (N = 144; AgeRange = 6–10 years). Participants chose between risky and safe options in private or during peer presence. In a third condition, children collaborated with peers before their risk preferences were assessed in those peers’ presence. Children from both societies were risk-averse, but Hai||om children showed greater risk aversion than their Ovambo counterparts. Across cultures and ages, boys were less averse to risks than girls. This effect was most pronounced during peer presence, whereas collaboration did not additionally affect risk preferences. These results suggest a dynamic interplay of individual, social, and cultural factors shaping children’s risk preferences.

Do socioeconomic factors and local human preference determine the hybridization of knowledge in local medical systems?

BackgroundHybridization between the local medical systems (LMSs) and biomedicine has been the focus of different studies in ethnobiology, primarily due to the increasing access to biomedicine by indigenous peoples and local communities. Studies on hybridization allow for an understanding of the process of developing and evolving local knowledge systems. In this study, we propose a hybridization score to determine how individuals’ socioeconomic characteristics and preference between LMS and biomedicine determine the complementarity of therapeutic options.MethodsWe conducted semistructured interviews and applied free listing technique in a rural community in Northeast Brazil to assess the treatments the local population sought and which were preferred.ResultsOur analyses showed that the level of schooling was the socioeconomic factor that negatively affected the hybridization process. Individuals with higher levels of schooling tended to prefer LMS strategies less and, consequently, showed a lower probability of hybridizing the two systems. Additionally, older people who preferred LMS strategies showed a greater tendency to adopt hybridization in human health-seeking behavior.ConclusionsOur findings provide further evidence of the complementarity between different medical systems and demonstrate that socioeconomic factors can affect local knowledge and are responsible for differences in individual propensity to hybridize distinct medical systems.

Increased color preference through the introduction of luminance noise in chromatic stimuli

Humans exhibit consistent color preferences that are often described as a curvilinear pattern across hues. The recent literature posits that color preference is linked to the preference for objects or other entities associated with those colors. However, many studies examine this preference using isoluminant colors, which don’t reflect the natural viewing experience typically influenced by different light intensities. The inclusion of random luminance levels (luminance noise) in chromatic stimuli may provide an initial step towards assessing color preference as it is presented in the real world. Employing mosaic stimuli, this study aimed to evaluate the influence of luminance noise on human color preference. Thirty normal trichromats engaged in a two-alternative forced-choice paradigm, indicating their color preferences between presented pairs. The chromatic stimuli included saturated versions of 8 standard hues, presented in mosaics with varying diameters under different luminance noise conditions. Results indicated that the inclusion of luminance noise increased color preference across all hues, specifically under the high luminance noise range, while the curvilinear pattern remained unchanged. Finally, women exhibit a greater sensitivity to the presence of luminance noise than men, potentially due to differences between men and women in aesthetic evaluation strategies.

The Importance of Timing—An Expert Evaluation on Latencies for Voice Assistants

In a driving simulator study, six experts interacted with an in-vehicle voice assistant (VA) and rated different latencies. The results suggest that in order to maintain drivers’ satisfaction with the interaction, in-vehicle VAs should have a latency of no more than 5 s. A slight delay of 1.5 s was rated best while shorter latencies caused highest variance amongst experts—indicating that the fastest response may not necessarily be the most desirable. Satisfaction may also depend on the complexity of the use case as experts showed higher tolerance toward longer latencies in the navigation-domain. Furthermore, our results raise thought-provoking insights about the importance of considering human expectations and preferences in the design of in-vehicle VAs. Despite technological advancements, humans might still expect a natural delay, similar to that in a human-human interaction. These findings emphasize the need to balance using cutting-edge technology with the desire for familiar interactions.

The consequences of AI training on human decision-making

AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid. To show this behavioral shift, we recruited participants to play the ultimatum game, where they were asked to decide whether to accept proposals of monetary splits made by either other human participants or AI. Some participants were informed their choices would be used to train an AI proposer, while others did not receive this information. Across five experiments, we found that people modified their behavior to train AI to make fair proposals, regardless of whether they could directly benefit from the AI training. After completing this task once, participants were invited to complete this task again but were told their responses would not be used for AI training. People who had previously trained AI persisted with this behavioral shift, indicating that the new behavioral routine had become habitual. This work demonstrates that using human behavior as training data has more consequences than previously thought since it can engender AI to perpetuate human biases and cause people to form habits that deviate from how they would normally act. Therefore, this work underscores a problem for AI algorithms that aim to learn unbiased representations of human preferences.

Selecting Source Tasks for Transfer Learning of Human Preferences

Selecting Source Tasks for Transfer Learning of Human Preferences

Enhancing text-audio generation by music classification and Retrieval-Augmented Generation

Recent advancements in deep learning have propelled the development of AI systems capable of generating music that resonates with human emotions and preferences. However, current music generation models still struggle to align generated music with detailed textual descriptions and maintain consistency, especially for longer compositions. This paper presents an innovative approach to address these challenges by integrating genre classification and retrieval-augmented generation (RAG) into the music generation pipeline. We train advanced CNN architectures, including ResNet-50, GoogleNet, and VGG16, for accurate genre classification. The classifier is then incorporated into a RAG framework, where the most relevant pre-classified music piece is retrieved based on the input text query. The retrieved audio and the text description are then fed into the MUSICGEN model to generate a new music piece that inherits attributes from both inputs. We evaluate our system through a double-blind human study, comparing the outputs of the original MUSICGEN model with our RAG-enhanced model. The results demonstrate a significant improvement in the ability of the RAG-enhanced model to generate music embodying specific stylistic elements, as evidenced by higher average confidence scores from participants. Our work represents a significant step towards more personalized and context-aware AI-generated musical experiences, laying the foundation for future advancements in this exciting field.

Logistical and preference bias in participatory science butterfly data

The volume of and interest in unstructured participatory science data has increased dramatically in recent years. However, unstructured participatory science data contain taxonomic biases—encounters with some species are more likely to be reported than encounters with others. Taxonomic biases are driven by human preferences for different species and by logistical factors that make observing certain species challenging. We investigated taxonomic bias in reports of butterflies by characterizing differences between a dedicated participatory semi‐structured dataset, eButterfly, and a popular unstructured dataset, iNaturalist, in spatiotemporally explicit models. Across 194 butterfly species, we found that 53 species were overreported and 34 species were underreported in opportunistic data. Ease of identification and feature diversity were significantly associated with overreporting in opportunistic sampling, and strong patterns in overreporting by family were also detected. Quantifying taxonomic biases not only helps us understand how humans engage with nature but also is necessary to generate robust inference from unstructured participatory data.

Interactive Skin Lesion Segmentation Considering Behavioral Preference in Clicking

Interactive Medical Image Segmentation (IMIS) aims to improve the accuracy of image segmentation by incorporating human guidance, primarily through click‐based interactions. IMIS for skin lesion segmentation is a challenging task because the edges of lesion regions on the skin are often ambiguous, and training IMIS models requires the generation of pseudo‐clicks to simulate human clicks. Most previous methods generate pseudo‐clicks by sampling from the entire mis‐segmented region. However, such clicks are inconsistent with human behavior, resulting in performance degradation, particularly for skin lesion segmentation. In this study, we address this issue by integrating human preference into the process of generating pseudo clicks to train the segmentation model, which is simple yet effective. Specifically, through a user study, we find that people are more inclined to click on larger mis‐segmented regions during interactive segmentation. Inspired by this, a roulette selection strategy is used to generate the pseudo‐clicks based on the area of the mis‐segmented subregions. Our proposed method, BehaviorClick, can be easily integrated with existing interactive segmentation models to improve the performance. The accuracy improvement on four dermoscopic datasets under six state‐of‐the‐art interactive segmentation methods is confirmed, which demonstrates the generalizability and effectiveness of our approach. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision making.

Research on momentum conversion in sports

Momentum, traditionally examined in sports, is an ability to increase or develop continuously, impacting mental and physical performance in a pattern akin to human preferences, which are complex and non-linear. This research explores momentum in sports, particularly tennis, integrating it with the economic concept of diminishing marginal utility to analyze momentum shifts. Using data from the 2023 Wimbledon men's singles, the study dissects momentum into absolute and relative measures, revealing server advantage and precise models with an R-square value of 0.877. It employs macro and micro perspectives to link momentum with success, affirming its significant impact on win rates and performance dimensions. A GARCH model identifies influential factors in momentum transitions, informing strategies for game control. Applying the model to basketball, specifically a 2024 Clippers-Celtics NBA game, maintains strong predictive capabilities, indicating its broader applicability. Practical recommendations for tennis coaches and players, grounded in empirical findings, conclude the study, emphasizing strategic momentum utilization.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

Tafāḋul al-Umam fī al-Qur’ān al-Karīm

This study aims to analyze the concept of differentiation, "Tafāḋul," one of the concepts of divine law in the Qur'an. This research is qualitative and uses a type of literature study. This research uses a semantic approach. The research data is in the form of the concept of differentiation "Tafāḋul" with the source of Qur'an data. The data collection technique uses documentation. Meanwhile, data analysis uses content analysis. The results of the study show that the concept of "al-Sunan Alelahia" divine laws refers to the divine laws governing history and nations. Differentiation between individuals and nations is a law established by God Almighty in the universe to enhance competition and effectiveness between humans to obtain the higher degrees of Paradise, and the divine preference for humans over all other creatures is The most comprehensive circle of preference is followed by the preference for those who carry messages, followed by the preference of nations over each other, and the preference for some groups of society, such as the preference for those who fight with their money and their lives over those who remain idle, that is, the preference for those with high determination over those who are passive and idle. So preference is for the one who carries out the message and advances the nation and elevates it. During the research, I explained the characteristics of the best nation and its descriptions in the Holy Qur’an, and how these characteristics are constant over time, deriving their stability from divine laws, but the best nations change over time according to their firmness and commitment to the divine commandments and their fulfillment of the charitable role assigned to them. If they regress and deviate from the path of righteousness, their fate is to disappear.