Search Results (22)

This research investigates how ingredient statement (IS) labels influence consumer perceptions of food processing and the downstream effects on behavioral intentions, with implications for sustainability. Across three studies, we demonstrate that consumers rely more heavily on IS labels than Nutrition Facts (NF) panels when judging the degree of food processing. Study 1 shows that IS labels attract greater attention and are perceived as more diagnostic for processing judgments. Study 2 confirms that IS labels elicit higher processing perceptions than NF labels across multiple food categories. Study 3 reveals that IS label length and ingredient familiarity interact to shape processing perceptions and behavioral intentions, with longer and less familiar ingredient lists reducing purchase and recommendation intentions. Theoretically, these findings support schema and dual-process models of consumer cognition, highlighting the role of heuristic cues in food evaluation. Practically, they suggest that simplifying ingredient lists may enhance consumer trust and product appeal. From a policy perspective, IS labels may serve as informal sustainability heuristics, nudging consumers toward less processed, potentially lower-impact foods. We discuss the implications for labeling regulation, product reformulation, and integrated health–sustainability frameworks, while identifying avenues for future research on real-world behavior and environmental metrics. Full article

This work introduces RAMA (Recursive Aesthetic Modular Approximation), a metaheuristic framework that models a restricted form of mathematical intuition inspired by the notebooks of Srinivasa Ramanujan. While Ramanujan often produced deep results without formal proofs, the heuristic processes guiding such discoveries remain poorly understood. RAMA treats large language models (LLMs) as proposal mechanisms within an iterative search that generates, evaluates, and refines candidate conjectures under an explicit energy functional balancing fit, description length, and aesthetic structure. A small set of Ramanujan-inspired heuristics—modular symmetries, integrality cues, aesthetic compression, and near-invariance detection—is formalized as micro-operators acting on symbolic states. We instantiate RAMA in two domains: (i) inverse engineering eta-quotients from partial q-series data and (ii) designing cyclotomic fingerprints with shadow gadgets for quantum circuits. In both settings, RAMA recovers compact structures from limited information and improves separation from classical baselines, illustrating how intuitive heuristic patterns can be rendered as explicit, reproducible computational procedures. Full article

Effective turn-taking is fundamental to conversational interactions, shaping the fluidity of communication across human dialogues and interactions with spoken dialogue systems (SDS). Despite its apparent simplicity, conversational turn-taking involves complex timing mechanisms influenced by various linguistic, prosodic, and multimodal cues. This review synthesises recent theoretical insights and practical advancements in understanding and modelling conversational timing dynamics, emphasising critical phenomena such as voice activity (VA), turn floor offsets (TFO), and predictive turn-taking. We first discuss foundational concepts, such as voice activity detection (VAD) and inter-pausal units (IPUs), and highlight their significance for systematically representing dialogue states. Central to the challenge of interactive systems is distinguishing moments when conversational roles shift versus when they remain with the current speaker, encapsulated by the concepts of “hold” and “shift”. The timing of these transitions, measured through Turn Floor Offsets (TFOs), aligns closely with minimal human reaction times, suggesting biological underpinnings while exhibiting cross-linguistic variability. This review further explores computational turn-taking heuristics and models, noting that simplistic strategies may reduce interruptions yet risk introducing unnatural delays. Integrating multimodal signals, prosodic, verbal, visual, and predictive mechanisms is emphasised as essential for future developments in achieving human-like conversational responsiveness. Full article

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article

Successful health promotion involves messages that are quickly captured and held long enough to permit eligibility, credibility, and calls to action to be coded. This research develops an exploratory eye-tracking atlas of breast cancer screening ads viewed by midlife women and a replicable pipeline that distinguishes early capture from long-term processing. Areas of Interest are divided into design-influential categories and graphed with two complementary measures: first hit and time to first fixation for entry and a tie-aware pairwise dominance model for dwell that produces rankings and an “early-vs.-sticky” quadrant visualization. Across creatives, pictorial and symbolic features were more likely to capture the first glance when they were perceptually dominant, while layouts containing centralized headlines or institutional cues deflected entry to the message and source. Prolonged attention was consistently focused on blocks of text, locations, and badges of authoring over ornamental pictures, demarcating the functional difference between capture and processing. Subgroup differences indicated audience-sensitive shifts: Older and household families shifted earlier toward source cues, more educated audiences shifted toward copy and locations, and younger or single viewers shifted toward symbols and images. Internal diagnostics verified that pairwise matrices were consistent with standard dwell summaries, verifying the comparative approach. The atlas converts the patterns into design-ready heuristics: defend sticky and early pieces, encourage sticky but late pieces by pushing them toward probable entry channels, de-clutter early but not sticky pieces to convert to processing, and re-think pieces that are neither. In practice, the diagnostics can be incorporated into procurement, pretesting, and briefs by agencies, educators, and campaign managers in order to enhance actionability without sacrificing segmentation of audiences. As an exploratory investigation, this study invites replication with larger and more diverse samples, generalizations to dynamic media, and associations with downstream measures such as recall and uptake of services. Full article

Product launch events serve as a crucial means of marketing and communication for technology brands. With the empowerment of multimodal technologies, the construction of meaning and the pathways of persuasion in these events have been reshaped. Drawing on grounded theory, this study systematically reviews and analyzes 258 smart product launch events organized by 20 leading technology brands. The findings reveal that product launch events consist of two major categories of content—namely, core information and peripheral information—which together form a reinforced persuasion mechanism, resonating with the additivity effects proposed in the Heuristic Systematic Model (HSM). Furthermore, the abundant multimodal cues embedded in these events contribute to the reinforcement mechanism through overlapping complementarity and dynamic supplementation. Finally, this study discusses the theoretical significance of the multimodality-assisted reinforced persuasion mechanism in relation to dual-process models and its appropriateness in contemporary communication contexts. By providing an in-depth investigation of smart product launch events as a novel form of content dissemination, the study conceptualizes a persuasion mechanism suitable for complex communication environments and offers practical guidance for industry marketing practice. Full article

Organic foods, functioning as credence goods in sustainable consumption, compel consumers to rely on extrinsic cues for quality evaluation. To address this challenge, this study employs a qualitative, phenomenological approach, conducting ten in-depth, semi-structured interviews with Romanian organic food consumers. The resulting data were systematically analyzed through thematic analysis to uncover decision-making patterns. The findings reveal a sequential hierarchy in which credible transnational certification (the EU organic logo) serves as a non-negotiable gatekeeper of trust, followed by country of origin—particularly local—which functions as an emotional and heuristic differentiator signaling authenticity and freshness, while price acts as a pragmatic arbiter, mediating trade-offs between ideal preferences and budget constraints. Based on these findings, this study proposes the Trust–Emotion–Pragmatism model as a nuanced framework for understanding organic food choice, suggesting that local producers can enhance competitiveness by first establishing trust through certification, then leveraging the emotional appeal of local origin, and finally adopting effective pricing strategies. Full article

Objectives: Artificial intelligence (AI) symptom-checker apps are proliferating, yet their everyday usability and transparency remain under-examined. This study provides a triangulated evaluation of three widely used AI-powered mHealth apps: ADA, Mediktor, and WebMD. Methods: Five usability experts applied a 13-item AI-specific heuristic checklist. In parallel, thirty lay users (18–65 years) completed five health-scenario tasks on each app, while task success, errors, completion time, and System Usability Scale (SUS) ratings were recorded. A repeated-measures ANOVA followed by paired-sample t-tests was conducted to compare SUS scores across the three applications. Results: The analysis revealed statistically significant differences in usability across the apps. ADA achieved a significantly higher mean SUS score than both Mediktor (p = 0.0004) and WebMD (p < 0.001), while Mediktor also outperformed WebMD (p = 0.0009). Common issues across all apps included vague AI outputs, limited feedback for input errors, and inconsistent navigation. Each application also failed key explainability heuristics, offering no confidence scores or interpretable rationales for AI-generated recommendations. Conclusions: Even highly rated AI mHealth apps display critical gaps in explainability and error handling. Embedding explainable AI (XAI) cues such as confidence indicators, input validation, and transparent justifications can enhance user trust, safety, and overall adoption in real-world healthcare contexts. Full article

Grounded in information processing theory, this study explores how ecotourism decisions were formed within the rural district of Akdağmadeni (Türkiye), integrating both heuristic and systematic decision-making processes. The research adopts a two-phase mixed-methods design: First, it employs a survey-based factorial analysis involving 383 participants to examine preferences for nature-based activities such as trekking, cycling, and cultural tourism. Second, it uses in-depth interviews to investigate participants’ strategic evaluations of local landscape and heritage assets. The results reveal that individuals flexibly switch between intuitive and analytical judgments based on contextual factors. Key decision drivers identified include alignment with local development, ecological integrity, and socioeconomic contribution. This dual-process insight is operationalized through a novel “feature-based evaluation model” that synthesizes landscape identity values with cognitive-perceptual cues, providing a new lens for assessing geoheritage-based tourism behavior. It was determined that participants used both intuitive and systematic information processing strategies in their decision-making processes, and factors such as harmony with nature, economic contribution, and local identity were found to affect preferences. The study draws attention to the need to develop sustainable tourism policies, raise public awareness, and support infrastructure investments, and provides a road map for the effective use of the region’s ecotourism potential. Full article

This study investigates the effects of unverifiable green signals—vague environmental claims, trust-substitute cues, and function-stacking—on consumer purchasing behaviors in e-commerce settings. Using detailed product-level data collected from two major Chinese online platforms, Taobao and Pinduoduo, during the peak shopping period in November 2023, we analyze the impact of these signals on product sales using ordinary least squares (OLS), instrumental variable (IV), and propensity score matching (PSM) methods. Results indicate that vague environmental language and function-stacking significantly boost sales across platforms, highlighting consumers’ preference for easily interpretable and seemingly comprehensive products. However, trust-substitute signals exhibit mixed effects, with them being beneficial on platforms with stronger credibility frameworks (Taobao) and less effective or even detrimental on platforms characterized by price competition and weaker governance (Pinduoduo). This study contributes to the literature on consumer trust and digital greenwashing by identifying platform-specific responses to unverifiable eco-claims and underscoring the importance of heuristic processing theories and trust formation mechanisms in digital marketing contexts. These findings underscore the complex dynamics of greenwashing strategies and stress the necessity for enhanced regulation and clearer communication standards to protect consumers and genuinely support sustainable consumption. Full article

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players’ facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience. Full article

This study aimed to demonstrate the effect of negative emotions elicited by COVID-19 on the revenge consumption of international travel through both rational and irrational buying intentions. The theory of planned behavior and heuristic cues were used to explain revenge consumption in terms of rational and irrational buying intentions, respectively. A survey was conducted using MTURK from 31 May 2023 to 2 June 2023 among adults who experienced COVID-19. A structural equation model (SEM) was used to test the hypotheses, and the Hayes PROCESS macro was used to test the mediation effect. The results revealed that negative emotions due to COVID-19 affected irrational buying intentions, but not rational buying intentions, and that both irrational and rational buying intentions significantly affected revenge consumption intentions for international travel. In addition, irrational buying intentions affected rational buying intentions. These results indicate that when making an international travel decision due to negative emotions caused by COVID-19, an irrational decision process was employed, whereas later, at the travel reservation and planning stage, individuals consumed and planned travel based on rational intentions. The significance of this study lies in the fact that it illuminates the phenomenon of revenge consumption following disasters such as pandemics. Full article

Understanding the intricate dynamics of social media users’ information-sharing behaviours during crises is essential for effective public opinion management. While various scholarly efforts have attempted to uncover the factors influencing information sharing through different lenses, the underlying mechanisms remain elusive. Building upon the heuristic–systematic model (HSM) and construal level theory (CLT), this study explores the complex mechanisms that govern social media users’ information-sharing behaviours. The results indicate that both cognition and emotion play crucial roles in shaping users’ information-sharing behaviours, with systematic cues having the most significant impact on information-sharing behaviours. In terms of heuristic cues, positive emotions are more influential on information-sharing behaviours than primary cognition and negative emotions. Furthermore, spatial distance emerges as a key moderator, influencing individuals’ levels of engagement in information sharing. Emotion also acts as a mediator, connecting cognition to information sharing. This study provides insights into the sophisticated mechanisms of information sharing during crises, offering valuable implications for emergency management agencies to utilise social media for targeted public opinion guidance. Full article

Mobile service robots often have to work in dynamic and cluttered environments. Multiple safety hazards exist for robots in such work environments, which visual sensors may not detect in time before collisions or robotic damage. An alternative hazard alert system using tactile methods is explored to pre-emptively convey surrounding spatial information to robots working in complex environments or under poor lighting conditions. The proposed method for robot-inclusive tactile paving is known as Passive Auto-Tactile Heuristic (PATH) tiles. These robot-inclusive tactile paving tiles are implemented in spatial infrastructure and are aimed to allow robots to pre-emptively recognize surrounding hazards even under poor lighting conditions and potentially provide improved hazard cues to visually impaired people. A corresponding Tactile Sensing Module (TSM) was used for the digital interpretation of the PATH tiles and was mounted onboard a mobile audit robot known as Meerkat. The experiment yielded a 71.6% improvement in pre-emptive hazard detection capabilities with the TSM using a customized Graph Neural Network (GNN) model. Full article

In this paper, a novel bilateral shared control approach is proposed to address the issue of strong dependence on the human, and the resulting burden of manipulation, in classical haptic teleoperation systems for vehicle navigation. A Hidden Markov Model (HMM) is utilized to handle the Human Intention Recognition (HIR), according to the force input by the human—including the HMM solution, i.e., Baum–Welch algorithm, and HMM decoding, i.e., Viterbi algorithm—and the communication delay in teleoperation systems is added to generate a temporary goal. Afterwards, a heuristic and sampling method for online generation of splicing trajectory based on the goal is proposed innovatively, ensuring the vehicle can move feasibly after the change in human intention is detected. Once the trajectory is available, the vehicle velocity is then converted to joystick position information as the haptic cue of the human, which enhances the telepresence. The shared teleoperation control framework is verified in the simulation environment, where its excellent performance in the complex environment is evaluated, and its feasibility is confirmed. The experimental results show that the proposed method can achieve simple and efficient navigation in a complex environment, and can also give a certain situational awareness to the human. Full article