Explore the vast range of titles available.

PurposeThis study examines the role of logotypes in advertising effectiveness on s-commerce platforms by analyzing the visual attention paid by the consumer to fashion branding – wordmarks or combination marks – and their subsequent recall.Design/methodology/approachThe study examines the main areas of visual representation of the brand (VRB) on the Instagram network and the user’s corresponding areas of interest on a mobile-device screen. Attention and recall of the VRB are assessed in light of different classification variables (users’ gender, age and level of experience in s-commerce tools) to better understand how VRB may be leveraged by fashion retailers to encourage purchasing behavior. To achieve this objective, a mixed experiment design based on the eye-tracking methodology and a self-administered questionnaire is carried out.FindingsThe results indicate that visual attention, gender, age and s-commerce experience all contribute to determining users’ recall of the brand logo to which they are exposed on-screen. By considering the different s-commerce user profiles that exhibit different visualization behaviors, fashion retailers will be better placed to improve their online advertising campaigns and, ultimately, increase brand sales. The findings also point to promising future research directions on the effectiveness of branding strategies.Originality/valueThis highly innovative study provides in-depth insights into advertising effectiveness in terms of attention and recall, according to the main types of VRB for two specific s-commerce tools used by a high-street fashion brand, namely, its profile on Instagram Shop and its profile on Instagram Stories.

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at theminimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: (1) noisy training data, (2) inappropriate deep architecture, and (3) suboptimal training procedure. We address all three issues. First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy.

Background Several isiXhosa home language (HL) learners are excluded from meaningful mathematics learning because they are taught in English. Not only do teachers lack epistemological and pedagogical confidence in using multiple languages when teaching mathematics, but there are no mathematical registers for African languages that allow for adequate mathematical teaching and learning. There is a scarcity of research on what constitutes effective mathematics instruction for isiXhosa HL learners in South African language of learning and teaching (LoLT) Grade 1 classrooms. Aim The purpose of this study was to explore the experiences of Grade 1 teachers using visual representations to enhance isiXhosa HL learners' understanding of mathematics in the English- LoLT in Grade 1 classrooms. Setting This study was conducted at four primary schools in the Western Cape's Metro East Education District. Methods This study employs a qualitative research approach in conjunction with an adapted interactive qualitative analysis (IQA) systems method to collect in-depth data about current mathematics practices in English LoLT in Grade 1 classrooms. The data were analysed using John Stuart Mill's analytical comparison technique. Results This study found that semiotics such as visual (and concrete) representations assist isiXhosa HL learners to grasp and understand mathematical concepts easily. Conclusion This study emphasises the significance of using sufficient visual representation strategies to enhance isiXhosa HL learners' mathematical understanding in the English LoLT in Grade 1 classrooms. Contribution The outcomes of this study can make a positive contribution to current mathematics practice in terms of supporting isiXhosa HL learners in English LoLT in Grade 1 classrooms.

Using graphical representations such as strips, bars, or diagrams can help solve word problems. This study explores how Grade 8 pupils’ use of bar models evolved after a brief instructional intervention in Kosovo, where visual representation strategies are not typically emphasised. The research focused on two questions: the types of bar models pupils create and the relationship between bar models and problem-solving success. The intervention involved 62 pupils who were initially unfamiliar with visual problem-solving strategies. After five lessons of practising the bar model method, pupils demonstrated improvements in both the frequency and quality of their bar models for complex two-step WPs. The findings revealed an association between the quality of the bar models and problem-solving success, with correct bar models leading to correct solutions. Even partially correct bar models with certain characteristics were associated with the right answers in more than 80% of cases. While some pupils quickly adopted the bar model method, others would require more tailored instruction to fully benefit from visual strategies. The study’s significance lies in demonstrating that a brief teaching intervention can be effective in a cultural context where visual representation and problem-solving skills are not typically emphasised in the classroom. Moreover, it provides more insight into the characteristics of bar models drawn by pupils when solving word problems. Future research should explore the long-term effects of bar models and how incomplete representations facilitate understanding.

In most STEM instruction, students interact with visual representations, which can be presented in either in a physical or a virtual mode or in a blended form that combines both modes. While much research has compared the effects of physical and virtual representations on students’ learning, the field is far from being able to predict when and why one representation mode is more effective than the other. One reason why making such predictions is particularly difficult is that multiple different theories have been used to explain differences between representation modes. The goal of this article is twofold. First, it presents a survey of the literature to examine which theoretical perspectives have been used to motivate comparisons of representation modes and what predictions they make about their effectiveness. A review of 54 articles reveals five theoretical perspectives: physical engagement, cognitive load, haptic encoding, embodied action schemas, and conceptual salience. While the first two make general predictions about the effectiveness of representation modes, the last three make concept-specific predictions. Second, this article compares these predictions to examine how they conflict and align. This comparison identified several conflicts between theories that predict opposite effects, as well as several alignments where theories make the same predictions but based on different mechanisms. Further, this comparison revealed common confounds in experimental designs of the reviewed studies. The article concludes with recommendations for research to address the identified conflicts and with recommendations for instructors and designers of blended technologies for appropriate choices of representation modes.

This study examined how the contents and timing of a visual stimulus affect the identification of mixed sounds recorded in a daily life environment. For experiments, we presented four environment sounds as auditory stimuli for 5 s along with a picture or a written word as a visual stimulus that might or might not denote the source of one of the four sounds. Three conditions of temporal relations between the visual stimuli and sounds were used. The visual stimulus was presented either: (a) for 5 s simultaneously with the sound; (b) for 5 s, 1 s before the sound (SOA between the audio and visual stimuli was 6 s); or (c) for 33 ms, 1 s before the sound (SOA was 1033 ms). Participants reported all identifiable sounds for those audio-visual stimuli. To characterize the effects of visual stimuli on sound identification, the following were used: the identification rates of sounds for which the visual stimulus denoted its sound source, the rates of other sounds for which the visual stimulus did not denote the sound source, and the frequency of false hearing of a sound that was not presented for each sound set. Results of the four experiments demonstrated that a picture or a written word promoted identification of the sound when it was related to the sound, particularly when the visual stimulus was presented for 5 s simultaneously with the sounds. However, a visual stimulus preceding the sounds had a benefit only for the picture, not for the written word. Furthermore, presentation with a picture denoting a sound simultaneously with the sound reduced the frequency of false hearing. These results suggest three ways that presenting a visual stimulus affects identification of the auditory stimulus. First, activation of the visual representation extracted directly from the picture promotes identification of the denoted sound and suppresses the processing of sounds for which the visual stimulus did not denote the sound source. Second, effects based on processing of the conceptual information promote identification of the denoted sound and suppress the processing of sounds for which the visual stimulus did not denote the sound source. Third, processing of the concurrent visual representation suppresses false hearing.

Objectives This study aimed to explore how the concepts of co‐production and co‐design have been defined and applied in the context of health and social care and to identify the temporal adoption of the terms. Methods A systematic scoping review of CINAHL with Full Text, Cochrane Central Register of Controlled Trials, MEDLINE, PsycINFO, PubMed and Scopus was conducted to identify studies exploring co‐production or co‐design in health and social care. Data regarding date and conceptual definitions were extracted. From the 2933 studies retrieved, 979 articles were included in this review. Results A network map of the sixty most common definitions and—through exploration of citations—eight definition clusters and a visual representation of how they interconnect and have informed each other over time are presented. Additional findings were as follows: (i) an increase in research exploring co‐production and co‐design in health and social care contexts; (ii) an increase in the number of new definitions during the last decade, despite just over a third of included articles providing no definition or explanation for their chosen concept; and (iii) an increase in the number of publications using the terms co‐production or co‐design while not involving citizens/patients/service users. Conclusions Co‐production and co‐design are conceptualized in a wide range of ways. Rather than seeking universal definitions of these terms, future applied research should focus on articulating the underlying principles and values that need to be translated and explored in practice. Patient and Public Contribution The search strategy and pilot results were presented at a workshop in May 2019 with patient and public contributors and researchers. Discussion here informed our next steps. During the analysis phase of the review, informal discussions were held once a month with a patient who has experience in patient and public involvement. As this involvement was conducted towards the end of the review, we agreed together that inclusion as an author would risk being tokenistic. Instead, acknowledgements were preferred. The next phase involves working as equal contributors to explore the values and principles of co‐production reported within the most common definitions.

Although the visualization of data is on the agenda for sociologists today, thanks to big data, the author raises the question of whether it may not also be possible to visualize theory and, especially, to improve it through visual thinking. The main purpose of this article, more precisely, is to open up a discussion of how visualization and visual thinking can be used as a tool for theorizing in sociology and thereby help produce new and creative theories. Three different types of visualization are discussed: theory pictures, visual sketches, and theorizing diagrams. Theory pictures summarize a theory that has already been developed. Visual sketches are used for early attempts to theorize; they are then typically discarded and replaced by new sketches. Theorizing diagrams draw on ideas from Charles Sanders Peirce and can be described as visual representations that are used to generate new theories. Examples are supplied.