There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

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Using social and behavioural science to support COVID-19 pandemic response.

Traditional approaches to human information processing tend to deal with perception and action planning in isolation, so that an adequate account of the perception-action interface is still missing On the perceptual side, the dominant cognitive view largely underestimates, and thus fails to account for, the impact of action-related processes on both the processing of perceptual information and on perceptual learning On the action side, most approaches conceive of action planning as a mere continuation of stimulus processing, thus failing to account for the goal-directedness of even the simplest reaction in an experimental task We propose a new framework for a more adequate theoretical treatment of perception and action planning, in which perceptual contents and action plans are coded in a common representational medium by feature codes with distal reference Perceived events (perceptions) and to-be-produced events (actions) are equally represented by integrated, task-tuned networks of feature codes – cognitive structures we call event codes We give an overview of evidence from a wide variety of empirical domains, such as spatial stimulus-response compatibility, sensorimotor synchronization, and ideomotor action, showing that our main assumptions are well supported by the data

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X01000103

The Theory of Event Coding (TEC): a framework for perception and action planning.

Doing Qualitative Research

Design processes can be influenced by their practice environments. Although design processes of industry engineers have been examined in multiple research studies, few studies have investigated design processes of academic engineers. As academia and industry have different sociocultural norms and constraints, their design processes likely also differ. To examine this question, we conducted semi-structured interviews with both academic and industry engineers who had successfully designed medical devices. Our qualitative findings revealed that engineers in industry described their design process as a sequence with problem definition, solution exploration, detail design, evaluation, and communication. Academic engineers, in contrast, described their design process as beginning with a discovered solution, then searching for application problems, evaluating compatibility between problems and solution, and finally, communicating their findings through publications. Understanding differences in design processes of academic and industry engineers can facilitate knowledge sharing and promote collaboration between academia and industry. The findings also highlight the impact of sociocultural norms on practices, even in disciplines with highly trained and clearly defined processes.

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Start With Problems or Solutions? Medical Device Design in Industry and Academia

Creative concept generation is an important source of successful innovation; thus, techniques that support creative conceptual designs are imperative to instruction on engineering design processes. One ideation tool, Design Heuristics, was developed through empirical studies of designs by professional engineers and in award-winning products. While the Design Heuristics strategies were identified and validated in the product design space, their application in biomedical engineering spaces has not been examined. In our study, we implemented a Design Heuristics lesson during one session of a semester-long biomedical engineering design course for upper-level undergraduates. In this paper, we present an analysis of three design teams from the course to examine the applicability and impact of Design Heuristics within biomedical engineering design problems. Findings revealed that the biomedical engineering students successfully engaged in idea generation using Design Heuristics to build new biomedical engineering concepts. The findings suggest that Design Heuristics can support idea generation in biomedical engineering contexts, demonstrating the value of Design Heuristics outside of the previously-documented mechanical engineering and industrial design contexts. Introduction A primary goal of the undergraduate engineering curriculum is to support students’ development of design skills, demonstrated by the ABET requirement that students should be able to “design a system, component, or process to meet desired needs.” ABET also emphasizes that these solutions will need to be innovative to address the complex problems in the world today. Idea generation and development are important processes in design that lead to innovative outcomes and instruction using idea generation tools can increase the likelihood of students’ success in these processes. One idea generation tool, called Design Heuristics, has been shown to be beneficial to student solution processes and outcomes in engineering design courses. The focus of these studies has been on product design in mechanical and multi-disciplinary engineering and industrial design courses. However, there is potential for Design Heuristics to be applied to other engineering disciplines, such as biomedical engineering. Biomedical engineering design courses focus on medical devices, and incorporate a wide variety of expertise and engineering disciplines, including mechanical, electrical, and chemical engineering. The breadth of experience represented on a design team can help to generate innovative and diverse ideas. Biomedical engineering design courses focus on the processes of reverse engineering, problem definition and conceptualization, preliminary design, final design selection, prototyping, testing, and marketing, as well as addressing some unique biomedical focus areas such as medical device regulation. Within the field of biomedical engineering design, there has been limited discussion of how to instruct students about the idea generation process. Instruction on idea generation appears in some biomedical engineering design textbooks with an emphasis on mindset, collaboration, and versatility in idea generation, along with guidelines for facilitation. However, textbooks in biomedical engineering design provide minimal information about techniques for idea generation. Design Heuristics have been shown to be valuable in multiple product design contexts and biomedical engineering product design may also be a relevant application area. Biomedical engineering design includes device-oriented designs that require physical interactions with users, and so may benefit from generative heuristics gleaned from enduser products. In this research, we explored the extent to which biomedical engineering students were able to apply Design Heuristics during idea generation, and whether they selected Design Heuristics-driven concepts as practical concepts to take forward in their design projects. Background The consideration of multiple and diverse concepts during ideation can lead to innovative solutions. Research indicates that diverse idea generation is difficult for students, and that limited resources exist for engineering educators on how to generate innovative concepts. Common teaching methods for engineering idea generation include “brainstorming” and morphological analysis; however there is little formal idea generation instruction or systematic approaches to idea generation. Tools to facilitate idea generation include brainstorming, brainwriting, conceptual combination, Design Heuristics, IDEO Method Cards, lateral thinking, morphological analysis, Synectics, SCAMPER, and TRIZ. Each of these tools has a unique approach to guiding idea generation, varying in their focus specificity, and usability. For example, SCAMPER aids in idea generation by defining general prompts; Synectics provides general theme suggestions to define the contextual meaning of product, and brainstorming sets rules to guide a team during idea generation sessions. Design Heuristics provide “rules of thumb” to introduce variation in design based on analyses of past products. Because of the existing evidence of the applicability of Design Heuristics in product design contexts, we selected this tool for use in a biomedical engineering design course. Design Heuristics are idea generation strategies to prompt designers to explore a wide variety of ideas during idea generation, leading to more diverse and creative design concepts. Cognitive heuristics as defined in the field of psychology facilitate “best guesses” at potential solutions, and their use has been shown to support expert performance. They are heuristics rather than algorithms because they provide a suggestion towards a possible solution rather than a deterministic outcome. A specific set of 77 cognitive heuristics for product design, called Design Heuristics, have been identified in studies of expert and advanced product designers and analysis of innovative products. Each of the 77 Design Heuristics provides a different, specific prompt, and is illustrated with a graphic representation and examples of its use in specific products. The heuristics are printed on an index card that can be considered by designers to prompt ideas. On the front of each Design Heuristic card, there is a title of the strategy, a graphic image, and a description of the heuristic (Figure 1). The back of each card provides two example products where the heuristic is evident, demonstrating how the heuristic can be applied to multiple products. One of these example is always a seating device, and the other example is a consumer product, represented by a variety of products throughout the set of 77 cards. Figure 1. Design Heuristic card #50, Provide Sensory Feedback. (a) Front features the Design Heuristic strategy and description with image and text. (b) Back features two examples of how the heuristic can be applied. For example, the Design Heuristic, Provide sensory feedback, prompts the designer to consider how to provide feedback to the user when they interact with the product. For example, in designing a prosthetic, this Design Heuristic could be applied by adding vibrating sensations for the user whenever their prosthetic touches a surface. By pushing the designer to consider aspects of designs noted by other designers, Design Heuristics can help novice engineers broaden their conceptions of the design space, consider non-obvious ideas, and generate multiple, diverse concepts. In a series of studies, the Design Heuristics cards have been empirically validated as effective in helping students generate conceptual solutions to address design problems. Designs developed with the cards were non-obvious and distinct, and led to diverse and creative ideas in later stages of the design process. In studies with first-year mechanical engineering students, design concepts generated with Design Heuristics were more original than those generated without Design Heuristics, which were often replications of known ideas or simply minor changes to existing products. Another study engaged first-year engineering students by teaching Design Heuristics as (1) a concept generation technique and (2) a concept transformation technique. More variety of solution concepts was observed in design concepts generated with the Design Heuristics. In a study with upper-level engineering students working in teams, concepts generated using Design Heuristics were observed as more practical, and were maintained from initial ideation through the final project design. In this study of teams working on different design projects, Design Heuristics were found to be applicable across a diverse range of problems. In sum, Design Heuristics have been established as a beneficial tool to assist mechanical engineers in generating diverse and create ideas. In the present study, we sought to determine whether this instructional method would also benefit students in the domain of biomedical engineering design. Research Methods Research Questions The focus of this study was to investigate whether and how Design Heuristics may assist students in generating design ideas in the context of biomedical engineering problems. We were also interested in how students developed their initial ideas as they refined and developed their concepts. Our project was guided by the following research questions:  How are Design Heuristics applied in concepts generated by biomedical engineering students?  To what extent are heuristic-driven ideas present in concepts considered worthwhile and selected to take forward in the design processes? Course Context and Participants The goal of the biomedical engineering design course at a large Midwestern university was to design, test, and build medical devices for stakeholders such as university departments, clinicians, and industry. Skills developed through the course included problem definition, concept gener

Design in biomedical engineering: Student applications of design heuristics as a tool for idea generation

Dr. Yilmaz is an Assistant Professor of Industrial Design who teaches design studios and lecture courses on developing creativity and research skills. For her research, she investigates design approaches and ideation, creative processes, and cross-disciplinary design team dynamics. She is the author of more than 20 peer-reviewed journals and conference proceedings. She also serves on review, advisory, and scientific boards of various journals and conferences. Her current research focuses on identifying impacts of different factors on ideation of designers and engineers, developing instructional materials for 77 cards, and designing innovation workshops for students without design or engineering background and teaching them design thinking methodologies. She received her PhD degree in Design Science in 2010 from University of Michigan. She is also a faculty in Human Computer Interaction Graduate Program and a research faculty in Center for e-Design.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1003&context=industrialdesign_conf

Design Heuristics: An Evidence-Based Tool to Improve Innovation

How far can teaching methods go to enhance learning? Optimal methods of teaching have been considered in research on supervised and unsupervised learning. Locally optimal methods are usually hybrids of teaching and self-directed approaches. The costs and benefits of specific methods have been shown to depend on the structure of the learning task, the learners, the teachers, and the environment.

Teacher and learner: Supervised and unsupervised learning in communities.

An innovative idea generation process explores a variety of diverse design ideas. While challenging to achieve even for expert designers, support tools can assist designers as they learn to generate more ideas, and more varied ideas, throughout their idea generation process. In this chapter, we summarize evidence identifying Design Heuristics in product design, and describe how they can be employed within a variety of lesson structures to foster idea generation. We describe alternative lesson plans using the 77 Design Heuristics: specifically, in initial idea generation, idea development, generation of subcomponent ideas, and team design. The Design Heuristics have been shown to support learning and innovative design outcomes across educational levels and design disciplines.

Colleen M. Seifert

Papers

Start With Problems or Solutions? Medical Device Design in Industry and Academia

Design in biomedical engineering: Student applications of design heuristics as a tool for idea generation

Design Heuristics: An Evidence-Based Tool to Improve Innovation

Teacher and learner: Supervised and unsupervised learning in communities.

Teaching Design Innovation Skills: Design Heuristics Support Creating, Developing, and Combining Ideas