This study aimed to identify and compare the EEG activities associated with semantic and episodic memory retrieval during creative processes. Episodic and semantic memory induction studies were conducted and EEG was used to collect data. The results showed that (i) Episodic and semantic memory retrieval are related to the frontal lobe area; (ii) Semantic memory retrieval is evoked more swiftly compared with episodic memory retrieval (ii) Prior to episodic memory retrieval, semantic memory retrieval is evoked first.

Artificial intelligence and cognitive science are two core research areas in design. Artificial intelligence shows the capability of analysing massive amounts of data which supports making predictions, uncovering patterns and generating insights in varying design activities, while cognitive science provides the advantage of revealing the inherent mental processes and mechanisms of humans in design. Both artificial intelligence and cognitive science in design research are focused on delivering more innovative and efficient design outcomes and processes. Therefore, this thematic collection on “Applications of Artificial Intelligence and Cognitive Science in Design” brings together state-of-the-art research in artificial intelligence and cognitive science to showcase the emerging trend of applying artificial intelligence techniques and neurophysiological and biometric measures in design research. Three promising future research directions: 1) human-in-the-loop AI for design, 2) multimodal measures for design, and 3) AI for design cognitive data analysis and interpretation, are suggested by analysing the research papers collected. A framework for integration of artificial intelligence and cognitive science in design, incorporating the three research directions, is proposed to inspire and guide design researchers in exploring human-centred design methods, strategies, solutions, tools and systems.

The study aims to identify the relations between creative design quality and content of the memorising precedents, association, and combination of information processes in a design context. 71 participants were recruited to finish a creative design task. Think aloud and interview were conducted during and after the creative design task to understand the content of the memorising precedents, association, and combination of information processes. The 71 creative designs were then assessed by five experts in creative design. The results from this study revealed that participants who generated high-creativity design tend to memorize various topic-related precedents, associate items based on topic-related information, and combine topic-related information with products. Participants who generated low-creativity design tend to memorize characteristics of a specific space of the design topic, associate items based on specific topic-related information, and add the topic-related pattern to a product.

Crowdfunding is becoming increasingly popular for funding projects, particularly in the domain of product design, by asking a large group of people. Previous studies have indicated that creativity plays a significant role in product design and is considered an important factor of success for new product design and development. However, these studies have not explicitly explored the role of creativity in crowdfunding product design projects. This paper investigates this issue by conducting a case study employing expert evaluations of selected successful and unsuccessful crowdfunding product design project samples. The results of the study show there is a positive relationship between the creativity of a product and the success of its crowdfunding campaign. Therefore, creativity can be considered a success factor of crowdfunding. The study also suggests creative products, especially useful ones, might have more potential to attract people's willingness to fund them. This paper has contributed to the research on design, creativity, product design and development, and funding business models. Most importantly, this paper has raised the significance of creativity in design and business.

Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. This paper proposes a knowledge-based approach to address the challenges. The approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. The structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. The knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID.

Neurotechnology has been applied to gain insights on creativity-related cognitive factors. Prior research has identified relations between cognitive factors and creativity qualitatively; while quantitative relations, such as the relative importance of cognitive factors and creativity, have not been fully determined. Therefore, taking the creative design process as an example, this study using electroencephalography (EEG) aims to objectively identify how creativity-related cognitive factors of retrieval, recall, association, and combination contribute to creativity. The theoretical basis for an EEG-based decoding method to objectively identify which cognitive factors occur in a creative process is developed. Thirty participants were recruited for a practical study to verify the reliability of the decoding method. Based on the methodology, relationships between the relative importance level of the cognitive factor and creative output quality levels were detected. Results indicated that the occurrence of recall and association are reported with a high reliability level by the decoding method. The results also indicated that association is the dominant cognitive factor for higher creative output quality levels. Recall is the dominant cognitive factor for lower creative output quality levels.

In the field of content generation by machine, the state-of-the-art text-to-image model, DALL⋅E, has advanced and diverse capacities for the combinational image generation with specific textual prompts. The images generated by DALL⋅E seem to exhibit an appreciable level of combinational creativity close to that of humans in terms of visualizing a combinational idea. Although there are several common metrics which can be applied to assess the quality of the images generated by generative models, such as IS, FID, GIQA, and CLIP, it is unclear whether these metrics are equally applicable to assessing images containing combinational creativity. In this study, we collected the generated image data from machine (DALL⋅E) and human designers, respectively. The results of group ranking in the Consensual Assessment Technique (CAT) and the Turing Test (TT) were used as the benchmarks to assess the combinational creativity. Considering the metrics’ mathematical principles and different starting points in evaluating image quality, we introduced coincident rate (CR) and average rank variation (ARV) which are two comparable spaces. An experiment to calculate the consistency of group ranking of each metric by comparing the benchmarks then was conducted. By comparing the consistency results of CR and ARV on group ranking, we summarized the applicability of the existing evaluation metrics in assessing generative images containing combinational creativity. In the four metrics, GIQA performed the closest consistency to the CAT and TT. It shows the potential as an automated assessment for images containing combinational creativity, which can be used to evaluate the images containing combinational creativity in the relevant task of design and engineering such as conceptual sketch, digital design image, and prototyping image.

Within the last few years, a need for renewed product personalisation has been observed in some markets such as consumer electronics, fashion to meet the exact demands of the customers. Product customisation emphasises the fulfilment of explicit requirements of a defined market segment, but product individualisation targets at satisfying the particular needs of a customer. Mass Individualisation (MI) is a new product design approach comprising of an open hardware platform and multiple modules to be integrated with the platform. It gives freedom to end-users to integrate different modules into the platform as per their choice. Technological and strategic integration of all actors involved in the design process is the primary focus of this research. This paper identifies key areas which need to be focussed on to realise this approach and convert it into an industrial practice by an explorative study of existing product design and customisation approaches. An industrial survey was conducted, and results for the industrial implication and insights on this approach are presented. The findings show that the end product from product design for MI will be more creative and innovative by the networking of all actors, and offers more individualised and technologically advanced products.

This paper asked participants to assess four selected expert-rated Taiwan International Student Design Competition (TISDC) products using four methods: Consensual Assessment Technique (CAT), Creative Product Semantic Scale (CPSS), Product Creativity Measurement Instrument (PCMI), and revised Creative Solution Diagnosis Scale (rCSDS). The results revealed that, between experts and non-experts, the ranking results by the CAT and CPSS were the same, while the ranking results of the rCSDS were different. The CAT, CPSS, and TISDC methods provided the same results indicating that raters may return the same results on creativity assessment, and the results are not affected by the selected methods.

If it is necessary to use non-experts to assess creativity and the creativity results are expected to be the same with that of experts, asking non-expert raters to use CPSS to assess creativity and then ranking the creativity score is more reliable. The study offers a contribution to the creativity domain on deciding which methods may be more reliable from a comparison perspective.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

Visual stimuli can be useful in supporting design ideation process. However, researchers still know very little about how stimuli should be delivered to designers during the early design stage. This question is crucial to the effective use of stimuli because previous researches have proved that ill-presented stimuli can have a negative impact on design creativity. Therefore, an empirical study was conducted with the aim of exploring if and how combinational pictorial stimuli can affect designers' creative performance. Results from a total of 36 participants show that the design outcomes presented by the group exposed to combinational pictorial stimuli were more creative than those given by the group exposed to no stimuli or randomly presented pictorial stimuli. These results imply that the form of stimuli delivery can affect creative design outcomes and combinational pictorial stimuli best support design creativity among these three conditions. These findings give us a better understanding of the roles that visual stimuli play in design, which is expected to bring us important implications for both design education and design support tool development

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.

Combinational creativity is a significant element of design in supporting designers to generate creative ideas during the early phases of design. There exists three driven approaches to combinational creativity: problem-, similarity- and inspiration-driven. This study provides further insights into the three combinational creativity driven approaches, exploring which approach could lead to ideas that are more creative in the context of practical product design. The results from a case study reveal that the problem- driven approach could lead to more creative and novel ideas or products compared with the similarity- and inspiration-driven approach. Products originating from the similarity- and inspiration-driven approach are at comparable levels. This study provides better understanding of combinational creativity in practical design. It also delivers benefits to designers in improving creative idea generation, and supports design researchers in exploring future ideation methods and design support tools employing the concept of 'combination'.

Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.

Idea generation is significant in design, but coming up with creative ideas is often challenging. This paper presents a computer-based tool, called the Combinator, for assisting designers to produce creative ideas. The tool is developed based on an approach simulating aspects of human cognition in achieving combinational creativity. It can generate combinational prompts in text and image forms through combining unrelated ideas. A case study has been conducted to evaluate the Combinator. The study results indicate that the Combinator, in its current formulation, has assisted the tool users involved in the case study in improving the fluency of idea generation, as well as increasing the originality, usefulness, and flexibility of the ideas generated. The results also indicate that the tool could benefit its users in generating high-novelty and high-quality ideas effectively. The Combinator is considered to be beneficial in expanding the design space, increasing better idea occurrence, improving design space exploration, and enhancing the design success rate.

Urban slums provide suitable conditions for infestation by rats, which harbour and shed a wide diversity of zoonotic pathogens including helminths. We aimed to identify risk factors associated with the probability and intensity of infection of helminths of the digestive tract in an urban slum population of Rattus norvegicus. Among 299 rats, eleven species/groups of helminths were identified, of which Strongyloides sp., Nippostrongylus brasiliensis and, the human pathogen, Angiostrongylus cantonensis were the most frequent (97, 41 and 39%, respectively). Sex interactions highlighted behavioural differences between males and females, as eg males were more likely to be infected with N. brasiliensis where rat signs were present, and males presented more intense infections of Strongyloides sp. Moreover, rats in poor body condition had higher intensities of N. brasiliensis. We describe a high global richness of parasites in R. norvegicus, including five species known to cause disease in humans. Among these, A. cantonensis was found in high prevalence and it was ubiquitous in the study area – knowledge which is of public health importance. A variety of environmental, demographic and body condition variables were associated with helminth species infection of rats, suggesting a comparable variety of risk factors for humans.