Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications

Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout

Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications

Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout

Characteristics of Pro-c Analogies and Blends between Research Publications

Dr Inventor is a tool that aims to enhance the professional (Pro-c) creativity of researchers by suggesting novel hypotheses, arising from analogies between publications. Dr Inventor processes original research documents using a combination of lexical analysis and cognitive computation to identify novel comparisons that suggest new research hypotheses, with the objective of supporting a novel research publication. Research on analogical reasoning strongly suggests that the value of analogy-based comparisons depends primarily on the strength of the mapping (or counterpart projection) between the two analogs. An evaluation study of a number of computer generated comparisons attracted creativity ratings from a group of practising researchers. This paper explores a variety of theoretically motivated metrics operating on different conceptual spaces, identifying some weak associations with user's creativity ratings. Surprisingly, our results show that metrics focused on the mapping appear to have less relevance to creativity than metrics assessing the inferences (blended space). This paper includes a brief description of a research project currently exploring the best research hypothesis generated during this evaluation. Finally, we explore PCA as a means of specifying a combined multiple metrics from several blending spaces as a basis for detecting comparisons to enhance researchers’ creativity

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

Embedding a Creativity Support Tool within Computer Graphics Research

We describe the Dr Inventor creativity support tool that aims to support and even enhance the creativity of active research scientists, by discovering un-noticed analogical similarities between publications. The tool combines text processing, lexical analysis and computational cognitive modelling to find comparisons with the greatest potential for a creative impact on the system users. A multi-year corpus of publications is used to drive the creativity of the system, with a central graph matching algorithm being adapted to identify the best analogy between any pair of papers. Dr Inventor has been developed for use by computer graphics researchers, with a particular focus on publications from the SIGGRAPH conference series and it uses this context in three main ways. Firstly, the pragmatic context of creativity support requires the identification of comparisons that are unlike pre-existing information. Secondly, the suggested inferences are assessed for quality within the context of a corpus of graphics publications. Finally, expert users from this discipline were asked to identify the qualities of greatest concern to them, which then guided the subsequent evaluation task

Interactive analogical retrieval: practice, theory and technology

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.PhDCommittee Chair: Goel, Ashok; Committee Member: Kolodner, Janet; Committee Member: Maher, Mary Lou; Committee Member: Nersessian, Nancy; Committee Member: Yen, Jeannett

Developing and Evaluating Visual Analogies to Support Insight and Creative Problem Solving

The primary aim of this thesis is to gain a richer understanding of visual analogies for insight problem solving, and, in particular, how they can be better developed to ensure their effectiveness as hints. While much work has explored the role of visual analogies in problem solving and their facilitative role, only a few studies have analysed how they could be designed. This thesis employs a mixed method consisting of a practice-led approach for studying how visual analogies can be designed and developed and an experimental research approach for testing their effectiveness as hints for solving visual insight problems

The development of reasoning heuristics in autism and in typical development

Reasoning and judgment under uncertainty are often based on a limited number of simplifying heuristics rather than formal logic or rule-based argumentation. Heuristics are low-effort mental shortcuts, which save time and effort, and usually result in accurate judgment, but they can also lead to systematic errors and biases when applied inappropriately. In the past 40 years hundreds of papers have been published on the topic of heuristics and biases in judgment and decision making. However, we still know surprisingly little about the development and the cognitive underpinnings of heuristics and biases. The main aim of my thesis is to examine these questions. Another aim is to evaluate the applicability of dual-process theories of reasoning to the development of reasoning. Dual-process theories claim that there are two types of process underlying higher order reasoning: fast, automatic, and effortless (Type 1) processes (which are usually associated with the use of reasoning heuristics), and slow, conscious and effortful (Type 2) processes (which are usually associated with rule-based reasoning). This thesis presents eight experiments which investigated the development of reasoning heuristics in three different populations: typically developing children and adolescents between the age of 5 and 16, adolescents with autism, and university students. Although heuristic reasoning is supposed to be basic, simple, and effortless, we have found evidence that responses that are usually attributed to heuristic processes are positively correlated with cognitive capacity in the case of young children (even after controlling for the effects of age). Moreover, we have found that adolescents with autism are less susceptible to a number of reasoning heuristics than typically developing children. Finally, our experiments with university students provided evidence that education in statistics increases the likelihood of the inappropriate use of a certain heuristic (the equiprobability bias). These results offer a novel insight into the development of reasoning heuristics. Additionally, they have interesting implications for dual-process theories of reasoning, and they can also inform the debates about the rationality of reasoning heuristics and biases