TOWARDS HUMAN-AI-COLLABORATION IN BRAINSTORMING: EMPIRICAL INSIGHTS INTO THE PERCEPTION OF WORKING WITH A GENERATIVE AI

Groups of humans or crowds can be remarkable when coming up with ideas. However, not everyone has a group of humans at their disposal to brainstorm. With recent advances in AI, however, generative large language models (LLM) might be capable of contributing ideas in a brainstorming session, turning individual work of a human into joint work of human and AI. It is, however, unclear, how group effects known from human brainstorming groups transfer to such a human-AI setting. In our mixed-method study (qualitative emphasis) with 24 participants, we investigate how a human brainstorms together with the generative LLM ‘GPT-3’, and how they perceived their experience. Our results highlight known effects like cognitive stimulation but also a risk of free riding. We thereby contribute to the understanding of how generative AI, which is becoming broadly available, can be used to address the challenge of human-AI collaboration for solving open-ended problems

The ideation compass: supporting interdisciplinary creative dialogues with real time visualization

This study presents the potential of live topic visualization in supporting creative dialogs during remote idea generation. We developed a novel Creativity Support Tool (CST) to explore the effects of the live topic visualization. The tool emphasizes the interdisciplinary knowledge background of participants. Using Natural Language Processing (NLP) and topic modeling, the tool provides users with a live visual mapping of the domains and topics being orally discussed. To understand the tool’s user perceived effects, we conducted evaluation sessions and interviews with participants (N = 10) from two different disciplinary backgrounds: design and bioscience. The findings show that live visualization of domains and topics supported self-reflection during individual and collaborative creativity and encouraged a balanced discussion, which can mitigate discipline-based fixation in ideation

Toward summarization of communicative activities in spoken conversation

This thesis is an inquiry into the nature and structure of face-to-face conversation, with a special focus on group meetings in the workplace. I argue that conversations are composed of episodes, each of which corresponds to an identifiable communicative activity such as giving instructions or telling a story. These activities are important because they are part of participants’ commonsense understanding of what happens in a conversation. They appear in natural summaries of conversations such as meeting minutes, and participants talk about them within the conversation itself. Episodic communicative activities therefore represent an essential component of practical, commonsense descriptions of conversations. The thesis objective is to provide a deeper understanding of how such activities may be recognized and differentiated from one another, and to develop a computational method for doing so automatically. The experiments are thus intended as initial steps toward future applications that will require analysis of such activities, such as an automatic minute-taker for workplace meetings, a browser for broadcast news archives, or an automatic decision mapper for planning interactions. My main theoretical contribution is to propose a novel analytical framework called participant relational analysis. The proposal argues that communicative activities are principally indicated through participant-relational features, i.e., expressions of relationships between participants and the dialogue. Participant-relational features, such as subjective language, verbal reference to the participants, and the distribution of speech activity amongst the participants, are therefore argued to be a principal means for analyzing the nature and structure of communicative activities. I then apply the proposed framework to two computational problems: automatic discourse segmentation and automatic discourse segment labeling. The first set of experiments test whether participant-relational features can serve as a basis for automatically segmenting conversations into discourse segments, e.g., activity episodes. Results show that they are effective across different levels of segmentation and different corpora, and indeed sometimes more effective than the commonly-used method of using semantic links between content words, i.e., lexical cohesion. They also show that feature performance is highly dependent on segment type, suggesting that human-annotated “topic segments” are in fact a multi-dimensional, heterogeneous collection of topic and activity-oriented units. Analysis of commonly used evaluation measures, performed in conjunction with the segmentation experiments, reveals that they fail to penalize substantially defective results due to inherent biases in the measures. I therefore preface the experiments with a comprehensive analysis of these biases and a proposal for a novel evaluation measure. A reevaluation of state-of-the-art segmentation algorithms using the novel measure produces substantially different results from previous studies. This raises serious questions about the effectiveness of some state-of-the-art algorithms and helps to identify the most appropriate ones to employ in the subsequent experiments. I also preface the experiments with an investigation of participant reference, an important type of participant-relational feature. I propose an annotation scheme with novel distinctions for vagueness, discourse function, and addressing-based referent inclusion, each of which are assessed for inter-coder reliability. The produced dataset includes annotations of 11,000 occasions of person-referring. The second set of experiments concern the use of participant-relational features to automatically identify labels for discourse segments. In contrast to assigning semantic topic labels, such as topical headlines, the proposed algorithm automatically labels segments according to activity type, e.g., presentation, discussion, and evaluation. The method is unsupervised and does not learn from annotated ground truth labels. Rather, it induces the labels through correlations between discourse segment boundaries and the occurrence of bracketing meta-discourse, i.e., occasions when the participants talk explicitly about what has just occurred or what is about to occur. Results show that bracketing meta-discourse is an effective basis for identifying some labels automatically, but that its use is limited if global correlations to segment features are not employed. This thesis addresses important pre-requisites to the automatic summarization of conversation. What I provide is a novel activity-oriented perspective on how summarization should be approached, and a novel participant-relational approach to conversational analysis. The experimental results show that analysis of participant-relational features is

Knowledge-Based Decision Making in Complex Environments: Methodological Aspects of Proactive Airport Security Management

An airport is the gateway which facilitates access to air transport. As a reaction to very diverse attacks on the air transport system during the last decades a broad range of security measures has been introduced to mitigate possible threats. The challenge to provide a trouble free experience for the passenger and, at the same time, to oper-ate more efficiently calls for a proactive approach. This requires the definition of future requirements that allow an adaptation of the security system. When dealing with uncertainty that future-oriented decisions inevitably display, it is important to gain as much knowledge as possible about a system’s general structure. The approach described in this paper systematically documents elements and relationships of the airport security system. It consists of threat scenario elements as well as security measures. The development of a software tool, the so-called Scenario Builder, is described and its application for the identification of possible future threats ex-plained. The presented approach offers intuitive access to the underlying structure of the airport security system. It provides decision makers with a possibility to interact with the system and anticipate effects of threat development, thereby enabling robust, future-oriented decisions

The role of phonology in visual word recognition: evidence from Chinese

Posters - Letter/Word Processing V: abstract no. 5024The hypothesis of bidirectional coupling of orthography and phonology predicts that phonology plays a role in visual word recognition, as observed in the effects of feedforward and feedback spelling to sound consistency on lexical decision. However, because orthography and phonology are closely related in alphabetic languages (homophones in alphabetic languages are usually orthographically similar), it is difficult to exclude an influence of orthography on phonological effects in visual word recognition. Chinese languages contain many written homophones that are orthographically dissimilar, allowing a test of the claim that phonological effects can be independent of orthographic similarity. We report a study of visual word recognition in Chinese based on a mega-analysis of lexical decision performance with 500 characters. The results from multiple regression analyses, after controlling for orthographic frequency, stroke number, and radical frequency, showed main effects of feedforward and feedback consistency, as well as interactions between these variables and phonological frequency and number of homophones. Implications of these results for resonance models of visual word recognition are discussed.postprin

Interactive effects of orthography and semantics in Chinese picture naming

Posters - Language Production/Writing: abstract no. 4035Picture-naming performance in English and Dutch is enhanced by presentation of a word that is similar in form to the picture name. However, it is unclear whether facilitation has an orthographic or a phonological locus. We investigated the loci of the facilitation effect in Cantonese Chinese speakers by manipulating—at three SOAs (2100, 0, and 1100 msec)—semantic, orthographic, and phonological similarity. We identified an effect of orthographic facilitation that was independent of and larger than phonological facilitation across all SOAs. Semantic interference was also found at SOAs of 2100 and 0 msec. Critically, an interaction of semantics and orthography was observed at an SOA of 1100 msec. This interaction suggests that independent effects of orthographic facilitation on picture naming are located either at the level of semantic processing or at the lemma level and are not due to the activation of picture name segments at the level of phonological retrieval.postprin