Alleviating the concerns with the SDT approach to reasoning: reply to Singmann and Kellen (2014).

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Motivated Reasoning and Response Bias: A Signal Detection Approach

The aim of this dissertation was to address a theoretical debate on belief bias. Belief bias is the tendency for people to be influenced by their prior beliefs when engaged in deductive reasoning. Deduction is the act of drawing necessary conclusions from premises which are meant to be assumed as true. Given that the logical validity of an argument is independent of its content, being influenced by your prior beliefs in such content is considered a bias. Traditional theories posit there are two belief bias components. Motivated reasoning is the tendency to reason better for arguments with unbelievable conclusions relative to arguments with believable conclusions. Response bias is the tendency to accept believable arguments and to reject unbelievable arguments. Dube et al. (2010) pointed out critical methodological problems that undermine evidence for traditional theories. Using signal detection theory (SDT), they found evidence for response bias only. We adopted the SDT method to compare the viability of the traditional and the response bias accounts. In Chapter 1 the relevant literature is reviewed. In Chapter 2 four experiments which employed a novel SDT-based forced choice reasoning method are presented, showing evidence compatible with motivated reasoning. In Chapter 3 four experiments which used the receiver operating characteristic (ROC) method are presented. Crucially, cognitive ability turned out to be linked to motivated reasoning. In Chapter 4 three experiments are presented in which we investigated the impact of cognitive ability and analytic cognitive style on belief bias, concluding that cognitive style mediated the effects of cognitive ability on motivated reasoning. In Chapter 5 we discuss our findings in light of a novel individual differences account of belief bias. We conclude that using the appropriate measurement method and taking individual differences into account are two key elements to furthering our understanding of belief bias, human reasoning, and cognitive psychology in general.School of Psycholog

Better but still biased: Analytic cognitive style and belief bias

Belief bias is the tendency for prior beliefs to influence people's deductive reasoning in two ways: through the application of a simple belief-heuristic (response bias) and through the application of more effortful reasoning for unbelievable conclusions (accuracy effect or motivated reasoning). Previous research indicates that cognitive ability is the primary determinant of the effect of beliefs on accuracy. In the current study, we show that the mere tendency to engage analytic reasoning (analytic cognitive style) is responsible for the effect of cognitive ability on motivated reasoning. The implications of this finding for our understanding of the impact of individual differences on belief bias are discussed

Using forced choice to test belief bias in syllogistic reasoning.

In deductive reasoning, believable conclusions are more likely to be accepted regardless of their validity. Although many theories argue that this belief bias reflects a change in the quality of reasoning, distinguishing qualitative changes from simple response biases can be difficult (Dube, Rotello, & Heit, 2010). We introduced a novel procedure that controls for response bias. In Experiments 1 and 2, the task required judging which of two simultaneously presented syllogisms was valid. Surprisingly, there was no evidence for belief bias with this forced choice procedure. In Experiment 3, the procedure was modified so that only one set of premises was viewable at a time. An effect of beliefs emerged: unbelievable conclusions were judged more accurately, supporting the claim that beliefs affect the quality of reasoning. Experiments 4 and 5 replicated and extended this finding, showing that the effect was mediated by individual differences in cognitive ability and analytic cognitive style. Although the positive findings of Experiments 3-5 are most relevant to the debate about the mechanisms underlying belief bias, the null findings of Experiments 1 and 2 offer insight into how the presentation of an argument influences the manner in which people reason

Logic brightens my day: Evidence for implicit sensitivity to logical validity.

A key assumption of dual process theory is that reasoning is an explicit, effortful, deliberative process. The present study offers evidence for an implicit, possibly intuitive component of reasoning. Participants were shown sentences embedded in logically valid or invalid arguments. Participants were not asked to reason but instead rated the sentences for liking (Experiment 1) and physical brightness (Experiments 2-3). Sentences that followed logically from preceding sentences were judged to be more likable and brighter. Two other factors thought to be linked to implicit processing-sentence believability and facial expression-had similar effects on liking and brightness ratings. The authors conclude that sensitivity to logical structure was implicit, occurring potentially automatically and outside of awareness. They discuss the results within a fluency misattribution framework and make reference to the literature on discourse comprehension.10 page(s

Spoken conversational search: audio-only interactive information retrieval

Speech-based web search where no keyboard or screens are available to present search engine results is becoming ubiquitous, mainly through the use of mobile devices and intelligent assistants such as Apple's HomePod, Google Home, or Amazon Alexa. Currently, these intelligent assistants do not maintain a lengthy information exchange. They do not track context or present information suitable for an audio-only channel, and do not interact with the user in a multi-turn conversation. Understanding how users would interact with such an audio-only interaction system in multi-turn information seeking dialogues, and what users expect from these new systems, are unexplored in search settings. In particular, the knowledge on how to present search results over an audio-only channel and which interactions take place in this new search paradigm is crucial to incorporate while producing usable systems. Thus, constructing insight into the conversational structure of information seeking processes provides researchers and developers opportunities to build better systems while creating a research agenda and directions for future advancements in Spoken Conversational Search (SCS). Such insight has been identified as crucial in the growing SCS area. At the moment, limited understanding has been acquired for SCS, for example how the components interact, how information should be presented, or how task complexity impacts the interactivity or discourse behaviours. We aim to address these knowledge gaps. This thesis outlines the breadth of SCS and forms a manifesto advancing this highly interactive search paradigm with new research directions including prescriptive notions for implementing identified challenges. We investigate SCS through quantitative and qualitative designs: (i) log and crowdsourcing experiments investigating different interaction and results presentation styles, and (ii) the creation and analysis of the first SCS dataset and annotation schema through designing and conducting an observational study of information seeking dialogues. We propose new research directions and design recommendations based on the triangulation of three different datasets and methods: the log analysis to identify practical challenges and limitations of existing systems while informing our future observational study; the crowdsourcing experiment to validate a new experimental setup for future search engine results presentation investigations; and the observational study to establish the SCS dataset (SCSdata), form the first Spoken Conversational Search Annotation Schema (SCoSAS), and study interaction behaviours for different task complexities. Our principle contributions are based on our observational study for which we developed a novel methodology utilising a qualitative design. We show that existing information seeking models may be insufficient for the new SCS search paradigm because they inadequately capture meta-discourse functions and the system's role as an active agent. Thus, the results indicate that SCS systems have to support the user through discourse functions and be actively involved in the users' search process. This suggests that interactivity between the user and system is necessary to overcome the increased complexity which has been imposed upon the user and system by the constraints of the audio-only communication channel. We then present the first schematic model for SCS which is derived from the SCoSAS through the qualitative analysis of the SCSdata. In addition, we demonstrate the applicability of our dataset by investigating the effect of task complexity on interaction and discourse behaviour. Lastly, we present SCS design recommendations and outline new research directions for SCS. The implications of our work are practical, conceptual, and methodological. The practical implications include the development of the SCSdata, the SCoSAS, and SCS design recommendations. The conceptual implications include the development of a schematic SCS model which identifies the need for increased interactivity and pro-activity to overcome the audio-imposed complexity in SCS. The methodological implications include the development of the crowdsourcing framework, and techniques for developing and analysing SCS datasets. In summary, we believe that our findings can guide researchers and developers to help improve existing interactive systems which are less constrained, such as mobile search, as well as more constrained systems such as SCS systems

Spoken conversational search: speech-only interactive information retrieval

This research investigates a new interface paradigm for interactive information retrieval (IIR) which forces us to shift away from the classic "ten blue links" search engine results page. Instead we investigate how to present search results through a conversation over a speech-only communication channel where no screen is available. Accessing information via speech is becoming increasingly pervasive and is already important for people with a visual impairment. However, presenting search results over a speech-only communication channel is challenging due to cognitive limitations and the transient nature of audio. Studies have indicated that the implementation of speech recognizers and screen readers must be carefully designed and cannot simply be added to an existing system. Therefore the aim of this research is to develop a new interaction framework for effective and efficient IIR over a speech-only channel: a Spoken Conversational Search System (SCSS) which provides a conversational approach to defining user information needs, presenting results and enabling search reformulations. In order to contribute to a more efficient and effective search experience when using a SCSS, we intend for a tighter integration between document search and conversational processes

Investigating reasoning with multiple integrated neuroscientific methods.

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