Tom Stafford

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.

In this panel six IS researchers from varying backgrounds will discuss whether epistemological anarchy, as proposed by the controversial philosopher Paul Feyerabend, has the potential to foster research progress and can help to create new insights in the IS field. Feyerabend is well known for his notion that "anything goes" in terms of methodology, and many scholars are concerned that this seemingly anarchistic sentiment can undermine efforts to systematically build and structure an epistemological and methodological foundation for an academic discipline. This panel, which will be moderated by Horst Treiblmaier, includes as panelists Andrew Burton-Jones, Shirley Gregor, Rudy Hirschheim, Michael Myers, and Tom Stafford. The outcome of this discussion will be incorporated into a paper published in the DATA BASE for Advances in Information Systems.

Piéron's Law describes the relationship between stimulus intensity and reaction time. Previously (Stafford & Gurney, 2004), we have shown that Piéron's Law is a necessary consequence of rise-to-threshold decision making and thus will arise from optimal simple decision-making algorithms (e.g., Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006). Here, we manipulate the color saturation of a Stroop stimulus. Our results show that Piéron's Law holds for color intensity and color-naming reaction time, extending the domain of this law, in line with our suggestion of the generality of the processes that can give rise to Piéron's Law. In addition, we find that Stroop condition does not interact with the effect of color saturation; Stroop interference and facilitation remain constant at all levels of color saturation. An analysis demonstrates that this result cannot be accounted for by single-stage decision-making algorithms which combine all the evidence pertaining to a decision into a common metric. This shows that human decision making is not information-optimal and suggests that the generalization of current models of simple perceptual decision making to more complex decisions is not straightforward

Understanding our ideological opponents is crucial for the effective exchange of arguments and the avoidance of escalation, and the reduction of conflict. We operationalize the idea of an “Ideological Turing Test” to measure the accuracy with which people represent the arguments of their ideological opponents. Crucially, this offers a behavioral measure of open-mindedness which goes beyond mere self-report. We recruited 200 participants from opposite sides of three topics with potential for polarization in the UK of the early 2020s (1200 participants total). Participants were asked to provide reasons both for and against their position. Their reasons were then rated by participants from the opposite side. Our criteria for “passing” the test was if an argument was agreed with by opponents to the same extent or higher than arguments made by proponents. We found evidence for high levels of mutual understanding across all three topics. We also found that those who passed were more open-minded toward their opponents, in that they were less likely to rate them as ignorant, immoral, or irrational. Our method provides a behavioral measure of open-mindedness and ability to mimic counterpartisan perspectives that goes beyond self-report measures. Our results offer encouragement that, even in highly polarized debates, high levels of mutual understanding persist.

Research programs in empirical psychology over the past few decades have led scholars to posit implicit biases. This is due to the development of innovative behavioural measures that have revealed aspects of our cognitions which may not be identified on self-report measures requiring individuals to reflect on and report their attitudes and beliefs. But what does it mean to characterise such biases as implicit? Can we satisfactorily articulate the grounds for identifying them as bias? And crucially, what sorts of cognitions are in fact being measured; what mental states or processes underpin such behavioural responses? In this paper, we outline some of the philosophical and empirical issues engaged when attempting to address these three questions. Our aim is to provide a constructive taxonomy of the issues, and how they interrelate. As we will see, any view about what implicit bias is may depend on a range of prior theoretical choices.

Using an observational sample of players of a simple online game, we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but it fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well-established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab and for experimental phenomenon to be put in the context of the performance of the whole task.

Implicit bias training (IBT) is now frequently provided by employers, in order to raise awareness of the problems related to implicit biases, and of how to safeguard against discrimination that may result. However, as Atewologun et al (2018) have noted, there is very little systematicity in IBT, and there are many unknowns about what constitutes good IBT. One important issue concerns the tone of information provided regarding implicit bias. This paper engages this question, focusing in particular on the observation that much bias training is delivered in exculpatory tone, emphasising that individuals are not to blame for possessing implicit biases. Normative guidance around IBT exhorts practitioners to adopt this strategy (Moss-Racusin et al 2014). However, existing evidence about the effects of moralized feedback about implicit bias is equivocal (Legault et al 2011; Czopp et al 2006). Through a series of studies, culminating in an experiment with a pre-registered analysis plan, we develop a paradigm for evaluating the impact of moralized feedback on participants’ implicit racial bias scores. We also conducted exploratory analyses of the impact on their moods, and behavioural intentions. Our results indicated that an exculpatory tone, rather than a blaming or neutral tone, did not make participants less resistant to changing their attitudes and behaviours. In fact, participants in the blame condition had significantly stronger explicit intentions to change future behaviour than those in the ‘no feedback’ condition (see experiment 3). These results indicate that considerations of efficacy do not support the need for implicit bias feedback to be exculpatory. We tease out the implications of these findings, and directions for future research.

Using an observational sample of players of a simple online game, we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but it fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well-established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab and for experimental phenomenon to be put in the context of the performance of the whole task.