One area of research examines cognitive models of multi-attribute judgment and heuristic decision making. The special issue of Judgment and Decision Making co-edited with Arndt Bröder details some early work and provides an overview of the field. One focus (in work with Michael Lee and Don van Ravenzwaaij) is on the application of evidence-accumulation models to multi-attribute judgment (e.g., Newell & Lee, 2011), and the development of hierarchical Bayesian methods for examining heuristic judgment (e.g., Lee & Newell, 2011; van Ravenzwaaij et al., 2014). More recent projects have attempted direct comparisons between evidence accumulation models (“the adjustable spanner”, Newell 2005) and “toolbox” models of multi-attribute choice – see Krefeld-Schwalb et al., 2018. In work with PhD student Garston Liang, we have examined the role that algorithms play in helping people to learn make judgments under uncertainty (see Liang et al. 2022).

Another strand of research focusses on choice under risk and uncertainty. We have examined a variety of topics such as the differences between experience and description-based choice (e.g., Camilleri & Newell, 2011; 2013; 2019) – see also a guest-edited (with Tim Rakow) special issue of the Journal of Behavioral Decision Making on this topic. Related investigations have looked at people’s tendency to probability match in experience-based choice (e.g., Newell & Rakow, 2007; Newell et al., 2013) and, with Christin Schulze, how this tendency is affected by competition and group reasoning (see Schulze et al., 2015; 2017; Schulze & Newell, 2016). Other projects have examined the role that ‘extreme outcomes’ play in experience-based risky choice (e.g., Konstantinidis et al., 2018; Vanunu et al., 2020) including a PNAS paper which explores the interplay between top-down and bottom-up processing in determining choice (Vanunu et al.,2021) the effect of experiencing outcomes on ambiguity aversion (e.g. Guney & Newell, 2011; 2015); and the relationship between risky choice and people’s understanding of the probabilities with which outcomes occur (Szollosi et al., 2019). An extension of this work has begun to question the role of probabilistic concepts in providing psychological explanations of human decision-making, offering an alternative perspective grounded in the notion that we are all “intuitive scientists” seeking the best explanations of our environments – see Szollosi & Newell, 2020; Szollosi, Donkin & Newell, 2022)

In work with Ashley Luckman and Chris Donkin we have explored the cognitive processes underlying choices that involve both risk and delay (e.g., Luckman et al., 2017; 2018). Related work with Emmanouil Konstantinidis, Don van Ravenzwaaij and Sule Guney examined the potential for evidence accumulation models to capture risky-intemporal choices (Konstatinidis et al., 2018). A large-scale comparison of models of risky-intertemporal choice was published in Psychological Review (Luckman et al., 2020)

I have two strands of research related to understanding how we learn to categorize and make inductions.

The first strand examines the popular notion that category learning can be explained through the operation of distinct ‘implicit’ and ‘explicit’ systems. In work initiated with David Shanks and David Lagnado we have investigated probabilistic categorization tasks in an attempt to characterize the evidence for the operation of such separate systems. In a similar vein, work with John Dunn and Mike Kalish, goes beyond the ‘systems’ debate by utilizing a technique known as state-trace analysis to uncover the inherent dimensionality of category learning (see Newell et al., 2011; Newell & Dunn, 2008; Kalish et al., 2017). More recent projects in this line have included an exhortation to use more sophisticated methods to model the ‘strategies’ people employ in learning categories (Donkin et al., 2015) and revisiting a claim for “one of the strongest dissociations” in the literature (Le Pelley, Newell & Nosofsky, 2019). The over-arching approach has been to consider the viability of single-system cognitive architectures for explaining category learning phenomena. (See also Newell & Shanks, 2014 and Newell & Le Pelley, 2018 for other detailed examinations of the value of dichotomizing cognitive processing).

The second strand (with Brett Hayes and Oren Griffiths) examines induction, and specifically the mechanisms underlying induction on the basis of uncertain categorization judgments. This so-called ‘as-if’ reasoning (reasoning as-if a given judgment is true when there is uncertainty associated with it) appears to be prevalent. We have documented some of the factors which affect the tendency to reason in this way (e.g. Griffiths et al., 2012; Newell et al, 2011; Hayes & Newell, 2009). A new project, also with Brett Hayes, starting in 2022 will examine the reasons why people tend to fall into so-called “learning-traps” – suboptimal methods for exploration which can lead to impaired learning of category structures.