Machine Thinking, Fast and Slow

Bonnefon, Jean‐François; Rahwan, Iyad

doi:10.1016/j.tics.2020.09.007

Cited by 23 publications

(21 citation statements)

References 45 publications

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“…Findings from behavioural economists have found their way into AI research, in particular with revelations about our inability to properly execute probabilistic reasoning ( Kahneman, 2011 ). This is eminently respectable work with a strong mathematical foundation and has been eagerly taken up by computer scientists ( Rossi and Loreggia, 2019 ; Bonnefon and Rahwan, 2020 , etc.). Magic has a different pedigree; to the uninitiated, seemingly haphazard and undisciplined.…”

Section: Discussionmentioning

confidence: 99%

Extended Goal Recognition: Lessons from Magic

Masters

Smith

Kirley

2021

Front. Artif. Intell.

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The “science of magic” has lately emerged as a new field of study, providing valuable insights into the nature of human perception and cognition. While most of us think of magic as being all about deception and perceptual “tricks”, the craft—as documented by psychologists and professional magicians—provides a rare practical demonstration and understanding of goal recognition. For the purposes of human-aware planning, goal recognition involves predicting what a human observer is most likely to understand from a sequence of actions. Magicians perform sequences of actions with keen awareness of what an audience will understand from them and—in order to subvert it—the ability to predict precisely what an observer’s expectation is most likely to be. Magicians can do this without needing to know any personal details about their audience and without making any significant modification to their routine from one performance to the next. That is, the actions they perform are reliably interpreted by any human observer in such a way that particular (albeit erroneous) goals are predicted every time. This is achievable because people’s perception, cognition and sense-making are predictably fallible. Moreover, in the context of magic, the principles underlying human fallibility are not only well-articulated but empirically proven. In recent work we demonstrated how aspects of human cognition could be incorporated into a standard model of goal recognition, showing that—even though phenomena may be “fully observable” in that nothing prevents them from being observed—not all are noticed, not all are encoded or remembered, and few are remembered indefinitely. In the current article, we revisit those findings from a different angle. We first explore established principles from the science of magic, then recontextualise and build on our model of extended goal recognition in the context of those principles. While our extensions relate primarily to observations, this work extends and explains the definitions, showing how incidental (and apparently incidental) behaviours may significantly influence human memory and belief. We conclude by discussing additional ways in which magic can inform models of goal recognition and the light that this sheds on the persistence of conspiracy theories in the face of compelling contradictory evidence.

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Section: Discussionmentioning

confidence: 99%

Extended Goal Recognition: Lessons from Magic

Masters

Smith

Kirley

2021

Front. Artif. Intell.

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“…It is presumably hard to overestimate the popularity of dual-process models in current-day psychology, economics, philosophy, and related disciplines (Chater & Schwarzlose, 2016; Melnikoff & Bargh, 2018). As De Neys (2021) clarified, they have been applied in a very wide range of fields including research on thinking biases (Evans, 2002; Kahneman, 2011), morality (Greene & Haidt, 2002), human cooperation (Rand, Greene, & Nowak, 2012), religiosity (Gervais & Norenzayan, 2012), social cognition (Chaiken & Trope, 1999), management science (Achtziger & Alós-Ferrer, 2014), medical diagnosis (Djulbegovic, Hozo, Beckstead, Tsalatsanis, & Pauker, 2012), time perception (Hoerl & McCormack, 2019), health behavior (Hofmann, Friese, & Wiers, 2008), theory of mind (Wiesmann, Friederici, Singer, & Steinbeis, 2020), intelligence (Kaufman, 2011), creativity (Barr, Pennycook, Stolz, & Fugelsang, 2015), fake news susceptibility (Bago, Rand, & Pennycook, 2020), and even machine thinking (Bonnefon & Rahwan, 2020). In addition, the dual-process framework is regularly featured in the popular media (Lemir, 2021; Shefrin, 2013; Tett, 2021) and has inspired policy recommendations on topics ranging from economic development (World Bank Group, 2015), over carbon emissions (Beattie, 2012), to the corona-virus pandemic (Sunstein, 2020).…”

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confidence: 99%

Advancing theorizing about fast-and-slow thinking

Neys

2022

Behav Brain Sci

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Human reasoning is often conceived as an interplay between a more intuitive and deliberate thought process. In the last 50 years, influential fast-and-slow dual process models that capitalize on this distinction have been used to account for numerous phenomena—from logical reasoning biases, over prosocial behavior, to moral decision-making. The present paper clarifies that despite the popularity, critical assumptions are poorly conceived. My critique focuses on two interconnected foundational issues: the exclusivity and switch feature. The exclusivity feature refers to the tendency to conceive intuition and deliberation as generating unique responses such that one type of response is assumed to be beyond the capability of the fast-intuitive processing mode. I review the empirical evidence in key fields and show that there is no solid ground for such exclusivity. The switch feature concerns the mechanism by which a reasoner can decide to shift between more intuitive and deliberate processing. I present an overview of leading switch accounts and show that they are conceptually problematic—precisely because they presuppose exclusivity. I build on these insights to sketch the groundwork for a more viable dual process architecture and illustrate how it can set a new research agenda to advance the field in the coming years.

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“…But I do not agree with the critics that the dual-process framework hampers studying thinking and would thwart scientific progress (Melnikoff & Bargh, 2018). Much to the contrary, as Tinghög et al and Sherman & Klein , I find the framework a very valuable heuristic that presents us with a great tool to communicate and organize our thinking about human (and machine, e.g., Bonnefon & Rahwan, 2020) cognition. However, although I agree with Tinghög et al 2 that the ultimate benefit of the dual-process framework lies in its role as a benchmark or meta-theory (see Evans & Stanovich, 2013), I also believe that our meta-theories should be viable and avoid conceptual paradoxes or homunculi.…”

Section: Dual Schmosses?mentioning

confidence: 98%

Further advancing fast-and-slow theorizing

Neys

2023

Behav Brain Sci

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The 34 commentaries on the target article span a broad range of interesting issues. I have organized my reply around five major themes that seemed to emerge: Remarks about the generalizability of the empirical findings, links with other models, necessary extensions, the utility of dual-process models, and more specific points. This allows me to clarify possible misconceptions and identify avenues for further advancement.

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Machine Thinking, Fast and Slow

Cited by 23 publications

References 45 publications

Extended Goal Recognition: Lessons from Magic

Extended Goal Recognition: Lessons from Magic

Advancing theorizing about fast-and-slow thinking

Further advancing fast-and-slow theorizing

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