Advancing rational analysis to the algorithmic level

Lieder, Falk; Griffiths, Thomas L.

doi:10.1017/s0140525x19002012

Cited by 10 publications

(6 citation statements)

References 267 publications

(333 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We base our model on best-first search (33); this algorithm iteratively expands nodes on the principal variation, the sequence of actions that lead to the best outcome for both players given the current decision tree. Best-first search is particularly appealing as a human planning algorithm, since it effectively allocates computational resources to relevant branches of the decision tree, and such computational efficiency is a hallmark of human intelligence (34)(35)(36)(37). Two players, black and white, alternate placing pieces on the board, and the first player to achieve 4-in-a-row wins the game.…”

mentioning

confidence: 99%

Revealing the impact of expertise on human planning with a two-player board game

Opheusden¹,

Galbiati²,

Kuperwajs

et al. 2021

Preprint

View full text Add to dashboard Cite

Do skilled decision-makers plan further into the future than novices? This question has been investigated for almost 75 years, traditionally by studying expert players in complex board games like chess. However, the complexity of these games poses a barrier to detailed modeling of human behavior. Conversely, common planning tasks in cognitive science are often lower-complexity and impose a ceiling for the depth to which any player can plan. Here, we investigate expertise by studying decision-making in a board game which is at the limit of complexity that can be precisely modeled using state-of-the-art statistical techniques, and which has ample opportunity for skilled players to plan deeply. We find robust evidence for increased planning depth with expertise in both laboratory and large-scale naturalistic data.

show abstract

mentioning

confidence: 99%

Revealing the impact of expertise on human planning with a two-player board game

Opheusden¹,

Galbiati²,

Kuperwajs

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…(It is unclear, however, how their model—which directly generates judgments of heaviness ratios—applies to sequential liftings with temporarily separated haptic signals and heaviness percepts, as in the present experiments.) More generally, for any such model (including the more recent approach proposed by Lieder & Griffiths, 2020 ; see also Wei & Stocker, 2015 ), the crux of the present challenge is for it to recast as “rational” not only improbable outcomes, but also impossible ones.…”

Section: Discussionmentioning

confidence: 99%

“Impossible” Somatosensation and the (Ir)rationality of Perception

2021

View full text Add to dashboard Cite

Impossible figures represent the world in ways it cannot be. From the work of M. C. Escher to any popular perception textbook, such experiences show how some principles of mental processing can be so entrenched and inflexible as to produce absurd and even incoherent outcomes that could not occur in reality. Surprisingly, however, such impossible experiences are mostly limited to visual perception; are there “impossible figures” for other sensory modalities? Here, we import a known magic trick into the laboratory to report and investigate an impossible somatosensory experience—one that can be physically felt. We show that, even under full-cue conditions with objects that can be freely inspected, subjects can be made to experience a single object alone as feeling heavier than a group of objects that includes the single object as a member—an impossible and phenomenologically striking experience of weight. Moreover, we suggest that this phenomenon—a special case of the size-weight illusion—reflects a kind of “anti-Bayesian” perceptual updating that amplifies a challenge to rational models of perception and cognition. Impossibility can not only be seen, but also felt—and in ways that matter for accounts of (ir)rational mental processing.

show abstract

“…Bhatia, 2013;Busemeyer & Townsend, 1993;Lee & Cummins, 2004;Roe et al, 2001;Turner et al, 2018;Usher & McClelland, 2004); and connectionist models are dynamical systems that describe decision making as the outcome of spreading activation processes in units of interconnected neurons (Glöckner & Betsch, 2008;Holyoak & Simon, 1999). Many of these models also make predictions about the specific information that is sampled and used in the decision, which can be 1 Although of course using all available information might not be adaptive or resource-rational (Lieder & Griffiths, 2020;Payne et al, 1988).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Although of course using all available information might not be adaptive or resource rational(Lieder & Griffiths, 2020;Payne et al, 1988).…”

mentioning

confidence: 99%

Process and content in decisions from memory.

Zhao¹,

Richie²,

Bhatia³

2022

Psychological Review

View full text Add to dashboard Cite

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this paper we present a general framework that can address the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought-listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to naturalistic binary choices from domains such as risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and social judgment.

show abstract

Advancing rational analysis to the algorithmic level

Cited by 10 publications

References 267 publications

Revealing the impact of expertise on human planning with a two-player board game

Revealing the impact of expertise on human planning with a two-player board game

“Impossible” Somatosensation and the (Ir)rationality of Perception

Process and content in decisions from memory.

Contact Info

Product

Resources

About