Generic properties of a computational task predict human effort and performance

Franco, Juan Pablo; Yadav, Nitin; Bossaerts, Peter; Murawski, Carsten

doi:10.1016/j.jmp.2021.102592

Cited by 12 publications

(36 citation statements)

References 63 publications

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“…In the TSP, participants received 0.3 per correct instance submitted plus 0.14 per instance if all instances were solved correctly. In the KP task (22), participants received a show-up fee of AUD 10 and earned AUD 0.7 for each correct answer.…”

Section: Methodsmentioning

confidence: 99%

“…Although the number of solution witnesses captures invariants of human behavior in all three problems, it does so only for satisfiable instances. We therefore now explore an alternative solution-space metric, instance complexity (IC), that can be used to study the difficulty of all instances (both satisfiable and unsatisfiable) (22). IC is related to the constrainedness of an instance and the order parameter ᾱ.…”

Section: Instance Complexitymentioning

confidence: 99%

“…We explored the effect of three generic complexity metrics on human performance. Importantly, while the proposed metrics capture the computational hardness of NP-complete problems, they can be applied to other decision problems in classes P or NP (14-16), and have been shown to be extendable to optimization problems (22). Each of the metrics can be used to unearth generalities in human behavior.…”

Section: Metrics Of Complexitymentioning

confidence: 99%

“…These optima were estimated using Gecode (40) for TSP and using the RC2 algorithm from the 'pysat' python library (41) for 3SAT. For the KP we used the metrics estimated in Franco et al (22).…”

Section: Derivation Of Metricsmentioning

confidence: 99%

“…Overall, these types of problems underlie many day-to-day cognitive tasks such as shopping, bin packing, way-finding and task scheduling (4,5). We specifically tested human performance in the Boolean satisfiability problem (3SAT) and the traveling salesperson problem (TSP), and compared these results to those previously obtained for the 0-1 knapsack problem (KP) (22), to test their generalizability across NPcomplete problems. While the SAT task is an abstract logical problem, TSP is presented as a visual navigation task and the KP is associated with arithmetic operations.…”

Section: Introductionmentioning

confidence: 99%

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Task-independent metrics of computational hardness predict human cognitive performance

Franco

Doroc

Yadav

et al. 2021

Preprint

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The survival of human organisms depends on our ability to solve complex tasks, which is bounded by our limited cognitive capacities. However, little is known about the factors that drive complexity of the tasks humans face and their effect on human decision-making. Here, using insights from computational complexity theory, we quantify computational hardness using a set of task-independent metrics related to the computational requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that these metrics predict both performance and effort allocation in three canonical cognitive tasks in a similar way. Our findings demonstrate that the ability to solve complex tasks can be predicted from generic metrics of their inherent computational hardness.

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Section: Methodsmentioning

confidence: 99%

Section: Instance Complexitymentioning

confidence: 99%

Section: Metrics Of Complexitymentioning

confidence: 99%

Section: Derivation Of Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Task-independent metrics of computational hardness predict human cognitive performance

Franco

Doroc

Yadav

et al. 2021

Preprint

Self Cite

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How can we apply decision‐making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Teichroeb,

Smeltzer,

Mathur

et al. 2023

American J Primatol

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Our understanding of decision‐making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision‐making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision‐making process. Based on the literature and evidence from our multi‐destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision‐making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio‐ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human‐induced rapid environmental change.

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