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

Opheusden, Bas van; Galbiati, G; Kuperwajs, Ionatan; Bnaya, Z; Y, li; Ji, Wei

doi:10.31234/osf.io/rhq5j

Cited by 25 publications

(50 citation statements)

References 33 publications

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“…Findings of this study gave insights into how individuals differ in their capacity to adapt to new situations. Recently, a notable approach has been presented, which aims at characterizing individual strategies in a setting where the complexity of the state space is relatively large ( van Opheusden et al, 2021 ). In this study, the rules of the game (equivalent of the statistics of stimuli in our case) and the relevant features (equivalent to the latent variables in our case) were assumed to be known by the participants.…”

Section: Discussionmentioning

confidence: 99%

Tracking the contribution of inductive bias to individualized internal models

Török

Nagy

Kiss

et al. 2020

Preprint

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Internal models are central to understand how human behaviour is adapted to the statistics of, potentially limited, environmental data. Such internal models contribute to rich and flexible inferences and thus adapt to varying task demands. However, the right internal model is not available for observers, instead approximate and transient internal models are recruited. To understand learning and momentary inferences, we need tools to characterise these approximate, yet rich and potentially dynamic models through behaviour. We used a combination of non-parametric Bayesian methods and probabilistic programming to infer individualised internal models from human response times in an implicit visuomotor learning task. Using this Cognitive Tomography approach we predict response times on a trial-by-trial basis and validate the internal model by showing its invariance across tasks and sensitivity to stimulus statistics. By tracking the performance of participants for multiple days, individual learning curves revealed transient subjective internal models and pronounced inductive biases.

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Section: Discussionmentioning

confidence: 99%

Tracking the contribution of inductive bias to individualized internal models

Török

Nagy

Kiss

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There are many differences between our study and previous planning studies in humans. Most of these studies relied on tasks without uncertainty (Classical planning) or tasks in which the uncertainty is limited to stochastic transitions between states (Markov Decision Processes, MDPs), and have focused on how people cope with the combinatorial explosion that occurs as the planning horizon increases [62][63][64], the depth with which people plan [65,66], or the extent to which people use model-based or model-free strategies when learning from reinforcement [62,67,68]. The present study is different because we focus on how people disambiguate a single hidden state from a sequence of information-seeking and reward-seeking actions.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Decision prioritization and causal reasoning in decision hierarchies

Zylberberg

2021

PLoS Comput Biol

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From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

show abstract

“…There are many differences between our study and previous planning studies in humans. Most of these studies relied on tasks without uncertainty (Classical planning) or tasks in which the uncertainty is limited to stochastic transitions between states (Markov Decision Processes, MDPs), and have focused on how people cope with the combinatorial explosion that occurs as the planning horizon increases (Keramati et al, 2016;Huys et al, 2012;Callaway et al, 2021), the depth with which people plan (Snider et al, 2015;van Opheusden et al, 2021), or the extent to which people use model-based or model-free strategies when learning from reinforcement (Daw et al, 2011(Daw et al, , 2005Keramati et al, 2016). The present study is different because we focus on how people disambiguate a single hidden state from a sequence of information-seeking and reward-seeking actions.…”

Section: Discussionmentioning

confidence: 99%

Decision Prioritization and Causal Reasoning in Decision Hierarchies

Zylberberg¹

2021

Preprint

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From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with $10^7$ latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

show abstract

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

Cited by 25 publications

References 33 publications

Tracking the contribution of inductive bias to individualized internal models

Tracking the contribution of inductive bias to individualized internal models

Decision prioritization and causal reasoning in decision hierarchies

Decision Prioritization and Causal Reasoning in Decision Hierarchies

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