scholarly journals Vicarious trial-and-error is associated with the use of place-strategies in human virtual navigation

2020 ◽  
Author(s):  
Neil Schmitzer-Torbert
Keyword(s):  
Trial And Error ◽  
Human Navigation ◽  
Response Strategies ◽  
Model Based ◽  
Model Free ◽  
Decision Points ◽  
Behavioral Index ◽  
Vicarious Trial And Error ◽  
Contrast Response ◽  
Navigation Strategies

Studies of decision-making in rodents have demonstrated that vicarious trial-and-error (VTE) is an important behavioral index of deliberation, when animals search through and evaluate the available options before making a decision. In rodents, VTE is enhanced during the use of hippocampally-dependent place strategies, which may represent a type of model-based behavior. While some evidence exists for VTE-like behaviors in humans during navigation, it is unknown if VTE in humans is specifically associated place-strategies, as would be predicted for model-based behaviors. To address this gap, humans were tested in navigation tasks in symmetrical environments, which allowed for the use of probe trials to assess navigation strategies (place or response) or impose them directly. The use of place strategies (on probe trials and place-training) was associated with increases in measures of VTE (reorientations and pausing) especially at high-cost decision points, similar to results from rodent studies. In contrast, response-strategies were associated with the development of efficient, stereotyped trajectories (consistent with model-free learning). These results support the identification of place- and response-strategies in human navigation with model-based and model-free learning, respectively, and demonstrate that VTE is specifically related to the use of place-strategies.

scholarly journals Conflict between place and response navigation strategies: Effects on vicarious trial and error (VTE) behaviors

2013 ◽  
Vol 20 (3) ◽  
pp. 130-138 ◽  
Author(s):  
B. Schmidt ◽  
A. Papale ◽  
A. D. Redish ◽  
E. J. Markus
Keyword(s):  
Trial And Error ◽  
Vicarious Trial And Error ◽  
Navigation Strategies

scholarly journals Human vicarious trial and error is predictive of spatial navigation performance

2018 ◽  
Author(s):  
Diogo Santos-Pata ◽  
Paul FMJ Verschure
Keyword(s):  
Spatial Learning ◽  
Spatial Configuration ◽  
Spatial Navigation ◽  
Head Orientation ◽  
Trial And Error ◽  
Trial Number ◽  
Decision Points ◽  
Scanning Behavior ◽  
Functional Relevance ◽  
Vicarious Trial And Error

ABSTRACTWhen learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

scholarly journals Metamers of Bayesian computation

2020 ◽  
Author(s):  
Hansem Sohn ◽  
Mehrdad Jazayeri
Keyword(s):  
Internal Models ◽  
Cost Functions ◽  
Trial And Error ◽  
Bayesian Decision ◽  
Visuomotor Rotation ◽  
Model Based ◽  
Model Free ◽  
Sensory Experiences ◽  
Timing Task ◽  
Decision Outcomes

AbstractThere are two sharply debated views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative model-free hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a novel paradigm that probes sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by model-based Bayesian theory. Our approach validates the model-based strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans implement model-based Bayesian computations.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Model-Based and Model-Free Control of DC-DC Converters with High-Order Dynamics and Limited Measurements

2020 ◽  
pp. 1-1
Author(s):  
Javier Loranca ◽  
Jonathan Carlos Mayo Maldonado ◽  
Gerardo Escobar ◽  
Carlos Villarreal-Hernandez ◽  
Thabiso Maupong ◽  
...  
Keyword(s):  
High Order ◽  
Model Based ◽  
Model Free ◽  
Model Free Control
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