scholarly journals The computational cost of active information sampling prior to decision making under uncertainty

2020 ◽  
Author(s):  
Pierre Petitet ◽  
Bahaaeddin Attaallah ◽  
Sanjay G Manohar ◽  
Masud Husain
Keyword(s):  
Time Pressure ◽  
Computational Cost ◽  
Computational Modelling ◽  
Cost Benefit ◽  
Theoretical Models ◽  
Cognitive Effort ◽  
Related Factor ◽  
Trade Off ◽  
Hidden Cost ◽  
Information Sampling

Humans often seek information to minimise the pervasive effect of uncertainty on decisions. Current theories explain how much knowledge people should gather prior to a decision, based on the cost-benefit structure of the problem at hand. Here, we demonstrate that this framework omits a crucial agent-related factor: the cognitive effort expended while collecting information. Using a novel paradigm, we unveil a speed-efficiency trade-off whereby more informative samples actually take longer to find. Crucially, under sufficient time pressure, humans can break this trade-off, sampling both faster and more efficiently. Computational modelling demonstrates the existence of a hidden cost of cognitive effort which, when incorporated into theoretical models, provides a better account of people's behaviour and also predicts self-reported fatigue accumulated during active sampling. By measuring metacognitive accuracy and uncertainty-reward preferences on a static, passive version of the task, we further validate the theoretical constructs captured by our model. Overall, the results show that the way people seek knowledge to guide their decisions is shaped not only by task-related costs and benefits, but also crucially by the quantifiable computational costs incurred.

Uncertainty Management in the Design of Multiscale Systems

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Ayan Sinha ◽  
Nilanjan Bera ◽  
Janet K. Allen ◽  
Jitesh H. Panchal ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Design Method ◽  
Computational Cost ◽  
Cost Benefit ◽  
Simulation Models ◽  
System Level ◽  
Model Refinement ◽  
Trade Off ◽  
Multiscale Systems ◽  
Model Parameter Uncertainty

In this paper, the opportunities for managing uncertainty in simulation-based design of multiscale systems are explored using constructs from information management and robust design. A comprehensive multiscale design problem, the concurrent design of material and product is used to demonstrate our approach. The desired accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. Our approach consists of integrating: (i) a robust design method for multiscale systems and (ii) an improvement potential based approach for quantifying the cost-benefit trade-off for reducing uncertainty in simulation models. Specifically, our approach focuses on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. Using this approach, system level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems.

scholarly journals Forced Choices Reveal a Trade-Off between Cognitive Effort and Physical Pain

2020 ◽  
Author(s):  
Todd A Vogel ◽  
Zachary M. Savelson ◽  
A Ross Otto ◽  
Mathieu Roy
Keyword(s):  
Cost Benefit Analysis ◽  
Computational Modelling ◽  
Cost Benefit ◽  
Cognitive Effort ◽  
Benefit Analysis ◽  
Physical Pain ◽  
Asymmetric Effects ◽  
Thermal Pain ◽  
Subjective Value ◽  
Decision Times

Cognitive effort is described as aversive, and people will generally avoid it when possible. This aversion to effort is believed to arise from a cost–benefit analysis of the actions available (e.g., study hard for an upcoming test or socialize with friends). The comparison of cognitive effort against other primary aversive experiences, however, remains relatively unexplored. Here, we offered participants choices between performing a cognitively demanding task or experiencing thermal pain. We found that cognitive effort can be traded off for physical pain and that people generally avoid exerting high levels of cognitive effort. We also used computational modelling to examine the subjective value of effort and its effects on response behaviours. Applying this model to decision times revealed asymmetric effects of effort and pain, suggesting that cognitive effort may not share the same basic influences on avoidance behaviour as more primary aversive stimuli such as physical pain.

Managing Uncertainty in Multiscale Systems via Simulation Model Refinement

2011 ◽  
Author(s):  
Ayan Sinha ◽  
Jitesh H. Panchal ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Simulation Model ◽  
Design Method ◽  
Information Economics ◽  
Computational Cost ◽  
Cost Benefit ◽  
Simulation Models ◽  
System Level ◽  
Model Refinement ◽  
Trade Off ◽  
Multiscale Systems

The motivating question for this article is: ‘How should a system level designer allocate resources for auxiliary simulation model refinement while satisfying system level design objectives and ensuring robust process requirements in multiscale systems? Our approach consists of integrating: (i) a robust design method for multiscale systems (ii) an information economics based approach for quantifying the cost-benefit trade-off for mitigating uncertainty in simulation models. Specifically, the focus is on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. A comprehensive multiscale design problem, the concurrent design of material and product is used for validation. The multiscale system is simulated with models at multiple length and time scales. The accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. System level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems using this approach.

scholarly journals Forced choices reveal a trade-off between cognitive effort and physical pain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Todd A Vogel ◽  
Zachary M Savelson ◽  
A Ross Otto ◽  
Mathieu Roy
Keyword(s):  
Cost Benefit Analysis ◽  
Computational Modelling ◽  
Cost Benefit ◽  
Cognitive Effort ◽  
Benefit Analysis ◽  
Physical Pain ◽  
Asymmetric Effects ◽  
Thermal Pain ◽  
Subjective Value ◽  
Decision Times

Cognitive effort is described as aversive, and people will generally avoid it when possible. This aversion to effort is believed to arise from a cost–benefit analysis of the actions available. The comparison of cognitive effort against other primary aversive experiences, however, remains relatively unexplored. Here, we offered participants choices between performing a cognitively demanding task or experiencing thermal pain. We found that cognitive effort can be traded off for physical pain and that people generally avoid exerting high levels of cognitive effort. We also used computational modelling to examine the aversive subjective value of effort and its effects on response behaviours. Applying this model to decision times revealed asymmetric effects of effort and pain, suggesting that cognitive effort may not share the same basic influences on avoidance behaviour as more primary aversive stimuli such as physical pain.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

scholarly journals Information Rate in Humans during Visuomotor Tracking

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 228
Author(s):  
Sze-Ying Lam ◽  
Alexandre Zénon
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Cost Benefit ◽  
Cognitive Effort ◽  
Task Demands ◽  
Information Rate ◽  
Present Contribution ◽  
Visuomotor Tracking ◽  
Processing Rate ◽  
Choice Tasks

Previous investigations concluded that the human brain’s information processing rate remains fundamentally constant, irrespective of task demands. However, their conclusion rested in analyses of simple discrete-choice tasks. The present contribution recasts the question of human information rate within the context of visuomotor tasks, which provides a more ecologically relevant arena, albeit a more complex one. We argue that, while predictable aspects of inputs can be encoded virtually free of charge, real-time information transfer should be identified with the processing of surprises. We formalise this intuition by deriving from first principles a decomposition of the total information shared by inputs and outputs into a feedforward, predictive component and a feedback, error-correcting component. We find that the information measured by the feedback component, a proxy for the brain’s information processing rate, scales with the difficulty of the task at hand, in agreement with cost-benefit models of cognitive effort.

scholarly journals High monetary reward rates and caloric rewards decrease temporal persistence

2017 ◽  
Vol 284 (1849) ◽  
pp. 20162759 ◽  
Author(s):  
Bowen J. Fung ◽  
Stefan Bode ◽  
Carsten Murawski
Keyword(s):  
Theoretical Models ◽  
Monetary Reward ◽  
Positive Energy ◽  
Opportunity Costs ◽  
Trade Off ◽  
Reward Rate ◽  
Potential Value ◽  
Monetary Rewards ◽  
Positive Energy Balance ◽  
Temporal Persistence

Temporal persistence refers to an individual's capacity to wait for future rewards, while forgoing possible alternatives. This requires a trade-off between the potential value of delayed rewards and opportunity costs, and is relevant to many real-world decisions, such as dieting. Theoretical models have previously suggested that high monetary reward rates, or positive energy balance, may result in decreased temporal persistence. In our study, 50 fasted participants engaged in a temporal persistence task, incentivised with monetary rewards. In alternating blocks of this task, rewards were delivered at delays drawn randomly from distributions with either a lower or higher maximum reward rate. During some blocks participants received either a caloric drink or water. We used survival analysis to estimate participants' probability of quitting conditional on the delay distribution and the consumed liquid. Participants had a higher probability of quitting in blocks with the higher reward rate. Furthermore, participants who consumed the caloric drink had a higher probability of quitting than those who consumed water. Our results support the predictions from the theoretical models, and importantly, suggest that both higher monetary reward rates and physiologically relevant rewards can decrease temporal persistence, which is a crucial determinant for survival in many species.

scholarly journals From atomistic interfaces to dendritic patterns

2018 ◽  
Vol 376 (2113) ◽  
pp. 20170210 ◽  
Author(s):  
P. K. Galenko ◽  
D. V. Alexandrov
Keyword(s):  
Computational Modelling ◽  
Experimental Tests ◽  
Theoretical Models ◽  
Boundary Integral ◽  
Transport Processes ◽  
Atomic Scale ◽  
Theme Issue ◽  
Two Phase ◽  
Field Methods ◽  
Atomistic Modelling

Transport processes around phase interfaces, together with thermodynamic properties and kinetic phenomena, control the formation of dendritic patterns. Using the thermodynamic and kinetic data of phase interfaces obtained on the atomic scale, one can analyse the formation of a single dendrite and the growth of a dendritic ensemble. This is the result of recent progress in theoretical methods and computational algorithms calculated using powerful computer clusters. Great benefits can be attained from the development of micro-, meso- and macro-levels of analysis when investigating the dynamics of interfaces, interpreting experimental data and designing the macrostructure of samples. The review and research articles in this theme issue cover the spectrum of scales (from nano- to macro-length scales) in order to exhibit recently developing trends in the theoretical analysis and computational modelling of dendrite pattern formation. Atomistic modelling, the flow effect on interface dynamics, the transition from diffusion-limited to thermally controlled growth existing at a considerable driving force, two-phase (mushy) layer formation, the growth of eutectic dendrites, the formation of a secondary dendritic network due to coalescence, computational methods, including boundary integral and phase-field methods, and experimental tests for theoretical models—all these themes are highlighted in the present issue. This article is part of the theme issue ‘From atomistic interfaces to dendritic patterns’.

Sign in / Sign up

Export Citation Format

Share Document