scholarly journals The construction and deconstruction of sub-optimal preferences through range-adapting reinforcement learning

2020 ◽  
Author(s):  
Sophie Bavard ◽  
Aldo Rustichini ◽  
Stefano Palminteri
Keyword(s):  
Reinforcement Learning ◽  
Task Difficulty ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Economic Values ◽  
Adaptive Coding ◽  
Large Sample ◽  
Locally Adaptive ◽  
Noise Ratio ◽  
And Performance

Converging evidence suggests that economic values are rescaled as a function of the range of the available options. Critically, although locally adaptive, range adaptation has been shown to lead to suboptimal choices. This is particularly striking in reinforcement learning (RL) situations when options are extrapolated from their original context. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: decreasing outcome uncertainty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of subjects performing variants of a RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing outcome uncertainty. Finally, we propose a range-adapting model and show that it is able to parsimoniously capture all the observed results.

scholarly journals The construction and deconstruction of sub-optimal preferences through range-adapting reinforcement learning

2020 ◽  
Author(s):  
Sophie Bavard ◽  
Aldo Rustichini ◽  
Stefano Palminteri
Keyword(s):  
Reinforcement Learning ◽  
Task Difficulty ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Economic Values ◽  
Adaptive Coding ◽  
Large Sample ◽  
Locally Adaptive ◽  
Noise Ratio ◽  
And Performance

AbstractConverging evidence suggests that economic values are rescaled as a function of the range of the available options. Critically, although locally adaptive, range adaptation has been shown to lead to suboptimal choices. This is particularly striking in reinforcement learning (RL) situations when options are extrapolated from their original context. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counter-intuitive prediction: decreasing outcome uncertainty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of subjects performing variants of a RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing outcome uncertainty. Finally, we propose a range-adapting model and show that it is able to parsimoniously capture all the observed results.

scholarly journals Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning

2021 ◽  
Vol 7 (14) ◽  
pp. eabe0340
Author(s):  
Sophie Bavard ◽  
Aldo Rustichini ◽  
Stefano Palminteri
Keyword(s):  
Reinforcement Learning ◽  
Task Difficulty ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Economic Values ◽  
Adaptive Coding ◽  
Large Sample ◽  
Locally Adaptive ◽  
And Performance ◽  
Two Sides

Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.

scholarly journals Design and Performance Evaluation of a Time Domain Microwave Imaging System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xuezhi Zeng ◽  
Andreas Fhager ◽  
Peter Linner ◽  
Mikael Persson ◽  
Herbert Zirath
Keyword(s):  
Time Domain ◽  
Measurement Accuracy ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Voltage Noise ◽  
Signal To Noise ◽  
Microwave System ◽  
Noise Ratio ◽  
And Performance

We design a time domain microwave system dedicated to medical imaging. The measurement accuracy of the system, that is, signal-to-noise ratio, due to voltage noise and timing noise, is evaluated. Particularly, the effect of coupling media on the measurement accuracy is investigated both numerically and experimentally. The results suggest that the use of suitable coupling media betters the measurement accuracy in the frequency range of interest. A signal-to-noise ratio higher than 30 dB is achievable in the range of 500 MHz to 3 GHz when the effective sampling rate is 50 Gsa/s. It is also indicated that the effect of the timing jitter on the strongest received signal is comparable to that of the voltage noise.

scholarly journals A design methodology using signal-to-noise ratio for plastic scintillation detectors design and performance optimization

2009 ◽  
Vol 36 (11) ◽  
pp. 5214-5220 ◽  
Author(s):  
Frédéric Lacroix ◽  
A. Sam Beddar ◽  
Mathieu Guillot ◽  
Luc Beaulieu ◽  
Luc Gingras
Keyword(s):  
Performance Optimization ◽  
Design Methodology ◽  
Signal To Noise Ratio ◽  
Scintillation Detectors ◽  
Signal To Noise ◽  
Plastic Scintillation ◽  
Noise Ratio ◽  
And Performance

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Sign in / Sign up

Export Citation Format

Share Document