Reversible VQ Data Hiding Based on Locally Adaptive Coding and Recursive Walking

Author(s):  
Cheng-Hsing Yang ◽  
Yi-Cheng Lin
2018 ◽  
Vol 463-464 ◽  
pp. 245-260 ◽  
Author(s):  
Wien Hong ◽  
Xiaoyu Zhou ◽  
Der-Chyuan Lou ◽  
Tung Shou Chen ◽  
Yuyang Li

2021 ◽  
Vol 7 (14) ◽  
pp. eabe0340
Author(s):  
Sophie Bavard ◽  
Aldo Rustichini ◽  
Stefano Palminteri

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.


Symmetry ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 254 ◽  
Author(s):  
Wien Hong ◽  
Xiaoyu Zhou ◽  
Shaowei Weng

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