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Reward / Control Information plots for each of the problem:

Gian Luca Lancia (13991392) ; Mattia Eluchans (14341772) ; Marco D'Alessandro (397205)..
https://figshare.com/articles/figure/Reward_Control_Information_plots_for_each_of_the_problem_/21833831. , 2023

Link: https://doi.org/10.1371/journal.pcbi.1010829.s001

RT Journal
T1 <Title>Reward / Control Information plots for each of the problem</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftunivfreestate:oai:figshare.com:article_21833831&Exemplar=1&LAN=DE
				
A1 Gian Luca Lancia (13991392)
A1 Mattia Eluchans (14341772)
A1 Marco D'Alessandro (397205)
A1 Hugo J. Spiers (14341775)
A1 Giovanni Pezzulo (324940)
YR 2023
K1 Cell Biology
K1 Neuroscience
K1 Cancer
K1 Science Policy
K1 Mental Health
K1 navigating back somewhere
K1 bounded rational </
K1 virtual reality map
K1 less optimal solutions
K1 instruction modulate flexibly
K1 div >< p
K1 want (" go
K1 might either retrace
K1 inforl correspond closely
K1 default policy ")
K1 control information required
K1 investing cognitive resources
K1 navigation </ p
K1 cognitive cost required
K1 instruction find shorter
K1 finding optimal shortcuts
K1 default policy
K1 cognitive resources
K1 cognitive cost
K1 virtual maze
K1 shorter path
K1 navigation strategies
K1 finding shortcuts
K1 informational cost
K1 using inforl
K1 two groups
K1 theoretic approach
K1 theoretic analysis
K1 taken together
K1 results illustrate
K1 previous research
K1 optimality refers
K1 navigational strategies
K1 navigational plans
K1 intrinsic difficulty
K1 humans account
K1 g
K1 firstly trained
K1 different routes
K1 characterize formally
K1 best trade
K1 ask whether
JF https://figshare.com/articles/figure/Reward_Control_Information_plots_for_each_of_the_problem_/21833831
LK http://dx.doi.org/https://doi.org/10.1371/journal.pcbi.1010829.s001
DO https://doi.org/10.1371/journal.pcbi.1010829.s001
SF ELIB - SuUB Bremen

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