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Keno Juechems
@
KJuechems
Oxford, England
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Junior Research Fellow in Experimental Psychology, Oxford @StJohnsOx @summerfieldlab
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160
Tweetovi
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533
Pratim
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325
Osobe koje vas prate
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Keno Juechems
@KJuechems
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27. sij |
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Taken together, our results offer a new perspective on human subjective utility and probability functions: that they represent an optimal adaptation to computational constraints imposed by biology. 12/12
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Keno Juechems
@KJuechems
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27. sij |
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Our results confirmed that participants adapted optimally to this manipulation and that only the uncertain group exhibited the canonical probability weighting function: 11/12 pic.twitter.com/S0ieaCyAYJ
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Keno Juechems
@KJuechems
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27. sij |
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In two experiments, we asked human participants to choose between two lotteries. We manipulated whether, after each choice, they received the expected value of their chosen lottery (certain feedback) or whether the lottery played out (uncertain feedback). 10/12
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Keno Juechems
@KJuechems
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27. sij |
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Our simulations make two key predictions: i) that human choices should fall within the optimal range for capacity limited agents and ii) that the probability weighting depends on feedback certainty. 9/12
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Keno Juechems
@KJuechems
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27. sij |
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We assume that humans seek to maximize reward whilst minimizing uncertainty and incorporate this into our optimization via the entropy of potential outcomes of a lottery. Thus equipped, our simulations fit empirical data quite well: 8/12 pic.twitter.com/zg7hUtifJM
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Keno Juechems
@KJuechems
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27. sij |
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Making these assumptions alone reproduces the canonical subjective value function. In other words, this nonlinear function is optimal for noisy agents. However, one additional assumption is necessary to also reproduce the probability function…7/12
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Keno Juechems
@KJuechems
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27. sij |
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We ask under what constraints these canonical functions would be optimal. We start with two assumptions: (i) that humans wish to maximise their expected value, and (ii) that they have only finite computational precision, i.e. that decisions are noisy. 6/12
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Keno Juechems
@KJuechems
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27. sij |
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Descriptively, these functions fit empirical data well. But why should humans have evolved such peculiar, non-linear functions for representing probability and value? Here, we address this question as an optimization problem. 5/12
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Keno Juechems
@KJuechems
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27. sij |
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For example, a Nobel-prize winning economic theory proposes that the probability and value functions take the form shown below. 4/12 pic.twitter.com/FJxrJfBG8x
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Keno Juechems
@KJuechems
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27. sij |
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Economic models can account for this phenomenon if subjective estimates of value and probability are nonlinear transforms of their objective counterparts. 3/12
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Keno Juechems
@KJuechems
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27. sij |
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The expected values were simply rescaled by a constant factor between scenarios, so a rational agent should prefer the same option in each scenario. However, most people prefer A in scenario 1, but B in scenario 2. This is an example of an irrational preference reversal. 2/12
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Keno Juechems
@KJuechems
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27. sij |
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New preprint with Jan Balaguer, Bernhard Spitzer and @summerfieldlab now out on this topic: Why are our decisions sometimes “irrational”? psyarxiv.com/6yhwg/
Take the example below. Which lottery do you prefer in scenario 1? What about scenario 2? 1/12 pic.twitter.com/222cbY94xf
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Andrew Saxe
@SaxeLab
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7. sij |
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Our paper "On the information bottleneck theory of deep learning" has been republished (with small edits) in J Stat Mech ML special issue: iopscience.iop.org/article/10.108… A wonderful collaboration with @whybansal @laika117 @advani_madhu Artemy Kolchinsky @brendantracey @neurobongo pic.twitter.com/kJOljN7U85
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Alizee Lopez-Persem
@LopezPersem
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16. sij |
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New pre-print on BiorXiv! Differential functional connectivity underlying asymmetric reward-related activity in human and non-human primates: biorxiv.org/cgi/content/sh…. Many thanks to @jerome_sallet @LeaRoumazeilles @davidefolloni @EFouragnan @nima_khalighi K Marche and M Rushworth
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Timothy Ballard
@timothyjballard
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11. pro |
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New preprint out! The culmination of many years of work with @AndrewHeathcot8, @psy_farrell, and others. A General Architecture for Modeling the Dynamics of Goal-Directed Motivation and Decision Making psyarxiv.com/ubh54 via @OSFramework
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Keno Juechems
@KJuechems
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12. stu |
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Excited to be in Paris for the OFC meeting! Could do without the torrential rain and hail, though...
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Quentin Huys
@docqhuys
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12. stu |
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How we differ in learning from experience shapes how we develop different values, beliefs and behaviours. See our @NatureHumBehav paper lead by Daniel Schad examining a neural and computational basis for this rdcu.be/bWEAK. pic.twitter.com/8BwSqv6xhY
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Davide Folloni
@davidefolloni
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6. stu |
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(Academically) there is nothing like seeing your first (scientific) love out there: Amygdala-Prefrontal Cortex connectivity in macaques and humans (elifesciences.org/articles/47175) now out in @eLife.
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Laurence Hunt
@LHuntNeuro
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1. stu |
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Yet another awesome paper from @SCavanaghNeuro! We find decision biases previously found in humans by @k_tsetsos/@summerfieldlab; study how they arise from an attractor network model; make behav predictions from the model of NMDAR hypofunction; and test these pharmacologically. twitter.com/SCavanaghNeuro…
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summerfieldlab
@summerfieldlab
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30. lis |
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Our theory about the role of posterior parietal cortex in structure learning is now out in Progress in Neurobiology! with @NeuroLuyckx and @hannahsheahan
sciencedirect.com/science/articl…
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