2020
DOI: 10.31234/osf.io/82y5e
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Switching competitors reduces win-stay but not lose-shift behaviour: The role of outcome-action association strength on reinforcement learning

Abstract:

Predictability is a hallmark of poor-quality decision-making during competition. One source of predictability is the strong association between current outcome and future action, as dictated by the reinforcement learning principles of win-stay and lose-shift. We tested the idea that predictability could be reduced during competition by weakening the associations between outcome and action. To do this, participants completed a competitive zero-sum game in which the opponent from the current trial was either … Show more

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citations
Cited by 6 publications
(9 citation statements)
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References 11 publications
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“…For example, in [18] (Experiment 1, no credit condition), the degree of shift behaviour is smaller following draws relative to losses (76.63% vs. 70.62%; t[39] = -2.267, p = .029; n = 40). Similar reductions in shift behaviour following draws relative to losses were also extracted from [10) (Experiment 1: 72.53% vs. 78.51%; t [35] = -2.530, p = .016; n = 36), [22] (baseline condition; 71.97% vs. 77.68%; t [35] = -2.120, p = .041; n = 36), and, [21] (70.63% vs. 75.89%; t[30] = -2.307, p = .028; n = 31). Given the attenuation of shift bias following draws relative to losses, then there is clearly some sense in which these are not identical examples of negative outcomes.…”
Section: Plos Onesupporting
confidence: 60%
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“…For example, in [18] (Experiment 1, no credit condition), the degree of shift behaviour is smaller following draws relative to losses (76.63% vs. 70.62%; t[39] = -2.267, p = .029; n = 40). Similar reductions in shift behaviour following draws relative to losses were also extracted from [10) (Experiment 1: 72.53% vs. 78.51%; t [35] = -2.530, p = .016; n = 36), [22] (baseline condition; 71.97% vs. 77.68%; t [35] = -2.120, p = .041; n = 36), and, [21] (70.63% vs. 75.89%; t[30] = -2.307, p = .028; n = 31). Given the attenuation of shift bias following draws relative to losses, then there is clearly some sense in which these are not identical examples of negative outcomes.…”
Section: Plos Onesupporting
confidence: 60%
“…All other ANOVA main effects and interactions were not significant: cumulative score main effect [F (1,35) Shift proportions were analysed across value (win-heavy, lose-heavy),cumulative score (present, absent) and outcome (win, lose, draw) in a three-way repeated-measures ANOVA (see Fig 1). A main effect of outcome was noted [F(2,70) = 14.632, MSE = 0.077, p < .001, p 2 = .295],…”
Section: Resultsmentioning
confidence: 99%
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“…40 individuals (31 female) from the University of Sussex community participated in the study; mean age was 21.13 years (SD = 4.37) and 39 were right-handed. Sample sizes were based on previous studies from the lab showing reliable lose-shift biases within zero-sum game contexts (e.g., [ 18 ], N = 31; [ 19 ], Ns = 36; [ 21 ], Ns = 40). Participants received course credit or £10 (their choice, unless course enrollment required them to take the credit) for their participation.…”
Section: Methodsmentioning
confidence: 99%
“…Changing draw value from 0 to +1 or -1, and evaluating performance against an unexploitable opponent in Experiment 1, we make the following predictions. This is guided by data from 12 previously published experiments that use a similar win (+1), draw (0), lose (-1) outcome value assignment against an opponent who cannot be beaten [ 10 , 18 , 20 22 , 35 ]. First, reaction times following draws should be faster than reaction times following wins ( post-draw speeding ).…”
Section: Methodsmentioning
confidence: 99%