Behavioural and neural limits in competitive decision making: The roles of outcome, opponency and observation

Dyson, Benjamin J.; Steward, Ben Albert; Meneghetti, Tea; Forder, Lewis

doi:10.1016/j.biopsycho.2019.107778

Cited by 19 publications

(26 citation statements)

References 64 publications

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“…Participants from refs. 19,21 provided informed consent from the University of Sussex community, and received either course credit or £20 for participation. Performance-independent compensation was offered as behavioural data were collected in the context of a longer electroencephalographic study.…”

Section: Methods Experiments 1-4mentioning

confidence: 99%

“…Specifically, reward mechanisms tend to shape behaviour to a greater degree than punishment mechanisms 12,15 , such that win-stay selections are more frequent than lose-shift within certain simple games (see 16 in the context of cooperative games, see 17 in the context of Matching Pennies). However, RPS can yield an over-use of lose-shift relative to win-stay behaviour [18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

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A micro-genesis account of longer-form reinforcement learning in structured and unstructured environments

Dyson

Asad

2021

npj Sci. Learn.

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We explored the possibility that in order for longer-form expressions of reinforcement learning (win-calmness, loss-restlessness) to manifest across tasks, they must first develop because of micro-transactions within tasks. We found no evidence of win-calmness or loss-restlessness when wins could not be maximised (unexploitable opponents), nor when the threat of win minimisation was presented (exploiting opponents), but evidence of win-calmness (but not loss-restlessness) when wins could be maximised (exploitable opponents).

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Section: Methods Experiments 1-4mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A micro-genesis account of longer-form reinforcement learning in structured and unstructured environments

Dyson

Asad

2021

npj Sci. Learn.

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“…An unwillingness to remain in a failure state also leads to a reduction in decision-making time following loss (e.g., [19][20][21][22][23][24]). This can also give rise to more predictable responding, as seen in the increases in lose-shift behavior relative to win-stay behavior, compared against the values expected by MS performance (e.g., [25,26]). While there are also contexts in which win-stay behavior is more in evidence than lose-shift behavior (e.g., [27,28]), the present concern is the degree to which individuals can regulate the expression of win-stay and lose-shift, and what environmental features might trigger them into doing so.…”

Section: Introductionmentioning

confidence: 98%

“…This approach is attractive in preserving the limits of human working memory with respect to the representation of action history only two trials back (see also [55], in the context of visual search performance). Also, different versions of this architecture have helped to resolve models of human performance, where ambiguously-valenced draws tend to be interpreted more as negative rather than positive outcomes (see also [25,56,57]). Nevertheless, the storage of action triads (with strength representing the likelihood of initiation) continue to preserve basic notions of reinforcement and punishment, and one could imagine fundamental principles of operant conditioning being represented within this architecture on the basis of an opponent expressing a particular item bias such as Rock.…”

mentioning

confidence: 99%

Switching Competitors Reduces Win-Stay but Not Lose-Shift Behaviour: The Role of Outcome-Action Association Strength on Reinforcement Learning

Srihaput

Craplewe

Dyson

2020

Games

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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 replayed (opponent repeat) thereby strengthening the association, or, replaced (opponent change) by a different competitor thereby weakening the association. We observed that win–stay behavior was reduced during opponent change trials but lose–shiftbehavior remained reliably predictable. Consistent with the group data, the number of individuals who exhibited predictable behavior following wins decreased for opponent change relative to opponent repeat trials. Our data show that future actions are more under internal control following positive relative to negative outcomes, and that externally breaking the bonds between outcome and action via opponent association also allows us to become less prone to exploitation.

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“…In addition to operating in environments such as gambling and education where feedback is salient and explicitly negative or positive in valence, there are other cases where information regarding our own performance is often ambiguous or incomplete (Muller, Moller, Rodriguez-Fornells & Munte, 2005). The interpretation of outcomes that do not have a clear valence, either as a result of the absence of feedback (ambiguous; Gu, Feng, Broster, Yuan, Xu, & Luo, 2017) or the explicit delivery of neutral feedback such as the zero value assigned to draw states (Dyson, Steward, Meneghetti & Forder, 2020) is a neglected feature of the decisionmaking literature. Examining the behavioural and neural responses following supposedly neutral outcomes offer a number of unique insights into the subjective aspects of decision-making.…”

Section: Introductionmentioning

confidence: 99%

Replicating and stress-testing behavioural profiles following feedback: Assessing the interpretation of neutral outcomes

Dahal¹,

K²,

Vavrek³

et al. 2020

Preprint

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Previous data point to neutral outcomes (draws) being subjectively assigned negative rather than positive value. The combined observations of faster rather than slower reaction times, subsequent actions defined by shift rather than stay behaviour, reduced flexibility, and, larger rather than smaller deviations from optimal performance all align with consequences of explicitly negative outcomes such as losses. We further tested the negative interpretation of draws via a value manipulation by assigning draws in a zero-sum game to +1, 0 or -1. If draws are inherently negative then the difference in behavioural profile comparisons should be greater when draw values are changed to a positive (+1) rather than negative (-1) value. Despite the observations of post-draw speeding and bias towards draw-shift behaviour, the degree of shift behaviour was equivalent between draw values of 0 and +1 and approached an approximation of optimal mixed-strategy performance. This was in contrast to draw values of -1 significantly increasing the degree of shift behaviour. This modification of draw behaviour was weaker when the same value modifications were applied to win or lose trials. Rather than viewing draws as neutral and valence-free outcomes, the processing cascade generated by stalemate produces a complex behavioural profile containing elements found in response to both explicitly positive and explicitly negative results.

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Behavioural and neural limits in competitive decision making: The roles of outcome, opponency and observation

Cited by 19 publications

References 64 publications

A micro-genesis account of longer-form reinforcement learning in structured and unstructured environments

A micro-genesis account of longer-form reinforcement learning in structured and unstructured environments

Switching Competitors Reduces Win-Stay but Not Lose-Shift Behaviour: The Role of Outcome-Action Association Strength on Reinforcement Learning

Replicating and stress-testing behavioural profiles following feedback: Assessing the interpretation of neutral outcomes

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