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

Post-loss speeding or post-win slowing? An empirical note on the interpretation of decision-making time as a function of previous outcome

Differences in response time following previous losses relative to previous wins are robust observations in behavioural science, often attributed to an increased (or decreased) degree of cognitive control exerted after negative feedback, hence, post-loss slowing (or post-loss speeding). This presumes that the locus of this effect resides in the specific modulation of decision time following negative outcomes. Across two experiments, I demonstrate how the use of absolute rather than relative processing speeds, and the sensitivity of processing speeds in response to specific experimental manipulations (Experiment 1: win rate, Experiment 2: feedback), provide clarity as to the relative weighting of post-win and post-loss states in determining these behavioural effects. Both experiments show that the speeding or slowing of decision-time is largely due to the flexibility generated by post-win cognitive states. Given that post-loss speeding may actually represent post-win slowing, conclusions regarding the modulation of decision-making time as a function of previous outcomes need to be more carefully considered.

Assessing behavioural profiles following neutral, positive and negative feedback

Previous data suggest zero-value, neutral outcomes (draw) are subjectively assigned negative rather than positive valence. 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 following draws all align with the consequences of explicitly negative outcomes such as losses. We further tested the relationships between neutral, positive and negative outcomes by manipulating value salience and observing their behavioural profiles. Despite speeded reaction times and a non-significant bias towards shift behaviour similar to losses when draws were assigned the value of 0 (Experiment 1), the degree of shift behaviour approached an approximation of optimal performance when the draw value was explicitly positive (+1). This was in contrast to when the draw value was explicitly negative (-1), which led to a significant increase in the degree of shift behaviour (Experiment 2). Similar modifications were absent when the same value manipulations were applied to win or lose trials (Experiment 3). Rather than viewing draws as neutral and valence-free outcomes, the processing cascade generated by draws produces a complex behavioural profile containing elements found in response to both explicitly positive and explicitly negative results.

Breaking the bonds of reinforcement: Effects of trial outcome, rule consistency and rule complexity against exploitable and unexploitable opponents

In two experiments, we used the simple zero-sum game Rock, Paper and Scissors to study the common reinforcement-based rules of repeating choices after winning (win-stay) and shifting from previous choice options after losing (lose-shift). Participants played the game against both computer opponents who could not be exploited and computer opponents who could be exploited by making choices that would at times conflict with reinforcement. Against unexploitable opponents, participants achieved an approximation of random behavior, contrary to previous research commonly finding reinforcement biases. Against exploitable opponents, the participants learned to exploit the opponent regardless of whether optimal choices conflicted with reinforcement or not. The data suggest that learning a rule that allows one to exploit was largely determined by the outcome of the previous trial.