Competitive environments in which individuals compete for mutually-exclusive outcomes require rational decision making in order to maximize gains but often result in poor quality heuristics. Reasons for the greater reliance on lose-shift relative to win-stay behaviour shown in previous studies were explored using the game of Rock, Paper, Scissors and by manipulating the value of winning and losing. Decision-making following a loss was characterized as relatively fast and relatively inflexible both in terms of the failure to modulate the magnitude of lose-shift strategy and the lack of significant neural modulation. In contrast, decision-making following a win was characterized as relatively slow and relatively flexible both in terms of a behavioural increase in the magnitude of win-stay strategy and a neural modulation of feedback-related negativity (FRN) and stimulus-preceding negativity (SPN) following outcome value modulation. The win-stay/lose-shift heuristic appears not to be a unified mechanism, with the former relying on System 2 processes and the latter relying on System 1 processes. Our ability to play rationally appears more likely when the outcome is positive and when the value of wins are low, highlighting how vulnerable we can be when trying to succeed during competition.
There are a number of serious and playful aspects of society in which individuals recursively engage with others for mutually-exclusive outcomes in a non-cooperative fashion1: there will be only one job awarded to the pool of applicants, there will be only one winner at poker. Success in these environments calls for rational decision-making and sound strategic planning (e.g., System 2) but due to the impact of emotion and arousal in these contexts2, performance is often compromised by reliance on more intuitive and impulsive action (e.g., System 1; ref. 3). Individuals who exhibit domain mastery appear to have subdued some of the tensions between System 1 and System 2: professional poker players regulate their emotional response to losses (ref. 4; i.e., reliance on System 2 rather than System 1), whereas for board game experts, their intuitive response tends also to be their best response (ref. 5; i.e., refinement of System 1 output). For the rest of us, these environments all-too-clearly betray the bounded rationality of human decision-making6. The game of Rock, Paper, Scissors (RPS) serves as one such context to study the tension between System 1 and System 2 activity, acting as a relatively simple game space but yielding complex and cyclical patterns of behaviour7.
The only guaranteed safe way to play RPS is to adopt the mixed equilibrium strategy (c.f., ‘minimax solution’; ref. 8) wherein one plays randomly with respect to the previous event9 while also ensuring that all three responses are played 33% of the time across the entire number of trials10. If both players adopt this strategy, then this unique Nash equilibrium leads to a zero-sum game11 where neither player experiences significant gains in the long run, but also ensures that neither player can be dominated by their opponent. Unfortunately, such processes are likely to be outside the bounds of human cognition, and instead individuals tend to employ heuristics during gameplay that run the risk of being dominated by opponents.
One common heuristic in RPS is win-stay lose-shift7,12,13. Such tendencies have their roots in behaviourism (c.f., ref. 14) where responses associated with reinforcement are more likely to be repeated and responses that are associated with punishment are more likely to be changed. Such principles may be fundamental properties of any successfully learning organism but in the context of a competitive environment, the predictability of repeating a response following a positive outcome and changing a response following a negative outcome is evolutionarily unsound. For example, an opponent savvy to a player overusing the win-stay strategy could begin to make dominating counter-moves so if, for example, a player won on Rock on the first trial, their opponent would be likely to play Paper on the second trial. In a previous paper7 we showed that deviations from rational (minimax) decision making in RPS were more likely following negative (e.g., lose and draw) rather than positive (e.g., win) trials (c.f., ‘tilting’ in poker; ref. 4). This was shown by the rough equivalence of the proportion response to each of the three available strategies: one stay response and two switch responses (upgrade, downgrade) following a win trial, but the predominance of switching strategies following negative outcome (specifically, downgrading [selecting the item that would have been beaten by the player’s previous response] following loss and upgrading [selecting the item that would have beaten the player’s previous response] following draw15). A similar reluctance to adopt win-stay relative to lose-shift behaviour has also been noted in human reversal learning16 and also spatial discrimination learning in rats17 (although see the primate work of12 for a contrary position). From an evolutionary point of view, failure to initiate behavioural change following a loss (or even the perceived threat of loss) is likely to be more damaging than the failure to repeat an action following a win. To wit: “Neither the mouse nor the gazelle can afford to learn to avoid”18 (p. 33, emphasis in original).
This paper focuses on why negative outcomes should be more likely to lead to irrational decision-making, and whether performance based on outcome-strategy contingencies can be modulated and possibly corrected. One reason why losses should have a more pronounced effect on behaviour is that the subjective value of wins and losses are not the same as their objective value. Specifically, losses are judged to be roughly twice as bad as gains19. In our previous RPS study7, we did not use a point or monetary system but we might assume that objectively a win and a loss trial were ‘worth’ the same value (i.e., +1 and −1) in the larger context of the game. Therefore, to examine decision making under conditions where wins are assigned +1, losses are assigned −1 and draws are assigned 0, is perhaps to produce an imbalance in the subjective value of loss (e.g., ≈ −2). It may be the larger subjective value of the loss rather than the negative valence of the loss per se that led to predictable deviation from rational decision making. To address this, we introduced a point system during RPS play, comparing performance in a baseline condition (where +1 for win, −1 for loss, 0 for draw) against two additional conditions (see Fig. 1a; following6). In the win-heavy condition, point values of +2, −1 and 0 were assigned to wins, losses and draws. Here, the assigned value of a win (+2) equated the subjective value of a loss (e.g., ≈ −1 * 2). If it is the subjective magnitude of the outcome rather than valence that drives an individual towards irrational decision making, then we would expect an increase in the observation of win-stay strategizing relative to baseline performance in this condition. In the lose-heavy condition, these values were +1, −2 and 0, respectively. By the same logic, in this condition losses have approximately four times the value as wins (e.g., ≈ −2 * 2 for loss versus +1 for win) so we would expect an exacerbation in lose-shift strategizing relative to baseline performance.
A second reason why losses might impact on subsequent performance to a greater extent than wins is due to the immediate attentional capture afforded by a losing relative to a winning action. In a variant of a dot-probe task using RPS20, participants were asked to observe play trials and then to respond to the quantity of dots displayed either behind the winning response or the losing response. It was found that dot-probe responses were faster when presented in the location of the losing response. We extrapolate these findings to the initiation of decisions following losing and winning. Since heuristics are characterised as being ‘fast and frugal’21, cases of irrational decision making (frugal) should also be associated with shorter RTs (fast). Therefore, in addition to a baseline tendency of increased irrational play following lose trials relative to win trials, the time to initiate these former responses should also be faster.
We also augment our behavioural predictions regarding heuristic modulation with the simultaneous recording of neural activity. A well-documented neural signature of outcome sensitivity is feedback-related negativity (FRN or fERN; ref. 22). The FRN is observed roughly 200–300 ms following the on-set of feedback, appears maximal at central and fronto-central electrodes, and originates from anterior cingulate cortex23,24. The FRN is thought to be distinct from a second type of performance monitoring known as ERN or error-related negativity22 (see also the dispute over medial-frontal negativity [MFN] and its potential role in sensitivity to financial outcome25,26). In the case of ERN, amplitude is larger as a result of internally generated failure such as an incorrect motor response, whereas FRN is larger as a result of externally generated failure such as losing against an opponent27. Both are thought to have their genesis in the release of dopamine in the basal ganglia28,29, although the relationship between FRN and dopamine also implicates norepinephrine, serotonin, GABA, and adenosine30. The FRN is then a potentially critical adaptive response where signals of success and failure ultimately project to a higher-order decision making network, where behaviour can be changed in the light of previous performance29.
The paradigm of RPS is well positioned to examine FRN for a number of reasons. First, RPS performance against an opponent adopting the mixed-equilibrium strategy ensures an approximately equal distribution of wins and losses across the series, thereby reducing the overall contribution of outcome expectancy effects24,31. This is similarly in keeping with the suggestion of32 who argue that during examination of FRN, trial outcome should not be controlled by the participant. Second, the possibility of draw trials in RPS, in addition to win and loss trials, satisfies the need in the FRN literature for the examination of neutral trials29,31. Third, by modulating the values assigned to the three types of trial outcome across baseline, win-heavy, and lose-heavy conditions we are able to adjudicate between competing theories of FRN (see ref. 32): For some researchers33, the FRN represents a binary process, distinguishing between positive and negative outcomes only. Evidence for this position is derived from the observation that FRN failed to modulate as a function of the degree of loss experienced by a participant34 and that under the examination of positive, negative and neutral feedback, neutral feedback appears similar to negative feedback29. Collectively, these observations would appear to conceptualise the FRN as an index of whether a current goal is achieved (e.g., win) or not achieved (e.g., lose or draw). Under the position that the FRN is a binary process responsive to valence, we should expect differences between goal-consistent and goal-inconsistent outcomes, but that these differences should not modulate as a function of baseline, win-heavy or lose-heavy condition. For others, the FRN is sensitive to the magnitude of certain trial outcomes and35 provide evidence for FRN modulation relative to the probability of winning across blocks of trials (but not the probability of losses, c.f., the discussion of29 above). Under the alternative position that the FRN is sensitive to the magnitude of positive or goal-consistent outcomes, then we should expect neural modulation across win trials as a function of the value and context within which the positive outcome is placed.
An alternative interpretation of FRN suggests that it is only generated when feedback represents non-redundant information32. This is potentially problematic in the current paradigm since there is full disclosure of response information (e.g., opponent played Paper, player played Rock) prior to a separate feedback display (e.g., lose; as in7). Hence, an attentive player may be able to infer a ‘loss’ trial upon the presentation of both responses, thereby rendering the explicit presentation of ‘lose’ in a separate feedback display redundant ref. 26 discuss a similar possibility in the context of ERN: “when the stimulus–response mappings are fixed and can be learned, the ERN should occur at the time of the error response and not at the time of feedback presentation…on such trials the system detects the error at the time of the response, producing a negative prediction error and a large ERN, and so by the time of feedback presentation the system has already detected the error, thus producing no change in prediction and no ERN…” (p. 251; see also36, in the context of FRN). One way to address this concern would be to examine neural activity prior to the on-set of the feedback display. Therefore, as a second metric of neural modulation, we also examined stimulus-preceding negativity (SPN; ref. 37) after the presentation of the response information but before the presentation of the feedback information. Such slow wave activity is thought to represent a variety of processes including sensitivity to forthcoming outcome and affective information38 and as such may contribute to FRN modulation further downstream.
Trial n item selection and outcome
In terms of the proportion of trials distributed across item and outcome, participants did not differ in their item selection at trial n [F(2, 70) = 2.09, MSE = 0.013, p = 0.132, ηp2 = 0.056] nor did this differ according to condition [F(4, 140) = 1.64, MSE = 0.002, p = 0.167, ηp2 = 0.045]. There was a numerical tendency to play Rock slightly more often than Paper or Scissors (35.16%, 32.50% and 32.35% trials, respectively), consistent with previous studies7,8,13. There was no significant effect of outcome at trial n [F(2, 70) = 0.02, MSE = 0.002, p = 0.982, ηp2 < 0.001] and no interaction with condition [F(4, 140) = 1.58, MSE = 0.002, p = 0.182, ηp2 = 0.043]. Winning, losing and drawing were all around 33.3% (33.38%, 33.26% and 33.36% trials, respectively), as would be expected from playing an opponent adopting the mixed-equilibrium strategy.
First-order repetition effects
Proportion data using last 149 trials in each condition (the first trial in each block had no previous history) were analysed according to a three-way repeated measures ANOVA using the factors of condition (baseline, win-heavy, lose-heavy), outcome at trial n (win, lose, draw) and strategy at trial n + 1 (stay, upgrade, downgrade; after ref. 7). The main effect of strategy was not significant [F(2, 70) = 1.04, MSE = 0.140, p = 0.359, ηp2 = 0.029], nor was the two-way interaction between condition × strategy at trial n + 1 [F(4, 140) = 2.24, MSE = 0.022, p = 0.068, ηp2 = 0.060]. However, a significant two-way interaction between outcome × strategy at trial n + 1 [F(4, 140) = 18.17, MSE = 0.051, p < 0.001, ηp2 = 0.342] was observed. This showed a larger proportion of stay trials relative to the two forms of switching, upgrading or downgrading, following a win (42.45%, 29.32% and 28.23%, respectively), thereby providing support for the win-stay heuristic. The lose-shift heuristic was also in evidence by a larger proportion of upgrading and downgrading (numerically in favour of downgrading) relative to staying following a loss (39.13%, 39.41% and 21.46%, respectively). Finally, upgrading was numerically more popular than both the other form of switch- downgrading- and staying during draw trials, although the differences between these forms of strategy (37.32%, 33.40% and 29.28%, respectively) were not statistically significant. The relationships between win-stay, lose-downgrade and draw-upgrade replicate the main findings of7 and show broad equivalence in performance between Canadian and UK samples.
A significant three-way interaction [F(8, 280) = 2.98, MSE = 0.012, p = 0.003, ηp2 = 0.079; see Fig. 1b] was relatively straightforward in its interpretation in that the distribution of stay, upgrade and downgrade responses remained stable across lose and draw trials, but the proportion of stay responses following win trials was significantly larger during win-heavy conditions relative to the baseline condition (48.44% versus 35.92%, respectively; Tukey’s HSD, p < 0.05). Win-stay responses were also increased during lose-heavy condition (42.99%) but this proportion was not statistically significant from the baseline condition (Tukey’s HSD, p > 0.05). Modulating the value of wins and losses increased the likelihood of the win-stay heuristic, but did not change the frequency of the lose-shift heuristic. These data suggest that participants retain more cognitive control over decision-making following a win (e.g., System 2) and retain less cognitive control over decision making following a loss or a draw (e.g., System 1).
To further test the idea that relatively controlled decisions follow positive outcome (win) but relatively automatic decisions follow negative outcome (loss or draw), median RT was calculated per participant for each of the nine possible outcome-strategy combinations for all three conditions. Four participants had to be rejected as a result of failing to have any observations in some of these 27 cells, and a further three participants were rejected as a result of their average median RT (2116, 2068.5 and 1476.5 ms) being at least twice as large as the group average median RT (583 ms). Data from the remaining 29 participants were entered into a three-way repeated measures ANOVA using the factors of condition (baseline, win-heavy, lose-heavy), outcome at trial n (win, lose, draw) and strategy at trial n + 1 (stay, upgrade, downgrade; see Fig. 1c). Main effects of condition, strategy, and interactions between condition × outcome, outcome × strategy, and, condition × outcome × strategy all failed to reach statistical significance (F < 1). An interaction between condition × strategy [F(4, 100) = 2.82, MSE = 42529, p = 0.029, ηp2 = 0.091] failed to show any pairwise statistical differences using Tukey’s HSD (p < 0.05; c.f.,39, non- consonance). However, the main effect of outcome [F(2, 56) = 17.99, MSE = 162685, p < 0.001, ηp2 = 0.391] revealed that responses following wins were slower than responses following losses or draws (708, 555 and 505 ms, respectively; Tukey’s HSD, p < 0.05). Slower responses are consistent with the increase in cognitive control participants are able to exert following win trials relative to lose or draw trials.
FRN mean amplitude was compared across the three conditions (baseline, win-heavy, lose-heavy) for each of the three trial outcomes (win, lose, draw; see Fig. 2a) within a two-way repeated measures ANOVA. The main effect of outcome was significant [F(2, 70) = 3.34, MSE = 1.806, p = 0.041, ηp2 = 0.087], revealing that draw trials generated significantly greater FRN than win trials (−1.57 versus −2.04 μV; Tukey’s HSD; p < 0.05). This further underscores the importance of considering draw trials as an additional example of negative outcome. The main effect of condition [F(2, 70) = 1.52, MSE = 1.927, p = 0.225, ηp2 = 0.042] and interaction failed to reach statistical significance [F(4, 140) = 2.37, MSE = 0.970, p = 0.055, ηp2 = 0.063]. On the basis of the principle of incoherence39 where omnibus statistics from ANOVA may fail to reveal specific pairwise comparisons, and predicated on the basis of previous research35 and our own behavioural data featuring the modulation of performance following win but not lose or draw trials, FRN mean amplitude was compared across the three conditions (baseline, win-heavy, lose-heavy) separately for each of the three trial outcomes (win, lose, draw). This revealed a main effect of condition during win trials [F(2, 70) = 5.07, MSE = 3.907, p = 0.009, ηp2 = 0.127], but not for lose [F(2, 70) = 0.02, MSE = 3.244, p = 0.978, ηp2 < 0.001] or draw [F(2, 70) = 0.61, MSE = 4.448, p = 0.545, ηp2 = 0.017] trials. The modulation of FRN was only apparent following win feedback, with larger negativity being generated for lose-heavy relative to win-heavy conditions (Tukey’s HSD; p < 0.05, see Fig. 3a).
A similar two-way repeated measures ANOVA on SPN mean amplitude (see Fig. 2b) failed to reveal main effects of condition [F(2, 70) = 2.35, MSE = 5.205, p = 0.109, ηp2 = 0.063] or outcome [F(2, 70) = 2.33, MSE = 2.107, p = 0.105, ηp2 = 0.062], or, a significant interaction between condition × outcome [F(2, 70) = 1.53, MSE = 1.268, p = 0.197, ηp2 = 0.041]. However as per FRN, with a separate consideration of each of the three potential outcomes, a main effect of condition was revealed during win trials [F(2, 70) = 3.72, MSE = 2.740, p = 0.029, ηp2 = 0.097], but not for lose [F(2, 70) = 0.89, MSE = 2.747, p = 0.417, ηp2 = 0.025] or draw [F(2, 70) = 1.54, MSE = 2.254, p = 0.221, ηp2 = 0.004] trials. The modulation of SPN was only apparent following win trials, with larger negativity being generated for win-heavy and lose-heavy relative to baseline conditions (Tukey’s HSD; p < 0.05, see Fig. 3b).
In contrast to previous research that demonstrate the use of relatively fast, automatic and emotional (System 1) versus more slow, controllable and reasoned (System 2) processing3 across separate tasks40, we show that such distinctions are available within the same game space (RPS) and in part determined by the outcome of the previous trial. Critically, we note the win-stay lose-shift heuristic is not a unified mechanism. Specifically, the relative speed with which responses following losing are initiated, the inflexibility in the magnitude of lose-shift strategy, and, the lack of significant neural FRN or SPN modulation within lose trials as a function of outcome value, point towards lose-shift behaviour relying heavily on System 1 processes. In contrast, the relatively slow responding following win trials, the flexibility in the magnitude of win-stay play, and, the modulation of FRN and SPN amplitude in win trials [see also35] following changes in outcome value, point towards win-stay behaviour relying heavily on System 2 processes.
We also found that this potential haven of System 2 operations following win outcomes in a baseline condition (see also7), wherein participants equally (and more slowly; ref. 41) distribute their responses across the three potential strategies, was compromised by manipulating outcome value. That is, making win trials more valuable (either by attempting to equate their subjective value with losses: win-heavy; or, making losses particularly damaging: lose-heavy) increased the likelihood of deploying a win-stay strategy, thereby increasing behavioural predictability and potentially compromising successful performance in a competitive environment. This increase in irrationality following a win in conditions where outcome value was manipulated was also marked by increases in FRN and SPN. These data are in alignment with the contention FRN may be sensitive to the magnitude of trial outcomes when that outcome is positive (win; ref. 35) rather than negative (lose; ref. 34). This provides further evidence that the processes evoked following the experience of a win are quite different from the processes evoked following the experience of a loss.
This is not to deny that win-stay mechanisms will always be more flexible than lose-shift mechanisms. For example, in a recent investigation of outcome value6, a win trial was set at a variable value of a (typically 2 and above) whereas draw trials were always assigned a value of 1 and loss trials a value of 0. It was found that as the variable value of a (incentive) increased, so too did the frequency of lose-downgrade responses (‘lose-left-shift’, in their terminology) but the frequency of win-stay responses actually decreased. Here, there appears to be contrast with the data from our win-heavy condition (where win = +2 and loss = −1 and so ‘incentive’ was also high) in which the frequency of win-stay responses actually increased relative to baseline condition, while the preponderance of lose-downgrade trials remained largely unchanged. One potential resolution may be found in comparing the differing ways in which the various payoff matrices have been conceptualized. There are two issues here: First, we implemented RPS under minimal reward conditions in that points accrued during the game were not converted into money at the end of the experimental session. Therefore, there may be broad differences in the perceived value of outcome when task performance is (and is not) linked to financial inventive7. Second, loss trials as described in6 do not have the negative valence traditionally associated with them but rather are defined by the absence of positive value, and draw trials are actually ascribed a positive value despite both behavioural and neural evidence (refs 6, 29, 42) suggesting that a tie with an opponent represents a negative rather than positive trial outcome. Therefore, although the relative differences between wins, losses and draws are identical between6 (where a = 2) and the current baseline condition, outcomes in the former case do not appear to have the same valence as outcomes in the latter case (2, 1, 0 versus +1, 0, −1, respectively). The framing of wins and losses is a well-established literature (see ref. 43 for a review) and it appears germane for future research to resolve these potential differences, especially given the potential difference between objective and subjective outcome values explored here. This is of course in addition to acknowledging that while we have focused on a group aggregate of performance here, there exists significant variation in individual strategy across games44.
In sum, our ability to maintain rational decisions in competitive environments appears limited to winning and only when the value of wins are low. The observation that we reliably perpetuate poor decisions following a loss4,7 and now also, following high-yield wins, pulls into focus the possible avenues for behavioural modification in problem gamblers or individuals predisposed to developing addictions, and highlights how vulnerable we can make ourselves when attempting to strategize in a competitive environment.
36 undergraduate students (30 female) participated in the study; mean age was 21.22 years (SD = 3.98) and all were right-handed. Three additional participants were removed due to noisy EEG data. The study was approved for testing by the Life Sciences and Psychology Research Ethics Committee (C-REC) at the University of Sussex (ER/BJD21/3), and the study was carried out in accordance with the approved guidelines. Informed consent was obtained from all participants, and all individuals received either course credit or £20 for participation. Compensation was independent of task performance.
Stimuli and apparatus
Composite pictures of two interacting hands making Rock, Paper and Scissors signs were displayed at a visual angle of approximately 12° × 6° with participants sat approximately 57 cm away from the screen. The presentation of stimuli was controlled by Presentation 18.1 (build 03.31.15; neurobs.com) and responses were recorded using a keyboard. Participants wore a white glove during experimentation.
Design and procedure
Participants completed 450 trials of RPS separated across 3 counterbalanced blocks (baseline, win-heavy, lose-heavy) of 150 trials each. At the bottom of the screen, the cumulative scores for both computer (on the left) and player (on the right) were displayed, in addition to the trial count within that block. In each block, the computer played Rock, Paper and Scissors 50 times in a random order. At each trial, participants were prompted to press one of three buttons corresponding to Rock, Paper and Scissors, promoted by the presentation of a fixation cross. At the time of pressing, the computer displayed a composite picture showing the result of the RPS trial, with its selection on the left (depicted by a blue glove) and the participant’s selection on the right (depicted by a white glove) for 1000 ms. Following the clearing of the picture (500 ms), feedback was provided for a further 1000 ms centre screen as to whether the participant won, lost or drew the trial. Scores were then updated during a 500 ms period according to the criteria set out in Fig. 1a and the next trial began with a fixation cross. Participants were informed that the computer would play in a certain way (which would be revealed after the experiment) and that they were to try to beat the computer across the course of the game.
To maintain parity with other RPS experiments in the lab, three short questionnaires were administered following the completion of each RPS block to assess individual’s degree of engagement with the block, the degree of anthropomorphism assigned to the computerized opponent, and co-presence felt between the player and opponent. First, engagement with specific blocks of RPS was assessed using a slightly-modified Game Engagement Questionnaire (GEQ; ref. 45). Eighteen from 19 original items were evaluated on a 5-point scale, encompassing the factors of absorption, flow, presence and immersion. The one item to be dropped from the revised GEQ was ‘I play longer than I meant to’ (related to presence). This item was deemed inappropriate given the fixed number of rounds in all RPS block. All items were also re-written to refer to the past tense (e.g., ‘I lose track of time’ became ‘I lost track of time’). Second, the degree of anthropomorphism attributed to the computer opponent was assessed on the basis of46 (Study 1) where five anthropomorphic states (‘mind of its own’, ‘intentions’, ‘free will’, ‘consciousness’, ‘experienced emotion’) and three non-anthropomorphic states (‘attractive’, ‘efficient’, ‘strong’) were adjudicated on an 11-point scale (c.f.47, Box A1). Third, aspects of self-reported co-presence and perceived other’s co-presence were adapted from the scales provided by48. Seven modified items, evaluated on a 5-point scale, were deemed appropriate for opponent interaction in the context of RPS (three related to self-reported co-presence, and four related to perceived other’s co-presence; see Appendix). A further five unique items were added to this particular questionnaire to assess the participant’s understanding of the opponent’s strategy: “I felt as though my opponent had a strategy that was based on the moves I was making” (e.g., other strategy), “I felt as though my opponent had a strategy that was based on the moves it was making” (e.g., self strategy), “My opponent exhibited a human-like strategy”, “I felt like my opponent was somehow cheating”, and “I found this block of RPS rewarding to play.” Finally, to assess the empathy of the participant, the Toronto Empathy Questionniare (TEQ; ref. 49) was administered after all blocks of RSP have been played. The TEQ consists of 16-items judged on a 5-point scale, yielding a single empathy factor.
Electrical brain activity was continuously digitized using a 64 channel ANT Neuro amplifier and a 1000 Hz sampling rate. Horizontal and vertical eye movements were also recorded using channels placed at the outer canthi and at inferior orbits, respectively. Data processing was conducted using BESA 5.3 Research (MEGIS; Gräfelfing, Germany). The contributions of both vertical and horizontal eye movements were reduced from the EEG record using the VEOG and HEOG artefact options in BESA following average referencing. Using a 0.1 Hz (12 db/oct; zero phase) high-pass and 30 Hz (24 db/oct; zero phase) low-pass filter, epochs were defined relative to the onset of the feedback (to assess FRN) and response presentation (to assess SPN). Epochs were baseline corrected according to a 200 ms pre-stimulus interval and neural activity was examined 800 ms post-feedback in the case of FRN and 2300 ms post-response presentation in the case of SPN. FRN mean amplitude was calculated on the basis of a 50 ms window centered around the peak latency (between 225 – 350 ms) reported for each specific condition and according to the anterior-posterior position on the scalp (F1, Fz, F2; FC1, FCz, FC2; C1, Cz, C2). SPN mean amplitude was calculated between 600 and 1600 ms from the same nine fronto-central electrodes. Individual epochs were rejected on the basis of amplitude difference exceeding 100 μV, gradient between consecutive time points exceeding 75 μV, or, signal lower than 0.01 μV, within any channel.
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The research was supported by a Research Development Fund from the University of Sussex (SA016-01).
The authors declare no competing financial interests.
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Forder, L., Dyson, B. Behavioural and neural modulation of win-stay but not lose-shift strategies as a function of outcome value in Rock, Paper, Scissors. Sci Rep 6, 33809 (2016). https://doi.org/10.1038/srep33809
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