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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC 2013 October 17.
Published in final edited form as:
PMCID: PMC3683537

Rapid brain responses independently predict gain-maximization and loss-minimization during economic decision-making


Success in many decision-making scenarios depends on the ability to maximize gains and minimize losses. Even if an agent knows which cues lead to gains and which lead to losses, that agent could still make choices yielding suboptimal rewards. Here, by analyzing event-related potentials (ERPs) recorded in humans during a probabilistic gambling task, we show that individuals’ behavioral tendencies to maximize gains and to minimize losses are associated with their ERP responses to the receipt of those gains and losses, respectively. We focused our analyses on ERP signals that predict behavioral adjustment: the fronto-central feedback-related negativity (FRN) and two P300 (P3) subcomponents: the fronto-central P3a and the parietal P3b. We found that, across participants, gain-maximization was predicted by differences in amplitude of the P3b for suboptimal versus optimal gains (i.e., P3b amplitude difference between the least good and the best possible gains). Conversely, loss-minimization was predicted by differences in the P3b amplitude to suboptimal versus optimal losses (i.e., difference between the worst and the least bad losses). Finally, we observed that the P3a and P3b, but not the FRN, predicted behavioral adjustment on subsequent trials, suggesting a specific adaptive mechanism by which prior experience may alter ensuing behavior. These findings indicate that individual differences in gain-maximization and loss-minimization are linked to individual differences in rapid neural responses to monetary outcomes.


In the last decade, individual differences in decision behavior have been linked to neural responses to positive and negative feedback (Frank et al., 2004; Klein et al., 2007). A common method for probing these behavioral effects has been the analysis of two hallmark event-related potentials (ERPs): the fronto-central feedback-related negativity (FRN) and the P300 (P3). The FRN is elicited by worse-than-expected outcomes (Holroyd and Coles, 2002), and its magnitude after losing has been found to predict whether participants will switch their choice on the subsequent trial (Cohen and Ranganath, 2007). In contrast, the P3 is thought to reflect attentional processes involved in stimulus evaluation and memory updating (Donchin and Coles, 1988; Nieuwenhuis et al., 2005), and is composed of two distinguishable subcomponents: the early, frontally-distributed P3a and the late, parietally-distributed P3b (Polich, 2007).

Across a number of studies, participants’ relative propensities to learn to avoid losses versus learning to achieve gains are correlated with the FRN amplitude difference between losses and gains (Frank et al., 2005, Eppinger et al., 2008, Cavanagh et al., 2011). However, loss avoidance and gain seeking do not necessarily imply optimal behavior in complex scenarios. For example, online poker players tend to adopt strategies that increase the frequency of gains vs. losses, although this results in decreased profits overall since the average magnitude of their losses significatively exceeds the average magnitude of their gains (Siler, 2010). Rather than relying on a simple, binary distinction between gains and losses, optimal decision-making probably relies on brain’s ability to distinguish the best (optimal) gain from all other gains, and similarly for losses. Although we hypothesized that this neural discrimination is reflected in the FRN, we also analyzed the P3 since it has also been found to predict behavioral adjustment (Chase et al., 2011). A previous study (Venkatraman et al., 2009) reported brain activity associated with a strategy that maximized gains and minimized losses versus a strategy that increased the probability of winning, but no study has so far has reported brain activity that independently predicts gain-maximization and loss-minimization.

Participants performed a decision-making task in which they selected the magnitude of their wager in response to a pair of probabilistic outcome-predicting cues on each trial. Gain-maximization was defined as the ability to choose the large bet on trials with greater than 50% probability of winning, and loss-minimization as the ability to choose the small bet on trials with greater than 50% probability of losing. We predicted that gain-maximization would correlate, across participants, with the difference between the FRN elicited by the smallest (worst) and largest (best) gains, whereas loss-minimization would correlate with the difference between the FRN elicited by the largest (worst) and smallest (best) losses. Finally, to further advance our understanding of the mechanisms underlying individual differences in choice behavior, FRN and P3 responses were assessed in terms of their ability to directly predict trial-to-trial behavioral adjustment.

Materials and Methods


Forty-five healthy, right-handed, adult volunteers (22 male) participated in this study [ages, 18–31 years; mean (M) = 23.05]. Participants gave written informed consent and were financially compensated for their time ($15/hour). They received an extra bonus (M = $12.21, standard deviation (SD) = $7.75) proportional to the points earned during the experimental session. All procedures were approved by the Duke University Health System Institutional Review Board. Four participants were excluded from further data analysis due to technical difficulties during their experimental sessions, leaving a final sample of 41 participants (20 male).

Stimuli and task

We designed a probabilistic decision-making task using elements from the experimental designs of Gehring and Willoughby (2002) and Frank et al. (2004). Participants sat in front of a computer screen and performed 800 trials over the course of a single experimental session divided into 40, roughly 1.7-minute blocks. Subjects were told that each trial would start with the presentation of two symbols, and that some symbols tended to precede losses and other symbols tended to precede gains. They were instructed to try to learn which symbols were associated with which outcomes and to use that information to bet either 2 points or 8 points on each trial. Also, they were told that the probabilistic relationship between symbols and gains/losses would remain constant during the entire task. Subjects were also informed that although a monetary bonus proportional to the points earned during the session would be given, no information regarding the conversion from points to money would be provided until the end of the experiment. Before data collection, participants completed a 20-trial practice session using a set of symbols different from that used during data collection.

The temporal sequence of the task as it unfolded over a single trial is shown in Figure 1A. Each trial started with the presentation of a pair of symbols (Higrana characters) and a fixation cross, which were displayed for 1500 ms. The pair of symbols presented on each trial was randomly selected, without replacement, from the set of 20 possible pairings of 5 unique symbols (Figure 1B). Considering that these were right-left counterbalanced, these 20 pairs actually corresponded to 10 unique (non-matching) combinations of symbols, so that each unique combination was presented twice per block.

Figure 1
Experimental design

After an inter-stimulus interval (ISI) jittered between 100 and 300 ms, two white squares with the numerals “8” and “2” depicting the wager-amount choices appeared randomly on the right and left of the fixation cross. Participants chose their wager amount for the trial by pressing a button with the hand corresponding to the side of the screen containing their wager preference. Feedback concerning the outcome of the trial was presented after an ISI jittered between 600 and 1000 ms and appeared as a green box around the chosen number if the participant won on that trial (i.e., gained that number of points) or as a red box around the chosen number if the participant lost that number of points. If no response was made within 1200 ms, the words “no response” and a box corresponding to losing 8 points were presented on the screen. The next trial started after an inter-trial interval (ITI) jittered between 800 and 1200 ms. Participants were instructed to maintain fixation on the fixation cross throughout the experimental runs.

The outcome’s valence (win or loss) on each trial was probabilistically determined according to the probability of winning [p(win)] associated with the presented stimulus pair (Figure 1B). The p(win) associated with each pair was calculated as an adjustment from 50% determined by each symbol: p(win)=0.5+pL+pR, where pL and pR are the adjustments associated with the symbol presented to the left and right of the screen, respectively (A = +0.3, B = +0.15, M = 0, Y = −0.15 and Z = −0.3). For example, the stimulus pair presented in Figure 1A corresponds to symbol labels A and Y, and following Figure 1B, p(win)AY = chance + p(win)A + p(win)Y = 0.5 + 0.3 − 0.15 = 0.65.

Most importantly for our research questions, participants could make a choice that would influence the magnitude of outcomes, but they had no control over the valence of the result. Optimal behavior entailed betting 8 points each time that a likely winning pair (i.e., p(win) > 0.5) was presented, and betting 2 points each time that a likely losing pair (i.e., p(win) < 0.5) was presented.

Besides magnitude (small or large) and valence (win or loss), feedback in the task also conveyed information about the relative value of the feedback compared to the outcome that “would-have-been” if the alternative wager amount had been selected. This variable, which accords roughly with intuitions of “rejoice” or “regret”, was labeled as “relative outcome”. This can be seen in Figure 1C. Thus, the +8 and −2 outcomes reflect the best possible gain and loss, respectively, given that in each case the alternative outcome would be 6 points worse (i.e. +2 and −8, respectively).

EEG recording and pre-processing

The electroencephalogram (EEG) was recorded continuously from 64 channels mounted in a customized, extended coverage, elastic cap (Electro-Cap International, using a bandpass filter of 0.01 – 100 Hz at a sampling rate of 500 Hz (SynAmps, Neuroscan). All channels were referenced to the right mastoid during recording. The positions of all 64 channels were equally spaced across the customized cap and covered the whole head from slightly above the eyebrows in front to below the inion posteriorly (Woldorff et al., 2002). Impedances of all channels were kept below 5k Ω, and fixation was monitored with electro-oculogram (EOG) recordings. Recordings took place in an electrically shielded, sound-attenuated, dimly lit, experimental chamber.

Offline, EEG data were exported to MATLAB (MathWorks) and processed using the EEGLAB software suite (Delorme and Makeig, 2004) and custom scripts. The data were low-pass filtered at 40 Hz using linear finite impulse response (FIR) filtering, down-sampled to 250 Hz and re-referenced to the algebraic average of the left and right mastoid electrodes. For each participant, we implemented a procedure for artifact removal based on independent component analysis (ICA). This approach has been used in a number of studies (Debener et al., 2005; Eichele et al., 2005; Scheibe et al., 2010) to obtain EEG data with diminished contribution of ocular/biophysical artifacts. First, we visually rejected unsuitable portions of the continuous EEG data. This procedure resulted in the exclusion of 20 trials on average (± SD = 8.36 trials) from the original 800-trial-long dataset for each participant. Secondly, we separated the data into 1200-ms feedback-locked epochs, spanning from 400 ms before to 800 ms after the onset of the feedback stimulus, with a prestimulus baseline period of 200 ms. Thirdly, we performed a temporal infomax ICA (Bell and Sejnowski, 1995). With this analysis, independent components with scalp topographies and signals that could be assigned to known stereotyped artifacts (e.g., blinks) based on their distribution across trials, their component waveform, and/or their spectral morphologies, were removed from the data (Jung et al., 2000a; Jung et al., 2000b; Delorme et al., 2007). The remaining components were back-projected to the scalp to create an artifact-corrected dataset.

Previous studies have consistently found that the FRN has a frontocentral distribution with a peak of amplitude over the standard 10–20 FCz location at around 250 ms after feedback onset (Miltner et al., 1997; Gehring and Willoughby, 2002; Nieuwenhuis et al., 2004). On the other hand, the P3 has been conceptualized as being formed by two subcomponents: the P3a with a frontocentral distribution and a maximum amplitude between 300 and 400 ms following stimulus presentation, and the P3b with a parietocentral distribution and a peak of amplitude occurring between 60 and 120 ms later (Nieuwenhuis et al., 2005; Polich, 2007). In order to assess the FRN and the P3a we used a region-of-interest (ROI) cluster of seven sensors centered on the canonical channel FCz as a frontal region of interest (frontal-ROI). In order to assess the P3b we used a parietal-ROI cluster of seven sensors centered on channel Pz.

On frontal sites, the FRN appears superimposed on the P3a, and as several studies have noted, the FRN peak can be shifted depending on the amplitude of this frontal P3 (Yeung and Sanfey, 2004; San Martin et al., 2010; Chase et al., 2011). This is consistent with the idea that scalp-recorded neuroelectrical activity corresponds to the linear sum of the activity of a discrete set of neural sources (Baillet et al., 2001). Thus, to more effectively quantify the FRN amplitude accounting for differences in the P3-induced baseline, we used a mean-amplitude-to-mean-amplitude approach. More specifically, the FRN amplitude for each trial was calculated in the frontal-ROI as the average potential across a 204–272 ms window post-feedback (i.e., relative to the feedback stimulus onset) minus the average voltage potential from a short 188–200 ms window preceding it (Note that the effective sampling rate was 250 Hz, and thus these window lengths were all multiples of 4 ms). This approach accounts in part for the overlap between the FRN and P3 (cf., Yeung and Sanfey, 2004; Frank et al., 2005; Bellebaum et al., 2010; Chase et al., 2011).

In addition, given that differences between conditions were in fact observed before the onset of the FRN, we decided to also include an earlier window into our analyses. We refer to this activity as the P2, noting that it may represent an early stage of the slower-wave P3a. We measured the P2 amplitude on the frontal-ROI as the average ERP voltage potentials from a 152–184 ms post-feedback window. The P3a was quantified as the average potential from a 284–412 ms window in the frontal-ROI and the P3b as the average potential from a 416–796 ms window in the parietal-ROI, both relative to prestimulus baseline.

Overview of the data analysis

Through our analyses we wished to explore the relationship between individual differences in feedback-elicited brain activity and individual differences in choice behavior, particularly in gain-maximization and loss-minimization. However, our paradigm has learning and choice components that are difficult to distinguish from each other during the initial part of the experiment. In order to focus on the choice components of the processing, we excluded from our analyses the trials from the first quarter of the experimental session, using only the last three quarters of the session, which we took as representative of stable learned behavior (see Figure 2A and Behavioral results section).

Figure 2
Behavioral results

Using behavioral metrics derived from subjects’ choices, we tested the hypothesis that neural differences between the worst gain (i.e., +2) and the best gain (i.e., +8) would scale with gain-maximization, while the neural differences between the worst loss (i.e., −8) and the best loss (i.e., −2) would scale with loss-minimization. In addition, we assessed the association between the amplitude of ERP components and trial-to-trial behavioral adjustment.

Behavioral data analysis

To extract individual scores in gain-maximization and loss-minimization, we characterized each subject by his/her observed probability to bet the larger amount on likely winning trials [p(win) > 0.5], neutral trials [p(win) = 0.5] and likely losing trials [p(win) < 0.5]. We then expressed these probabilities on a logit-function scale


, where p is the probability to bet the larger amount on a given trial. This logit transform allows for better characterizations of differences in probability at the low and high ends of the scale. γ coefficients were estimated for each of the three types of trials for each participant. For all our subsequent analyses, the strength of gain-maximization was measured by the γ estimate for likely winning pairs: the more positive that value was for a participant, the more likely the participant was to bet high on likely winning trials. On the other hand, the strength of loss-minimization was measured by the γ estimate for likely losing pairs multiplied by −1, such that the more positive this value was for a participant the more likely the participant was to bet low on likely losing trials.

Gain-maximization, loss minimization, and ERPs for worst vs. best outcomes

Our first ERP data analysis tested the hypothesis that the difference between the neural responses elicited by feedback stimuli indicating the worst (+2) versus best (+8) gains would scale with gain-maximization and that the difference between the neural responses elicited by feedback stimuli indicating the worst (−8) versus best (−2) losses would scale with loss-minimization. As such, we computed four ERPs for each participant, each ERP corresponding to averaged EEG activity time-locked to the presentation of each of the four feedback stimulus types. After removing the first-quarter of the data to minimize the effect of learning (see above), 154 trials on average went into the ERP for +8 (SD = 32.70), 139 into the ERP for +2 (SD = 39.18), 207 trials into the ERP for −2 (SD = 40.01), and 84 trials into the ERP for −8 (SD = 35.45). Then we computed the difference in the ERP signal for conditions +2 minus +8 and −8 minus −2 for each participant. Finally, we performed a multiple linear regression using gain-maximization and loss-minimization scores for each participant (i.e., γgain-max and γloss-min) as explanatory variables for such ERP differences, according to the following equations:



where ε is a vector of error terms.

We performed this analysis separately for the P2, FRN, P3a and P3b components. For correction of multiple comparisons, we applied the step-down method described by Holm (1979).

ERPs correlates of behavioral adjustment

The last analysis was intended to assess directly the association between each ERP component and the behavioral adjustment on subsequent trials. We considered each trial t in terms of the two cue symbols that were presented (S1t & S2t) and the bet that was wagered. We then asked whether the wager choice (high or low) was the same or different on the next trial that presented S1t and/or S2t. This analysis was done separately for the next appearance of each one of these symbols, regardless if they appeared paired together or individually with other symbols. That is, a trial on which symbols labeled A and Y were presented was compared both with the next trial to present A and the next trial to present Y (which may have been the same trial). We defined a switch variable that assumed the value 0 if the same bet was chosen on each of these trials, 1 if the trial with one of the two cue symbols involved the same bet and the other the opposite, and 2 if neither trial resulted in the same bet. Note that this particular scoring ignores the temporal delay between subsequent presentations of the stimuli and precludes any interaction between the elements of each pair.

Evoked responses corresponding to the P2, FRN, P3a, and P3b components were each entered as dependent variables to fit a linear mixed model using different levels of ‘outcome’ and ‘adjustment level’ as fixed effects and a participant’s identifier (μ) as random effect.


where ‘outcome’ was a categorical variable with three levels (+2, −2 and −8), and ‘adjustment level’ was likewise a categorical variable with two levels (1 = switching for one symbol, 2 = switching for both symbols). We used the +8 outcome and the not switching condition as constant for the model. In other words, the β estimates that we report below for the fixed effects reflect expected deviations from the ERP components elicited by the best gain (i.e., +8), when such a result was followed by the same choice (i.e., a large bet) on the next immediately next trial(s) wherein the same cue symbols were presented. For correction of multiple comparisons we applied the method described by Holm (1979).

After removing the data from the first-quarter of the trials, 49 trials went into each of these 12 ERPs on average (SD = 27.64). The condition with the maximum number of trials across participants was ‘not switching after −2′ [Mean (M) = 93.23; SD = 38.85; range = 11 – 167] and the condition with the minimum number of trials was ‘not switching after −8′ (M = 20.51; SD = 19.31; range = 8 – 109). No condition was associated with fewer than 7 trials in any of the 41 participants included in the analysis.


Behavioral results

A visual inspection of choice behavior across blocks (Figure 2A) suggests that the participants quickly learned the contingencies of the task. Indeed, an ANOVA on the probability of high bets on the first block showed that, overall, participants’ choices already distinguished between trial types within this block (F(2, 80)=5.3993, p < 0.01), with a greater proportion of high bets on positive trials compared to both neutral (p = 0.02, Tukey’s range post-hoc tests) and negative trials (p = 0.01). However, in order to minimize the effect of the early stages of learning, and to instead focus on the choice behavior effects, all the analyses that we report below were restricted to the last three quarters of the experimental session (blocks 11 to 40), when learning has roughly converged on a stable pattern of choice behavior (see marking lines in Figure 2A).

Gain-maximization and loss-minimization

As expected, γ estimates for the tendency to bet high on likely winning trials were positive (t(40) = 6.60, p < 0.0001) (i.e., subjects overall chose to bet high on likely winning trials), and γ estimates for the tendency to bet high on likely losing trials were negative (t(40) = −13.37, p < 0.0001) (i.e., subjects overall chose to bet low on likely losing trials). Estimates of γ, reflecting the tendency to bet high on neutral trials, were also negative (t(40) = −4.11, p < 0.0005), revealing that subjects generally chose to bet low on neutral trials (Figure 2B)..

Hereafter, values of the γ estimate for the probability of betting high on likely winning trials will be referred as gain-maximization scores for each participant. Similarly, the additive inverse of the γ estimates, reflecting the probability of betting high on likely losing trials (i.e. -γ), will be referred to as loss-minimization scores. Note that most of the participants were better at loss-minimization than gain-maximization (35 out of the 41 below the dashed line in Figure 2C).

By definition, high gain-maximization and high loss-minimization scores should predict high earnings in our task, and their net effects on earnings should not differ significantly. As expected, we found that both gain-maximization (r = 0.67, p < 0.0001) and loss-minimization (r = 0.70, p < 0.0001) were correlated with participant’s earnings. We also found a positive correlation between gain-maximization and loss-minimization values across subjects (r = 0.37, p = 0.02). Importantly, the last correlation suggests that we had success in differentiating our behavioral measures from participants’ overall risk preferences. Specifically, if gain-maximization were referring to risk seeking and loss-minimization were referring to risk aversion, we should have found a negative correlation between these measures – but instead we observed a positive correlation.

Finally, to confirm that there was no difference between the effects of gain-maximization and loss-minimization in earnings, we compared the points earned by two groups of participants identified through a median split procedure. The first group consisted of those participants that had high gain-maximization and low loss-minimization scores (n=8), and the second groups consisted of those participants (n=8) who showed the opposite pattern (i.e., low gain-maximization and high loss minimization scores). A t-test revealed no significant difference in earnings between these two groups (p = 0.69).

ERP results

ERP responses to monetary feedback independently predict gain-maximization and loss-minimization across participants

Figure 3 presents the grand-average ERP waveforms extracted for the four possible outcomes on each trial. We asked whether individual differences in neural responses to different possible outcomes might correlate with individuals’ gain-maximization and loss-minimization scores. Indeed, gain-maximization was positively associated across subjects with the difference between the P3b elicited by the worst gains (+2) and that elicited by the best gains (+8; see Table 1 for statistics). On the other hand, loss-minimization scores were positively associated with the differences between the amplitudes for the P2, the P3a, and the P3b elicited by the worst (−8) and best (−2) losses. In contrast, the amplitude of the FRN did not significantly correlate with any of these behavioral measures.

Figure 3
Feedback-locked ERPs and their distribution over the scalp

Strikingly, these results show that gain-maximization and loss-minimization have different patterns of association with the P3b (Figure 4). The contrast ‘worst > best’ between the two types of gains was significantly associated with gain-maximization (β = 2.34, p < 0.0001) but not with loss-minimization (β = −0.56, p =0.24). The contrast ‘worst > best’ between the two types of losses was significantly associated with loss-minimization (β = 3.66, p < 0.0001), but not with gain-maximization (β = −0.74, p = 0.38). In order to further support this finding we performed a stepwise regression procedure. First, we removed loss-minimization as a predictor from our original model to predict the +2 > +8 P3b differences across participants. We found that the performance of this new model (which included only gain-maximization and constant) was not statistically different from the performance of the original one (F(1, 38)=1.45, p > 0.1). However, when we removed gain-maximization as a predictor from the original model, the new model (which included only loss-minimization and a constant) was significantly outperformed by the original one (F(1, 38)=27.14, p < 0.00001). Then, we performed the same procedure to evaluate the association between loss-minimization and the −8 > −2 P3b. We found the opposite pattern of results. The performance of a model that excluded gain-maximization was not statistically different from the performance of the original model (F(1, 38)=0.79, p > 0.1), but the original model significantly outperformed a model that excluded only loss-minimization (F(1, 38)=21.46, p < 0.00005).

Figure 4
Neural differentiation between gain-maximization and loss-minimization

In sum, although gain-maximization and loss-minimization were correlated at the behavioral level, gain-maximization and loss-minimization were associated with different contrasts at the neural level. Specifically, gain-maximization was associated with the differences in P3b amplitude between the worst (+2) and the best gain (+8) across participants, while loss-minimization was associated with the differences in P3b amplitude between the worst (−8) and the best loss (−2).

The results from our original models also suggest a temporal difference between the neural processing associated to these two types of choices, since the neural processing associated with loss-minimization was also found in earlier latencies (i.e., P2 and P3a time-windows), while the first index of neural processing associated with gain-maximization was found much later, in the 416 – 796 ms post-feedback window (i.e., P3b).

ERP predictors of behavioral adjustment

Given the previously reported associations between the P3 and behavioral adjustment (Chase et al., 2011), we asked whether neural responses in our task predicted changes in behavior on a trial-to-trial basis. Finding such a relationship would strengthen the argument that the P3 activity not only reflects, but also contributes to, adjustments aimed toward gain maximization and loss minimization. To test this, we designed a linear mixed model (see Materials and Methods section) using different levels of ‘outcome’ (−8, −2, +2, +8) and ‘adjustment level’ (0 = not switching, 1 = switching for one symbol, 2 = switching for both symbols) as predictors of neural responses corresponding to the P2, FRN, P3a, and P3b components.

We found that the P2 elicited by the −8 feedback stimuli was statistically indistinguishable from the P2 elicited by the outcome stimuli coded in the constant condition (i.e., +8). On the other hand, the P2 for −2 and +2 was smaller than the constant P2 (Figure 3, see Table 2 for statistics). This result is consistent with a previous study by Goyer et al. (2008), which found that, compared to small magnitude outcomes, large magnitude monetary outcomes elicited a greater positive potential in pre-FRN latencies (staring ~150 ms in their case). The FRN was larger (i.e., more negative) for −8, −2 and +2, compared to the best possible outcome (+8), which is consistent with previous studies showing that the FRN responds to feedback along a general good-bad dimension (reviewed in San Martin, 2012). P3a and P3b amplitudes showed similar modulations as a function of outcome, with larger responses to −8 and smaller responses to −2 compared to +8, although the P3a was also smaller for +2 compared with +8. Overall, these P3 results are consistent with previous studies showing that the P3 is greater both for large vs. small magnitude outcomes, and for suboptimal vs. optimal outcomes (Chase et al., 2011; Yeung and Sanfey, 2004).

Table 2
Association between ERP components and variables ‘outcome’ and ‘behavioral adjustment’.

Most importantly, both the P3a and P3b amplitudes scaled with ‘adjustment level’ (Figure 5). However, only the relationship between P3a and the largest adjustment (2 = switching for both symbols) survived our rather conservative approach to multiple comparisons (see Table 2 for statistics). Importantly, the FRN showed no significant association with behavioral adjustment, not even according to uncorrected p-values. In sum, the larger the P3a was on a given trial, the more likely the subjects were to change their choice behavior on the next appearance of the same symbols.

Figure 5
Feedback-locked ERPs as a function sequential behavioral adjustment


When confronted by choices from among competing options, simply avoiding losses and seeking gains may be an insufficient strategy for generating optimal behavior. In other words, in order to achieve long-term positive outcomes, decision-makers must not only be concerned with the relative frequency of gains and losses, but also with the relative magnitude of gains and losses. Previous behavioral studies have found that deficits in gain-maximization and loss-minimization are associated with negative life outcomes in gambling (Siler, 2010) and depression (Maddox et al., 2012). Here, we contribute to the identification and functional characterization of the neural mechanisms that may underlie such effects by showing that the amplitude of two P3 subcomponents predicted individual differences in gain-maximization and loss-minimization (P3b) and the subsequent behavioral adjustment (P3a). These findings suggest the P3 may reflect brain activity involved in adjusting choice behavior in support of maximizing gains and minimizing losses.

Different neural signatures for gain-maximization and loss-minimization

Previous studies (Frank et al., 2005; Eppinger et al., 2008; Cavanagh et al., 2011) have suggested that the ability to learn to avoid negative outcomes scales with the sensitivity of the FRN to losses. We employed a task in which losses could not be avoided, but could be minimized, and in which gains could not be ensured, but could be maximized. From this, we expected to find a dissociation between gain-maximization and loss-minimization involving the FRN: gain-maximization would be associated with the neural differentiation of gains, whereas loss-minimization would be associated with the neural differentiation of losses. We did indeed find this pattern of results in the ERP activity, but in the P3b rather than in the FRN. A possible reason for the discrepancy between our results and those of the previous studies is that in those studies valence and relative outcome information (i.e., best vs. worst outcome relative to the outcome that “would-have-been” if the alternative decision had been made) were always correlated, and the FRN is known to be primarily sensitive to valence (Yeung and Sanfey, 2004). In contrast, our task distinguished outcome valence from relative outcome. Accordingly, this manipulation was able to decouple valence evaluation from relative outcome information, which thereby allowed us to distinguish the functional processes reflected by the FRN and the P3b.

The differentiation between gain-maximization and loss-minimization that we found in the P3b is in many ways consistent with the context-updating hypothesis for the P3 (Donchin, 1981; Donchin and Coles, 1988), which proposes that the P3 reflects the amount of cognitive resources employed during the revision of an internal model of the environment. Such a model would be revised whenever discrepancies between a stimulus and model-derived predictions bring the validity of the model into question. This model is complemented by the context-updating/LC-P3 hypothesis (Nieuwenhuis et al., 2005; Nieuwenhuis, 2011), which builds on the similarities between the context-updating hypothesis and the role of the locus coeruleus-norepinephrine (LC-NE) system in learning (Aston-Jones and Cohen, 2005; Yu and Dayan, 2005; Dayan and Yu, 2006), to propose that the P3 reflects cortical activity resulting from phasic modulation by the LC-NE system.

In considering the application of this model to the present study, it indeed seems likely that the participants would have been dynamically updating an internal model of the association between various symbols and the probability of winning [p(win)] that informed decisions on each trial. In this scenario, high gain-maximization scores would be associated with a strong tendency to modify representations leading to future choices likely to produce suboptimal gains while maintaining representations leading to choices that maximize gains. According to the context-updating/LC-P3 hypothesis, this would be reflected in a large P3b for suboptimal compared to optimal gains (i.e., +2 > +8), as was the case. On the other hand, high loss-minimization scores would be associated with a tendency to modify representations leading to choices likely to produce suboptimal losses while maintaining representations leading to choices that minimize losses. According to the context-updating/LC-P3 hypothesis, this would be associated with a large P3b for suboptimal compared to optimal losses (i.e., −8 > −2), as was also the case. Below we discuss the additional analyses that advance a mechanistic explanation of these results.

P3 sensitivity to large magnitude and choice bias

The P3 response has previously been found to be an index of attentional allocation (Schupp et al., 2004; Gao et al., 2011). In our study, the findings of positive-polarity feedback-magnitude effects starting rather early (P2) and lasting for a long period (P3a and P3b, where large magnitude outcomes were, overall, associated with large ERP amplitudes) suggests that people pay more attention to large outcomes. Large outcomes probably induce greater arousal than do small ones because they have a greater impact on cumulative earnings in economic scenarios. Neuroimaging studies have identified a number of regions that are sensitive to outcome magnitude, including the orbitofrontal cortex, insula, and ventral striatum (Elliott et al., 2000; Knutson et al., 2000; Breiter et al., 2001; Delgado et al., 2003). Although these frontal and subcortical regions may not directly contribute to the outcome-sensitive P3 subcomponents reported here (i.e., as generators), some of these regions may be involved in allocating additional cognitive resources to the processing of large outcomes, which may in turn be reflected by the P3 activity

This additional allocation of attention to large magnitude outcomes may explain why participants were overall better at loss-minimization than gain-maximization. Specifically, an increased amount of cognitive resources marshaled in response to large outcomes might benefit loss-minimization by selectively enhancing the processing of large-magnitude outcomes that indicate the need for behavioral adjustment (i.e., −8 is not just a negative outcome but also a large-magnitude one). On the other hand, gain-maximization requires adjusting behavior after a small-magnitude outcome (i.e., +2, the suboptimal gain) and thus would not benefit from an attentional bias towards large-magnitude outcomes.

P3 subcomponents predict behavioral adjustment

We found that the P3a, rather than the FRN, predicted behavioral adjustment between trial occurrences in the trial sequence in which the same symbols were presented. The P3b also tended to distinguish between the absence of adjustment and the largest adjustments, but such difference did not survive our conservative method of correcting for multiple comparisons. We propose an interpretation of these results that is also consistent with the context-updating/LC-P3 hypothesis, namely that decisions on each trial were informed by an internal model of the symbol/p(win) contingencies, and the P3 amplitude reflects the extent of the feedback-triggered revision of such a model.

With respect to the roles of the FRN and the P3 in feedback-guided decision-making, our results suggest that the P3 is involved in using outcome-predicting cues to adjust behavior when the goal is maximizing gains and minimizing losses, whereas the FRN might be involved in using such cues when the goal is approaching gains and avoiding losses (Frank et al., 2005). An issue for future research will be to determine how the behavioral goals and context of the task affect the relative involvement of different brain signals in feedback-guided decision-making.

Time course of feedback processing

The temporal resolution of ERPs allows us to propose a time course of feedback processing. According to its distribution over the scalp, and in line with previous studies (Yeung and Sanfey, 2004; Goyer et al., 2008; San Martin et al., 2010), we interpret the P2 activity (~180 ms after feedback onset) as an early stage of the P3, probably preceding stimulus awareness (Sergent et al., 2005; Del Cul et al., 2007) and related to an implicit appreciation of the task-relevance of the stimulus (Potts et al., 2006). As our results suggest, such appreciation can be biased (e.g., greater attentional sensitivity toward large magnitude outcomes), and according to our interpretation it can selectively enhance later stages of feedback processing. The FRN (~250 ms) seems to index the evaluation of outcome value (“how much value was acquired/lost”), presumably in terms of a reward prediction error (Holroyd and Coles, 2002). Such a mechanism might not directly adjust behavior, but might rather inform subsequent processes that update the representation of probabilistic contingencies. We interpret the P3 amplitude as reflecting such revisions. Interestingly, recent work has been able to accurately simulate the P3 under the assumption that it is driven, in part, by pre-computed prediction errors (Feldman and Friston, 2010). In line with the context-updating/LC-P3 hypothesis, the P3 amplitude may reflect the amount of attention engaged during the feedback-induced revision of an internal model of the environment that informs choice behavior. During the course of the P3, the P3a (~350 ms) may serve as a link between a stimulus-driven attentional process that recruits a frontal circuit initially indexed by the P2/early-P3, and a memory updating process that recruits a temporo-parietal circuit and that is indexed by the P3b (~450 ms) (Polich, 2007).

Overall, our results suggest that the P3 response to monetary outcomes reflect an adaptive mechanism by which prior experience may alter ensuing choice behavior. Moreover, our results suggest that individual differences in this process, as reflected in the P3, are linked to individual differences in gain-maximization and loss-minimization during economic decision-making.


This work was supported by a Fulbright scholarship and a CONICYT grant to R.S.M., and by an NIMH grant (R01-MH060415) to M.G.W. We thank Kenneth C. Roberts for helpful assistance during the preparation of the experiment, Stevan Budi for assistance with data collection, and R. McKell Carter for helpful assistance and comments during data analysis.


Conflict of Interest: The authors declare that there are no competing financial interests


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