The principal events of interest in the task included: (1) the presentation of the gain-loss context cue, followed by the spatial cues; (2) the delay period interposed between visual cue presentation and the motor response; (3) the execution of the motor response; and (4) feedback indicating the gain or loss acquired in a particular trial, contingent on the correct (gain) or incorrect (loss) response (Figure 1). By imposing a long delay between instructive visual cues and the contingent motor response, this task structure permits delineation between neuronal contributions due to sensory, motor, and intervening preparatory processes.

After initial baseline fixation, the gain-loss contingencies for the trial were displayed, followed by a brief presentation of spatial cues specifying the required movements for the trial. After a long delay, subjects performed a speeded motor response, and received immediate feedback (gain or loss) based on their performance.

To make a response, subjects operated a trackball with their right index finger to guide a cursor sequentially to five remembered out of nine possible target locations, in the exact order in which they were previously cued. Subjects were allowed a limited time in which to complete their motor responses, prompting them to prepare movements in advance. Brief cue presentation, high planning load and constrained response time made successful trial completion difficult. Therefore, subjects trained extensively on the task before scanning. This training helped to minimize learning effects and to stabilize performance during the experimental session, promoting stable expectations of action outcomes throughout the task (see below).

Model predictions and hypothetical BOLD responses in motor-preparatory ROIs encoding value (A), stakes (B), and absolute value (C). For the purpose of illustration, three-component response profiles (cue peak, sustained delay, and response peak)—typically observed in motor preparatory regions—are depicted. The delay period is shaded in gray. Examples are given for both good (>50%) and bad performance (<50%).

The reward contexts comprised five combinations of potential gains and losses: $0/−$0, $1/−$1, $1/−$5, $5/−$1, and $5/−$5. These combinations enabled predictions as to the hypothetical modulation of neural signals due to various parameters of the expected action outcome for different performance levels. Figure 2 illustrates the models based on three such parameters, and corresponding predictions for fMRI activity in motor preparatory areas, averaged over “good” (>50%) and “bad” (<50%) performances:

Across gain-loss contexts, reaction times were indistinguishable (Friedmans ANOVA: X2(4,960) = 2.36, p = 0.67). Total movement time to complete responses was shortest for the $5/−$1 condition. Yet this trend did not reach significance (Friedmans ANOVA: X2(4,960) = 4.68, p = 0.32). From these observations, individual subjects' behavioral measures show no significant disparities across conditions, yielding a relatively fixed probability of success for each subject during the experimental session.

(A) Actual (objective) versus self-reported (subjective) performance. “Subjective good” subjects assumed net winning money; “subjective bad” subjects assumed the converse. For comparison, “x”s are subjects who actually won money; “o”s, subjects who lost money. (B) Subjects' average preference rankings ([1] low; [5] high) of gain-loss contexts, grouped by objective (left graph) and subjective (right graph) performance. (C) Subjects' average motivation rankings for varying gain-loss contexts, grouped by objective (left) and subjective (right) performance. Error bars reflect the standard error (SE) of the mean.

The 17 subjects who participated in this study achieved drastically different levels of performance, ranging from 10% to 70% correct responses ( Figure 3A ). However, performance levels across the five gain-loss contexts were indistinguishable (Friedman's ANOVA: X2 (4,64) = 0.82, p = 0.94). To assess if performance changed throughout the scanning session, trials were evenly divided into six successive blocks. No significant differences in success rates across blocks of trials emerged (Friedman's ANOVA: X2(2,32) = 0.22, p = 0.89), indicating that no learning occurred during the course of the fMRI experiment.

Subjects' motivation ( Figure 3C ) displayed a strikingly different trend: objective good and bad groups showed no dissimilarity in ratings (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 29.4, p < 0.05; group: F(1,15) = 0, p = 1.0; group x context: F(4,60) = 0.2, p = 0.95). Yet context-dependant group ratings that were divided on the basis of subjective performance diverged significantly (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 38.7, p < 0.05; group: F(1,15) = 0, p = 1.0; group x context: F(4,60) = 3.3, p <0.05). Subjective good subjects rated the high-gain contexts ($5/−$1, $5/−$5) equivalently, followed by the low-gain contexts, indicating their motivation rating solely depended on the gains. However, the subjectively bad group showed the reverse pattern, i.e. contexts involving high losses were more motivating than high-gain contexts, congruent with the notion that they believed themselves more likely to perform poorly. Also note that the ANOVA statistics indicate that grouping subjects by subjective as compared to objective performance—through the interaction of gain-loss context and performance group—accounts for a greater proportion of the variance in both preference and motivation ratings.

Additionally, subjects rated the gain-loss contexts in terms of their motivation during and their preference for related trials. Figure 3B depicts the mean preference: on the left, the ratings for the objective good versus objective bad subjects, and on the right, subjective good versus subjective bad subjects. Intuitively, these ratings should parallel the value associated with the gain-loss contexts. Accordingly, subjects in all groups most preferred the high-gain/low-loss context ($5/−$1), and least preferred the converse, low-gain/high-loss context ($1/−$5). Between the objective good and bad groups, no significant differences existed in the ratings of these and all remaining contexts (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 31.0, p < 0.05; group: F(1,15) = 0, p = 1.0; group x context: F(4,60) = 0.3, p = 0.89). A similar picture surfaced for the subjective good and bad groups (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 32.7, p < 0.05; group: F(1,15) = 0, p = 1.0; group x context: F(4,60) = 0.60, p = 0.66).

Upon completion of the scanning session, but prior to receiving any feedback about their overall performance and net winnings, all subjects completed a questionnaire: first, 16 of 17 subjects claimed to pay attention to the presented gain-loss contexts; all subjects reported investing maximal effort on all trials, independent of the gain-loss context, as instructed (see Experimental Procedures). Based on feedback received at the end of each trial, subjects also estimated whether they had net won money, net lost money, or broke even. Given the task structure and the fact that performance did not differ between conditions, net winning required >50% performance on trials resulting in earning increments/decrements; net losing required <50% performance on these trials. Figure 3A portrays the relationship between perceived (subjective) task winnings and subjects' average (objective) performance across all trials. The subjective “good” group claimed a net gain based on their performance during the task (n = 11); the “bad” group claimed net losses (n = 6). For comparison, subjects denoted by “x” actually net won money (n = 6) during the scanning session, and those by “o” net lost (n = 11). Note that the objective and subjective performances were uncorrelated (Behrens-Fisher two-sampled t-test comparing actual performance of the subjective good versus subjective bad groups: p = 0.70). Because of this dichotomy, we will present all further results as a function of both objective and subjective performance estimates.

As this study chiefly concerns modulation of motor preparatory activity, the focus lies primarily upon sustained BOLD activity during the delay period that precedes the motor response. FMRI-responses elicited by the cue and the feedback stimulus are described in the supplemental results and discussion ( Text S1 ).

Motor Preparatory ROIs

The primary analysis identified “motor preparatory ROIs” as those clusters that (i) exhibited increased levels of fMRI activity during the delay period (i.e. the time preceding a motor response), irrespective of the gain-loss context, and that (ii) have been previously shown to exhibit specific motor-preparatory activity (see Materials and Methods for details). By this approach, a group analysis revealed significant delay period activity in the left superior parietal lobule (SPL), along the medial bank and fundus of both the most posterior and most anterior aspects of the intraparietal sulcus (postIPS, antIPS), the dorsal premotor cortex (PMd), and the (pre-)supplementary motor area (SMA) (see Figure 4A and Table S1). While we later discuss that these areas exhibited delayed activity most likely due to their role in prospective motor planning, retrospective spatial memory and attention might have contributed to their activity as well.

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larger image TIFF original image Download: Figure 4. Modulation of delay activity by reward context. (A) Regions exhibiting significant delay period activity, across all gain-loss conditions (p(FWE) < 0.01, k > 5). (B) Average BOLD signal time-courses for different gain-loss contexts (shaded error bars: ±SE), extracted from left Superior Parietal Lobule (SPL) of each subject. Vertical lines demarcate delay period onset at 0 s and average movement onset at 15 s; combined “cues presentation” (gain-loss context cue, spatial cue, and mask) lasts 3.7 s prior to the onset of the delay period. (C) Average beta values (±SE) for the corresponding delay-period regressors, averaged across all subjects. https://doi.org/10.1371/journal.pbio.1000444.g004

Among the motor preparatory ROIs, the left SPL demonstrated the most robust delay period activity; left SPL BOLD time-courses averaged across all subjects (±SE), sorted by gain-loss context, are illustrated in Figure 4B. Expressed in %-signal change relative to the last 4 s of the initial fixation period, the exemplary time-courses of this region show four main components: (1) a transient (high-amplitude) signal increase time-locked to the cue, peaking approximately 6 s after cue-presentation; (2) a sustained level of activity during the delay period, but of a smaller magnitude than the earlier cue-related and the later movement-related peak amplitudes; (3) a transient (high-amplitude) signal increase time-locked to the initiation of movement, again peaking approximately 6 s after movement onset; and (4) a smaller transient increase time-locked to the feedback (receipt of reward/punishment), often obscured by the decay of the larger, movement-related signal.

To better isolate delay period modulations consequent of gain-loss contexts (without residual contributions from the cue epoch), the corresponding beta values are depicted in Figure 4C. As these beta values are regression coefficients that represent the “weight” of each predictor in order to best fit the observed signal relative to the residual baseline activity, they constitute a normalized estimate of the signal change due to each predictor—in this figure, the delay periods under each gain-loss context. Averaged over all subjects, the preferred high-gain/low-loss (+$5/−$1) context produced the largest signal. While this tentatively suggests that the BOLD response may reflect the value associated with the trial or subjects' preference rankings, the remaining gain-loss contexts do not generate levels of activity proportional to either the value model or subjective preference—most notably, the beta value associated with the low-gain/high-loss context (+$1/−$5) exceeds those associated with the low-gain/low-loss and neutral context (+$1/−$1 and +$0/−$0, respectively). This strongly suggests that the absolute value associated with successful trial completion may play an explicative role in shaping delay period responses.

In a next step we tried to elucidate any relationship between objective and subjective performance levels and context-dependant delay period activity: the “objective good” group yielded no clear order of delay period beta values, except that the neutral context would have led to the lowest beta estimate in this and in all other groupings. The “objective bad” group exhibited a pattern similar to that of the overall group of subjects, with the high-gain/low-loss context (+$5/−$1) being highest, and the low-gain/high-loss context (+$1/−$5) being greater than the low-gain/low-loss and the neutral contexts (Figure 5A). This grouping on the basis of objective performance does not explain more of the overall variance in delay period beta values than when considering gain-loss contexts alone (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 4.595, p < 0.01; group: F(1,15) = 0.475, p = 0.5; group x context: F(4,60) = 0.54, p = 0.71).

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larger image TIFF original image Download: Figure 5. SPL activity correlates best with subjective absolute value. Left SPL delay period beta values for (A) objective good versus bad and (B) subjective good versus bad subjects. (C) Corresponding BOLD time-courses for subjective good (left) and bad (right) subjects (figure conventions as in Figure 4). (D) The chart depicts the average R2-values of the linear regression between the different explanatory models and individual subject's beta estimates in the left SPL for different reward contexts. Significant differences in R2-values of different models are indicated by “X.” https://doi.org/10.1371/journal.pbio.1000444.g005

Alternatively, subjects were divided according to their subjective performance estimate. Delay period beta values (Figure 5B) disclose a trend for an interaction between gain-loss context and subjective performance grouping (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 6.248, p < 0.001; group: F(1,15) = 0.32, p = 0.56; group x context: F(4,60) = 2.267, p = 0.07). For the “subjective good” group, the beta values of +$5 contexts exceed those of the +$1 contexts, with the highest beta associated with the +$5/−$1 context. For the “subjective bad” group, the −$5 contexts garner a larger hemodynamic response than the high-gain/low-loss context (+$5/−$1), which in turn produces a larger response than the low-gain/low-loss and the neutral context. Collectively considered, these findings concur best with the absolute value model for both subjective good and subjective bad performance (cf. Figure 2C). They do not concur with the value or stakes model, nor do they with the subjective rankings about preference and motivation. To demonstrate the task-dependent modulation of activity throughout the trial, Figure 5C renders the left SPL BOLD signal time-courses for both the subjective good and the subjective bad group.

The profile of BOLD activity in other motor preparatory ROIs echoed that in SPL. Figure 6 portrays the analogous time-courses, for subjective good and bad subjects, for left postIPS (Figure 6A), left antIPS (Figure 6B), left PMd (Figure 6C), and SMA (Figure 6D). Across these posterior parietal and premotor areas, neural activity developed similarly, likely reflecting a modulation of BOLD responses by the absolute value tied to task completion (compare Figure 2C). Amongst these areas, the left postIPS revealed the most robust context-dependent responses (Figure 6D). Moreover, supporting our findings for the left SPL, delay period beta values in neighboring left postIPS reveal a significant interaction between gain-loss context and subjective performance grouping (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 9.03, p < 0.001; group: F(1,15) = 0.342, p = 0.57; group x context: F(4,60) = 2.589, p < 0.05) but not for objective performance grouping (two-way mixed-design ANOVA: context [repeated measure]: F(4,60) = 6.597, p < 0.001; group: F(1,15) = 2.119, p = 0.17; group x context: F(4,60) = 0.503, p = 0.73). This implies that the absolute value model for subjective performance might account best for posterior parietal planning activity.

To further corroborate these findings, a series of ROI analyses was conducted to directly probe context-dependent modulations: on a single-subject level the beta values for each reward context were extracted for a given ROI and entered into linear regression analyses. These analyses revealed individual coefficients of determination (R2-values) for several explanatory models that could account for the modulation of the delay-related beta estimates in a given ROI due to the gain-loss contexts. Separate models were calculated for modulations according to the value, the stakes, the absolute value, and the subjective motivation model. Since the earlier three models also incorporate estimates of performance, we calculated both “objective performance” and “subjective performance” models. For the “objective performance” models, these hypothesized modulations for each subject were determined by their objective performance and for “subjective performance” models, by their subjective performance estimate (see Experimental Procedures, Table 1 for values used for these hypothesized modulations). For all ROIs, subjects' beta estimates were best explained by the absolute value model which was based on subjective performance. This is evident from the average R2-values in Figure 7: in each ROI the respective R2-value for the subjective absolute value model was the highest. In other words, this model was the best to account for the variance of motor-preparatory activity due to our reward-context. For instance, it explained more than 50% of the variance in the left SPL and in the postIPS of both hemispheres. Conversely, the least amount of variance was captured by the objective value model. Finally, for all ROIs and for all performance-based models, those based on subjective performance estimates always explained more variance than their objective counterpart, i.e. the same model but based on objective-performance estimates.

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larger image TIFF original image Download: Figure 7. Subjective absolute value coding in motor preparatory ROIs. For each ROI the average R2-values of the linear regression between the different explanatory models and individual subject's beta estimates for different reward contexts are depicted. https://doi.org/10.1371/journal.pbio.1000444.g007

In order to allow a statistical comparison between models, we performed a one-way repeated-measures ANOVA for each ROI, which was calculated across subjects' R2-values for the different models. In case of a significant influence of the factor “model,” additional pair-wise comparisons between models were performed (see Materials and Methods for details). A significant influence of the factor “model” was revealed for SPL (F(6,96) = 2.7, p < 0.05), postIPS (left: F(6,96) = 3.0, p<0.05; right: F(6,96) = 2.41, p < 0.05), and the SMA (F(6,96) = 2.6, p < 0.05). For the left SPL the results of the post-hoc comparisons between the different models are shown in Figure 5D. The figure reveals (i) that in the left SPL the subjective absolute value model explains significantly more variance than all other models but the subjective stakes model and (ii) that the objective expected value model performs significantly worse than all other models. The same principal pattern of results also surfaced for the postIPS in both hemispheres, except that the subjective absolute value model was not significantly better than the subjective motivation model (compare Table S2). All other ROIs display similar trends, though in these regions only a small subset of models could be statistically distinguished, if at all (i.e. for the SMA) (compare Figure 7 and Table S2).

Finally, we conducted a second set of full-brain group analyses to directly probe brain regions that exhibit context-dependent modulation. General linear models (GLMs) were defined for each individual subject that employed a single regressor for each task epoch. For the cue, delay, and response epochs, an additional regressor captured the hypothesized parametric modulation of the fMRI signal due to gain-loss contexts. Separate models were calculated for the value model, the stakes model, and the absolute value model (based both on subjective and objective performance estimates; see Table 1) as well as for subjects' preference and motivation. On the second level, group analyses exclusively utilized contrast images from individual subjects which assessed the beta values of each parametric regressor capturing the respective modulation of delay-related BOLD signals in accordance with each of our explanatory models. By this approach, all voxels in which a particular model could significantly account for delay period activity were mapped. Furthermore, we were able to directly contrast our main models using multiple pair-wise comparisons.

For second-level GLMs predicated upon stakes and value (either rooted in objective or subjective performance estimates) or predicated upon subjective preference and motivation, this contrast produced no significant voxels (up to an uncorrected voxel level threshold of p<0.05). However, confirming the results of our previous ROI analyses, absolute value models based on subjective performance yielded significant clusters in parietal and premotor cortex (p<0.05 corrected at cluster-level; k > 5 voxels; threshold at the voxel-level: p<0.05 FDR-corrected), rendered in green in Figure 8A (also compare Tables S1 and S3). Models of absolute value based on objective performance also highlighted a subset of these clusters, but these voxels did not survive the statistical threshold criteria. Superimposed on the statistical map for subjective absolute value in Figure 8A are the motor preparatory ROIs, which exhibited a significant main effect of the delay period (red). The extensive overlap suggests that these major motor preparatory ROIs were also the regions most significantly encoding subjective absolute value-related information.

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larger image TIFF original image Download: Figure 8. Subjective absolute value coding in Posterior Parietal Cortex. (A) Voxels revealing a significant main effect of the delay period are shown in red (p(FWE) < 0.01, k > 5); voxels revealing a significant parametric modulation of absolute value, based on subjective performance, are depicted in green (p(FDR) < 0.05, k > 5). Circled clusters of overlap (depicted in yellow) are significant at p < 0.05 (corrected at cluster level). (B–F) Cortical sites that exhibited significant differences when comparing between our six main models on the second level (p(FDR) < 0.05, k > 5; inclusive mask for delay period activity at p(FWE) < 0.01; k > 5 voxels [mask shown in red in A]). Note that the pairwise comparisons between models often suffered from the high degree of correlation between models in a subset of our subjects. Thus, the distinction between the models improved markedly by focusing on those subjects in whom the predictions of both models under comparison differed maximally. This principle is exemplified in (F): the comparison of subjective absolute value versus objective value, in which all subjects are included except those with both good subjective and good objective performance estimates, i.e. excluding those subjects in whom the predictions of both models converge (cf. Figure 2). https://doi.org/10.1371/journal.pbio.1000444.g008

To further assess the ability of one model to better account for the observed patterns of BOLD activation, paired t-test comparisons between our six main models (Figure 2) were performed for all possible model combinations. For example, in order to compare objective value and subjective value models, the two contrast images corresponding to the parametric modulation for the two models were extracted from each subject (first-level GLMs) and considered as pairs in the paired t-test comparison (resulting in 17 pairs for 17 subjects for each paired t-test). In this analysis, only the subjective absolute value model, when compared to other models, yielded significant activation: the contrasts of subjective absolute value > objective absolute value (Figure 8B), subjective absolute value > subjective stakes (Figure 8C), subjective absolute value > objective stakes (Figure 8D), and subjective absolute value > objective value (Figure 8E,F) all exhibited significant voxels within right and left SPL (p(FDR) < 0.05, inclusive mask for delay period activity at p(FWE) < 0.01; k > 5 voxels [Figure 8A]; also compare Table S4). No suprathreshold clusters for any other comparisons, including the inverse contrasts (e.g. objective value > subjective absolute value), were revealed.