Edited by: Scott A. Huettel, Duke University, USA
Reviewed by: Lasana Harris, New York University, USA; Daniel Houser, George Mason University, USA
*Correspondence: Gregory S. Berns, Department of Economics, 1602 Fishburne Drive, Atlanta, GA 30322, USA. e-mail:
This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.
This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
The majority of decision-related research has focused on how the brain computes decisions over outcomes that are positive in expectation. However, much less is known about how the brain integrates information when all possible outcomes in a decision are negative. To study decision-making over negative outcomes, we used fMRI along with a task in which participants had to accept or reject 50/50 lotteries that could result in more or fewer electric shocks compared to a reference amount. We hypothesized that behaviorally, participants would treat fewer shocks from the reference amount as a gain, and more shocks from the reference amount as a loss. Furthermore, we hypothesized that this would be reflected by a greater BOLD response to the prospect of fewer shocks in regions typically associated with gain, including the ventral striatum and orbitofrontal cortex. The behavioral data suggest that participants in our study viewed all outcomes as losses, despite our attempt to induce a status quo. We find that the ventral striatum showed an increase in BOLD response to better potential gambles (i.e., fewer expected shocks). This lends evidence to the idea that the ventral striatum is not solely responsible for reward processing but that it might also signal the relative value of an expected outcome or action, regardless of whether the outcome is entirely appetitive or aversive. We also find a greater response to worse gambles in regions previously associated with aversive valuation, suggesting an opposing but simultaneous valuation signal to that conveyed by the striatum.
Many real world decisions involve the possibility of both good and bad outcomes, but sometimes the choices are between bad and worse. Consider, for example, an individual who purchases a cell phone plan only to realize that the reception with that carrier is terrible. The individual is then faced with the decision to either stay with the carrier and suffer bad reception, or pay an exorbitant cancellation fee. In either case, the outcome is bad. The recent advent of neuroeconomics has brought new methods of analysis to the study of human decision-making, but the vast majority of these studies have focused on decisions in which all possible outcomes are non-negative (Knutson et al.,
There is some evidence that the striatum and other orbitostriatal structures are involved in both gain and loss processing (Delgado et al.,
To test these hypotheses, we used fMRI along with a gambling task involving electric shocks. In a manner similar to that in the task used by Tom et al. (
Thirty-six participants (18 female, 18 male; 18–45 years) were recruited from the Emory University campus. All participants were right-handed, reported no psychiatric or neurological disorders, or other characteristics that might preclude them from safely undergoing fMRI, and provided informed consent to experimental procedures approved by the Emory University Institutional Review Board. Participants received a base pay of $40.
A Biopac STM100C stimulator module with a STMISOC isolation unit (Biopac Systems, Inc., CA, USA) was used to deliver electric shocks cutaneously to the dorsum of the left foot through shielded, gold electrodes placed 2–4 cm apart. The STMISOC unit controlled current output to the electrodes, with each pulse lasting 15 ms. The stimulator module was connected via a serial-interface to a laptop which controlled the timing and delivery of the shocks.
Prior to scanning, shock intensity was calibrated by finding each participant's “maximum shock intensity”,
To gain familiarity with the different numbers of shock outcomes, participants were passively exposed to all possible outcomes. An attempt to induce a status quo of 10 shocks was made by subjecting participants to the 10 shocks at the beginning of each trial. On each outcome, the number of shocks (SN) was evenly spaced in time over 340 ms, yielding an inter-pulse interval of 340/(SN-1). This was done to avoid confounding the number of shocks with the total duration of shocks. The number of shocks, SN, within a trial was 2, 3, 4, 5, 6, 8, 10, 12, 15, or 18. These numbers were determined based on previous literature that suggests that the Weber fraction for many stimuli range from 0.01 to 0.10, meaning that a difference of at least 1–10% between stimuli is needed in order to be distinguishable from each other (Teghtsoonian,
Following the calibration phase of the experiment, participants entered the scanner to begin the experimental phase, which was modeled after a monetary gambling paradigm used by Tom et al. (
Two seconds after presentation of the gamble, participants were allowed to “Accept” or “Reject” the gamble by using a button box in the scanner. If participants accepted the gamble, a pink ball flipped between options for a varying amount of time between 3 and 6 s, landing with a 50/50 chance on the more shocks or fewer shocks outcome. The side on which the ball landed turned yellow, indicating the outcome of the gamble and impending shocks, which occurred 4.7 s after the outcome was revealed. If participants rejected the gamble, an identical presentation including the ball-flip and outcome selection occurred. However, in this case the reference shocks were the only possible outcome. After the shocks were administered, the outcome remained on screen for 3 s, and was followed by an inter-trial interval (ITI) of 3 s. The experimental phase consisted of three runs with 18 trials per run (54 trials in total). Trials were randomly ordered for each run within-subjects, but remained the same between-subjects. COGENT 2000 (FIL, University College London) was used for stimulus presentation and response acquisition for this phase.
To confirm that participants could distinguish between the different numbers of shocks and that increasing shocks were increasingly averse, participants rated all possible sets of shocks relative to the reference shocks (after the above procedure but while still in the scanner). A visual analog scale (VAS) was presented on screen, with a white arrow in the center labeled as “reference shocks.” Participants were given the reference shocks, and then were given another set of shocks, blinded to the number. They were asked to rate “How much better or worse it is from your reference,” by moving the arrow on screen either left (“better”) or right (“worse”). All possible sets of shocks were given three times each for a total of 30 data points.
Functional imaging was performed with a Siemens 3 T Trio whole-body scanner. T1-weighted images (TR = 2300 ms, TE = 3.04 ms, flip angle = 8,192 × 146 matrix, 176 sagittal slices, 1 mm cubic voxel size) were acquired for each subject prior to the three experimental runs. For each experimental run, T2*-weighted images using an echo-planar imaging sequence were acquired, which show blood oxygen level-dependent (BOLD) responses (echo-planar imaging, TR = 2350 ms, TE = 30 ms, flip angle = 90, FOV = 192 mm × 192 mm, 64 × 64 matrix, 35 3-mm thick axial slices, and 3 mm3 voxels).
fMRI data were analyzed using SPM5 (Wellcome Department of Imaging Neuroscience, University College London) using a standard 2-stage random-effects regression model. Data were subjected to standard preprocessing, including motion correction, slice timing correction, normalization to an MNI template brain and smoothing using an isotropic Gaussian kernel (full-width half-maximum = 8 mm).
Four main regressors were included in the first-level models. (1) The status quo shock at the beginning of each trial was modeled as an impulse function. (2) The “decision” period, during which a decision to accept or reject the gamble was required, was modeled from the onset of gamble presentation until button press. The expected value of the gamble was also included as a parametric modulator for this period. (3) The “ball” period, in which the gamble outcome was resolved over a varying period of time between 3 and 6 s, was modeled as a variable duration function. (4) The “wait” period was modeled from the display of the gamble outcome to the receipt of the shocks. For this period, the number of shocks received was included as a parametric modulator. Subject motion parameters were also included as regressors. All regressors were convolved to the standard HRF function.
Because we were interested in investigating the neural basis of decision parameters that affect choice, the second-level analysis focused on the decision period (#2 above). To identify regions involved in valuation during choice, we first identified regions showing correlations with the expected value of the gamble. We assumed shocks are “bad” and have negative value; for example, the reference shocks would have an expected value (EV) of −10. We calculated the expected value of the gambles with the equation: EVgamble =
Finally, another first-level model was constructed in order to extract BOLD responses for each individual gamble type during the decision period. Instead of a single lottery period modulated by the number of shocks less and number of shocks more than the reference amount, this model included each lottery period associated with a different gamble as a separate regressor, such that there were 18 columns in the design matrix for the decision period, along with the remaining regressors that appeared in the primary first-level model described above. This allowed the average BOLD activity during the decision period for each separate gamble to be extracted. These values were then used to create “heat maps” of activation which give snapshots of how a particular region responds to all possible gambles.
For monetary payments, if an individual prefers to receive a certain payment rather than a gamble with the same expected value, he is said to be risk averse. If he instead prefers the gamble, he is said to be risk seeking. Prior research with monetary payments shows that on average, individuals are risk-averse (risk-seeking) for positive (negative) payoffs. We consider whether the shock quantities in our experiment are treated in the same way. To consider the issue, participant behavior in symmetric lotteries was analyzed. Symmetric lotteries were lotteries with the same amount of shocks less and more than the reference amount, and therefore had the same expected value as the reference shocks. Averaged across all runs for all participants, the symmetric lotteries were chosen over the reference shocks 74% of the time, which suggests risk-seeking behavior. For the individual symmetric lotteries of 8/8, 5/5, and 2/2, participants chose the lottery 56%, 78%, and 89% of the time, respectively. Interestingly, this was significantly different between the three symmetric lottery types (
As another indicator of overall risk-preference, the average indifference point across participants was determined by graphing the probability of choosing the lottery as a function of the expected value of the gambles. A sigmoidal curve, shown in Figure
To determine individual risk-preference, the curvature of the utility function,
The expected value of the gambles, EVgamble, was used to identify brain regions involved in the valuation of gambles during the decision period (see Figure
Structure | L/R | Voxels | Maximum |
|||
---|---|---|---|---|---|---|
Primary visual | L | 1250 | −6 | −84 | −6 | 8.59 |
Pre-motor | L | 143 | −36 | −9 | 48 | 5.47 |
Intraparietal sulcus | R | 410 | 30 | −45 | 54 | 5.31 |
Pre-motor | R | 82 | 42 | −3 | 48 | 4.1 |
Frontal eye fields | R | 15 | −3 | 66 | 3.37 | |
Cuneus | R | 28 | 18 | −90 | 18 | 4.67 |
Frontal eye fields | L | 29 | −24 | −6 | 63 | 4.34 |
Cerebellum | R | 15 | 24 | −51 | −18 | 4.33 |
Insula | R | 16 | 39 | −21 | 21 | 3.92 |
Ventral striatum | L | 14 | −12 | 9 | −9 | 3.85 |
Superior temporal gyrus | L | 19 | −45 | −39 | 12 | 3.79 |
Mid-cingulate gyrus | R | 13 | 12 | −18 | 48 | 3.7 |
Anterior cingulate/middle frontal | 1467 | 0 | 30 | 45 | 6.07 | |
Anterior insula/inferior orbital | R | 152 | −42 | 24 | −12 | 5.94 |
Posterior cingulate | 103 | 3 | −24 | 36 | 4.87 | |
Angular gyrus | R | 229 | 39 | −66 | 54 | 4.82 |
Angular gyrus | L | 38 | −39 | −60 | 57 | 4.44 |
Middle temporal gyrus | L | 43 | −66 | −42 | −6 | 4.34 |
Middle frontal gyrus | R | 40 | 39 | 15 | 57 | 4.12 |
Angular gyrus | L | 16 | −33 | −75 | 48 | 4.05 |
Precuneus | R | 20 | 6 | −63 | 39 | 3.91 |
Middle frontal gyrus | R | 11 | 45 | 36 | 30 | 3.52 |
To determine how these regions responded to the individual components of the gambles (less or more potential shocks), beta values for the
Contrary to the simplest form of the dual-systems view, which would predict no response from the ventral striatum to gambles consisting solely of losses, our results indicate that the ventral striatum encodes information regarding value irrespective of the type of outcome (e.g., “more” or “less” shocks) and whether the outcomes are globally “good” or “bad” (e.g., appetitive or aversive). In particular, the positive correlation of left ventral striatal activity with the expected value of the shock lotteries supports its role in valuation and extends this to include the relative valuations of “bads.” While previous neuroimaging studies have demonstrated the role of the striatum in integrating the value of rewards with a variety of costs (Tom et al.,
That these decisions were viewed as occurring in the loss domain is reinforced by the fact that, despite being exposed to the reference shocks for each trial, participants viewed every outcome as a “loss.” This was evidenced by consistent risk-seeking behavior over the full range of lotteries and a larger slope for less shocks than more shocks relative to the status quo for the VAS ratings. These results are consistent with past research showing risk-seeking behavior over hypothetically painful outcomes (Eraker and Sox,
It is important to distinguish between the loss of something desirable, which has been investigated in a considerable number of prior studies, and the receipt of something undesirable, which has received less attention. Previous neuroeconomic studies of loss aversion have shown that the ventral striatum deactivates to the prospect of monetary loss (Tom et al.,
Beyond the striatum's adaptive coding of value, its more general role in pain processing has been hotly debated (Leknes and Tracey,
Although the aforementioned discussion pertains to the role of the striatum in relative valuation, we also find evidence for such signals in cortical regions classically associated with pain and punishment evaluation (Bechara et al.,
In addition to valuation, the increase in BOLD response to worse gambles that we observed could be related to attention or cognitive control in general, which refers to the process by which attention, memory, and other cognitive abilities are shifted to accomplish a variety of goals. In addition to the lateral OFC and ACC, we found that the DMPFC signaled worse gambles with above-baseline activation in a location that has been recently implicated in decision-related control (Venkatraman et al.,
The current body of research in decision-making points to the idea of a universal valuation system that signals how “good” or “bad” a potential outcome is, relative to some reference point. Structures that were originally thought to be involved solely in reward processing during decision-making are increasingly being shown to be involved in the processing of punishing stimuli as well. Similar activity in these orbitofrontal-striatal regions is observed between more abstract punishments (e.g., monetary losses) and painful stimuli as we have shown here, much like the similar activation patterns for a wide variety of rewarding stimulus modalities. Future research might focus on how a baseline is determined for this valuation activity and whether it is directly related to the status quo, and whether loss aversion can be observed for non-monetary outcomes once a status quo has been set.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supported by grants from NIDA (R01 DA016434 and R01 DA025045).