The overall goal of the following experiment is to examine the neural encoding of the subjective value of risky and ambiguous options using functional MRI. This is achieved by scanning participants while they make choices between risky and ambiguous lotteries, varying in amount, probability, and ambiguity level. The choices made by each participant are then used to estimate his or her attitudes towards risk and ambiguity, which in turn are used to estimate how risk and ambiguity affect participants valuation of the lotteries.
Next, these estimates are used as regressors in a general linear model in order to identify brain areas in which activity is correlated with subjective value of risky and or ambiguous lotteries. The results show correlation with subjective values of both risky and ambiguous options in overlapping regions of the medial prefrontal cortex and the striatum. The main advantage of this technique over other methods that compare general activity for risky and ambiguous lotteries is that the individual behavior of each participant is used at the analysis of brain activation.
This method can help answer key questions in the neuroeconomics field, such as how value is represented in the brain and how it's used to make choices. The first step in this protocol is to design the visual stimuli representing risky and ambiguous choices to be presented while scanning use images such as those seen here to represent envelopes filled with poker chips called lottery bags in stimuli for risky lotteries, the winning probability being the ratio of red to blue chips should be illustrated using both a graphic stimulus and numbers. Here, the red and blue areas of each image are proportional to the number of red and blue chips.
In a real envelope, a minimum of three outcome probabilities is recommended for ambiguous lotteries. Part of the information about the probability should be missing such that the possible ratio of red to blue chips is bounded but not specified. Rendering the winning probability, partially ambiguous.
Increasing the occluded size increases the ambiguity level. We recommend using at least three levels of occlusion present each winning probability or ambiguity level with outcome amount displayed next to the winning color and zero next to the other color. Here, drawing a red chip would result in winning 18 while drawing a blue chip would result in a zero outcome.Five.
Reward levels are recommended spanning a wide range of amounts in each trial. The subject will choose between two lotteries. However, for simplicity, one of the options can be kept constant throughout the experiment and offscreen, while only the displayed option is varied.
Each combination of probability or ambiguity level and amount should be presented at least four times to ensure sufficient statistical power. In analyses, set the paradigm as a slow event related design so that each lottery is presented as a stimulus for two seconds, followed by a delay period of six seconds to allow for neurovascular response. Then the response should be made within one to two seconds.
Use a simple image as a brief feedback to indicate that the response has been recorded and separate the trials by rest periods of at least 10 seconds. Also, prepare physical bags or envelopes to match each lottery image used. In the experiment.
Fill each envelope with a total of 100 red and blue chips with proportions corresponding to the probability of drawing a chip of each color from that bag shown in the display. These should be shown to subjects before the task as they will be used later to play randomly chosen trials for payoff. Paying subjects based on their choices in the experiment is important in order to encourage them to express their true preferences.
Using physical bags with real chips in them to play the lotteries ensures to subject that the outcome probabilities are indeed the one stated prior to scanning. First, have the subject fill out a consent form as well as an MRI screening questionnaire. Then be sure that the subject removes all metal to ensure safety in the scanner environment.
Next, provide the subject with detailed instructions about the experiment and the task. Make sure that the subject understands how probabilities and amounts are conveyed in each image, but do not reveal any information that could influence choices. A two button response box will be used in the scanner to record the subject's choices.
Also, explain the payment mechanism so that the subject understands payment will be made. According to the choices. Seal the previously prepared bags and have the subject sign his or her name across the seal.
Explain that this will enable verification at the end of the experiment that the contents of the bags were not altered as the subject will be allowed to look into the bags to make sure they confirm to the stated probability or ambiguity level. Using a three Tesla MRI scanner first acquire a high resolution image of the subject's brain using a T one weighted sequence. This structural volume will be used for 3D reconstruction.
Any high resolution sequence can be used for this purpose. Next, use a T two star weighted EPI sequence to acquire functional imaging. During the task paradigm, make sure to position the slices to include the brain areas of interest.
Typically, the prefrontal cortex, parietal cortex, and the basal ganglia scanning parameters should be optimized for the specific scanner using a TR of two seconds and three by three by three millimeter voxels. After scanning, retrieve the behavioral data from the computer that has recorded the subject's button. Press responses.
Then allow the subject to randomly select one or a few trials for payment. This can be done by having the subject draw a numbered poker chip out of an opaque bag that contains chips with all trial numbers. For each selected trial, show the subject, the presented option, and the choice they made on that trial.
Then ask the subject to draw a chip from the bag chosen on that trial and pay them according to the drawn color and the amount presented on the trial. To analyze the behavioral data, use maximum likelihood to fit the choice data of each subject to a logistic function as seen. Here where PV is the probability that the subject chose the variable lottery.
SV sub F and SV sub V are the subjective values of the fixed and variable options respectively and gamma is the slope of the logistic function, which is a subject specific parameter. An alternative approach is to use a probate distribution to model the subjective value of each option For each subject, use a model that takes into account the amount, probability, and ambiguity level of the option and the attitudes of the individual subject towards risk and ambiguity. We chose to use a power function that includes a linear effect of ambiguity on the perceived probability.
One of several alternative approaches is to include ambiguity as an exponential effect, fitting the choice data with the choice function, thus provide estimates for the risk attitude, alpha and ambiguity attitude beta for each subject, initially perform standard pre-processing of the functional data, including slice, scan, time correction, motion correction, and removal of low frequencies related to physiological noise and scanner Drifts. Then coregister the functional data of each subject to the anatomical data For analysis at the single subject level, use a standard hemodynamic response function and model the activity of each voxel as a sustained response during the entire trial. Use a general linear model with two predictors of subjective value, one for risky trials and one for ambiguous trials.
Use the individual subject specific parameters derived from the behavioral fit to calculate the subjective value of each lottery. Also include two dummy predictors, one for risky trials, and one for ambiguous trials. To capture general activations such as visual and motor, the test for significance should take into account the multiple comparisons performed here.
The minimum cluster size was limited to six contiguous functional voxels. Other methods such as false discovery rate can also be used. Look for voxels in which the coefficients of subjective value under risk and or ambiguity are significant.
Here we see behavioral results of three representative subjects. Each panel presents the choice data and model fit results for one subject under either risk or ambiguity. The graphs depict the proportion of trials in which the subject chose the variable lottery as a function of amount separately for each level of probability or ambiguity.
As can be seen, subjects may vary a lot in their attitudes toward risk and ambiguity. Here we see imaging results for one, subject highlighted voxels are ones in which the coefficient of the subjective value predictor under ambiguity or risk was significantly different from zero. In this representative subject, significant correlation was found in medial prefrontal cortex and the striatum under both conditions.
These areas are the most consistent across subjects, but significant correlations can also be expected in areas in medial and lateral parietal cortex as well as the amygdala. This technique allows researchers in the field of neuroeconomics to explore changes in risk and ambiguity attitudes, and their neural correlates in different populations under different contexts. After watching this video, you should have a good understanding of how to elicit risk and ambiguity attitudes, and how to use them to analyze FMRI Data obtained while participants were making choices.
