In this dissertation we investigate the neural correlates of two different types of social decision-making. This thesis discusses some aspects of the neural basis of economic and moral decision-making in the human brain.
Neuroeconomics
A bet on any color pays $20 if a ball of the chosen color is drawn and $0 otherwise. In a situation like this, most people prefer to draw a ball from Urn A over Urn B regardless of color, preferring known probabilities over unknown probabilities.
Experiments in Neuroeconomics
Furthermore, it is non-invasive and does not require any labeling of the blood with radioisotopes (as PET does, for example). If the fMRI results have determined a specific area of the brain that will be activated in a certain condition, then the same experiment can be administered to patients with localized lesions in that area.
MRI Physics
This results in a much faster decay of the transverse magnetization with a time constant T2*, which depends on the nature of the tissue. T1-weighted MR imaging is commonly used to obtain high-resolution anatomical images of the brain (Figure 5).
Measuring the BOLD Signal
It has been argued that the increased blood flow fills the demand for oxygen and glucose needed to restore the energy supply during neuronal activity. One of the drawbacks of using the KREPEG signal to measure neuronal activity is the slow hemodynamic response (~20 seconds compared to the neuronal spike, which is on the order of milliseconds).
Once the images are obtained, the next step is to analyze them to determine which parts of the brain are activated and what the nature of this activation is. In the next two parts, I will describe how the functional time series and the anatomical image will be combined into a statistical activation map of the brain.
Even before spatial smoothing is performed, neighboring voxels are correlated due to the way fMRI data is acquired. This turns out to be very useful for some parts of the subsequent statistical analysis.
But before solving this equation, one must consider the fact that the BOLD response in the brain to a punctuate stimulus is not punctate. Another major weakness of the GLM is that it is a model-driven analysis, i.e. the experimenter must have an a priori hypothesis about the variables that produce the neural activity (their shape through the hrf and their timing).
Background
McCabe, Houser et al. 2001) performed one of the first experiments in neuroeconomics by implementing a simplified version of the trust game. 8 seconds after the Trustee's decision, the results of the Payback Phase are revealed to both players simultaneously.
Behavioral Results
A return ratio below the green line indicates that the Trustee earned more on average than the Investor in that round. No investor played the perfectly selfish Nash equilibrium in which the investor invests nothing in each round.
Strategic Uncertainty and Prediction Analysis
Local maxima of clusters in the Trustee brain showing increased activity for detecting investment outcomes relative to the payback display (df=47, uncorrected p<0.001, cluster size k≥8). Left panel: coronal view (y=0) of Trust's brain showing significant differential activation in the bilateral caudate for the βinv - βrep contrast. Cumulative distribution function (CDF) of signal magnitudes in the left caudate for reciprocal and nonreciprocal events 6 s after investments were detected.
It is therefore striking that strategic choice can be accurately predicted by an encoding of strategic uncertainty in the signal magnitude of caudate and midbrain. Local maxima of clusters in the Investor Brain showing increased activity for the disclosure of the payback results relative to the investment screen (df=461, p<0.001 uncorrected, cluster size k≥8). For each strategic choice (reciprocal, non-reciprocal and neutral) at round i we have the probability of an investment increase pin increase (when Ii+1−Ii >1), decrease p-decrease (Ii+1−Ii <−1) determine, determine or no change neutral (|Ii+1−Ii |≤1) in the next round.
Dynamic Cross-Brain Analysis
The two regressors of interest were βinv and βrep, corresponding to the disclosure of the investment and redemption screens, respectively. Activations in the investor's brain when the results of the investment phase (left panel) and the repayment phase (right panel) are revealed (p≤0.001, minimum cluster size: 5, x=-30, y=6, z= 0 ). Activations in the investor's brain at the moment when the results of the investment phase (left panel) and the repayment phase (right panel) are revealed (p≤0.001, minimum cluster size: 5, x=-30, y=6, z= - 3).
Note that for brain interactions we calculated the mutual information (MI) and correlation coefficient (CC) between the time series from the entire round, while for the other types of interactions we calculated the MI and CC from the pooled time-courses of the screens of investment and repayment. A similar pattern is found in right BA9 when correlation coefficient is used instead of mutual information (Fig. 35B). This justifies the combined use of mutual information and the correlation coefficient to detect dependencies in the brain, which has been shown to be a source of nonlinearity.
Trust and Reciprocity
For example, benevolent reciprocity from the investor is expected to generate the intention to increase repayment (trust) in the trustee's brain. However, the peak of the cross-correlogram between investors' MCC activity and the trustee's 'intention to trust' signal in the caudate showed a clear 14-s shift from early to late rounds (green traces). 39, the source of the shift lies in the “intent to trust” signal from the trustee's tail.
The peak of the cross-correlogram between the investor's MCC activity and the caregiver's “trust intention” signal in the caudate shows a distinct 14-second shift from early to late rounds (green traces). This shift suggested that the signal would be associated with the development of a model of the investor in the caregiver's brain. At the beginning of the exchange, the trustee's intention to increase trust occurs after the disclosure of the investor's decision to increase the investment (Figure 39A); this means that the increase in investment is surprising.
Agency Attribution
Given the previously reported activations in the anterior and posterior portions of the medial cingulate during a social exchange (Rilling, Sanfey et al. 2004), a detailed analysis of the cingulate cortex was performed in each pair of subjects. This is a remarkable finding, because visual presentation of the subject's own decision elicited little response in the cingulate cortex. However, the peak amplitude of these different response types was not uniform across the anterior-posterior axis of the cingulate.
In this group, there is a clear predominance of responses in the anterior and posterior poles of the cingulate. A statistical comparison of responses in each of the cingulate domains revealed that responses differed significantly between the conventional trust task and the control tasks. The rows labeled “Investors” and “Trustees” show the consistency of responses in the two different roles (n = 100 for each).
Conclusions
This chapter analyzes various neural aspects of the trade-off between efficiency and fairness in moral decision-making. It begins with some background on moral decision-making and a review of the cognitive neuroscience literature on moral judgment.
Background
Most issues in morality and moral decision-making have been around for a long time, and are part of the big questions of the great thinkers, e.g. Plato or Socrates (who, by the way, is often considered the father of ethics and moral philosophy in Western societies). Again, the trade-off here is between economic growth of the country and growth of inequality (for example between internal regions of the country). One of the most famous moral dilemmas that investigate moral intuition is the trolley problem (Fig. 45A): A trolley is out of control and runs down a track.
There are several arguments that can be made for this inconsistency: in the trolley dilemma, there is no direct intention to harm anyone, whereas harming the fat man is part of the plan to save the five people. The importance of the prefrontal cortex is emphasized in another lesion study showing that patients with lesions in the ventromedial prefrontal cortex produce a utilitarian pattern of judgments about personal moral dilemmas (Koenigs, Young et al. 2007). The aim of the experiment is to study the preferences of others when people have to make trade-offs between different distributional criteria (efficiency and fairness).
Experimental Design and Methods
The number of meals that each child can possibly receive is indicated next to the picture of the child. The group that will receive the meals is indicated by the direction of the lever in the middle of the screen. The nature of the moral dilemma in the task stems from the fact that there are several criteria (number of children, number of meals and distribution of meals) that cannot all be satisfied at the same time.
Utility based on a combined measure of the number and distribution of meals: subjects try to find a trade-off between providing the largest number of meals (or taking away the smallest number of meals) and distributing the meals as evenly as possible. The utility of the chosen allocation is called Uc and the utility of the non-chosen allocation is called Uu. Subjects' choices are determined by the absolute difference in meals between the allocations (i.e., ΔM) and by the nature of the dilemma (Give or Take).
Note that only the voxels actually located in the insula are included in #Voxels. There was also a weak correlation in the right insula when the projectile hit the lever. We also found that the delta utility (ΔU) was positively correlated with brain activity in the caudate at the moment the projectile hits the children (Fig. 58).
Positive parametric activation in the caudate relative to ΔU when the projectile hits children. Positive parametric activation in the caudate relative to ΔG when the projectile hits children. Note that only voxels that are actually in the caudate were considered in #Voxel.
Discussion and Conclusion
34;Consequences, action, and intention as factors in moral judgments: An fmri investigation." Journal of Cognitive Neuroscience. 34;Cooperation under the shadow of the future: Experimental evidence from infinitely repeated games." American Economic Review. 34; Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning." J Neurophysiol.
34;The Neural Correlates of Moral Sensitivity: A Functional Magnetic Resonance Imaging Investigation of Basic and Moral Emotions." J Neurosci. 34;Error Rate and Outcome Predictability Affect Neural Activation in Prefrontal Cortex and Anterior Cingulate Under Decision-Making." Neurobillede. 34; Neurale korrelater af en reverserende læringsopgave med en affektivt neutral baseline: en begivenhedsrelateret Fmri-undersøgelse." Neuroimage.
