Updated: Nov 22, 2020
Rationalists assume that humans only value their own outcome given a social decision-making scenario. Then, do humans not care about the consequences of their actions on others? It turns out, they do care. But, it is a qualified affirmation.
Hu et al. (2017), in their study titled ‘Social value orientation modulates the processing of outcome evaluation involving others’, have conducted an Event-Related Potential (ERP) study to measure outcome evaluation, for both self and other, under conditions of social decision-making. They devised a simple gambling task where the participant (also called the decision-maker) had to choose one out of two cards. Every choice led to an outcome, one for the decision-maker him/herself and one for the recipient, the other (a confederate). For example, the outcome may be a gain of 20 for self and a loss of 20 for the other. So, outcomes varied in terms of valence but not in terms of magnitude. Hence, the amount of gain or loss was always restricted to 20. The experiment involved four conditions: 1) self-gain (+20) and other-gain (+20), 2) self-loss (-20) and other-loss (-20), 3) self-gain (+20) and other-loss (-20), and 4) self-loss (-20) and other-gain (+20). The experiment was constructed in order to elicit brain response to an outcome, involving both self and other, which was presented to the participant (or decision-maker). In addition, the authors were also interested to see whether individuals who were more individualist and competitive in nature viewed outcome distributions in contrast to individuals who were cooperative and valued equality of distribution. Therefore, they categorised these two types of individuals as pro-selfs and pro-socials, respectively. This categorisation was carried out with the help of the Triple-Dominance Scale.
In order to understand the different ERP measures the following few terminologies and definitions will be of use. The Event-Related Potential (ERP) approach is a way of understanding the neural correlates of brain processes. In other words, ERPs are brain waves measured using electrodes. These waves are brain potentials measured in microvolts (µV), along the time axis. Hence, they are temporal in nature. Also, ERPs are generated as a response by the brain to a stimulus. Since ERPs are related to an event, which can also be a stimulus, they are called Event-Related Potentials.
Therefore, it is clear that a given stimulus will generate a certain waveform. Let’s take an example illustrated in figure 1. The graph shows four ERP waveforms. These are waveforms generated in the brain upon presentation of a stimulus. For the purposes of this study, the stimulus is the outcome presentation screen, which includes the outcome for both the self and the other.
Now, let us try to understand the specifics of this graph so that a few terminologies are clear. The x-axis represents time in milliseconds (ms) and the y-axis represents potential in microvolts (µV). Now, it is easy to understand that an ERP waveform is temporal in nature. On the y-axis, it is important to note that negative is plotted upwards and positive is plotted downwards. On the x-axis, zero (0) is the point in time at which the outcome screen is presented. Additionally, this graph concentrates on the total time window. But, the total time window can be further broken up into several smaller time windows.
For the purpose of understanding a cognitive process, in figure 2 (panel A) and figure 3 (panel A), we will focus only on that portion of the waveform which lies within the shaded time window. The waveform within a time window has a certain amplitude. Amplitude of a wave is the difference in potential between the x-axis and the peak of the wave. It can also be said that amplitude is a measure of how prominent a waveform is. All statistical analyses carried out for waveforms in this study is with reference to amplitude.
Every ERP component is measured by a waveform. A waveform can be called negative-going if it peaks in the negative direction, along the y-axis, but not necessarily peaking in the negative section of the graph. Also, the more negative-going a waveform is the stronger or more prominent the corresponding ERP component is. The phrases ‘negative-going’ and ‘negativity’ will be used interchangeably in this post. The same definition holds for positive-going as well.
In order to understand the underlying cognitive processes involved during outcome evaluation the authors analysed three temporally separated processes as identified by three different ERP components. The ERP components that the authors focused on were the Feedback-Related Negativity (FRN), the P3 and the Late Positive Component (LPC).
The first of these components, the FRN, measures losses. In other words, the greater the monetary loss the greater will be the negativity of FRN. The FRN waveforms for both groups have been presented in figure 2 (panel A). Clearly, the FRN waveform is negative-going for the selected time window, indicated by a vertical grey bar.