Neuroeconomists investigate how the human brain analyzes and makes decisions about financial situations. They use functional magnetic resonance imaging (fMRI) of subjects who participate in economic games. Here we present three such experiments.
In the first experiment, we investigate how the brain recombines expected reward (ER) and risk. Recent fMRI results show that the brain decomposes a gamble in terms of these two metrics. However, economic theory predicts that the brain must recombine them in order to obtain an effective evaluation of the gamble. It was not clear what biological mechanism directs such recombination. Here we show that the brain uses the correlation of noise to recombine signals. We implement a new technique based on canonical correlation analysis and we show that ER is added to risk to form a metric that activates the medial prefrontal cortex.
In the second experiment, we investigate how the brain encodes two gambles instead of one. The brain is likely to encode the utility of each gamble in a common area but in separate groups of neurons. However, it is unknown how the brain indexes the gambles. Indeed, which group of neuron encodes which gamble can be decided in many ways. We hypothesized that the brain would use either the physical position of the gambles or an idiosyncratic parameter, such as ER or risk. Here we introduce a new analysis technique based on Hotelling T-squared statistics and we show that the brain uses risk as an index.
In the third experiment, we investigate a much more complex situation: a stock market. Contrary to what standard finance theory predicts, we hypothesize that the brain does not use mathematical models but instead heuristically uses a social cognition approach. Specifically, we posit that humans understand stock markets by using Theory of Mind (ToM), the ability to attribute to others mental states different from one's own. Here we show that humans engage brain structures related to ToM (paracingulate cortex, anterior cingulate cortex, insula, and amygdala). Subsequent behavioral tests show that ToM, rather than mathematical, abilities are better predictors of success in forecasting stock markets.
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