My main passion is all about doing research in the fields of Neuroeconomics and Decision Making. This is the reason why I founded NeuroLabX back in 2020. My primary goal is to gather the most interesting insights from research and present them to you as my audience. Neuroeconomics and Behavioral Economics manages to bridge the gap between formal modeling and human behavior.

Cheng and Hsu (2018) investigate the neural response from different genders brains in specific consumer behavior research by using the fMRI method

» TRUE PASSION: NEUROECONOMICS

» THE POWER OF NEUROECONOMIC RESEARCH «

“Don’t Ask the People – Ask the Brain”

Many people believe that we are fully aware of our actions and believes, yet the most of us cannot explain sufficiently their behavior. Especially when it comes to make a decision most of us tend to be biased.

Recent research has indeed shown, that such biases can even reverse our preferences There are many different biases we can fall into. You can read more about cognitive biases and other behavioral phenomena here.

Chib et al. (2009) show that a “common currency” is formed in the same brain area, namely vmPFC. The image depicts which parts of the brain are being used to encode value for money, trinkets, appetitive and aversive food.

Behavioral Finance: Why do so many of us struggle to save money, even when we know we should?

Helping People Investing into their Future

From a neuroeconomic perspective, imaging studies reveal that self-control in saving decisions is correlated with activity in the dorsolateral prefrontal cortex (dlPFC), the same region involved in regulating impulses and enabling future-oriented thinking.

While classical economic models assume rational planning and intertemporal utility maximization, behavioral research displays a different picture:

People tend to underestimate future value. Several cognitive and behavioral biases might play a role, e.g.:

  • Present Bias: As shown in studies by Laibson and others, we overweight immediate gratification and undervalue future rewards, which can lead to procrastination in saving.
  • Mental Accounting: People tend to separate money into mental buckets, affecting how much they save or spend.
  • Reference Dependence: Our saving behavior depends on perceived changes in income, not just the absolute level. A loss relative to our expected income reduces saving more than a gain increases it.

» Analyzing Consumer Experience with Behavioral Data Analytics «

With NeuroVision & AI support, visual aspects can be analyzed:

What captures customers’ interest? Is their gaze driven by impulse? Where are “decoy” items placed?

And we care about all these things, because…??

Research by Armel et al. (2010) suggests that there is a positive correlation between the duration of viewing a product and the creation of positive value associated with it!

« EXPERIMENTAL Consumer Research

However, analyses like the one above do not establish a causal relationship. For many entrepreneurs, the more important question is:

«Which factors do really influence purchasing behavior?»

By leveraging econometric methods and (field) experiments, such phenomena can be identified.

Artificial Intelligence & Human Behavior

Ferrante et al. (2019) introduce Explainable Artificial Intelligence (XAI) that manages to provide a mechanistic understanding of the relationships between inputs and outputs.

By observing the brains neural activity we are finally able to understand what is going on – deep inside our minds. We have learned which brain region creates a chocolate’s value, and we know that a brain lesion can impair memories that are an important factor when it comes to evaluating choices.

INCREASING DURAION OF VISUAL STIMULUS INCREASES VALUE
Armel et al. (2010) shows the effects of a visual stimulation on our choice. A longer visual exposure to some certain product results into a higher decision bias towards the product that has been viewed for a longer amount of time.

TO SHED SOME LIGHT ON MY CURRENT RESEARCH INTERESTS:

Neuroeconomics, Decision Making, Behavioral Economics

Microeconomic Modeling, Asymmetric Information, Game Theory

I AM ALSO INTERESTED IN:

Neurobiology

SOFTWARE & PROGRAMMING LANGUAGES I AM CURRENTLY WORKING WITH

Python, R Studio, Stata, PHP, MySQL, C++, MATLAB, KNIME, ChatGPT API

REFERENCES

(1) Hsu, M.Y.-T. and Cheng, J.M.-S. (2018), “fMRI neuromarketing and consumer learning theory: Word-of-mouth effectiveness after product harm crisis”, European Journal of Marketing, Vol. 52 No. 1/2, pp. 199-223. https://doi.org/10.1108/EJM-12-2016-0866

(2) Chib, V. S., Rangel, A., Shimojo, S. & O’Doherty, J. P. Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J. Neurosci. 29, 12315–12320 (2009).

(3) Fellous JM, Sapiro G, Rossi A, Mayberg H, Ferrante M. Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation. Front Neurosci. 2019 Dec 13;13:1346. doi: 10.3389/fnins.2019.01346. PMID: 31920509; PMCID: PMC6923732.

(4) Krajbich, Ian & Armel, Carrie & Rangel, Antonio. (2010). Visual fixations and comparison of value in simple choice. Nature neuroscience. 13. 1292-8. 10.1038/nn.2635.