How do we turn down rewards today, for larger rewards in the future?

I use neuroscientific and behavioral methods to investigate the cognitive processes crucial to increasing human patience.

Humans behave as if future rewards are worth less than present ones, known as temporal discounting. Valuation and cognitive control systems have been identified as crucial mechanisms in reducing temporal discounting. However, two crucial components have largely been ignored:

The role of episodic future thought in temporal discounting. Is thinking about the future special?

To care about the future, perhaps we need to be able to imagine it. The ability to prospect about the future, or episodic future thought, may enable us to prioritize our future, reducing temporal discounting. However, such theories have failed to appropriately account for general processes involved during episodic future thought, common to all episodic processes, that may have an independent role in modulating decision-making.

Is temporal discounting inherently social? Does a lack of trust impair our ability to wait for the future?

Decision-making does not occur in a social vaccum. I am interested in highlighting the role that social processes have in modulating temporal discounting. For example, if you do not trust that you will receive the future reward as promised, you may show increased temporal discounting despite having strong cognitive control.

Unified model of individual differences

It is not clear what the crucial components underlying major differences between individual in temporal discounting are. I am interested in using advanced analytical methods and large-scale samples to better answer this question and estimate to what extent these differences are stable and explainable by a hyperboilc model.





Using informatics & open science to understand the mind and brain

I use large-scale informatics & machine learning algorithms to inform neurocognitive theories

Large-scale decision-making studies

I use Amazon's mechanical turk to deploy large scale decision-making studies in order to better quantify the contribution of differnt cognitive components during intetemporal choice

Machine learning in neurosynth

In collaboration with Tal Yarkoni, I am implementing machine learning algorithms in the neurosynth framework in order to do spatial classification of the brain using features (i.e. words from papers). This project should give new insights into what cognitive theories differentiate regions and networks. In particular, I am interesting in using such data-driven analytics to give further insight into regions crucial for decision-making such as vmPFC. Check out the neurosynth project on github for more information.