New Research from NYU, Princeton and UCL Researchers Published in PLoS Computational Biology

February 16, 2017

The decisions we make are only as good as the information we base those decisions off of, but a lot of that information comes to us via our senses as incomplete and uncertain. Understanding how humans interpret their world requires a new approach to understanding the processes behind human decision-making. But how do people evaluate uncertain information they receive and act upon it?

In a new paper published in PLoS Computational Biology, NYU professors Elyse Norton and Michael Landy, along with Stephen Fleming (University College London) and Nathaniel Daw (Princeton), upend 60 years of signal detection theory.

While it was previously understood that people address uncertainty using a static set of criterion to evaluate that data, the researchers’ hypothesized that such static criterion cannot keep up with the rapidly changing and dynamic nature of the real world, and set out to understand how people balance static criterion with dynamically-created criterion for addressing uncertain information or situations.

Professor Norton was inspired to look into this research because she noticed that results can be uncertain in a dynamic environment which is similar to the ever-changing environment we live in.  Past data is irrelevant in this environment and so dynamic criterion can actually be updated by understanding the learning process over time.

Another author of the paper, IISDM and Center for Neural Science Professor, Michael Landy, explains the results:

“Independent of whether the environment is static or dynamic, human observers dynamically adjust sensory decision criteria, although they do so suboptimally.”