[Abstract Title]. - Society for Neuroscience

Poster
289. Human Decision Making
Time: Sunday, November 16, 2008, 1:00 pm - 5:00 pm
Program#/Poster#: 289.3/RR39
Topic: F.01.g. Decision making and reasoning
Support: P50 MH062196
P30 EY001583
McKnight Foundation
R01 EY015260
Sloan Foundation
Title: Computational, behavioral and physiological correlates of the value of uncertain
information in a dynamic parameter estimation task
Authors: *M. R. NASSAR, B. HEASLY, J. I. GOLD;
Neurosci., Univ. Pennsylvania, Philadelphia, PA
Abstract: The deliberative process of decision-making often requires updating beliefs that must
take into account the uncertainty associated with possible interpretations or outcomes. Such
belief updating is particularly challenging when the uncertainty itself takes various forms.
Among the many forms of uncertainty are noise, which reflects the variance of a parameter, and
volatility, which represents the instability of the parameter over time. Both forms of uncertainty
are apparent in new evidence that deviates from a prior belief, but they prescribe opposite
courses of action. Noise implies that new evidence should be de-emphasized, whereas volatility
implies that new evidence should weigh heavily into the updated belief. We studied belief
updating in human subjects using a dynamic parameter estimation task that includes both noise
and volatility.
The task required subjects to estimate the expected value of a set of sequentially presented
numbers. The numbers were generated at random from a normal distribution. Within blocks of
sequential trials, the mean and variance (noise) remained fixed. However, at random intervals the
mean changed (volatility). We used a Bayesian model to determine the ability of an optimal
agent to distinguish volatility from noise and generate appropriate estimates. The model, like the
subjects, placed more value on information acquired immediately after a volatile change, and this
effect was greater when the variance of the distribution was small. Subjects appeared to use the
size of their errors as a heuristic to achieve this near-optimal behavior.
A volatility signal has been reported previously in the anterior cingulate cortex (ACC) of
subjects performing a two-choice reward probability tracking task (Behrens et al., 2007). The