Disentangling weighting, evaluation and attention in

Multi-Attribute Value-Based Choice

BACKGROUND

01
Weighted Sum

Decisions are often captured as a weighted sum over multiple attributes1:

Summed Value = w1*a1 + w2*a2 + … + wn*an

where a is how “good” the attribute is (its evaluation) and w how “important” (its weight).

.

02
Contextual Re-weighting

“Good” decisions require flexibly weighting attributes according to context or goals2.

.

03
Gaps

The neurocomputational processes enabling attribute evaluation and flexible weighting remain poorly understood.

It is also unclear how value and attention interact.

1Belton, Valerie. (1986). A Comparison of the Analytic Hierarchy Process and a Simple Multi-Attribute Value Function. European Journal of Operational Research 26 (1): 7–21.

2Wiecki TV, Sofer I and Frank MJ (2013). HDDM: Hierarchical Bayesian estimation of the Drif-Difusion Model in Python. Front. Neuroinform. 7:14. doi: 10.3389/ fninf.2013.00014

GOALS

01

Develop fMRI and EEG-compatible paradigm for tracking value and attention during multi-attribute choice

.

02

Investigate influence of flexible attribute weighting on attention.

.

03

Investigate influence of attention on attribute valuation and weighting.

Download a copy of the poster presented at the 2017 Society for Neuroeconomics Conference.

Poster