|
Decoding and predicting human decisions| old_uid | 11080 |
|---|
| title | Decoding and predicting human decisions |
|---|
| start_date | 2012/03/16 |
|---|
| schedule | 16h-17h |
|---|
| online | no |
|---|
| location_info | RdC, salle 01-02 |
|---|
| summary | Multivariate pattern recognition has recently emerged as a powerful
tool in human cognitive neuroscience. Its power lies in its ability to
go beyond traditional brain activation studies to investigate the
specific task-related "information" encoded in cortical
representations. Here we will present work from our lab that has
applied this approach to visual perception and human decision making.
In one series of experiments we investigated the information flow
through the visual system during varying levels of conscious access.
Using pattern recognition techniques we were able to reveal that the
brain appears to use different networks for perceptual decision-making
depending on the visibility of sensory information: Whereas choices
for highly visible objects were best predicted from high-level sensory
regions, choices under low visibility were best predicted from
supramodal regions of parietal cortex. In another series of
experiments we investigated "free" decisions and were able to predict
subjects' choices for different actions (button presses) several
seconds before the choices entered awareness. This long-term
predictive activity was confirmed by further studies using other
choices (adding / subtracting). Furthermore, we found that free
choices have highly similar cortical representations as choices made
during perceptual decision when subjects believe to be "purely
guessing". In an extension of this research we also investigated
reward encoding and choices where subjects have a clear preference for
one or the other option. For this we studied the evaluation of
consumer products (cars). We were able to predict with
high accuracy which car someone would buy, even when the car was
presented outside the focus of attention. Taken together these results
reveal the power of multivariate pattern recognition for investigating
human choice behavior. |
|---|
| responsibles | San-Galli |
|---|
| |
|