TdcsFrameBetween

Excitatory stimulation of the ventromedial prefrontal cortex reduces cognitive gambling biases via improved feedback learning in a gambling task

Abstract Humans are subject to a variety of cognitive biases, such as the framing-effect or the gambler’s fallacy, that lead to decisions unfitting of a purely rational agent. Previous studies have shown that the ventromedial prefrontal cortex (vmPFC) plays a key role in making rational decisions and that stronger vmPFC activity is associated with attenuated cognitive biases. Accordingly, dysfunctions of the vmPFC are associated with impulsive decisions and pathological gambling. By applying a gambling paradigm in a between-subjects design with 33 healthy adults, we demonstrate that vmPFC excitation via transcranial direct current stimulation (tDCS) reduces the framing-effect and the gambler’s fallacy compared to sham stimulation. Corresponding magnetoencephalographic data suggest improved inhibition of maladaptive options after excitatory vmPFC-tDCS. Our analyses indicate that the underlying mechanism is improved reinforcement learning, as effects only emerge over time. These findings encourage further investigations of whether excitatory vmPFC-tDCS has clinical utility in treating pathological gambling or other behavioral addictions.

Data

Behavioral and neural data (MEG - MN5) of a study comparing excitatory and sham stimulation and their influence on behavioral and neural correlates of gambling. The neural data can be read with the free emegs software (Peyk, De Cesarei & Junghöfer, 2011). The data is in the so-called source space, which means that the underlying sources of the MEG signal were estimated. For this purpose the L2-Minimum-Norm estimation was used (Hämäläinen and Ilmoniemi, 1994).

We provided three datasets: The choice data set contains the independent variables frame (gain frame = ...10.100.MN5, ...11.100.MN5, loss frame = ...12.100.MN5, ...13.100.MN5), stimulation (sham = ...100.MN5, excitatory = ...200.MN5) and decision (keep = ...10.100.MN5, ...12.100.MN5, gamble = ...11.100.MN5, ...13.100.MN5).

The risk data set contains the independent variables risk (20% = ...20.100.MN5, 40% = ...40.100.MN5, 60% = ...60.100.MN5, 80% = ...80.100.MN5) and stimulation (sham = ...100.MN5, excitatory = ...200.MN5).

The feedback data set contains the independent variables outcome (gain (keep) = ...55.100.MN5, gain (gamble) = ...52.100.MN5, loss (keep) = ...56.100.MN5, loss (gamble) = ...51.100.MN5), decision (keep = ...55.100.MN5, ...56.100.MN5, gamble = ...51.100.MN5, ...52.100.MN5) and stimulation (sham = ...100.MN5, excitatory = ...200.MN5).

The data can be organized with the provided Matlab Scripts: Find...Files.

The behavioral data can be analyzed with the provided R-script. Choice behavior depends here on the risk-of-losing, the frame and the stimulation. Furthermore trial number has an important influence on decision-making as it indicates learning. The R-script will recode all the independent variables into a meaningful form. Regarding the Choice: 1 = gamble, 0 = keep.