Regression analysis.

<p>Regression analysis.</p

Goodness of fit for the exponential and the hyperbolic model.

<p>left: Summed BIC-scores as a Goodness of fit, comparing the exponential and the hyperbolic model by condition (light grey = control; dark grey = episodic); Fig 2 right: Individual Pseudo adjusted R2 values as a Goodness of fit, comparing the exponential (x-axis) and the hyperbolic (y-axis) model by condition (light grey = control; dark grey = episodic).</p

Descriptive statistics of discounting variables.

<p>Descriptive statistics of discounting variables.</p

Descriptive statistics on reported alcohol consumption.

<p>Descriptive statistics on reported alcohol consumption.</p

Descriptive statistics of control variables for subsample (n = 32).

<p>Descriptive statistics of control variables for subsample (n = 32).</p

Episodic future thinking reduces temporal discounting in healthy adolescents

<div><p>Episodic Future Thinking has proven efficient in reducing impulsive behavior in several adult populations. Whether it also has a beneficial impact on decision making in adolescents is not known. Here the impact of episodic future thinking on discounting behavior was investigated in a sample of healthy adolescents (n = 44, age range 13–16 years). Discounting behavior in trials including episodic future thinking was significantly less impulsive than in control trials (<i>t</i> = 2.74, <i>p</i> = .009, <i>d</i>_<i>z</i> = .44). In a subsample we controlled for executive function, alcohol use and developmental measures. Neither executive function nor alcohol use but developmental measures explained variability in the effect of episodic future thinking. These findings reveal that episodic future thinking can improve adolescent decision making while the effect is to some degree modulated by developmental measures.</p></div

Goodness of fit for the exponential and the hyperbolic model.

<p>left: Summed BIC-scores as a Goodness of fit, comparing the exponential and the hyperbolic model by condition (light grey = control; dark grey = episodic); Fig 2 right: Individual Pseudo adjusted R2 values as a Goodness of fit, comparing the exponential (x-axis) and the hyperbolic (y-axis) model by condition (light grey = control; dark grey = episodic).</p

Episodic discounting-task.

<p>The Episodic Discounting task.</p

The episodic effect, n = 44.

<p>The episodic effect. Mean discount rate (parameter log(<i>k</i>) values); <i>p</i> = .009 (left); mean area under the curve (AUC) values; <i>p</i> < .001 (middle); n = 44. Averaged ID-points (right); n = 41.</p

IMAGEN_DIS_Supplemental_Material_final – Supplemental material for Extending the Construct Network of Trait Disinhibition to the Neuroimaging Domain: Validation of a Bridging Scale for Use in the European IMAGEN Project

<p>Supplemental material, IMAGEN_DIS_Supplemental_Material_final for Extending the Construct Network of Trait Disinhibition to the Neuroimaging Domain: Validation of a Bridging Scale for Use in the European IMAGEN Project by Sarah J. Brislin, Christopher J. Patrick, Herta Flor, Frauke Nees, Angela Heinrich, Laura E. Drislane, James R. Yancey, Tobias Banaschewski, Arun L. W. Bokde, Uli Bromberg, Christian Büchel, Erin Burke Quinlan, Sylvane Desrivières, Vincent Frouin, Hugh Garavan, Penny Gowland, Andreas Heinz, Bernd Ittermann, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Dimitri Papadopoulos Orfanos, Luise Poustka, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, Patricia Conrod, Argyris Stringaris, Maren Struve, Betteke van Noort, Yvonne Grimmer, Tahmine Fadai, Gunter Schumann, and Jens Foell in Assessment</p