Abstract
Impairments in decision-making have been reported in brain-damaged (stroke/traumatic brain injury) patients with a wide range of lesion sites. Here, we propose that the performances of patients in complex sequential decision-making (DM) tasks can be explained by their negative affectivity, leading to deliberative processing associated with poor DM performances. We assumed that a slow-paced breathing (SPB) training, by reducing negative affectivity would improve performances in a complex DM task. For 24 days, 34 brain-damaged patients (16 males and 18 females; 12 had a hemorrhagic stroke, 17 with an ischemic stroke and 5 with a TBI), practiced either daily SPB or sham trainings for five min, three times a day. Before and after training, we assessed their vagal tone (electrocardiogram—ECG), affectivity (Positive and Negative Affect Schedule—PANAS) and certainty level (Dimensional Ratings Questionnaire—DRQ) and their performance on the Iowa Gambling Task. All participants showed initial weak performance, which improved only for patients in the SPB training condition. These results suggest that DM disorders in brain-damaged patients can be the consequence of their poor information processing strategy rather than an impairment in their DM abilities. Second, we showed that SPB could be efficient to normalize DM processes in brain injury patients.
Acknowledgments
We thank Thierry Bollon and Mélody Maillez for their help with the decision-making processes related to certainty. This work was supported by grants from NeuroCoG IDEX UGA under the “Investing for the future program” [ANR-15-IDEX-02] and from the FONDATION Université Savoie Mont Blanc for the “Heart-brain project”.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
In accordance with good research practices, an OSF project has been created under private embargo until publication (https://osf.io/jp6yx/?view_only=75213826c8a1458ea4f9e8e04af78836). It includes all our data and scripts to perform data analysis.