OBJECTIVE: Alcohol use disorder (AUD) is associated with passive avoidance learning (PAL) deficits. This study investigated PAL deficits in AUD by using a novel growth model approach to quantify patterns of PAL as changes in false alarms over time, rather than the typical index of total false alarms in a PAL task. METHOD: Subjects, 112 (58 men; 54 women) with an AUD and 110 controls (44 men; 66 women), were administered a monetary incentive Go/No-Go task. Subjects could win $0.25 for a hit (response after a GO) or lose $0.25 for a false alarm. RESULTS: PAL rate was quantified as the slope of initial learning phase (across the first 5 blocks) on the Go/No-Go task. The PAL curves indicated rapid learning in first 5 blocks followed by a later slower learning across blocks 6-9 (consolidation phase). A piecewise growth model with random intercepts indicated that AUD status was significantly associated with a slower initial PAL (i.e. learning phase), with B = -0.69, p < 0.001 for the control group and a PAL slope of 0.13 higher for the AUD group indicating a slower learning rate in the AUD group. This effect was not observed in the consolidation phase. CONCLUSIONS: The results suggest that those with an AUD have greater difficulty learning to avoid negative consequences compared with controls. The results also suggest that measuring PAL rate by focusing on the rate of learning early in the task may be a better index of PAL learning than simply looking at overall false alarm rate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
OBJECTIVE: Alcohol use disorder (AUD) is associated with passive avoidance learning (PAL) deficits. This study investigated PAL deficits in AUD by using a novel growth model approach to quantify patterns of PAL as changes in false alarms over time, rather than the typical index of total false alarms in a PAL task. METHOD: Subjects, 112 (58 men; 54 women) with an AUD and 110 controls (44 men; 66 women), were administered a monetary incentive Go/No-Go task. Subjects could win $0.25 for a hit (response after a GO) or lose $0.25 for a false alarm. RESULTS: PAL rate was quantified as the slope of initial learning phase (across the first 5 blocks) on the Go/No-Go task. The PAL curves indicated rapid learning in first 5 blocks followed by a later slower learning across blocks 6-9 (consolidation phase). A piecewise growth model with random intercepts indicated that AUD status was significantly associated with a slower initial PAL (i.e. learning phase), with B = -0.69, p < 0.001 for the control group and a PAL slope of 0.13 higher for the AUD group indicating a slower learning rate in the AUD group. This effect was not observed in the consolidation phase. CONCLUSIONS: The results suggest that those with an AUD have greater difficulty learning to avoid negative consequences compared with controls. The results also suggest that measuring PAL rate by focusing on the rate of learning early in the task may be a better index of PAL learning than simply looking at overall false alarm rate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Authors: A Zeynep Enkavi; Ian W Eisenberg; Patrick G Bissett; Gina L Mazza; David P MacKinnon; Lisa A Marsch; Russell A Poldrack Journal: Proc Natl Acad Sci U S A Date: 2019-03-06 Impact factor: 11.205
Authors: Ian W Eisenberg; Patrick G Bissett; Jessica R Canning; Jesse Dallery; A Zeynep Enkavi; Susan Whitfield-Gabrieli; Oscar Gonzalez; Alan I Green; Mary Ann Greene; Michaela Kiernan; Sunny Jung Kim; Jamie Li; Michael R Lowe; Gina L Mazza; Stephen A Metcalf; Lisa Onken; Sadev S Parikh; Ellen Peters; Judith J Prochaska; Emily A Scherer; Luke E Stoeckel; Matthew J Valente; Jialing Wu; Haiyi Xie; David P MacKinnon; Lisa A Marsch; Russell A Poldrack Journal: Behav Res Ther Date: 2017-10-05