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Background: Recent work indicates that increasing the drinking rate of a virtual bar-goer (VB) increases the rate of drinking for participants in a virtual reality (VR) bar environment. Here, we test the hypothesis that biopsychosocial factors including typical drinking pattern and expectancy that alcohol enhances social interactions would moderate this effect., Methods: We assessed the drinking topography (DT) of participants (N=20) in a VR environment with a programmable VB during two testing sessions: one with a fast-drinking VB (30-60s sip interval) and one in which the VB drank slowly (60-120s sip interval). In this secondary analysis, linear mixed models were used to characterize potential interactions of typical daily alcohol intake (quantity-frequency index [QFI]), maximal alcohol consumed in one bout over the past six months (maxQ), Alcohol Use Disorder Identification Test (AUDIT) score, and expectancy that alcohol enhances social and physical pleasures (SPP) with time in simulation and condition on sip interval and volume., Results: Individuals with higher MaxQ showed a reduced effect of time on sip volume such that more intense recent binge episodes were associated with consistent drinking. Greater AUDIT scores were associated with lower sip intervals. In addition, greater SPP expectancy was associated with higher sip volumes, but only in the fast-drinking VB condition., Conclusions: Greater drinking behavior and social expectancies were associated with more rapid drinking topography. In addition, findings suggest challenging alcohol outcome expectancies related to social enhancement could reduce alcohol-related risks by slowing the rate of alcohol intake in social situations., Competing Interests: Declaration of Competing Interest This study was supported by the University of Florida Center for Pain Research and Behavioral Health. The University of Florida holds a copyright on INTACT VR. Authors may receive a portion of proceeds if a license is purchased from the university in the future. The software is available to non-commercial entities under a GNU GPL-2.0 open-source license. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors., (Copyright © 2023 Elsevier B.V. All rights reserved.)