The proposed project seeks to develop a maximally effective computer-delivered brief intervention (CDBI) for reducing heavy alcohol use. To accomplish this, we will use the Multiphase Optimization Strategy (MOST), an efficient method for optimizing intervention content, beginning with factorial designs evaluating main and interaction effects of specific intervention components. Our selection of components will be guided by: (a) Common Factors Theory, which highlights the tremendous contribution of non-specific factors, such as empathy and positive regard, to therapy outcomes, but which is of unknown relevance to CDBIs; and (b) Media Equation Theory, which suggests that people automatically respond to computers in social ways, particularly when those computers replicate human characteristics. To accomplish these goals, we will examine outcomes of computer-delivered brief interventions in which common factors (empathy & positive regard), use of a voice, and use of an animated narrator are systematically manipulated using a factorial design. We will also systematically manipulate the presence vs. absence of motivational content in order to examine possible interactions between common factors and specific motivational techniques. Participants will be 352 undergraduates who are randomly assigned to 1 of 16 intervention conditions. Mean drinks per day over the past 30 days will be measured at 1 and 3-month follow-ups. Secondary analyses will also examine past month heavy drinking days, alcohol-related consequences, and intention to reduce alcohol use. We hypothesize that there will be significant main effects for (1) the factor consistent with Common Factors Theory (empathy and positive regard), (2) the two factors consistent with Media Equation Theory (voice and narrator), and (3) the presence of motivational content. We further hypothesize that mean drinks/day will be lower when (1) the common factors (empathy, positive regard) are combined with a voice and/or narrator or (2) motivational content is combined with common factors and/or a voice/narrator.