Abstract
A long-standing debate in the political psychology literature considers whether individuals update their beliefs and attitudes in the direction of evidence or grow more confident in their convictions when confronted with counter-attitudinal arguments and information. Though recent studies have shown that instances of the latter tendency, which scholars have termed attitude polarization and "belief backfire, are rarely observed in settings involving hot-button issues or viral misinformation, we know surprisingly little about how participants respond to information directly targeting deeply held attitudes, a key condition for triggering attitude polarization according to theories of motivated reasoning. To address this gap, we develop a tailored experimental design that measures participants' positions regarding their most important issues and randomly assigns them to different mixtures of personalized pro-attitudinal and counter-attitudinal information using the large language model GPT-3. We fail to recover evidence consistent with attitude polarization.