Implicit attitudes are often defined as residing beyond conscious awareness. This definition has been challenged by robust evidence demonstrating highly accurate predictions of implicit attitudes. However, relevant tests have all been conducted using well-known targets (e.g., racial groups), about which participants possess ample relevant knowledge. Therefore, accurate predictions may have emerged from inferential mechanisms rather than privileged first-person awareness. Here we probe participants' (N = 4,448) ability to report their own experimentally created implicit attitudes across four studies where implicit attitudes and their explicit counterparts (representing an obvious source of inference) were manipulated to shift in opposite directions. Predicted and actual implicit attitudes were either unrelated to each other, or predictive accuracy was limited to participants whose implicit and explicit attitudes were aligned. Echoing classic and contemporary accounts, these data suggest that implicit attitudes are (largely) unconscious, and successful implicit attitude predictions are likely subserved by inference rather than introspection.

Normative accounts of decision-making predict that people attempt to balance the immediate rewards associated with correct responses against the costs of deliberation. However, humans frequently deliberate longer than normative models say they should. We propose that people try to optimize not only their rate of material rewards, but also their rate of information gain. A computational model that implements this idea successfully mimics human decision makers, reproducing key patterns of behavior not predicted by alternative models. Moreover, simulations reveal a normative basis for our model: An agent that exchanges even a small amount of immediate reward for information will improve its decision-making ability through learning, allowing it to earn more reward in the long run than an agent disinterested in information. Maximizing a combination of reward and information rate is a simple yet effective strategy for solving the speed-accuracy tradeoff that may resolve lingering mysteries about human decision-making.