Balancing exploration and exploitation is difficult, and across a wide variety of situations under-exploration ofuncertain alternatives appears prevalent. We propose that one possible cause of under-exploration is present bias, wherebyimmediate rewards (like those gained from exploitation) loom larger than future rewards (like those gained from exploration).This possible cause of under-exploration is not addressed by past studies, in which choices generally yield token rewards thatare converted to money at the end of the experiment, removing the inter-temporal aspect of the decision-making process. Toaddress this issue, we developed an exploratory choice task with immediately-consumed rewards. We then tested whetherwhether imposing a temporal delay before the consumption of rewards increased exploration by decreasing present bias, andreport on our results.

People often make repeated decisions from experience. In such scenarios, persistent biases of choice can develop, most notably the “hot stove effect” (Denrell & March, 2001) in which a prospect that is mistakenly believed to be negative is avoided and thus belief-correcting information is never obtained. In the existing literature, the hot stove effect is generally thought of as developing through interaction with a single, stochastic prospect. Here, we show how a similar bias can develop due to people’s tendency to selectively attend to a subset of features during categorization. We first explore the bias through model simulation, then report on an experiment in which we find evidence of a decisional bias linked to selective attention. Finally, we use these computational models to design novel interventions to “de-bias” decision-makers, some of which may have practical application

What are the major topics of the Cognitive Science Societyconference? How have they changed over the years? To an-swer these questions, we applied an unsupervised learning al-gorithm known as dynamic topic modeling (Blei & Lafferty,2006) to the 2000–2017 Proceedings of the Cognitive Sci-ence Society. Unlike traditional topic models, a dynamic topicmodel is sensitive to the temporal context of documents andcan characterize the evolution of each topic across years. Us-ing this model, we identify historical trends in the popularity oftopics over time, and shifts in word use within topics indicativeof changing focuses within the field. We also measure the cor-relation across topics, and use the model to highlight the topicstructure of particular papers and labs. We believe dynamictopic models present an important tool towards understandingCognitive Science as it continues to grow and evolve over time