Categorization has a large impact on how people perceive theworld, especially when used to make inferences about uncer-tain features of new objects. While making these inferences,people tend to draw information from only one possible cate-gorization of a new object; in addition, people are sensitive topre-existing correlations between features. Here, we explainthese trends of feature inference using a priming-based cogni-tive process model, and show that our model is distinguishedin that it can explain not only these two main trends, but alsocases where people seem to reverse the first trend and base in-ferences on information from multiple categories.

Retrieval-induced forgetting (RIF) is a paradigm where re-peated study and cue-based retrieval of words impair retrievalof related, but unstudied, words. We present a process model,situated in the ACT-R/E cognitive architecture, that accountsfor the RIF task using the architecture’s overarching theory ofassociative learning. In this theory, studying words strengthenstheir association with their related cues; this, in turn, weakensthe association between those cues and any other words theyare related to. We show this account fits a recent dataset thatexplores cueing in the RIF task (Perfect et al., 2004).

We present a computational cognitive model of mind wandering, an important cognitive phenomenon whose mechanisms are involved in insight, problem-solving, and creativity. The model posits that mind wandering begins when one is not engaged in goal-oriented cognition, whether when between tasks or when in the middle of a task but not actively thinking about one’s goal. At such times, the model thinks about other, highly-activated thoughts in memory. This model sheds light on both how task-oriented and more basic cognitive processes interact, as well as how mind wandering content is generated; both unresolved questions for mind wandering research. We compare our model against data presented by McVay and Kane (2013), who induced mind wandering in a laboratory setting by embedding participants’ personal goals and concerns in a lexical SART task. Overall, our model matched the data’s mind wandering rates very well. We discuss implications and future work on the model.

Associative learning is an important part of human cognition, and is thought to play key role in list learning. We present here an account of associative learning that learns asymmetric itemto- item associations, strengthening or weakening associations over time with repeated exposures. This account, combined with an existing account of activation strengthening and decay, predicts the complicated results of a multi-trial free and serial recall task, including asymmetric contiguity effects that strengthen over time (Klein, Addis, & Kahana, 2005).

We describe two different cognitive process models of a wellknown experiment on social influence (Salganik, Dodds, &Watts, 2006). One model, the social influence model,reproduced the choices that participants took by modelingboth the cognitive processes the participant engaged in andthe social influences that the participant saw. The secondmodel, the pure cognitive model, used only cognitivecapabilities and did not model any social influences that theparticipant saw. Somewhat surprisingly, the two modelsshowed no difference in quality of fit (the pure cognitivemodel actually fit slightly better than the social influencemodel), suggesting that social influence models should takecognitive functions into account in their theories.