Higher cognitive functions are the product of a dynamic interplay of perceptual, mnemonic, and other cognitive processes. Modeling the interplay of these processes and generating predictions about both behavioral and neural data can be achieved with cognitive architectures. However, such architectures are still used relatively rarely, likely because working with them comes with high entry-level barriers. To lower these barriers, we provide a methodological primer for modeling higher cognitive functions and their constituent cognitive subprocesses with arguably the most developed cognitive architecture today-ACT-R. We showcase a principled method of generating individual response time predictions, and demonstrate how neural data can be used to refine ACT-R models. To illustrate our approach, we develop a fully specified neurocognitive model of a prominent strategy for memory-based decisions-the take-the-best heuristic-modeling decision making as a dynamic interplay of perceptual, motor, and memory processes. This implementation allows us to predict the dynamics of behavior and the temporal and spatial patterns of brain activity. Moreover, we show that comparing the predictions for brain activity to empirical BOLD data allows us to differentiate competing ACT-R implementations of take the best.

In multiple‐cue probabilistic inferences, people infer alternatives' unknown values on decision criteria, using alternatives' attributes as cues. Some inferential strategies, like take‐the‐best, assume that people consider relevant cues sequentially in order of decreasing validity. This assumption has been deemed cognitively implausible by some, who suggest memory retrieval principles to guide cue order. We test whether memory‐based inferences are better described by a model considering cues in order of validity or in order of memory retrieval. In an experiment, we manipulated the frequency with which cues appeared in a learning phase, increasing retrieval fluency of cue values related to the more frequently appearing cue. In a subsequent decision phase, participants made a series of two‐alternative decisions based on the learned cue values. We compared two sequential sampling models, which differed in whether cues are sampled in order of subjective cue validity or in order of retrieval fluency. To model retrieval order of cues in the fluency sampling model, we used the declarative memory theory embedded in the ACT‐R cognitive architecture. Most participants' decisions were best described by the model sampling cues in order of memory retrieval. Only a minority of participants were classified as sampling cues by validity. Our result suggests that retrieval fluency is the primary driver of cue order in inferences from memory, irrespective of the cues' validities. Copyright © 2017 John Wiley & Sons, Ltd.

We studied collaborative skill acquisition in a dynamic setting with the game Co‐op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT‐R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters using both a reinforcement learning procedure and a new to ACT‐R supervised learning procedure. This is the first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuable insights into the multiple cognitive processes that are involved in learning to collaborate.