Evaluating a range of learning schedules: hybrid training schedules may be as good as or better than distributed practice for some tasks.

We investigated theoretically and empirically a range of training schedules on tasks with three knowledge types: declarative, procedural, and perceptual-motor. We predicted performance for 6435 potential eight-block training schedules with ACT-R's declarative memory equations. Hybrid training schedules (schedules consisting of distributed and massed practice) were predicted to produce better performance than purely distributed or massed training schedules. The results of an empirical study (N = 40) testing four exemplar schedules indicated a more complex picture. There were no statistical differences among the groups in the declarative and procedural tasks. We also found that participants in the hybrid practice groups produced reliably better performance than ones in the distributed practice group for the perceptual-motor task--the results indicate training schedules with some spacing and some intensiveness may lead to better performance, particularly for perceptual-motor tasks, and that tasks with mixed types of knowledge might be better taught with a hybrid schedule. PRACTITIONER SUMMARY: We explored distributed and massed training schedules as well as hybrids between them with respect to three knowledge types based on theories and an empirical study. The results suggest that industrial and operator training in complex tasks need not and probably should not be done on a distributed training schedule.

Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment.

Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm.

A functional model of sensemaking in a neurocognitive architecture.

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

Modeling surgical skill learning with cognitive simulation.

We used a cognitive architecture (ACT-R) to explore the procedural learning of surgical tasks and then to understand the process of perceptual motor learning and skill decay in surgical skill performance. The ACT-R cognitive model simulates declarative memory processes during motor learning. In this ongoing study, four surgical tasks (bimanual carrying, peg transfer, needle passing, and suture tying) were performed using the da Vinci© surgical system. Preliminary results revealed that an ACT-R model produced similar learning effects. Cognitive simulation can be used to demonstrate and optimize the perceptual motor learning and skill decay in surgical skill training.