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. (shrink)

Memory should make more available things that are more likely to be needed. Across multiple environmental domains, it has been shown that such a system would match qualitatively the memory effects involving repetition, delay, and spacing (Schooler & Anderson, 2017). To obtain data of sufficient size to study how detailed patterns of past appearance predict probability of being needed again, we examined the patterns with which words appear in large two data sets: tweets from popular sources and comments on popular (...) subreddits. The two data sets show remarkably similar statistics, which are also consistent with earlier, smaller studies of environmental statistics. None of a candidate set of mathematical models of memory do well at predicting the observed patterns in these environments. A new model of human memory based on the environmental model proposed by Anderson and Milson (1989) did better at predicting the environmental data and a wide range of behavioral studies that measure memory availability by probability of recall and speed of retrieval. A critical variable in this model was range, the span of time over which an item occurs, which was discovered in mining the environmental data. These results suggest that theories of memory can be guided by mining of the statistical structure of the environment. (PsycInfo Database Record (c) 2023 APA, all rights reserved). (shrink)

Open‐ended tasks can be decomposed into the three levels of Newell's Cognitive Band: the Unit‐Task level, the Operation level, and the Deliberate‐Act level. We analyzed the video game Co‐op Space Fortress at these levels, reporting both the match of a cognitive model to subject behavior and the use of electroencephalogram (EEG) to track subject cognition. The Unit Task level in this game involves coordinating with a partner to kill a fortress. At this highest level of the Cognitive Band, there is (...) a good match between subject behavior and the model. The EEG signals were also strong enough to track when Unit Tasks succeeded or failed. The intermediate Operation level in this task involves legs of flight to achieve a kill. The EEG signals associated with these operations are much weaker than the signals associated with the Unit Tasks. Still, it was possible to reconstruct subject play with much better than chance success. There were significant differences in the leg behavior of subjects and models. Model behavior did not provide a good basis for interpreting a subject's behavior at this level. At the lowest Deliberate‐Act level, we observed overlapping key actions, which the model did not display. Such overlapping key actions also frustrated efforts to identify EEG signals of motor actions. We conclude that the Unit‐task level is the appropriate level both for understanding open‐ended tasks and for using EEG to track the performance of open‐ended tasks. (shrink)

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools (...) and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. (shrink)