Many theories of complex cognitive-motor skill learning are built on the notion that basic cognitive processes group actions into easy-to-perform sequences. The present work examines predictions derived from laboratory-based studies of motor chunking and motor preparation using data collected from the real-time strategy video game StarCraft 2. We examined 996,163 action sequences in the telemetry data of 3,317 players across seven levels of skill. As predicted, the latency to the first action is delayed relative to the other actions in the (...) group. Other predictions, inspired by the memory drum theory of Henry and Rogers, received only weak support. (shrink)

Using an observational sample of players of a simple online game, we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but (...) it fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well-established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab and for experimental phenomenon to be put in the context of the performance of the whole task. (shrink)

Using an observational sample of players of a simple online game, we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but (...) it fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well-established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab and for experimental phenomenon to be put in the context of the performance of the whole task. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on (...) learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on (...) learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

Cognitive Science, Volume 46, Issue 5, May 2022.

Why games? How could anyone consider action games an experimental paradigm for Cognitive Science? In 1973, as one of three strategies he proposed for advancing Cognitive Science, Allen Newell exhorted us to “accept a single complex task and do all of it.” More specifically, he told us that rather than taking an “experimental psychology as usual approach,” we should “focus on a series of experimental and theoretical studies around a single complex task” so as to demonstrate that our theories of (...) human cognition were powerful enough to explain “a genuine slab of human behavior” with the studies fitting into a detailed theoretical picture. Action games represent the type of experimental paradigm that Newell was advocating and the current state of programming expertise and laboratory equipment, along with the emergence of Big Data and naturally occurring datasets, provide the technologies and data needed to realize his vision. Action games enable us to escape from our field's regrettable focus on novice performance to develop theories that account for the full range of expertise through a twin focus on expertise sampling and longitudinal studies of simple and complex tasks. (shrink)

Conceptual knowledge is dynamic, fluid, and flexible, changing as a function of contextual factors at multiple scales. The Covid-19 pandemic can be considered a large-scale, global context that has fundamentally altered most people's experiences with the world. It has also introduced a new concept, COVID (or COVID-19), into our collective knowledgebase. What are the implications of this introduction for how existing conceptual knowledge is structured? Our collective emotional and social experiences with the world have been profoundly impacted by the Covid-19 (...) pandemic, and experience-based perspectives on concept representation suggest that emotional and social experiences are critical components of conceptual knowledge. Such changes in collective experience should, then, have downstream consequences on knowledge of emotion- and social-related concepts. Using a naturally occurring dataset derived from the social media platform Twitter, we show that semantic spaces for concepts related to our emotional experiences with Covid-19 (i.e., emotional concepts like FEAR)—but not for unrelated concepts (i.e., animals like CAT)—show quantifiable shifts as a function of the emergence of COVID-19 as a concept and its associated emotional and social experiences, shifts which persist 6 months after the onset of the pandemic. The findings support a dynamic view of conceptual knowledge wherein shared experiences affect conceptual structure. (shrink)

Games are more varied and occupy more of daily life than ever before. At the same time, the tools available to study game play and players are more powerful than ever, especially massive data sets from online platforms and computational engines that can accurately evaluate human decisions. This essay offers six suggestions for future cognitive science research on games: Don't forget about chess, Look beyond action games and chess, Use -optimal play to understand human play and players, Investigate social phenomena, (...) Raise the standards for studies of games as treatments, Talk to real experts. (shrink)

While acoustic analysis methods have become a commodity in voice emotion research, experiments that attempt not only to describe but to computationally manipulate expressive cues in emotional voice...