A cornerstone of behavioral modeling and decision-making research includes characterizing the strategies people employ to make decisions. We often study strategies by collecting sequences of choices which hold considerable information about human cognition and behavior. However, it can be difficult to identify patterns and extract strategies, (e.g., `win-stay-lose-shift'), from noisy, raw sequence data. Consequently, many standard analytical approaches aggregate choice data (e.g., over time, across multiple participants, etc.). Unfortunately, this process obscures temporal patterns that may provide insight about strategies employed, strategy switches, or even adaptation of strategies over time. As illustrated by McCormick, Blaha, and Gonzalez (2020b), we can generate novel insights about decision making strategies by characterizing patterns over choice sequences with recurrence quantification analysis.

The capacity coefficient function is a well-established, modelbased measure comparing performance with multiple sources of information together to performance on each of those information sources in isolation. Because it is a function across time, it may contain a large amount of information about a participant. In many applications, this information has been ignored, either by using qualitative assessment of the function or by using a single summary statistic. Recent work has demonstrated the efficacy of functional principal components analysis for extracting important information about the capacity function. We extend this work by applying clustering techniques to examine individual capacity differences in configural learning.

Aggregate statistics, such as percentage of choices, drive manyinsights about sequential behavior in decision making re-search. However, aggregation leaves usable information andpotential insights unexamined. Here, we introduce the useof recurrence plots (RP) and recurrence quantification anal-ysis (RQA) to explore individual choice sequences and de-termine generalized patterns of decision making strategies ina dynamic decision task. We illustrate the insights that RPsand RQAs reveal in a data set collected in a past study in-volving a dynamic, binary choice task (McCormick et al., inpreparation). Patterns of recurrence reveal multiple, distin-guishable, individual choice patterns among participants whowere equally successful in adapting to the dynamic environ-ment. We discuss how RQA of choice behavior can augmentour understanding of decision strategies when paired with tra-ditional aggregate assessments.

Adaptive Control of Thought-Rational (ACT-R) and the Linear Ballistic Accumulator (LBA) were compared in a model mimicry simulation of the Psychomotor Vigilance Task (PVT), a simple, reaction time (RT) task requiring sustained attention. The models use different formalisms to capture the full response profile of the PVT. The parameters were varied systematically to illustrate the ranges of the models’ predictions, to assess the models’ estimation properties, and to determine which parameters in the models correspond with each other. Both models produced skewed RT distributions typical of empirical data, including false starts and lapses. The simulation study demonstrated that both models and their parameters are recoverable. Lastly, isolated parameters in the LBA model captured the effects of varying parameters in the ACT-R model, but the reverse was not always true. These interesting correspondences across different modeling formalisms suggest the possibility of integrating ACT-R and the LBA in future work