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An Algorithm for Clustering Decision-Making Phenotypes from Behavioural Data
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Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.
Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting
patterns in data. Such “exemplars” can be found by randomly choosing an initial subset of data points and then iteratively
refining it, but this works well only if that initial choice is close to a good solution. We devised a method called “affinity
propagation,” which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged
between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation
to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify
cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other
methods, and it did so in less than one-hundredth the amount of time.