Three challenges for connecting model to mechanism in decision-making

Abstract

Recent years have seen a growing interest in understanding the neural mechanisms that support decision-making. The advent of new tools for measuring and manipulating neurons, alongside the inclusion of multiple new animal models and sensory systems has led to the generation of many novel datasets. The potential for these new approaches to constrain decision-making models is unprecedented. Here, we argue that to fully leverage these new approaches, three challenges must be met. First, experimenters must design well-controlled behavioral experiments that make it possible to distinguish competing behavioral strategies. Second, analyses of neural responses should think beyond single neurons, taking into account tradeoffs of single-trial versus trial-averaged approaches. Finally, quantitative model comparisons should be used, but must consider common obstacles.

Figure 1. Stochastic fluctuations in stimulus signal intensity can offer insight into behavioral strategy

Top: Schematic single-trial traces showing stimulus intensity (i.e, motion strength or repetition rate) fluctuating over time. Values above zero indicate evidence in favor of one decision category, described as “right” because the subject might report the decision by making an eye or body movement to the right; values below zero indicate evidence in favor of the other decision category (“left”). Left: Examples that ultimately led a (hypothetical) subject to select “left”. Right: Examples that ultimately led a (hypothetical) subject to select “right”. Bottom: Schematic traces reflecting averages over many trials of the kind shown at top. Values close to zero (dashed line) indicate moments in time in which the stimulus had little impact on the eventual choice. Negative values indicate evidence at the corresponding time led to a leftwards choice. Positive values indicate evidence at the corresponding time led to a rightwards choice. Colors indicate two possible behavioral strategies. Red: support for a strategy in which subjects increase the weight assigned to evidence as it arrives over time. Early evidence (left side of red traces) is largely ignored (values are close to 0). Blue: support for an alternate strategy in which subjects decrease the weight assigned to evidence as it arrives over time. Late evidence (right side of blue traces) is largely ignored (values are close to 0). Left: computed from examples leading to a leftwards choice. Right: computed from examples leading to a rightwards choice.