Three challenges for connecting model to mechanism in decision-making

- A new influx of behavioral and neural data can constrain decision-making models.
- Decision-making tasks must allow post hoc analyses to uncover the subject's strategy.
- Neural analyses must consider single-trial versus trial-averaged trade-offs.
- Quantitative model comparisons should be used, but must consider common obstacles.

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.