Inactivation of superior colliculus neurons affects interactive competition during rhesus monkey decision making
Understanding the decision-making process is crucial to any theory of cognition. A popular framework for the mathematical modeling of decision-making is the sequential sampling framework. Support for this framework comes from converging evidence from animal studies showing the implementation of processes similar to evidence accumulation in several brain regions. While there is continued debate about which brain regions play critical roles in the perceptual decision-making process, several recent studies suggest the superior colliculus (SC) is involved. In one such study, rhesus monkeys completed a simple perceptual decision-making task with and without inactivation of neurons in the intermediate layers of the SC via muscimol injection. The monkeys made fewer responses to targets presented in the inactivated receptive field and the correct responses made towards the inactivated field were slower than in the pre-inactivation condition. Previous work found that a Diffusion Decision Model (DDM) allowing the drift rate parameters to vary across the injection conditions was the preferred model for these data, implying that the inactivation of the SC affected the rate of evidence accumulation. Since muscimol is a GABA agonist and there are GABAergic neurons in the SC, it is possible that the muscimol inactivation affected the competitive dynamics instead of simply the drift rate. Subsequently, we build upon the prior work by fitting (in addition to the DDM) two models that instantiate competition, or the lack thereof, differently than the DDM: the race model and Leaky Competing Accumulator (LCA) model. When fitting to the data, we allowed either the drift rate, decision threshold, neural leak, or lateral inhibition to vary across the pre-inactivation and post-inactivation conditions. Regardless of which parameter was manipulated across conditions, the LCA models provided a better fit to more sessions than the DDM or race models. The two winning models were the LCA model where the drift rates decreased in the post-inactivation condition relative to the pre-inactivation condition, and the LCA model where the neural leak increased in the post-inactivation condition relative to the pre-inactivation condition. Our modeling results provide further evidence that the SC is involved in decision-making, and that interactive competition plays a key role in the dynamics of the accumulation process.
Hello Ryan, thank you for your talk. Just a point of clarification, in the original Jun et al. In Press Nature Neuroscience paper, we did not fit LCA models, just Hierarchical DDMs, DDMs, and Urgency Gating Models (UGMs). So your finding that the LCAs fitting this data better is not counter to our findings, just new, important work. Do you kn...
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Basso, M., Jun, E.,