Many decisions under uncertainty entail the temporal accumulation of evidence that informs about the state of the environment. When environments are subject to hidden changes in their state, maximizing accuracy and reward requires non-linear accumulation of the evidence. How this adaptive, non-linear computation is realized in the brain is unknown. We analyzed human behavior and cortical population activity (measured with magnetoencephalography) recorded during visual evidence accumulation in a changing environment. Behavior and decisionrelated activity in cortical regions involved in action planning exhibited hallmarks of adaptive evidence accumulation, which could also be implemented by a recurrent cortical microcircuit. Decision dynamics in action-encoding parietal and frontal regions were mirrored in a frequency-specific modulation of the state of visual cortex that depended on pupil-linked arousal and the expected probability of change. These findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related feedback to sensory cortex..

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Decision-making under uncertainty commonly entails the accumulation of decision-relevant 'evidence' over time. In natural environments, this accumulation process is complicated by the existence of hidden changes in the state of the environment. Optimal behavior in such contexts requires a rapid, non-linear tuning of the evidence accumulation. It is unknown if and how this adaptive computation is realized in neural circuits. To illuminate this issue we combined a visuo-motor choice task entailing hidden changes in the evidence source with computational model-based assessment of human behavior and cortical population dynamics, measured via magnetoencephalography. We identified diagnostic signatures of the normative evidence accumulation process in behavior as well as the selective population dynamics in sensory, parietal, and (pre)motor cortex. These signatures could be explained by an interplay of recurrent cortical circuit dynamics and phasic, pupil-linked arousal signals. Our findings link normative computations for adaptive decision-making to the organization and function of neural circuits.

Perceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization , in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making. 5/23/2020 Copy of Theoretical paper_post_thesis_biorxiv_nolinks -Google Docs https://docs.google.com/document/d/1GzJnoKh80LGPHNONiRWI8FqU4SV5qtRlgl-Cr8hPWBs/edit# 2/47stimulus evidence linearly until one of the bounds is reached 1 . The DDMA and its different variations have been successfully used to fit psychometric and chronometric curves 2 , to capture the speed accuracy trade off 1,3,4 , to account for history dependent choice biases 5 , changes of mind 6 , confidence reports 7 or the Weber's law 8 . Although the absorbing bounds were originally thought of as a mechanism to terminate the integration process, the DDMA has also been applied to fixed duration tasks 9,10 . In motion discrimination tasks, for instance, it can reproduce the subjects' tendency to give more weight to early rather than late stimulus information, which is called a primacy effect 9,11-15 . However, depending on the details of the task and the stimulus, subjects can also give more weight to late rather than to early evidence (i.e. a recency effect) 16,17 or weigh the whole stimulus uniformly 18 . In order to account for these differences, the DDMA needs to be modified by using reflecting instead of absorbing bounds or by removing the bounds altogether 19 . Despite their considerable success in fitting experimental data, the DDMA and its many variants remain purely phenomenological descriptions of sensory integration. This makes it difficult to link the DDMA to the actual neural circuit mechanisms underlying perceptual decision making.These neural circuit mechanisms have been studied with biophysical attractor network models that ca...