Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments

Abstract

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..

Flexible Sensory-Motor Mapping Rules Manifest in Correlated Variability of Stimulus and Action Codes Across the Brain

“…2f). We focused on two sets of regions implicated in orientation perception (Reynolds et al, 2000; Haynes and Rees, 2005; Kamitani and Tong, 2005) and action planning or execution (Picard and Strick, 2001; Cisek and Kalaska, 2005; Kamitani and Tong, 2005; Murphy et al, 2021), respectively: early visual cortex (V1-V4) and dorsal premotor cortex (PMd). The multi-voxel patterns of evoked fMRI responses in these regions during the trials of the primary decision task reliably encoded the stimulus orientation (for V1-V4) or the participants’ action choice (for PMd, Fig.…”

“…This task, called ‘inferred rule’ in the following, required participants to continuously select the currently active rule by accumulating the noisy evidence (higher-order decision) and apply the selected rule to report their decision for orientation judgment (lower-order decision). Because the generative state for the higher-order decision could undergo unpredictable and hidden state changes, perfect (i.e., lossless) accumulation of all the evidence is suboptimal, and optimal performance requires accumulating the evidence in an adaptive fashion that strikes a balance between stable evidence accumulation and sensitivity to change points (Glaze et al, 2015; Piet et al, 2018; Murphy et al, 2021). We asked the same participants to perform this task, which they did reasonably well (Fig.…”

“…). This can be understood by noting that belief magnitude in our task roughly corresponds to the complement of relative uncertainty (Murphy et al, 2021). h. The magnitude of the sensory evidence in the form of a log-likelihood ratio between rules.…”

“…Following previous work (Glaze et al, 2015;Murphy et al, 2021), we fitted the model separately to each participant's data, by minimizing the cross-entropy between the choices of that participant and the model:…”

“…4e, purple). A third source is suboptimality of the higher-order decision, in the form of noise or biases corrupting the evidence accumulation and/or resulting rule selection (Drugowitsch et al, 2016;Murphy et al, 2021). Based on these considerations, we examined the relationship between individual differences in performance, and the rule-related change of correlation patterns in the sensory-motor network.…”