Supplementary Figure 2Properties of subgraphs. (a) Subgraphs differ in the extent of within-versus betweensystem edge strength. For each subgraph, the strength of within-system edges (edges linking two nodes that both belong to the same system; Fig. 3b) was averaged, and the strength of between-system edges (edges linking one node from one system to another node from another system; Fig. 3b) was averaged. The ten subgraphs are ordered according to the relative strength of within-versus between-system edges. To form a normalized relative strength, we subtracted the average strength of between-system edges from the average strength of within-system edges and then divided this difference by their sum. A high relative strength means that a subgraph has stronger within-system edges than between-system edges (e.g., subgraph 1). The 95% confidence interval of each subgraph was estimated by boostrapping 10,000 times on the edges of that subgraph. (b) Subgraphs differ in the extent of within-system strength. For each subgraph, the strength of within-system edges was averaged. For demonstration, the ten subgraphs are ordered according to within-system strength. The 95% confidence interval of each subgraph was a b c 1 2 3 4 5 6 7 8 9 10
When making choices, collecting more information is beneficial but comes at the cost of sacrificing time that could be allocated to making other potentially rewarding decisions. To investigate how the brain balances these costs and benefits, we conducted a series of novel experiments in humans and simulated various computational models. Under six levels of time pressure, subjects made decisions either by integrating sensory information over time or by dynamically combining sensory and reward information over time. We found that during sensory integration, time pressure reduced performance as the deadline approached, and choice was more strongly influenced by the most recent sensory evidence. By fitting performance and reaction time with various models we found that our experimental results are more compatible with leaky integration of sensory information with an urgency signal or a decision process based on stochastic transitions between discrete states modulated by an urgency signal. When combining sensory and reward information, subjects spent less time on integration than optimally prescribed when reward decreased slowly over time, and the most recent evidence did not have the maximal influence on choice. The suboptimal pattern of reaction time was partially mitigated in an equivalent control experiment in which sensory integration over time was not required, indicating that the suboptimal response time was influenced by the perception of imperfect sensory integration. Meanwhile, during combination of sensory and reward information, performance did not drop as the deadline approached, and response time was not different between correct and incorrect trials. These results indicate a decision process different from what is involved in the integration of sensory information over time. Together, our results not only reveal limitations in sensory integration over time but also illustrate how these limitations influence dynamic combination of sensory and reward information.
14Learning effectively from errors requires using them in a context-dependent manner, for example 15 adjusting to errors that result from unpredicted environmental changes but ignoring errors that 16 result from environmental stochasticity. Where and how the brain represents errors in a context-17 dependent manner and uses them to guide behavior are not well understood. We imaged the 18 brains of human participants performing a predictive-inference task with two conditions that had 19 different sources of errors. Their performance was sensitive to this difference, including more 20 choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. 21Using multi-voxel pattern classification, we identified context-dependent representations of error 22 magnitude and past errors in posterior parietal cortex. These representations were distinct from 23 representations of the resulting context-dependent behavioral adjustments in dorsomedial frontal, 24 anterior cingulate, and orbitofrontal cortex. The results provide new insights into human brain 25 that represent and use errors in a context-dependent manner to support adaptive behavior. 26 27 flexible learning in uncertain and dynamic environments: responding to the same exact errors 58 differently in different contexts. 59To identify such context-dependent neural responses to errors, we adapted a paradigm 60 from our previous single-unit recording study (Li et al., 2019). In this paradigm, we generated 61 two different dynamic environments by varying the amount of noise and the frequency that 62 change-points occur (i.e., hazard rate; Behrens et al.unstable environment, noise was 64 absent and the hazard rate was high, and thus errors unambiguously signaled a change in state. In 65 the high-noise environment, noise was high and the hazard rate was low, and thus small errors 66 were ambiguous and could indicate either a change in state or noise. Thus, effective learning 67 requires treating errors in the two conditions differently, including adjusting immediately to 68 errors in the unstable environment but using the size of errors and recent error history as cues to 69 aid interpretation of ambiguous errors in the high-noise condition. 70In our previous study, we found many single neurons in the anterior cingulate cortex 71 (ACC) or posterior cingulate cortex (PCC) that responded to errors or the current context, but we 72 found little evidence that single neurons in these regions combined this information in a context-73 dependent manner to discriminate the source of errors or drive behavior. In the current study, we 74 used whole-brain fMRI and multi-voxel pattern classification to identify context-dependent 75 neural responses to errors and activity predictive of context-dependent behavioral updating in the 76 human brain. The results show context-dependent encoding of error magnitude and past errors in 77 PPC and encoding of behavioral shifts in a large array of frontal regions including ACC, DMFC, 78 DLPFC and orbitofrontal cortex ...
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