Noradrenaline blockade specifically enhances metacognitive performance

Hauser, Tobias U.; Allen, Micah; Purg, Nina; Moutoussis, Michael; Rees, Geraint; Dolan, Raymond J.

doi:10.7554/elife.24901

Cited by 78 publications

(90 citation statements)

References 67 publications

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“…Rodent studies showed that one-shot learning can occur with noradrenergic input to the hippocampus (Wagatsuma et al, 2018). More generally, a role for noradreline in confidence-weighted learning is consistent with the fact that increased pupil size co-occurs with increased updating (Nassar et al, 2012) and reset of current strategies (Devauges and Sara, 1990), and that more volatile learning contexts are associated with larger pupil size (Pulcu and Browning, showing that manipulation of noradrenergic activity impacts the estimation of confidence in a decision task (Hauser et al, 2017), learning dynamics when unexpected changes occur (Marshall et al, 2016) and the effect of surprise on update (Jepma et al, 2016(Jepma et al, , 2018.…”

Section: Discussion (1500)mentioning

confidence: 70%

Brain dynamics for confidence-weighted learning

Meyniel

2019

Preprint

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Learning in a changing and uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions.Bayesian models use this confidence to regulate learning: for a given surprise, the update is smaller when confidence is higher. We explored the human brain dynamics sub-tending such a confidenceweighting using magneto-encephalography. During our volatile probability learning task, subjects' confidence reports conformed with Bayesian inference. Several stimulus-evoked brain responses reflected surprise, and some of them were indeed further modulated by confidence. Confidence about predictions also modulated pupil-linked arousal and beta-range (15-30 Hz) oscillations, which in turn modulated specific stimulus-evoked surprise responses. Our results suggest thus that confidence about predictions modulates intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations. Meyniel et al., 2015b), the weight of evidence (Rohe et al., 2019), the precision of predictions (Iglesias et al., 2013;Mathys et al., 2014;Vossel et al., 2014) discussed at the end of this article.Here, we propose to use optimal Bayesian models as a benchmark to formalize, at a computational level, the learning process. In particular, we formalize the notion of discrepancy between

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Section: Discussion (1500)mentioning

confidence: 70%

Brain dynamics for confidence-weighted learning

Meyniel

2019

Preprint

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“…Previous research has demonstrated that metacognitive efficiency is affected by fatigue (Maniscalco, McCurdy, Odegaard, & Lau, 2017), working memory demands (Maniscalco & Lau, 2015), and can be enhanced pharmacologically via noradrenaline blockade (Hauser et al, 2017). All of these previous findings rely on taxing subjects' "resources" for metacognition.…”

Section: Discussionmentioning

confidence: 91%

Sensory noise increases metacognitive efficiency

Bang

Shekhar

Rahnev

2017

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Visual metacognition is the ability to employ confidence ratings in order to predict the accuracy of one's decisions about visual stimuli. Despite years of research, it is still unclear how visual metacognitive efficiency can be manipulated. Here we show that a hierarchical model of confidence generation makes a counterintuitive prediction: Higher sensory noise should increase metacognitive efficiency. The reason is that sensory noise has a large negative influence on the decision (where it is the only corrupting influence) but a smaller negative influence on confidence (where it is one of two corrupting influences; the other one being metacognitive noise). To test this prediction, we used a perceptual learning paradigm to decrease the amount of sensory noise. In Experiment 1, seven days of training led to significant decrease in noise as well as a corresponding decrease in metacognitive efficiency. Experiment 2 showed the same effect in a brief 97---trial learning for each of two different tasks. Finally, in Experiment 3, we experimentally manipulated stimulus contrast to increase sensory noise and observed a corresponding increase in metacognitive efficiency. Our findings demonstrate the existence of a robust positive relationship between sensory noise and metacognitive efficiency. These results could not be captured by a standard model in which decision and confidence judgments are made based on the same underlying information. Thus, our study provides a novel way to directly manipulate metacognitive efficiency and suggests the existence of metacognitive noise that corrupts confidence but not the perceptual decision.

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“…The hierarchical model proposes that metacognition and primary decision-making are two distinct cognitive processes: the primary decision-making is implemented at a first order, while a metacognitive process access both sensory evidence for the decision-making and other sources of information to form a judgement (Fleming & Daw, 2017). Many studies have reported such dissociation between metacognition and decision-making, with the PFC playing a central role in the hierarchical monitoring (Allen et al, 2017;Bang et al, 2019;Fleming et al, 2014;Hauser et al, 2017b;Qiu et al, 2018;Rounis et al, 2010;Shekhar and Rahnev, 2018).…”

Section: Discussionmentioning

confidence: 99%

Diffusion property and functional connectivity of superior longitudinal fasciculus underpin human metacognition

Zheng

Wang

et al. 2020

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Metacognition as the capacity of monitoring one's own cognition operates across domains.Here, we addressed whether metacognition in different cognitive domains rely on common or distinct neural substrates with diffusion tensor imaging (DTI) and inter-areal functional connectivity techniques. After acquiring DTI and resting-state fMRI data, we asked participants to perform a temporal-order memory judgement task and a perceptual discrimination task, followed by trial-specific confidence judgments. DTI analysis revealed that the structural integrity (fractional anisotropy) in the anterior portion of right superior longitudinal fasciculus (SLF) was associated with both perceptual and mnemonic metacognitive abilities. When the mnemonic metacognitive ability was disrupted by TMS, the mnemonic metacognition scores did not correlate with SLF structural integrity anymore, revealing the causal relevance of this tract in memory metacognition. Furthermore, taking the dorsolateral PFC and IPL (both of which are connected by SLF) as seeds, we found perceptual and mnemonic metacognitive abilities to be associated with functional connectivity between DLPFC and VLPFC, whereas mnemonic metacognitive ability was selectively associated with connectivity between IPL and precuneus. These results illustrate the importance of SLF and a distinct white-matter grey-matter circuitry that supports human metacognition. Introduction:The capacity of reflecting on one's own cognitive process is known as metacognition (Flavell, 1979;Fleming and Dolan, 2012;Yeung and Summerfield, 2012). Metacognition has been considered as the most crucial function emerged during evolution (Hayes, 2016). Given its crucial functions, researchers have endeavored to understand how a metacognitive judgment is computed (Fleming and Daw, 2017;Kepecs et al., 2008;Zylberberg et al., 2016), how it is disrupted in psychiatric disorders (Hauser et al., 2017a;Rouault et al., 2018a), and how its accuracy can be improved (Carpenter et al., 2019). Metacognition is an umbrella term for the higher-level cognition about the lower-level cognition in various domains (e.g., perception and memory). As cognitive processes are implemented in the brain, an interesting and important question is whether the neural circuit supporting metacognition is the same or distinct across different cognitive domains.A large body of fMRI work have increasingly demonstrated a nuanced picture for this domaingenerality issue of metacognition. For example, the dorsolateral prefrontal cortex (DLPFC) might be involved in reading out the information of primary decision-making and using it for computing both perceptual and mnemonic metacognitive judgements (Chua and Ahmed, 2016;Fleming and Dolan, 2012;Kwok et al., 2019;Rounis et al., 2010). Morales et al. (2018) reported that the dorsal anterior cingulate cortex (dACC) was active during metacognitive evaluation in both memory and perception tasks but also found the activation patterns decoded from a perception task in the posterior medial frontal cortex and ven...

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Noradrenaline blockade specifically enhances metacognitive performance

Cited by 78 publications

References 67 publications

Brain dynamics for confidence-weighted learning

Brain dynamics for confidence-weighted learning

Sensory noise increases metacognitive efficiency

Diffusion property and functional connectivity of superior longitudinal fasciculus underpin human metacognition

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