Explaining Distortions in Metacognition with an Attractor Network 2 Model of Decision Uncertainty
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bioRxiv preprint doi: https://doi.org/10.1101/2020.09.25.313619; this version posted June 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Explaining distortions in metacognition with an attractor network 2 model of decision uncertainty 3 Nadim A. A. Atiya1,2, Quentin J. M. Huys1,4,5, Raymond J. Dolan1,2, Stephen M. Fleming1,2,3 4 5 1 Max Planck University College London Centre for Computational Psychiatry and Ageing 6 Research, University College London, London WC1B 5EH, UK 7 2 Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, 8 London WC1N 3BG, UK 9 3 Department of Experimental Psychology, University College London, 26 Bedford Way, 10 London WC1H 0AP, UK 11 4 Division of Psychiatry, University College London, 149 Tottenham Court Road, London 12 W1T 7NF, UK 13 5 ORCID: 0000-0002-8999-574X 14 Corresponding Author: Nadim A. A. Atiya ([email protected]) 15 Abstract 16 Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. 17 Distortions in metacognition are common in mental health disorders, though the neural 18 underpinnings of such dysfunction are unknown. One reason for this is that models of key 19 components of metacognition, such as decision confidence, are generally specified at an 20 algorithmic or process level. While such models can be used to relate brain function to 21 psychopathology, they are difficult to map to a neurobiological mechanism. Here, we 22 develop a biologically-plausible model of decision uncertainty in an attempt to bridge this 23 gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of 24 metacognition, namely mean confidence level (bias) and the accuracy of metacognitive 25 judgments (sensitivity). We show that dissociable shifts in metacognition are associated 26 with isolated disturbances at higher-order levels of a circuit associated with self- 27 monitoring, akin to neuropsychological findings that highlight the detrimental effect of 28 prefrontal brain lesions on metacognitive performance. Notably, we are able to account for 29 empirical confidence judgements by fitting the parameters of our biophysical model to 30 first-order performance data, specifically choice and response times. Lastly, in a reanalysis 31 of existing data we show that self-reported mental health symptoms relate to disturbances 32 in an uncertainty-monitoring component of the network. By bridging a gap between a 33 biologically-plausible model of confidence formation and observed disturbances of 34 metacognition in mental health disorders we provide a first step towards mapping 35 theoretical constructs of metacognition onto dynamical models of decision uncertainty. In 36 doing so, we provide a computational framework for modelling metacognitive performance 37 in settings where access to explicit confidence reports is not possible. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.25.313619; this version posted June 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 38 Author Summary 39 In this work, we use a biologically-plausible model of decision uncertainty to show that 40 shifts in metacognition are associated with disturbances in the interaction between 41 decision-making and higher-order uncertainty-monitoring networks. Specifically, we show 42 that stronger uncertainty modulation is associated with decreased metacognitive bias, 43 sensitivity, and efficiency, with no effect on perceptual sensitivity. Our approach not only 44 enables inferences about uncertainty modulation (and, in turn, these facets of 45 metacognition) from fits to first-order performance data alone – but also provides a first 46 step towards relating dynamical models of decision-making to metacognition. We also 47 relate our model’s uncertainty modulation to psychopathology, and show that it can offer 48 an implicit, low-dimensional marker of metacognitive (dys)function – opening the door to 49 richer analysis of the interaction between metacognitive performance and 50 psychopathology from first-order performance data. 51 Introduction 52 Computational psychiatry (Friston et al. 2014; Huys, Maia, and Frank 2016; Wang and 53 Krystal 2014; Montague et al. 2012) employs mechanistic and theory-driven models to 54 relate brain function to phenomena that characterise mental health disorders (Ratcliff 55 1978; Ratcliff, Smith, and McKoon 2015; Rescorla, Wagner et al. 1972; Huys, Maia, and 56 Frank 2016; Sutton and Barto 2018). Typically, algorithmic-level models (Marr and Poggio 57 1976) describe the computational processes that realise specific brain functions and return 58 theoretically meaningful parameters that may vary between subjects. Some of these 59 algorithmic models (e.g. reinforcement learning; Sutton and Barto 2018) closely relate to 60 the functions of discrete brain circuits (Schultz 1999; Dayan and Balleine 2002; Dolan and 61 Dayan 2012). However, there remains a high degree of imprecision when relating diverse 62 sets of algorithms to circuit-level disturbances, potentially limiting our understanding of, 63 and treatments for, mental disorders. 64 One proposal is that the same neural circuit disturbances can be associated with several 65 (often unrelated) changes in behaviour (Stephan et al. 2016). Here detailed biophysical 66 models (Murray et al. 2014; Krystal et al. 2017; Rolls, Loh, and Deco 2008) may provide 67 tools for understanding mental health disorders in terms of precise disturbances at the 68 microcircuit level. For instance, Murray et al. (2014) showed that an imbalance in 69 excitatory/inhibitory synaptic connections in a spiking neural network model can explain 70 working memory deficits associated with schizophrenia. However, the complex nature of 71 such models renders it challenging to fit them to individual subjects’ behavioural data. At 72 the level of neural systems, simpler biologically-grounded models (Dima et al. 2009; Yang 73 et al. 2014) have been employed to relate macrocircuit-level dysfunctions to symptoms of 74 mental health disorders, and motivate non-invasive experimental neuroimaging to probe 75 such dysfunctions (Cohen and Servan-Schreiber 1992). Such (connectionist) biologically- 76 motivated models retain a mapping between neurobiology and behaviour, while allowing 77 faster computation and fewer free parameters. 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.25.313619; this version posted June 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 78 Here our focus is on developing similar biologically-plausible models of subjective 79 confidence and metacognition – the ability to reflect upon and evaluate aspects of our own 80 cognition and behaviour. Recent advances in metacognition research has led to the 81 development of precision assays for different facets of metacognitive ability (Maniscalco 82 and Lau 2012; Fleming 2017). Within a signal detection theory (SDT) framework, 83 metacognitive bias refers to a subject’s overall (mean) confidence level on a task. In 84 contrast, metacognitive sensitivity refers to whether subjects’ confidence ratings effectively 85 distinguish between correct and incorrect decisions, as quantified by the SDT metric 86 ͙͕ͨ͡ Ǝ ͘ɑ. Furthermore, metacognitive sensitivity can be compared to another SDT 87 measure, ͘ɑ, which quantifies how effectively a subject processes information related to the 88 task (Howell 2009; Rounis et al. 2010). The ratio ͙͕ͨ͡ Ǝ ͘ɑ/ɑ thus yields a measure of 89 metacognitive efficiency, i.e. metacognitive sensitivity for a given level of task performance 90 (Fleming and Lau 2014). 91 Experimental evidence suggests that these facets of metacognitive ability are dissociable 92 from task performance, and may have a distinct neural and computational basis (Del Cul et 93 al. 2009; Fleming et al. 2010; Fleming and Dolan 2012; Fleming et al., 2014; Lak et al., 2014; 94 Bang & Fleming, 2018; Miyamoto et al., 2018). Interestingly, self-reported mental health 95 symptoms have been linked to changes in metacognition, often in the absence of 96 differences in task performance (Rouault et al. 2018; Moses-Payne et al. 2019; Hoven et al., 97 2019; Seow & Gillan, 2020). Developing a biologically-motivated model of metacognition 98 has the potential to cast light on how this dissociable mechanism is implemented at a 99 circuit level, as well as provide a direct bridge between circuit-level dysfunction and 100 psychopathology. 101 Theoretical work addressing perceptual decision-making has proposed dynamical reduced 102 accounts (Wong and Wang 2006; Roxin and Ledberg 2008) that provide detailed 103 biophysical models of decision making (Wang 2002), enabling more rigorous theoretical 104 analyses and faster computation. For instance, Wong and Wang (2006) have accounted for 105 most of the behavioural results addressed by Wang (2002)’s model using the two slowest 106 N-Methyl-D-aspartic acid (NMDA) dynamical variables. More recently, Atiya et al. (2019) 107 extended