Synaptic plasticity in the orbitofrontal cortex explains how risk attitude adapts to the range of risk prospects

Brochard, J.; Daunizeau, Jean

doi:10.1101/2020.09.08.287714

“…This would extend the portfolio of recent empirical studies of neural noise perturbations to learning-relevant computations ( Drugowitsch et al, 2016 ; Wyart and Koechlin, 2016 ; Findling et al, 2019 ). Reciprocally, using some variant of mediation analysis ( MacKinnon et al, 2007 ; Lindquist, 2012 ; Brochard and Daunizeau, 2020 ), one may extract neuroimaging estimates of neural noise that can inform DDM-based behavioral data analysis. Alternatively, one may model neural and behavioral data in a joint and symmetrical manner, with the purpose of testing some predefined DDM variant ( Rigoux and Daunizeau, 2015 ; Turner et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Feltgen

¹

,

Daunizeau

²

2021

Front. Artif. Intell.

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Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

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“…This would extend the portfolio of recent empirical studies of neural noise perturbations to learning-relevant computations (Drugowitsch et al, 2016; Findling et al, 2019; Wyart and Koechlin, 2016). Reciprocally, using some variant of mediation analysis (Brochard and Daunizeau, 2020; Lindquist, 2012; MacKinnon et al, 2007), one may extract neuroimaging estimates of neural noise that can inform DDM-based behavioral data analysis. Alternatively, one may model neural and behavioral data in a joint and symmetrical manner, with the purpose of testing some predefined DDM variant (Rigoux and Daunizeau, 2015; Turner et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

An overcomplete approach to fitting drift-diffusion decision models to trial-by-trial data

Feltgen¹,

Daunizeau

²

2020

Preprint

Self Cite

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Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches do not provide reliable parameter estimates when, e.g., evidence strength is varied over trials. In this note, we propose a fast and efficient approach that is based on fitting a self-consistency equation that the DDM fulfills. Using numerical simulations, we show that this approach enables one to extract relevant information from trial-by-trial variations of RT data that would typically be buried in the empirical distribution. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

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“…The limitation, however, lies in the interpretability of such results. Neural mediation tests put the nervous system as an intermediate processing step between stimulus and behaviour (Brochard & Daunizeau, 2022; Rigoli et al, 2016). Yet they can surprisingly provide little insight into neural organisations.…”

Section: Introductionmentioning

confidence: 99%

Neural shape mediation analysis

Brochard,

Combrisson,

Chatard

et al. 2023

Preprint

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0

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Neural signal shapes convey significant information about their generating processes. In this study, we introduce a data-driven methodology to identify sensory and behaviourally-relevant traces within neural responses. We present a phenomenological model that characterises temporal variations in intracranial EEG using eight interpretable parameters: peak time, peak intensity, initial and final baselines, accumulation and depletion period, and their respective concavities. This model effectively captures subtle signal variations, especially in sensory decision-making tasks. By decomposing the signals in this manner, we then conduct a comprehensive brain mediation analysis on iEEG data's shape, pinpointing regions that mediate behavioural processes. Importantly, we can determine which signal dynamics specifically reflect underlying behavioural processes, enhancing the depth of analysis and critique of their role in behaviour. Preliminary applications on a cohort of epileptic patients reveal that our model explains over a third of the signal variance at the trial level across all brain regions. We identified four key regions--encompassing sensory, associative, frontal, and premotor areas--that mediate the impact of task difficulty on reaction time. Notably, in these regions, it was the depletion period, rather than signal amplitude, that correlated with behavioural speed. This study highlights the potential of our approach in providing detailed insights into the neural mechanisms linking stimuli to behaviour.

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Synaptic plasticity in the orbitofrontal cortex explains how risk attitude adapts to the range of risk prospects

Cited by 3 publications

References 133 publications

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

An overcomplete approach to fitting drift-diffusion decision models to trial-by-trial data

Neural shape mediation analysis

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