Biology subjects

Daunizeau, J.

Publications and source records attributed to Daunizeau, J..

12 recordsLinked to original sources

bioRxiv2026

Starting strong: A descending analgesic dosing scheme enhances pain relief compared to a conventional ascending dosing scheme

Managing pain while minimizing opioid-related harm remains a central clinical challenge. Enhancing drug-related placebo effects has been proposed as one strategy for reducing opioid consumption, yet current dosing guidelines which recommend starting analgesic treatment at a low dose and titrating upward may inadvertently generate negative treatment expectations, since initial drug encounters are often associated with minimal pain relief. This study tested whether the inverse approach of a descending dosing scheme that starts with a strong analgesic dose more effectively enhances placebo-related analgesia than the conventional ascending scheme. Eighty-two healthy participants underwent a heat pain paradigm involving a sham analgesic treatment delivered at three different efficacy levels (low, medium, high). Participants were randomly assigned to a descending (high-to-low) or ascending (low-to-high) treatment-history sequence before receiving an identical, medium-intensity treatment during a subsequent test phase. The descending group showed significantly greater pain relief during the test phase than the ascending group, despite receiving identical treatment. These findings suggest that initiating analgesic treatment with a high, effective dose can amplify placebo-related analgesia relative to conventional dosing schemes, offering a potential strategy for reducing cumulative opioid use without compromising pain relief.

neuroscience↗

bioRxiv2025

Biological profits of irrational computations in the orbitofrontal cortex

Making good decisions is essential for survival and success, yet humans and animals often exhibit perplexing irrational decision-making whose biological origin remains poorly understood. Recent empirical and computational work suggests that altered computations in perceptual, motor and memory systems in the brain may arise from informational, metabolic or robustness constraints on their internal connectivity structure. However, whether and how such neurobiological constraints may have molded the architecture of decision systems (such as the orbitofrontal cortex) and eventually distorted decision-relevant computations, remains largely unknown. We first train cohorts of artificial neural nets to perform ten variants of rational decision-relevant computations. Those variants that operate under a specific option-encoding format exhibit most of the electrophysiological coding properties observed in orbitofrontal neurons of monkeys making decisions under risk. We then distort these neural nets internal wiring to reproduce monkeys irrational choices. This induces deterministic spillover interferences in decision-relevant computations that generalize across individuals, at both the behavioral and neural level. Importantly, although irrational nets do not seem to bring informational or metabolic benefits, they display enhanced tolerance to damage and noise when compared to their rational counterparts. This suggests that some forms of irrational behavior may be the incidental outcome of distal evolutionary pressure on the robustness of orbitofrontal circuits.

neuroscience↗

bioRxiv2023

Neural shape mediation analysis

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 datas 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.

neuroscience↗

bioRxiv2023

The metacognitive control of decisions predicts whether and how mice override their default policy

Decisions permeate every aspect of our lives but decision-making policies seem to vary tremendously across time and individuals. Rather than processing all decision-relevant information, we sometimes rely on habitual and/or intuitive responses, which can lead to irrational biases and errors. This raises the twofold issue of whether a single normative principle can (i) determine when cognitive control is engaged, and (ii) account for the diverse behavioral signature of cognitive control regulation. In line with recent theoretical proposals, our working hypothesis is that cognitive control is flexibly deployed to optimize a cost-benefit trade-off. Using Markov Decision Processes, we derive the optimal online control allocation policy in standard decision-making tasks, under arbitrary default biases. This bridges the gap between motivational theories of cognitive control regulation and accumulation-to-bound models of decision making. Under the model, response times, default response rates, decision accuracy and interference effects are not independent behavioral phenomena, but distinct manifestations of the same underlying arbitration process. We then test the distinct behavioral predictions of the model using a customized pseudo-ecological set-up, whereby mice engage with a self-administered perceptual decision-making task over several weeks. In this context, processing decision-relevant cues engages cognitive control, whereas default responses are determined by learned, cue-independent, action-outcome associations. The model accurately predicts both performance and response time interference effects, their interactions, as well as their modulation by reward and task demands. This work provides novel computational means for assessing the brain circuits that operate the arbitration between default and controlled decision processes, in rodent models of both healthy and neuropsychiatric conditions.

animal behavior and cognition↗

bioRxiv2023

The online metacognitive control of decisions

Difficult decisions typically involve mental effort, which scales with the deployment of cognitive (e.g., mnesic, attentional) resources engaged in processing decision-relevant information. But how does the brain regulate mental effort? A possibility is that the brain optimizes a resource allocation problem, whereby the amount of invested resources balances its expected cost (i.e. effort) and benefit. Our working assumption is that subjective decision confidence serves as the benefit term of the resource allocation problem, hence the "metacognitive" nature of decision control. Here, we present a computational model for the online metacognitive control of decisions or oMCD. Formally, oMCD is a Markov Decision Process that optimally solves the ensuing resource allocation problem under agnostic assumptions about the inner workings of the underlying decision system. We demonstrate how this makes oMCD a quasi-optimal control policy for a broad class of decision processes, including -but not limited to- progressive attribute integration. We disclose oMCDs main properties (in terms of choice, confidence and response time), and show that they reproduce most established empirical results in the field of value-based decision making. Finally, we discuss the possible connections between oMCD and most prominent neurocognitive theories about decision control and mental effort regulation.

animal behavior and cognition↗

bioRxiv2022

Seeing speech: The cerebral substrate of tickertape synesthesia.

Reading acquisition is enabled by deep changes in the brains visual system and language areas, and in the links subtending their collaboration. Disruption of those plastic processes commonly results in developmental dyslexia. However, atypical development of reading mechanisms may occasionally result in ticker-tape synesthesia (TTS), a condition described by Francis Galton in 1883 wherein individuals "see mentally in print every word that is uttered (...) as from a long imaginary strip of paper". While reading is the bottom-up translation of letters into speech, TTS may be viewed as its opposite, the top-down translation of speech into internally visualized letters. In a series of functional MRI experiments, we studied MK, a man with TTS. We showed that a set of left-hemispheric areas were more active in MK than in controls during the perception of normal than reversed speech, including frontoparietal areas involved in speech processing, and the Visual Word Form Area, an occipitotemporal region subtending orthography. Those areas were identical to those involved in reading, supporting the construal of TTS as upended reading. Using dynamic causal modeling, we further showed that, parallel to reading, TTS induced by spoken words and pseudowords relied on top-down flow of information along distinct lexical and phonological routes, involving the middle temporal and supramarginal gyri, respectively. Future studies of TTS should shed new light on the neurodevelopmental mechanisms of reading acquisition, their variability and their disorders. Significance statementSome individuals, whenever they are hearing speech, see vividly in their minds eye the corresponding words in written form, as mental subtitles. This unusual condition, termed ticker-tape synesthesia (TTS), far from being purely anecdotal, actually touches on the core of the brain mechanisms of normal and impaired reading acquisition. Through 3 fMRI experiments, plus brain connectivity analyses, we propose an in-depth study of a single individual with such ticker-tape synesthesia. We propose that TTS, a situation in some sense symmetrical to developmental dyslexia, reflects an inverted flow of information through the reading system, such that speech is automatically translated into internally visualized letters. Future studies of TTS should shed new light on the neurodevelopmental mechanisms of reading acquisition.

neuroscience↗

bioRxiv2020

Meet me in the middle: brain-behavior mediation analysis for fMRI experiments

Functional outcomes (e.g., subjective percepts, emotions, memory retrievals, decisions, etc...) are partly determined by external stimuli and/or cues. But they may also be strongly influenced by (trial-by-trial) uncontrolled variations in brain responses to incoming information. In turn, this variability provides information regarding how stimuli and/or cues are processed by the brain to shape behavioral responses. This can be exploited by brain-behavior mediation analysis to make specific claims regarding the contribution of brain regions to functionally-relevant input-output transformations. In this work, we address four challenges of this type of approach, when applied in the context of mass-univariate fMRI data analysis: (i) we quantify the specificity and sensitivity profiles of different variants of mediation statistical tests, (ii) we evaluate their robustness to hemo-dynamic and other confounds, (iii) we identify the sorts of brain mediators that one can expect to detect, and (iv) we disclose possible interpretational issues and address them using complementary information-theoretic approaches. En passant, we propose a computationally efficient algorithmic implementation of the approach that is amenable to whole-brain exploratory analysis. We also demonstrate the strengths and weaknesses of brain-behavior mediation analysis in the context of an fMRI study of decision under risk. Finally, we discuss the limitations and possible extensions of the approach.

neuroscience↗

bioRxiv2020

Blaming blunders on the brain: can indifferent choices be driven by range adaptation or synaptic plasticity?

Many deleterious behaviors, such as procrastinating on urgent matters or sustaining a toxic relationship, are irrational. But is irrational behavior the incidental outcome of biological constraints imposed on neural information processing? In particular, can those constraints alter decisions, even when people know the consequences of alternative actions? Recent studies indicate that orbitofrontal neurons encode decision value in relative terms, i.e. value signals in OFC neurons are normalized with respect to the context. Value-based decisions may thus exhibit irrational context-dependence effects. A candidate explanation is "efficient coding": OFC neurons may mitigate information loss by adapting their (bounded) output firing properties to the recent value range. This is seducing, because it suggests that relative value coding is the brains best attempt to produce rational behavior, given its own hard-wired biological constraints. However, whether the behavioral implications of this scenario are met, how it generalizes to realistic situations in which OFC neurons construct value from multiple decision-relevant attributes, and what its neurophysiological bases are, is unclear. Here, we address these issues by re-analyzing two open fMRI datasets from the OpenNeuro.org initiative, where people have to integrate prospective gains and losses to decide whether to gamble or not. First, we show that peoples risk attitudes critically depend on the range of gain prospects they are exposed to. Importantly, counter to simple efficient coding scenarios, differences in gain ranges induce progressive changes in peoples sensitivity to both gains and losses. Second, we use artificial neural network models to show that hebbian rewiring processes between attribute-specific and attribute-integration neurons predict (out-of-sample) both context-dependence effects in peoples risk attitude and multivariate patterns of fMRI activity in the OFC. Under mild conditions on distributed population codes for decision attributes, hebbian plasticity eventually translates the distribution of reweighted attribute signals towards the responsive range of integration neurons. In turn, integration units exhibit the known features of range adaptation, including (but not limited to), relative value coding. Our results demonstrate how hebbian plasticity within brain networks may result in range adaptation, eventually yielding complex though predictable irrational behavior.

neuroscience↗

bioRxiv2020

Fitting drift-diffusion decision models to trial-by-trial data

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. But 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.

animal behavior and cognition↗

bioRxiv2019

Trading Mental Effort for Confidence: The Metacognitive Control of Value-Based Decision-Making

Why do we sometimes opt for actions or items that we do not value the most? Under current neurocomputational theories, such preference reversals are typically interpreted in terms of errors that arise from the unreliable signaling of value to brain decision systems. But, an alternative explanation is that people may change their mind because they are reassessing the value of alternative options while pondering the decision. So, why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide initial (and largely uncertain) representations of options values, yielding prior estimates of decision difficulty. These uncertain value representations are then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty. The ensuing MCD model predicts response time, subjective feeling of effort, choice confidence, changes of mind, and choice-induced preference change and certainty gain. We test these predictions in a systematic manner, using a dedicated behavioral paradigm. Our results provide a quantitative link between mental effort, choice confidence, and preference reversals, which could inform interpretations of related neuroimaging findings.

neuroscience↗

bioRxiv2019

Is choice-induced preference change due to pre- or post-decision cognitive dissonance resolution?

For more than 60 years, it has been known that people report higher (lower) subjective values for items after having selected (rejected) them during a choice task. This phenomenon is coined \"choice-induced preference change\" or CIPC, and its established interpretation is that of \"cognitive dissonance\" theory. In brief, if people feel uneasy about their choice, they later convince themselves, albeit not always consciously, that the chosen (rejected) item was actually better (worse) than they had originally estimated. While this might make sense from a pragmatic psychological standpoint, it is challenging from a theoretical evolutionary perspective. This is because such a cognitive mechanism might yield irrational biases, whose adaptive fitness would be unclear. In this work, we assume that CIPC is mostly driven by the refinement of option value representations that occurs during (and not after) difficult choices. This makes CIPC the epiphenomenal outcome of a cognitive process that is instrumental to the decision. Critically, our hypothesis implies novel predictions about how observed CIPC should relate to two specific meta-cognitive processes, namely: choice confidence and subjective certainty regarding pre-choice value judgments. We test these predictions in a behavioral experiment where participants rate the subjective value of food items both before and after choosing between equally valued items; we augment this traditional design with reports of choice confidence and subjective certainty about value judgments. The results confirm our predictions and provide evidence against the standard post-choice cognitive dissonance reduction explanation. We then discuss the relevance of our work in the context of the existing debate regarding the putative cognitive mechanisms underlying cognitive dissonance reduction.

neuroscience↗

bioRxiv2018

A reverse Turing-test for predicting social deficits in people with Autism

AbstractSocial symptoms of autism spectrum disorder (ASD) are typically viewed as consequences of an impaired Theory of Mind, i.e. the ability to understand others covert mental states. Here, we test the assumption that such \"mind blindness\" may be due to the inability to exploit contextual knowledge about, e.g., the stakes of social interactions, to make sense of otherwise ambiguous cues (e.g., idiosyncratic responses to social competition). In this view, social cognition in ASD may simply reduce to non-social cognition, i.e. cognition that is not informed by the social context. We compared 24 adult participants with ASD to 24 neurotypic participants in a repeated dyadic competitive game against artificial agents with calibrated mentalizing sophistication. Critically, participants were framed to believe that they were competing against humans (social framing) or not (non-social framing), hence the \"reverse Turing test\". In contrast to control participants, the strategy of people with ASD is insensitive to the games framing, i.e. they do not constrain their understanding of others behaviour with the contextual knowledge about the game (cf. competitive social framing). They also outperform controls when playing against simple agents, but are outperformed by them against recursive algorithms framed as human opponents. Moreover, computational analyses of trial-by-trial choice sequences in the game show that individuals with ASD rely on a distinctive cognitive strategy with subnormal flexibility and mentalizing sophistication. These computational phenotypes yield 79% diagnosis classification accuracy and explain 62% of the severity of social symptoms in people with ASD.

neuroscience↗