Biology subjects

Parr, T.

Publications and source records attributed to Parr, T..

17 recordsLinked to original sources

bioRxiv2026

Why Speech Motor Blocks Emerge in a Communicative Context: An Active Inference Model of Stuttering

Adults who stutter can speak fluently when speech is not addressed to another person, but stuttering emerges when they aim to convey information to a listener. The value of the information being conveyed to the listener also affects the likelihood of stuttering. Why should the mere absence of a listener neutralise a profound motor deficit, and why does a word's predictability affect whether it is spoken fluently? To resolve this socio-motor paradox, we develop a computational model of stuttering within an active inference architecture. The model represents the communicative context, including whether a listener is present and whether the agent is speaking or listening. It was designed around two candidate mechanisms for stuttering, a prior for silence and rigid phoneme sequencing precision. Using both, the model produced fluent private speech and more stuttering-like events during social speech. In the same parameter regime, the model also showed more stuttering-like events on words with higher information value, and produced a word-length effect, in which disfluency increased with longer words. To our knowledge, this is the first model of stuttering to generate both the private speech and the information-value effect from inferred communicative context. By representing the listener as a hidden state that makes the sensory consequences of resuming speech ambiguous, the model offers a computational link between social cognition and speech-motor instability, and suggests that speech fluency depends on whether the speaker believes anyone is present. Clinically, it may offer testable hypotheses and a route to personalising treatment, since the same overt severity can arise from different combinations of parameters.

neuroscience↗

bioRxiv2025

Inferential planning in the frontal cortex

How the brain plans and maintains sequences of future actions remains a central question in systems neuroscience. Recent studies in the frontal cortex have revealed that multiple elements of a sequence are represented simultaneously in separable neural subspaces, challenging classical serial models of sequential planning. Here, we show that these representations emerge naturally under inferential planning in which sequential actions are inferred from sensory evidence and goals. Using a hierarchical generative model, we reproduce key neural phenomena observed in primate frontal cortex, including the simultaneous activation of multiple plan elements, the emergence of (almost) orthogonal memory subspaces, and their reuse across forward and backward sequence tasks. Our approach provides a mechanistic account of how probabilistic inference over control states gives rise to distributed and dynamic neural representations of plans. This framework not only unifies previously disparate findings on planning, working memory, and motor preparation, but also generates novel, testable predictions about the dynamics of active inference, the role of sensory subspaces, and the impact of uncertainty on sequence processing.

neuroscience↗

bioRxiv2025

Dynamic updating of spatial working memory across eye movements: a computational investigation of transsaccadic integration

The brain continuously integrates rapidly changing visual input across eye movements to maintain stable perception, yet the precise mechanisms underpinning dynamic working memory and how these break down in brain diseases remain unclear. We developed a novel eye-tracking paradigm and computational models to investigate how spatial and colour information are updated across saccades. Our findings reveal that saccades selectively impair spatial but not colour memory. Computational modelling identified that spatial representations are maintained in a dual eye-centred frame of reference which is actively updated by a noisy memory of saccades but is vulnerable to interference. Using this model, we found that specific mechanistic failures in initial encoding and memory decay, rather than the saccadic updating process itself, account for spatial working memory deficits in Alzheimers and Parkinsons disease. These results provide a mechanistic understanding of how dynamic spatial memory operates in health and its disruption in neurodegenerative disorders.

neuroscience↗

bioRxiv2020

Deep Active Inference and Scene Construction

Adaptive agents must act in intrinsically uncertain environments with complex latent structure. Here, we elaborate a model of visual foraging - in a hierarchical context - wherein agents infer a higher-order visual pattern (a scene) by sequentially sampling ambiguous cues. Inspired by previous models of scene construction - that cast perception and action as consequences of approximate Bayesian inference - we use active inference to simulate decisions of agents categorizing a scene in a hierarchically-structured setting. Under active inference, agents develop probabilistic beliefs about their environment, while actively sampling it to maximise the evidence for their internal generative model. This approximate evidence maximization (i.e. self-evidencing) comprises drives to both maximise rewards and resolve uncertainty about hidden states. This is realised via minimization of a free energy functional of posterior beliefs about both the world as well as the actions used to sample or perturb it, corresponding to perception and action, respectively. We show that active inference, in the context of hierarchical scene construction, gives rise to many empirical evidence accumulation phenomena, such as noise-sensitive reaction times and epistemic saccades. We explain these behaviours in terms of the principled drives that constitute the expected free energy, the key quantity for evaluating policies under active inference. In addition, we report novel behaviours exhibited by these active inference agents that furnish new predictions for research on evidence accumulation and perceptual decision-making. We discuss the implications of this hierarchical active inference scheme for tasks that require planned sequences of information-gathering actions to infer compositional latent structure (such as visual scene construction and sentence comprehension). Finally, we propose experiments to contextualise active inference in relation to other formulations of evidence accumulation (e.g. drift-diffusion models) in tasks that require planning in uncertain environments with higher-order structure.

neuroscience↗

bioRxiv2020

Active Listening

This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The active aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next.

neuroscience↗

bioRxiv2019

Transferring structural knowledge across cognitive maps in humans and models

Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.

neuroscience↗

bioRxiv2019

Active inference, stressors, and psychological trauma: A neuroethological model of (mal)adaptive explore-exploit dynamics in ecological context

This paper offers a formal account of emotional inference and stress-related behaviour, using the notion of active inference. We formulate responses to stressful scenarios in terms of Bayesian belief-updating and subsequent policy selection; namely, planning as (active) inference. Using a minimal model of how creatures or subjects account for their sensations (and subsequent action), we deconstruct the sequences of belief updating and behaviour that underwrite stress-related responses - and simulate the aberrant responses of the sort seen in post-traumatic stress disorder (PTSD). Crucially, the model used for belief-updating generates predictions in multiple (exteroceptive, proprioceptive and interoceptive) modalities, to provide an integrated account of evidence accumulation and multimodal integration that has consequences for both motor and autonomic responses. The ensuing phenomenology speaks to many constructs in the ecological and clinical literature on stress, which we unpack with reference to simulated inference processes and accompanying neuronal responses. A key insight afforded by this formal approach rests on the trade-off between the epistemic affordance of certain cues (that resolve uncertainty about states of affairs in the environment) and the consequences of epistemic foraging (that may be in conflict with the instrumental or pragmatic value of fleeing or freezing). Starting from first principles, we show how this trade-off is nuanced by prior (subpersonal) beliefs about the outcomes of behaviour - beliefs that, when held with unduly high precision, can lead to (Bayes optimal) responses that closely resemble PTSD.

neuroscience↗

bioRxiv2019

Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance

Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena - such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity - characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals - and provide a principled basis for personalized precision medicine.

neuroscience↗

bioRxiv2019

A Bayesian account of generalist and specialist formation under the Active Inference framework

This paper offers a formal account of policy learning, or habitual behavioural optimisation, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a two-step maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behaviour for any preferred maze direction. Conversely, in conservative or static environments, agents adopt a specialist strategy; forming strong preferences for policies that result in approach to a small number of previously-observed reward locations. The pros and cons of the two strategies are tested and discussed. In general, specialization offers greater benefits, but only when contingencies are conserved over time. We consider the implications of this formal (Active Inference) account of policy learning for understanding the relationship between specialisation and habit formation.\n\nAuthor SummaryActive inference is a theoretical framework that formalizes the behaviour of any organism in terms of a single imperative - to minimize surprise. Starting from this principle, we can construct simulations of simple \"agents\" (artificial organisms) that show the ability to infer causal relationships and learn. Here, we expand upon currently-existing implementations of Active Inference by enabling synthetic agents to optimise the space of behavioural policies that they can pursue. Our results show that by adapting the probabilities of certain action sequences (which may correspond biologically to the phenomenon of synaptic plasticity), and by rejecting improbable sequences (synaptic pruning), the agents can begin to form habits. Furthermore, we have shown our agents habit formation to be environment-dependent. Some agents become specialised to a constant environment, while other adopt a more general strategy, each with sensible pros and cons. This work has potential applications in computational psychiatry, including in behavioural phenotyping to better understand disorders.

neuroscience↗

bioRxiv2019

Simulating emotions: An active inference model of emotional state inference and emotion concept learning

The ability to conceptualize and understand ones own affective states and responses - or "emotional awareness" (EA) - is reduced in multiple psychiatric populations; it is also positively correlated with a range of adaptive cognitive and emotional traits. While a growing body of work has investigated the neurocognitive basis of EA, the neurocomputational processes underlying this ability have received limited attention. Here, we present a formal Active Inference (AI) model of emotion conceptualization that can simulate the neurocomputational (Bayesian) processes associated with learning about emotion concepts and inferring the emotions one is feeling in a given moment. We validate the model and inherent constructs by showing (i) it can successfully acquire a repertoire of emotion concepts in its "childhood", as well as (ii) acquire new emotion concepts in synthetic "adulthood," and (iii) that these learning processes depend on early experiences, environmental stability, and habitual patterns of selective attention. These results offer a proof of principle that cognitive-emotional processes can be modeled formally, and highlight the potential for both theoretical and empirical extensions of this line of research on emotion and emotional disorders.

neuroscience↗

bioRxiv2019

An active inference model of concept learning

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning - and specifically state-space expansion and reduction - within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) slots that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning - associated with these slots - can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this models ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of one-shot generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer.

neuroscience↗

bioRxiv2019

In the Body’s Eye: The Computational Anatomy of Interoceptive Inference

A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of emotion and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and confidence. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to fear perception. We further show that simulated interoceptive lesions blunt fear expectations, induce psychosomatic hallucinations, and exacerbate metacognitive biases. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers the possibility to computationally phenotype disordered brain-body interaction.

neuroscience↗

bioRxiv2019

Hallucinations both in and out of context: An Active Inference Account

Hallucinations, including auditory verbal hallucinations (AVH), occur in both the healthy population and in psychotic conditions such as schizophrenia (often developing after a prodromal period). In addition, hallucinations can be in-context (they can be consistent with the environment, such as when one hallucinates the end of a sentence that has been repeated many times), or out-of-context (such as the bizarre hallucinations associated with schizophrenia). In previous work, we introduced a model of hallucinations as false (positive) inferences based on a (Markov decision process) formulation of active inference. In this work, we extend this model to include content - to disclose the computational mechanisms behind in- and out-of-context hallucinations. In active inference, sensory information is used to disambiguate alternative hypotheses about the causes of sensations. Sensory information is balanced against prior beliefs, and when this balance is tipped in the favor of prior beliefs, hallucinations can occur. We show that in-context hallucinations arise when (simulated) subjects cannot use sensory information to correct prior beliefs about hearing a voice, but beliefs about content (i.e. the sequential order of a sentence) remain accurate. When hallucinating subjects also have inaccurate beliefs about state transitions, out-of-context hallucinations occur; i.e. their hallucinated speech content is disordered. Furthermore, subjects with inaccurate beliefs about state transitions but an intact ability to use sensory information do not hallucinate and are reminiscent of prodromal patients. This work demonstrates the different computational mechanisms that may underlie the spectrum of hallucinatory experience - from the healthy population to psychotic states.

neuroscience↗

bioRxiv2018

Thalamocortical dynamics underlying spontaneous transitions in beta power in Parkinsonism

Parkinsons disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15-35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic dynamic causal modelling framework, we recursively optimised a set of plausible models of the thalamocortical circuit (n=144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. We therefore hypothesize that beta synchronisation at the cortical level could selectively be modulated via interventions that are capable of finely regulating cortical excitability in a spatial (delivered to either the superficial or deep cortical laminae) and time specific manner.

neuroscience↗

bioRxiv2018

Generalised free energy and active inference: can the future cause the past?

We compare two free energy functionals for active inference under Markov decision processes. One of these is a functional of beliefs about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy); priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to counterfactual (i.e., future) observations, we equip the generative model with a prior over policies that ensure preferred (i.e., priors over) outcomes are realised. In other words, selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional - that effectively treats counterfactual observations as hidden states - we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.

neuroscience↗

bioRxiv2018

Social intelligence model with multiple internal models

To exhibit social intelligence, animals have to recognize who they are communicating with. One way to make this inference is to select among multiple internal generative models of each conspecific. This induces an interesting problem: when receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning under multiple generative models by integrating active inference and (online) Bayesian model selection. This scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability it could have generated the current input (i.e., model evidence). We show that a synthetic bird who employs the proposed scheme successfully learns and distinguishes (real zebra finch) birdsongs generated by several different birds. These results highlight the utility of having multiple internal models to make inferences in complicated social environments.

neuroscience↗

bioRxiv2017

Biological Self-organisation and Markov blankets

Biological self-organisation is a process of spontaneous pattern formation; namely the emergence of coherent and stable systemic configurations that distinguish themselves from their environment. This process can occur at various spatial scales: from the microscopic (giving rise to cells) to the macroscopic (the emergence of organisms). Self-organisation at each level is essential to account for the hierarchical organisation of living organisms (organelles within cells, within tissues, within organs, etc.). In this paper, we pursue the idea that Markov blankets - statistical boundaries separating states that are external to a system from its internal states - emerge at every possible level of the description of the (living) system. Through simulations, we show that the concept of a Markov blanket is fundamental in defining biological systems and underwrites the nature and form of interactions between successive levels of hierarchical structure. We demonstrate the validity of our argument using simulations, based on the normative principle of variational free energy minimisation. Specifically, we adopt a top-down approach to provide a proof of concept for the claim that the self-organisation of Markov blankets (and blankets of blankets) underwrites the self-evidencing, autopoietic behaviour of living systems.

systems biology↗