Human social interactions depend on the ability to resolve uncertainty about the mental states of others. The context in which social interactions take place is crucial for mental state attribution ...as sensory inputs may be perceived differently depending on the context. In this paper, we introduce a mental state attribution task where a target-face with either an ambiguous or an unambiguous emotion is embedded in different social contexts. The social context is determined by the emotions conveyed by other faces in the scene. This task involves mental state attribution to a target-face (either happy or sad) depending on the social context. Using active inference models, we provide a proof of concept that an agent's perception of sensory stimuli may be altered by social context. We show with simulations that context congruency and facial expression coherency improve behavioural performance in terms of decision times. Furthermore, we show through simulations that the abnormal viewing strategies employed by patients with schizophrenia may be due to (i) an imbalance between the precisions of local and global features in the scene and (ii) a failure to modulate the sensory precision to contextualise emotions.
Artificial intelligence has recently attained humanlike performance in a number of gamelike domains. These advances have been spurred by brain-inspired architectures and algorithms such as ...hierarchical filtering and reinforcement learning. OpenAI Gym is an open-source platform in which to train, test, and benchmark algorithms—it provides a range of tasks, including those of classic arcade games such as Doom. Here we describe how the platform might be used as a simulation, test, and diagnostic paradigm for psychiatric conditions.
To illustrate how active inference models of game play could be used to test mechanistic and algorithmic properties of psychiatric disorders, we provide two exemplar analyses. The first speaks to the impact of aging on cognition, examining game-play behaviors in a model of aging in which we compared age-dependent changes of younger (n = 9, 22 ± 1 years of age) and older (n = 7, 56 ± 5 years of age) adult players. The second is an illustration of a putative feature of anhedonia in which we simulated diminished sensitivity to reward.
These simulations demonstrate how active inference can be used to test predicted changes in both neurobiology and beliefs in psychiatric cohorts. We show that, as well as behavioral measures, putative neural correlates of active inference can be simulated, and hypothesized (model-based) differences in local field potentials and blood oxygen level–dependent responses can be produced.
We show that active inference, through epistemic and value-based goals, enables simulated subjects to actively develop detailed representations of gaming environments, and we demonstrate the use of a principled algorithmic and neurobiological framework for testing hypotheses in psychiatric illness.
In this thesis I present a number of computational models and biologically interpretable agents that embody theoretical constructs of neural computation, namely the Free Energy Principle and Active ...Inference. The Free Energy Principle is a unifying theory of the brain that integrates concepts from statistical physics, theoretical neuroscience and machine learning; its central claim, that any self-organizing system at equilibrium with its environment must minimize its free energy, is supported by a mathematical formulation of how adaptive systems resist a natural tendency to disorder. Active Inference is a corollary of the Free Energy Principle that explains how action, perception and learning are unified by this objective. Using the discrete-time Markovian formulation of Active Inference, I show that individual differences in behaviour can be explained in terms of model parameters rather than (or in addition to) traditional behavioural metrics, allowing one to make meaningful comparisons between real and simulated behaviours. I also use a model of predictive coding to simulate perceptual inference, drawing a comparison between learned visual representations in the brain and the latent variables of deep generative models. In the final chapter, I combine these predictive coding and decision-making models to simulate both categorical perception and saccade planning in a novel visual search task. This novel contribution demonstrates that complex task stimuli can be accounted for explicitly within active inference models, enabling their use in a wider range of experimental paradigms.
Dopamine (DA) is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although the ...dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridges from molecular to neuronal circuit level is still lacking. In this study, the authors aim to develop a realistic yet efficient computational model of a dopaminergic pre-synaptic terminal. They first systematically perturb the variables/substrates of an established computational model of DA synthesis, release and uptake, and based on their relative dynamical timescales and steady-state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. They show that the original and reduced models exhibit rather similar steady and perturbed states, whereas the reduced models are more computationally efficient and illuminate the underlying key mechanisms. They then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, they successfully include autoreceptor-mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step toward an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.
We propose a computational model of visual search that incorporates Bayesian interpretations of the neural mechanisms that underlie categorical perception and saccade planning. To enable meaningful ...comparisons between simulated and human behaviours, we employ a gaze-contingent paradigm that required participants to classify occluded MNIST digits through a window that followed their gaze. The conditional independencies imposed by a separation of time scales in this task are embodied by constraints on the hierarchical structure of our model; planning and decision making are cast as a partially observable Markov Decision Process while proprioceptive and exteroceptive signals are integrated by a dynamic model that facilitates approximate inference on visual information and its latent causes. Our model is able to recapitulate human behavioural metrics such as classification accuracy while retaining a high degree of interpretability, which we demonstrate by recovering subject-specific parameters from observed human behaviour.
We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5th 2020. Two distinct ...generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model and second: a partially observable Markov Decision Process (MDP) or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (S0), as well as epidemic parameters. Parameter estimation (data fitting) was performed using a standard Bayesian scheme (variational Laplace) designed to allow for latent unobservable states and uncertainty in model parameters. Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. The peaks of the current waves were predicted to be in the past for four countries (Italy, Spain, Germany and Switzerland) and to emerge in 0.5-2 weeks in Ireland and 1-3 weeks in the UK. For France one model estimated the peak within the past week and the other in the future in two weeks. Crucially, Maximum a posteriori (MAP) estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. With a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% for Germany and 12.9% for Switzerland. Our results indicate that after the current wave, a large proportion of the total population will remain without immunity.
Dopamine is an important neurotransmitter responsible for regulating various brain functions such as learning and cognition. Dysfunctions within the dopaminergic system are implicated in many ...neurological and neuropsychiatric disorders. To understand such a complex system, biologically realistic multiscale computational models are necessary. Such models require the extraction of relevant and important factors or processes from one scale to bridge and interact with systems at other scales. In this paper, we analyze an influential computational model of dopamine synthesis and release within a pre-synaptic terminal by systematically perturbing its variables/substrates. Based on the relative changes in steady states and the time to reach the new perturbed steady states, we found that the substrates within the cascade of intracellular biochemical reactions can vary widely in terms of influence and timescale. We then categorize the substrates according to their relative timescales and changes in steady states. The perturbation results are then used to guide our selection for the most appropriate equations and functions to be approximated in developing reduced models of the original model. Our preliminary simulation results show that either a slow or fast version of the reduced model can be simulated significantly faster than the original model. Our work demonstrates, through perturbation analysis, the feasibility of reduced models of the dopaminergic presynaptic terminal to improve computational efficiency, implement in multiscale modelling, and in silico neuropharmacology.
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