Biology subjects

Priorelli, M.

Publications and source records attributed to Priorelli, M..

6 recordsLinked to original sources

bioRxiv2024

Embodied decisions as active inference

Decision-making is often conceptualized as a serial process, during which sensory evidence is accumulated for the choice alternatives until a certain threshold is reached, at which point a decision is made and an action is executed. This decide-then-act perspective has successfully explained various facets of perceptual and economic decisions in the laboratory, in which action dynamics are usually irrelevant to the choice. However, living organisms often face another class of decisions - called embodied decisions - that require selecting between potential courses of actions to be executed timely in a dynamic environment, e.g., for a lion, deciding which gazelle to chase and how fast to do so. Studies of embodied decisions reveal two aspects of goal-directed behavior in stark contrast to the serial view. First, that decision and action processes can unfold in parallel; second, that action-related components, such as the motor costs associated with selecting a particular choice alternative or required to "change mind" between choice alternatives, exert a feedback effect on the decision taken. Here, we show that these signatures of embodied decisions emerge naturally in active inference - a framework that simultaneously optimizes perception and action, according to the same (free energy minimization) imperative. We show that optimizing embodied choices requires a continuous feedback loop between motor planning (where beliefs about choice alternatives guide action dynamics) and motor inference (where action dynamics finesse beliefs about choice alternatives). Furthermore, our active inference simulations reveal the normative character of embodied decisions in ecological settings - namely, achieving an effective balance between a high accuracy and a low risk of missing valid opportunities. Author summaryIn this study, we introduce a novel modeling approach to explore embodied decision-making, where decisions and actions occur simultaneously in dynamic environments. Unlike traditional models that treat decision and action as separate, our framework, based on active inference, reveals that crucial features of embodied decisions - such as feedback loops between decision and action dynamics - emerge naturally. By simulating real-time decision-making tasks, we show how organisms continuously refine their choices by integrating sensory information and motor dynamics. This allows them to strike a balance between decision accuracy and the need for fast, adaptive actions. Our model offers a new perspective on how decisions are influenced by the actions taken, highlighting the importance of considering motor control as an integral part of decision processes. This approach broadens the scope of decision-making research and provides new insights into behavior in ecologically valid, time-sensitive contexts, with potential implications for neuroscience, cognitive science, and fields involving human and animal behavior.

neuroscience↗

bioRxiv2023

Dynamic Inference by Model Reduction

How can agents infer the intentions of others by simply observing their behavior? And how can they generate fast and accurate actions such as grasping a moving object on the fly? Recent advances in Bayesian model reduction have led to innovative, biologically plausible approaches to actively infer the state of affairs of the world and perform planning with continuous signals. However, reducing the surrounding environment into a small set of simpler hypotheses remains a challenge in highly dynamic contexts. In this study, we propose an approach, based on active inference, that employs dynamic priors sampled from reduced versions of a generative model. Each dynamic prior corresponds to an alternative evolution of the world, which the agent can evaluate by accumulating continuous data. We test our approach on two everyday tasks: inferring a trajectory and grasping a moving object. Our findings reveal how agents can smoothly infer and enact dynamic intentions, and emphasize the key role of intentional gain or precision in motor learning.

neuroscience↗

bioRxiv2023

Slow but flexible or fast but rigid? Discrete and continuous processes compared

AO_SCPLOWBSTRACTC_SCPLOWA tradeoff exists when dealing with complex tasks composed of multiple steps. High-level cognitive processes can find the best sequence of actions to achieve a goal in uncertain environments, but they are slow and require significant computational demand. In contrast, lower-level processing allows reacting to environmental stimuli rapidly, but with limited capacity to determine optimal actions or to replan when expectations are not met. Through reiteration of the same task, biological organisms find the optimal tradeoff: from action primitives, composite trajectories gradually emerge by creating task-specific neural structures. The two frameworks of active inference - a recent brain paradigm that views action and perception as subject to the same free energy minimization imperative - well capture high-level and low-level processes of human behavior, but how task specialization occurs in these terms is still unclear. In this study, we compare two strategies on a dynamic pick-and-place task: a hybrid (discrete-continuous) model with planning capabilities and a continuous-only model with fixed transitions. Both models rely on a hierarchical (intrinsic and extrinsic) structure, well suited for defining reaching and grasping movements, respectively. Our results show that continuous-only models perform better and with minimal resource expenditure but at the cost of less flexibility. Finally, we propose how discrete actions might lead to continuous attractors and compare the two frameworks with different motor learning phases, laying the foundations for further studies on bio-inspired task adaptation.

neuroscience↗

bioRxiv2023

Active vision in binocular depth estimation: a top-down perspective

AO_SCPLOWBSTRACTC_SCPLOWDepth estimation is an ill-posed problem: objects of different shapes or dimensions, even if at different distances, may project to the same image on the retina. Our brain uses several cues for depth estimation, including monocular cues such as motion parallax and binocular cues like diplopia. However, it is still unclear how the computations required for depth estimation are implemented in biologically plausible ways. State-of-the-art approaches to depth estimation based on deep neural networks implicitly describe the brain as a hierarchical feature detector. Instead, we propose an alternative approach that casts depth estimation as a problem of active inference. We show that depth can be inferred by inverting a hierarchical generative model that simultaneously predicts the eyes projections from a 2D belief over an object. Model inversion consists of a series of biologically plausible, homogeneous transformations based on Predictive Coding principles. Under the plausible assumption of a nonuniform fovea resolution, depth estimation favors an active vision strategy that fixates the object with the eyes, rendering the depth belief more accurate. This strategy is not realized by first fixating on a target and then estimating the depth, but by combining the two processes through action-perception cycles, with a similar mechanism of the saccades during object recognition. The proposed approach requires only local (top-down and bottom-up) message passing that can be implemented in biologically plausible neural circuits.

neuroscience↗

bioRxiv2023

Deep kinematic inference affords efficient and scalable control of bodily movements

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on Optimal Control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, Active Inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce a novel Active Inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.

neuroscience↗

bioRxiv2022

Flexible Intentions in the Posterior Parietal Cortex: An Active Inference Theory

AO_SCPLOWBSTRACTC_SCPLOWWe present a normative computational theory of how neural circuitry may support visually-guided goal-directed actions in a dynamic environment. The model builds on Active Inference, in which perception and motor control signals are inferred through dynamic minimization of generalized prediction errors. The Posterior Parietal Cortex (PPC) is proposed to maintain constantly updated expectations, or beliefs over the environmental state, and by manipulating them through flexible intentions it is involved in dynamically generating goal-directed actions. In turn, the Dorsal Visual Stream (DVS) and the proprioceptive pathway implement generative models that translate the high-level belief into sensory-level predictions to infer targets, posture, and motor commands. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC putatively hosts its core intention mechanism. More broadly, the study provides a normative basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.

neuroscience↗