Biology subjects

Angelaki, D. E.

Publications and source records attributed to Angelaki, D. E..

14 recordsLinked to original sources

bioRxiv2026

Primate Gaze Behavior Reflects Change of Mind in a Naturalistic Navigation Task

Revising a decision while acting on it is a fundamental aspect of natural behavior, yet the underlying computations are difficult to track in real time. To explore how such belief updates unfold, we tested whether eye movements reveal a subject's change of mind during a naturalistic navigation task with a binary target choice. Humans and monkeys were required to navigate to one of two goal locations presented either simultaneously or sequentially in a virtual environment where explicit position cues were absent. Our findings reveal that gaze continuously tracks the latent variable, including changes of mind. We found that the target tracking accuracy before the second target is even presented during sequential target trials predicts whether the subject's navigational goal persists (first target choice) or changes (second target choice). These results underscore the importance of gaze in action planning and changes of mind, supporting the hypothesis that eye movements embody not only the current latent variable but also its uncertainty.

neuroscience↗

bioRxiv2026

Temporal Structure of Reward Availability and Sensory Uncertainty Modulate Allocation Dynamics in Naturalistic Foraging

Adaptive foraging requires animals to combine uncertain sensory cues with predictions about when rewards are likely to occur. While theoretical models describe how animals should allocate their effort under variable-interval reward schedules, it remains unclear how the timing or rewards and the reliability of sensory cues affects behavior. We developed a continuous foraging task in which freely moving macaque monkeys navigated among three reward patches. Rewards became available at unpredictable times, with their availability signaled by a visual cue of varying reliability. We also varied the schedule of reward availability: in some conditions, rewards were equally likely to become available at any moment (exponentially distributed intervals), while in others the interval distribution was more concentrated around a particular mean (gamma-distributed intervals) which increased the cost of premature responses. Under exponential schedules, monkeys eventually allocated their time at different patches according to reward schedules, and cue reliability had only modest effects. Under gamma-distributed intervals, monkeys more quickly learned to differentiate between patches. Their choices were more strongly dependent on predicted reward timing, particularly when sensory cues were highly reliable. These results show that both the timing of rewards and reliability of sensory cues shape how animals allocate their time and effort in continuous naturalistic foraging tasks.

animal behavior and cognition↗

bioRxiv2023

Belief embodiment through eye movements facilitates memory-guided navigation

Neural network models optimized for task performance often excel at predicting neural activity but do not explain other properties such as the distributed representation across functionally distinct areas. Distributed representations may arise from animals strategies for resource utilization, however, fixation-based paradigms deprive animals of a vital resource: eye movements. During a naturalistic task in which humans use a joystick to steer and catch flashing fireflies in a virtual environment lacking position cues, subjects physically track the latent task variable with their gaze. We show this strategy to be true also during an inertial version of the task in the absence of optic flow and demonstrate that these task-relevant eye movements reflect an embodiment of the subjects dynamically evolving internal beliefs about the goal. A neural network model with tuned recurrent connectivity between oculomotor and evidence-integrating frontoparietal circuits accounted for this behavioral strategy. Critically, this model better explained neural data from monkeys posterior parietal cortex compared to task-optimized models unconstrained by such an oculomotor-based cognitive strategy. These results highlight the importance of unconstrained movement in working memory computations and establish a functional significance of oculomotor signals for evidence-integration and navigation computations via embodied cognition.

neuroscience↗

bioRxiv2022

Inductive biases of neural networks for generalization in spatial navigation

The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. Behaviors of agents with this architecture also resemble macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brains modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.

neuroscience↗

bioRxiv2022

Dynamical Latent State Computation in the Posterior Parietal Cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkeys displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

neuroscience↗

bioRxiv2021

Eye movements reveal spatiotemporal dynamics of active sensing and planning in navigation

Goal-oriented navigation is widely understood to depend upon internal maps. Although this may be the case in many settings, humans tend to rely on vision in complex, unfamiliar environments. To study the nature of gaze during visually-guided navigation, we tasked humans to navigate to transiently visible goals in virtual mazes of varying levels of difficulty, observing that they took near-optimal trajectories in all arenas. By analyzing participants eye movements, we gained insights into how they performed visually-informed planning. The spatial distribution of gaze revealed that environmental complexity mediated a striking trade-off in the extent to which attention was directed towards two complimentary aspects of the world model: the reward location and task-relevant transitions. The temporal evolution of gaze revealed rapid, sequential prospection of the future path, evocative of neural replay. These findings suggest that the spatiotemporal characteristics of gaze during navigation are significantly shaped by the unique cognitive computations underlying real-world, sequential decision making.

neuroscience↗

bioRxiv2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video -- specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral "syllables," and estimation of interpretable "disentangled" low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose.

animal behavior and cognition↗

bioRxiv2019

Tracking the mind’s eye: Primate gaze behavior during virtual visuomotor navigation reflects belief dynamics

To take the best actions, we often need to maintain and update beliefs about variables that cannot be directly observed. To understand the principles underlying such belief updates, we need tools to uncover subjects belief dynamics from natural behaviour. We tested whether eye movements could be used to infer subjects beliefs about latent variables using a naturalistic, visuomotor navigation task. We observed eye movements that appeared to continuously track the goal location even when no visible target was present there. Accurate goal-tracking was associated with improved task performance, and inhibiting eye movements in humans impaired navigation precision. By using passive stimulus playback and manipulating stimulus reliability, we show that subjects eye movements are likely voluntary, rather than reflexive. These results suggest that gaze dynamics play a key role in action-selection during challenging visuomotor behaviours, and may possibly serve as a window into the subjects dynamically evolving internal beliefs.

animal behavior and cognition↗

bioRxiv2019

Multiplexed code of navigation variables in the anterior limbic system

The brains navigation system integrates multimodal cues to create a sense of position and orientation. Here we used a multimodal model to systematically assess how neurons in the anterior thalamic nuclei, retrosplenial cortex and anterior hippocampus of mice, as well as in the cingulum fiber bundle and the white matter regions surrounding the hippocampus, encode an array of navigational variables when animals forage in a circular arena. In addition to coding head direction, we found that some thalamic cells encode the animals allocentric position, similar to place cells. We also found that a large fraction of retrosplenial neurons, as well as some hippocampal neurons, encode the egocentric position of the arenas boundary. We compared the multimodal model to traditional methods of head direction tuning and place field analysis, and found that the latter were inapplicable to multimodal regions such as the anterior thalamus and retrosplenial cortex. Our results draw a new picture of the signals carried and outputted by the anterior thalamus and retrosplenial cortex, offer new insights on navigational variables represented in the hippocampus and its vicinity, and emphasize the importance of using multimodal models to investigate neural coding throughout the navigation system.

neuroscience↗

bioRxiv2018

Choice-Related Activity during Visual Slant Discrimination in Macaque CIP but Not V3A

Creating three-dimensional (3D) representations of the world from two-dimensional retinal images is fundamental to many visual guided behaviors including reaching and grasping. A critical component of this process is determining the 3D orientation of objects. Previous studies have shown that neurons in the caudal intraparietal area (CIP) of the macaque monkey represent 3D planar surface orientation (i.e., slant and tilt). Here we compare the responses of neurons in areas V3A (which is implicated in 3D visual processing and which precedes CIP in the visual hierarchy) and CIP to 3D oriented planar surfaces. We then examine whether activity in these areas correlates with perception during a fine slant discrimination task in which monkeys report if the top of a surface is slanted towards or away from them. Although we find that V3A and CIP neurons show similar sensitivity to planar surface orientation, significant choice-related activity during the slant discrimination task is rare in V3A but prominent in CIP. These results implicate both V3A and CIP in the representation of 3D surface orientation, and suggest a functional dissociation between the areas based on slant-related decision signals.\n\nSignificance StatementSurface orientation perception is fundamental to visually guided behaviors such as reaching, grasping, and navigation. Previous studies implicate the caudal intraparietal area (CIP) in the representation of 3D surface orientation. Here we show that responses to 3D oriented planar surfaces are similar in CIP and V3A, which precedes CIP in the cortical hierarchy. However, we also find a qualitative distinction between the two areas: only CIP neurons show robust choice-related activity during a fine visual orientation discrimination task.

neuroscience↗

bioRxiv2017

Speed tuning in the macaque posterior parietal cortex

Neurons in the macaque posterior parietal cortex are known to encode the direction of self-motion. But do they also encode ones speed? To test this, we performed neural recordings from area 7a while monkeys were passively translated or rotated at various speeds. Visual stimuli were delivered as optic flow fields and vestibular stimuli were generated by a motion platform. Under both conditions, the responses of a fraction of neurons scaled linearly with self-motion speed, and speed-selective neurons were not localized to specific layers or columns. We analyzed ensembles of simultaneously recorded neurons and found that the precision of speed representation was sufficient to support path integration over modest distances. Our findings describe a multisensory neural code for linear and angular self-motion speed in the posterior parietal cortex of the macaque brain, and suggest a potential role for this representation.

neuroscience↗

bioRxiv2017

A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration

Path integration is a navigation strategy by which animals track their position by integrating their self-motion velocity over time. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and found that subjects biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration performance is limited largely by biases in processing optic flow rather than by suboptimal signal integration.

neuroscience↗

bioRxiv2017

Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

The precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, in order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

neuroscience↗

bioRxiv2017

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally-generated ( passive) movements. However, these neurons show reduced responses during self-generated ( active) movements, indicating that the brain computes the predicted sensory consequences of motor commands in order to cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations established during passive movements remain unknown. Here we construct a Kalman filter that incorporates motor commands into a previously-established model of optimal passive self-motion estimation. We find that the simulated sensory error and feedback signals match experimentally measured neuronal response during active and passive head and trunk rotations and translations. We conclude that a single internal model of head motion can process motor commands and sensory afferent signals optimally, and we describe how previously identified neural responses in the brainstem and cerebellum may represent distinct nodes in these computations.

neuroscience↗