Dataset Information

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

ABSTRACT: Path integration is a strategy by which animals track their position by integrating their self-motion velocity. 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 traveled beyond the goal location. Such a behavior could stem from leaky integration of unbiased self-motion velocity estimates or from a prior expectation favoring slower speeds that causes velocity underestimation. Testing both alternatives using a probabilistic framework that maximizes expected reward, we 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 buildup of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration in noisy environments is limited largely by biases in processing optic flow rather than by leaky integration.

SUBMITTER: Lakshminarasimhan KJ

PROVIDER: S-EPMC6190923 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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

Lakshminarasimhan Kaushik J KJ Petsalis Marina M Park Hyeshin H DeAngelis Gregory C GC Pitkow Xaq X Angelaki Dora E DE

Neuron 20180621 1

Path integration is a strategy by which animals track their position by integrating their self-motion velocity. 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 traveled beyond the goal location. Such a behavior could stem from leaky integration of unbiased self-motion velocity estimates or from a prior expectation favoring slower speeds that causes velocity undere ...[more]

PMID: 29937278

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Project description:Mathematical model observers are intended for efficient assessment of diagnostic image quality, but model-observer studies often are not representative of clinical realities. Model observers based on a visual-search (VS) paradigm may allow for greater clinical relevance. The author has compared the performances of several VS model observers with those of human observers and an existing scanning model observer for a study involving nodule detection and localization in simulated Tc-99m single-photon emission computed tomography (SPECT) lung volumes.A localization receiver operating characteristic (LROC) study compared two iterative SPECT reconstruction strategies: an all-corrections (AllC) strategy with compensations for attenuation, scatter, and distance-dependent camera resolution and an "RC" strategy with resolution compensation only. Nodules in the simulation phantom were of three different relative contrasts. Observers in the study had access to the coronal, sagittal, and transverse displays of the reconstructed volumes. Three human observers each read 50 training volumes and 100 test volumes per reconstruction strategy. The same images were analyzed by a channelized nonprewhitening (CNPW) scanning observer and two VS observers. The VS observers implemented holistic search processes that identified focal points of Tc-99m uptake for subsequent analysis by the CNPW scanning model. The level of prior knowledge about the background structure in the images was a study variable for the model observers. Performance was scored by area under the LROC curve.The average human-observer performances were respectively 0.67 ± 0.04 and 0.61 ± 0.03 for the RC and AllC strategies. Given approximate knowledge about the background structure, both VS models scored 0.69 ± 0.08 (RC) and 0.66 ± 0.08 (AllC). The scanning observer reversed the strategy ranking in scoring 0.73 ± 0.08 with the AllC strategy and 0.64 ± 0.08 with the RC strategy. The VS observers exhibited less sensitivity to variations in background knowledge compared to the scanning observer.The VS framework has the potential to increase the clinical similitude of model-observer studies and to enhance the ability of existing model observers to quantitatively predict human-observer performance.

Project description:The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity.

Dataset Information

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

Publications

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

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