Dataset Information

Neural representation of object-specific attentional priority.

ABSTRACT: Humans can flexibly select locations, features, or objects in a visual scene for prioritized processing. Although it is relatively straightforward to manipulate location- and feature-based attention, it is difficult to isolate object-based selection. Because objects are always composed of features, studies of object-based selection can often be interpreted as the selection of a combination of locations and features. Here we examined the neural representation of attentional priority in a paradigm that isolated object-based selection. Participants viewed two superimposed gratings that continuously changed their color, orientation, and spatial frequency, such that the gratings traversed the same exact feature values within a trial. Participants were cued at the beginning of each trial to attend to one or the other grating to detect a brief luminance increment, while their brain activity was measured with fMRI. Using multi-voxel pattern analysis, we were able to decode the attended grating in a set of frontoparietal areas, including anterior intraparietal sulcus (IPS), frontal eye field (FEF), and inferior frontal junction (IFJ). Thus, a perceptually varying object can be represented by patterned neural activity in these frontoparietal areas. We suggest that these areas can encode attentional priority for abstract, high-level objects independent of their locations and features.

SUBMITTER: Liu T

PROVIDER: S-EPMC4803527 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neural representation of object-specific attentional priority.

Liu Taosheng T

NeuroImage 20160126

Humans can flexibly select locations, features, or objects in a visual scene for prioritized processing. Although it is relatively straightforward to manipulate location- and feature-based attention, it is difficult to isolate object-based selection. Because objects are always composed of features, studies of object-based selection can often be interpreted as the selection of a combination of locations and features. Here we examined the neural representation of attentional priority in a paradigm ...[more]

PMID: 26825437

Similar Datasets

Project description:Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

Dataset Information

Neural representation of object-specific attentional priority.

Publications

Neural representation of object-specific attentional priority.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets