Search arXivSearch

arXiv · 1411.0291

Primary visual cortex as a saliency map: parameter-free prediction of behavior from V1 physiology

Abstract

It has been hypothesized that neural activities in the primary visual cortex (V1) represent a saliency map of the visual field to exogenously guide attention. This hypothesis has so far provided only qualitative predictions and their confirmations. We report this hypothesis' first quantitative prediction, derived without free parameters, and its confirmation by human behavioral data. The hypothesis provides a direct link between V1 neural responses to a visual location and the saliency of that location to guide attention exogenously. In a visual input containing many bars, one of them saliently different from all the other bars which are identical to each other, saliency at the singleton's location can be measured by the shortness of the reaction time in a visual search task to find the singleton. The hypothesis predicts quantitatively the whole distribution of the reaction times to find a singleton unique in color, orientation, and motion direction from the reaction times to find other types of singletons. The predicted distribution matches the experimentally observed distribution in all six human observers. A requirement for this successful prediction is a data-motivated assumption that V1 lacks neurons tuned simultaneously to color, orientation, and motion direction of visual inputs. Since evidence suggests that extrastriate cortices do have such neurons, we discuss the possibility that the extrastriate cortices play no role in guiding exogenous attention so that they can be devoted to other functional roles like visual decoding or endogenous attention.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li Zhaoping, Li Zhe. 2014-11-02. Primary visual cortex as a saliency map: parameter-free prediction of behavior from V1 physiology. https://doi.org/10.1371/journal.pcbi.1004375

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Only what exists can cause: An intrinsic powers view of free will

This essay addresses the implications of integrated information theory (IIT) for free will. IIT is a theory of what consciousness is and of how its presence and quality can be accounted for in physical terms. According to IIT, the presence of consciousness is accounted for by a maximum of cause-effect power in the brain. Moreover, the way an experience feels is accounted for by how that cause-effect power is structured. If IIT is right, we do have free will in a genuine sense: we have alternatives, reasons, and values, we make decisions, and we-not our neurons or atoms-are the cause of our willed actions and bear responsibility for them. IIT's argument for genuine free will hinges on the proper understanding of consciousness as intrinsic existence, captured by its intrinsic powers ontology: what exists absolutely, in physical terms, are intrinsic entities, and only what exists can cause.

q-bio.NC

Seeing the imagined: latent functional alignment in visual imagery decoding from fMRI data

Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art (SOTA) perception decoder (DynaDiff) can be adapted to reconstruct imagined content from the NSD-Imagery benchmark. We propose a latent functional alignment (LFA) approach that maps imagery-evoked activity to the pretrained model's semantic content-enriched conditioning space, by adding a simple alignment module, while keeping the original remaining components frozen. To mitigate the limited amount of matched imagery-perception supervision, we further introduce a neural retrieval-based augmentation strategy that selects semantically related NSD perception trials from the same participants. Across four subjects, LFA consistently improves high-level semantic reconstruction metrics relative to the frozen pretrained baseline and a voxel-space ridge alignment baseline, and enables above-chance decoding from multiple cortical regions. These results suggest that semantic structure learned from perception can be leveraged to stabilize and improve visual imagery decoding under out-of-distribution conditions.

q-bio.NC

Deep Learning in Infant Functional Neuroimaging: Challenges, Advances, and Future Directions

Infancy is a critical developmental window characterized by rapid functional brain reorganization, during which large-scale networks emerge, individualized connectome signatures continue to form, and early deviations may shape long-term cognitive and clinical outcomes. Functional MRI (fMRI) offers an opportunity to study these processes in vivo, yet extracting developmentally meaningful information from it remains challenging due to comparatively short scan duration, structured motion artifacts, variable scan states, and rapid brain maturation. Amid these challenges, deep learning has expanded the capacity of computational neuroimaging by learning robust representations from noisy, high-dimensional data, integrating complex spatial and temporal information, and capturing the nonlinear and rapidly evolving organization of the developing brain. Here, we review recent advances in deep learning for infant functional neuroimaging, synthesizing progress across input representation formatting, population and individualized brain mapping, longitudinal trajectory forecasting, robust and explainable model evaluation, and biological translation. Collectively, these methodological advances mark a paradigm shift in infant functional neuroimaging from descriptive, group-level analyses toward reliable, individualized, and developmentally grounded models. Future progress will depend on larger and more diverse longitudinal datasets, developmentally appropriate model designs, rigorous and standardized evaluation, and integration of computational predictions with biological mechanisms towards clinically meaningful outcomes. Addressing these priorities will help establish deep learning as a robust framework for understanding early functional brain development, identifying developmental variation at the individual level, and ultimately supporting earlier and precise assessment of neurodevelopmental risk.

q-bio.NC