Visual attention selects only a tiny fraction of visual input information for further processing and recognition. This makes human blind to most of visual inputs. Attentional selection starts at the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea (center of gaze) to selected visual locations via gaze shifts. This motivates a new framework that views vision as consisting of encoding, selection, and decoding stages, placing selection on center stage. It suggests a massive loss of non-selected information from V1 downstream along the visual pathway to higher cortical areas. Hence, feedback from downstream areas to V1 for better decoding (recognition), through analysis-by-synthesis, should query for additional information and be mainly directed at the foveal region. Accordingly, non-foveal vision is not only poorer in spatial resolution, but also more susceptible to many illusions (which are akin to adversarial attack in artificial neural networks). I will review studies quantifying the attentional bottleneck, the discovery of V1’s saliency map (V1SH) built from recurrent processing in V1, signatures of information loss from V1 to downstream brain areas along the visual pathway, visual illusions manifesting the bottleneck, and evidence for, and the workings of, top-down feedback (from higher to lower visual areas like V1) in light of the bottleneck for visual recognition mainly in the central vision. Computational, psychophysical, neurobiological, and engineering approaches can hopefully combine to understand vision in humans and machines.
Some references are:
Zhaoping L. (2014) Understanding Vision: theory, models, and data. Oxford University Press (https://www.lizhaoping.org/zhaoping/VisionBook.html).
Zhaoping, L. (2019) A new framework for understanding vision from the perspective of the primary visual cortex Current Opinion in Neurobiology, volume 58, page 1-10 (https://www.lizhaoping.org/zhaoping/NewPathPaperEtc_2019.html).
Zhaoping, L. (2023) Peripheral and central sensation: Multisensory orienting and recognition across species Trends for Cognitive Sciences, Vol 27, issue 6, page 539-552 (https://www.lizhaoping.org/zhaoping/prints/Zhaoping_TICS_2023.pdf).