Topic: visual attention

AI systems increasingly influence both which images people encounter and predictions of how those images attract attention. This session examines the relationship between automated image selection, predicted visual attention and measured human responses. Peter Steidl presents Visual GEO and a two-stage account of visual communication: an image must first be recognized and selected by a machine before human attention can be measured. Hedda Martina Šola compares machine predictions with human measurements in two published studies using the same material, including a case where an AI-created des

We use rapid eye, head, and body movements to extract information from a new part of the visual scene upon each new gaze fixation. But the consequences of such visual actions go beyond their intended sensory outcomes. On the one hand, intrinsic consequences accompany movement preparation as covert internal processes (e.g., predictive changes in the deployment of visual attention). On the other hand, visual actions have incidental consequences, side effects of moving the sensory surface to its intended goal (e.g., global motion of the retinal image during saccades). In this talk, I will present

Visual attention refers to a set of processes allowing selection of relevant and filtering out of irrelevant information in the visual environment. A large amount of research on visual attention has involved visual search paradigms, where observers are asked to report whether a single target is present or absent. However, recent studies have revealed that these classic single-target visual search tasks only provide a snapshot of how attention is allocated in the visual environment, and that multitarget visual foraging tasks may capture the dynamics visual attention more accurately. In visual f

Humans are incredible learners, a talent supported by our ability to detect and transfer relational similarities between items and events. Spotting these common relations despite perceptual differences is challenging, yet there’s evidence that this ability begins early, with infants as young as 3 months discriminating same and different (Anderson et al., 2018; Ferry et al., 2015). How? To understand the underlying mechanisms, I examine how learning outcomes in the first year correspond with changes in input and in infant age. I discuss the commonalities in this process with that seen in older

Cases with visuospatial abnormalities provide opportunities for understanding the underlying cognitive mechanisms. Three cases of visual mirror-reversal have been reported: AH (McCloskey, 2009), TM (McCloskey, Valtonen, & Sherman, 2006) and PR (Pflugshaupt et al., 2007). This research reports a fourth case, BS -- with focal occipital cortical dysgenesis -- who displays highly unusual visuospatial abnormalities. They initially produced mirror reversal errors similar to those of AH, who -- like the patient in question -- showed a selective developmental deficit. Extensive examination of BS revea

Visual attention selects only a tiny fraction of visual input information for further processing. Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea 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. Hence, feedback from downstream visual cortical areas to V1 for better decoding (recognition), through

The dynamic nature of sleep in most animals suggests distinct stages which serve different functions. Genetic sleep induction methods in animal models provide a powerful way to disambiguate these stages and functions, although behavioural methods alone are insufficient to accurately identify what kind of sleep is being engaged. In Drosophila, activation of the dorsal fan-shaped body (dFB) promotes sleep, but it remains unclear what kind of sleep this is, how the rest of the fly brain is behaving, or if any specific sleep functions are being achieved. Here, we developed a method to record calci

Visual search, the act of finding a target among multiple visually presented items, is a key paradigm in studying visual attention. While much is known about the brain networks underlying visual search, our understanding of the neural computations driving this behavior is limited, resulting in challenges in simulating such behavior in-silico. To address this gap, we developed an image-computable general-architecture visual search model (GVSM). We trained an artificial neural network to perform naturalistic visual search [Fig. A-B] using the same stimulus configuration as human psychophysics ex

Attention selectively prioritizes goal-related information to facilitate visual perception. Various studies have demonstrated that visual attention enhanced the efficacy of neuronal pairs’ interactions between the hierarchically structured visual cortex. The primary visual cortex (V1) also exhibits a hierarchical connection between simple (S) and complex (C) cells. We wonder about the impact of visual attention on neuronal interactions within V1, encompassing both the extent and manner in which it occurs. When monkeys engaged in an attention-involved task, we simultaneously recorded neuronal p