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An attentional limbo: Saccades become momentarily non-selective in between saliency-driven and relevance-driven selection.

ABSTRACT: Human vision involves selectively directing the eyes to potential objects of interest. According to most prominent theories, selection is the quantal outcome of an ongoing competition between saliency-driven signals on the one hand, and relevance-driven signals on the other, with both types of signals continuously and concurrently projecting onto a common priority map. Here, we challenge this view. We asked participants to make a speeded eye movement towards a target orientation, which was presented together with a non-target of opposing tilt. In addition to the difference in relevance, the target and non-target also differed in saliency, with the target being either more or less salient than the non-target. We demonstrate that saliency- and relevance-driven eye movements have highly idiosyncratic temporal profiles, with saliency-driven eye movements occurring rapidly after display onset while relevance-driven eye movements occur only later. Remarkably, these types of eye movements can be fully separated in time: We find that around 250 ms after display onset, eye movements are no longer driven by saliency differences between potential targets, but also not yet driven by relevance information, resulting in a period of non-selectivity, which we refer to as the attentional limbo. Binomial modeling further confirmed that visual selection is not necessarily the outcome of a direct battle between saliency- and relevance-driven signals. Instead, selection reflects the dynamic changes in the underlying saliency- and relevance-driven processes themselves, and the time at which an action is initiated then determines which of the two will emerge as the driving force of behavior.

SUBMITTER: van Heusden E

PROVIDER: S-EPMC8979483 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.

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An attentional limbo: Saccades become momentarily non-selective in between saliency-driven and relevance-driven selection.

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