Dataset Information

Rapid feature-driven changes in the attentional window.

ABSTRACT: Spatial attention must adjust around an object of interest in a manner that reflects the object's size on the retina as well as the proximity of distracting objects, a process often guided by nonspatial features. This study used ERPs to investigate how quickly the size of this type of "attentional window" can adjust around a fixated target object defined by its color and whether this variety of attention influences the feedforward flow of subsequent information through the visual system. The task involved attending either to a circular region at fixation or to a surrounding annulus region, depending on which region contained an attended color. The region containing the attended color varied randomly from trial to trial, so the spatial distribution of attention had to be adjusted on each trial. We measured the initial sensory ERP response elicited by an irrelevant probe stimulus that appeared in one of the two regions at different times after task display onset. This allowed us to measure the amount of time required to adjust spatial attention on the basis of the location of the task-relevant feature. We found that the probe-elicited sensory response was larger when the probe occurred within the region of the attended dots, and this effect required a delay of approximately 175 msec between the onset of the task display and the onset of the probe. Thus, the window of attention is rapidly adjusted around the point of fixation in a manner that reflects the spatial extent of a task-relevant stimulus, leading to changes in the feedforward flow of subsequent information through the visual system.

SUBMITTER: Leonard CJ

PROVIDER: S-EPMC3859374 | biostudies-other | 2013 Jul

REPOSITORIES: biostudies-other

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Publications

Rapid feature-driven changes in the attentional window.

Leonard Carly J CJ Lopez-Calderon Javier J Kreither Johanna J Luck Steven J SJ

Journal of cognitive neuroscience 20130228 7

Spatial attention must adjust around an object of interest in a manner that reflects the object's size on the retina as well as the proximity of distracting objects, a process often guided by nonspatial features. This study used ERPs to investigate how quickly the size of this type of "attentional window" can adjust around a fixated target object defined by its color and whether this variety of attention influences the feedforward flow of subsequent information through the visual system. The tas ...[more]

PMID: 23448524

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Project description:BACKGROUND:Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ?C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. OBJECTIVE:In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. METHODS:We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. RESULTS:We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). CONCLUSIONS:Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

Dataset Information

Rapid feature-driven changes in the attentional window.

Publications

Rapid feature-driven changes in the attentional window.

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