Transient Signals and Inattentional Blindness in a Multi-object Tracking Task

Palmer, Dakota; Yamani, Yusuke; Bobrow, Taylor L.; Karpinsky, Nicole D.; Krusienski, Dean J.

doi:10.1177/2041669518754595

Cited by 4 publications

(2 citation statements)

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“…Such a factor can play a role in other animal models, with honeybees preferencing 20 -25 Hz visual flicker and avoiding 2-4 Hz (Van De Poll et al, 2015). Even a single luminance change is enough to break inattentional blindness in humans (Palmer et al, 2018). To minimize this possibility, we distributed the two tagging frequencies across two spatial locations (T 1 and T 2 ) as well as testing our entire experimental paradigm at two different frequency tagged pairs.…”

Section: Discussionmentioning

confidence: 99%

A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

et al. 2019

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The visual world projects a complex and rapidly changing image onto the retina of many animal species. This presents computational challenges for those animals reliant on visual processing to provide an accurate representation of the world. One such challenge is parsing a visual scene for the most salient targets, such as the selection of prey amid a swarm. The ability to selectively prioritize processing of some stimuli over others is known as 'selective attention'. We recently identified a dragonfly visual neuron called 'Centrifugal Small Target Motion Detector 1Ј (CSTMD1) that exhibits selective attention when presented with multiple, equally salient targets. Here we conducted in vivo, electrophysiological recordings from CSTMD1 in wild-caught male dragonflies (Hemicordulia tau), while presenting visual stimuli on an LCD monitor. To identify the target selected in any given trial, we uniquely modulated the intensity of the moving targets (frequency tagging). We found that the frequency information of the selected target is preserved in the neuronal response, while the distracter is completely ignored. We also show that the competitive system that underlies selection in this neuron can be biased by the presentation of a preceding target on the same trajectory, even when it is of lower contrast than an abrupt, novel distracter. With this improved method for identifying and biasing target selection in CSTMD1, the dragonfly provides an ideal animal model system to probe the neuronal mechanisms underlying selective attention.

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Section: Discussionmentioning

confidence: 99%

A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

et al. 2019

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“…Such a factor can play a role in other animalmodels, with honeybees preferencing 20-25 Hz and avoiding 2-4 Hz visual flicker (Van De Poll et al, 2015). Even a single luminance change is enough to break inattentional blindness in humans(Palmer et al, 2018). To minimise this possibility, we distributed the two tagging frequencies across the two spatial locations (T1 and T2) as well as testing our entire experimental paradigm at two different frequency-tagged pairs.…”

mentioning

confidence: 99%

Target-detecting neurons in the dragonfly lock on to selectively attended targets

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Evans

Fabian

et al. 2018

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The visual world projects a complex and rapidly changing image on to the retina, presenting a computational challenge for any animal relying on vision for an accurate view of the world. One such challenge is parsing a visual scene for the most salient targets, such as the selection of prey amidst a swarm. The ability to selectivity prioritize processing of some stimuli over others is known as 'selective attention'. Previously, we identified a dragonfly visual neuron called 'Centrifugal Small Target Motion Detector 1' (CSTMD1) that exhibits selective attention when presented with multiple, equally salient features. Here we conducted electrophysiological recordings from CSTMD1 neurons in vivo, whilst presenting visual stimuli on a monitor display. To identify the target selected in any given trial, we modulated the intensity of moving targets, each with a unique frequency (frequency-tagging). We find that the frequency information of the selected stimulus is preserved in the neuronal response, whilst the distracter is completely ignored. We show that the competitive system that underlies selection in this neuron can be biased by the presentation of a preceding target on the same trajectory, even when it is of lower contrast to the distracter. With an improved method of identifying and biasing target selection in CSTMD1, the dragonfly provides an effective animal model system to probe the mechanisms underlying neuronal selective attention.Significance Statement: This is a novel application of frequency tagging at the intracellular level, demonstrating that frequency information of a flickering stimulus is preserved in the response of an individual neuron. Using this technique, we show that the selective attention mechanism in an individual dragonfly visual neuron is able to lock on to the selected stimuli, in the presence of distracters, even those of abrupt onset or higher contrast. Conversely, unidentified factors allow selection to occasionally switch mid-trial to the other target. We therefore show that this neuronal network underlying selective attention is more complex than the traditionally modelled winner-takes-all framework.

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Exploring the Effects of Perceptual Separability on Human-Automation Team Efficiency

et al. 2021

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Transient Signals and Inattentional Blindness in a Multi-object Tracking Task

Cited by 4 publications

References 12 publications

A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

Target-detecting neurons in the dragonfly lock on to selectively attended targets

Exploring the Effects of Perceptual Separability on Human-Automation Team Efficiency

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