Visual working memory (VWM) representations interact with attentional guidance, but there is controversy over whether multiple VWM items simultaneously influence attentional guidance. Extant studies relied on continuous variables like response times, which can obscure capture – especially if VWM representations cycle through interactive and non-interactive states. Previous conflicting findings regarding guidance when under high working memory (WM) load may be due to the use of noisier response time measures that mix capture and non-capture trials. Thus, we employed an oculomotor paradigm to characterize discrete attentional capture events under both high and low VWM load. Participants held one or two colors in memory, then executed a saccade to a target disk. On some trials, a distractor (sometimes VWM-matching) appeared simultaneously with the target. Eye movements were more frequently directed to a VWM-matching than a non-matching distractor for both load conditions. However, oculomotor capture by a VWM-matching distractor occurred less frequently under high compared with low load. These results suggest that attention is automatically guided toward items matching only one of two colors held in memory at a time, suggesting that items in VWM may cycle through attention-guiding and not-guiding states when more than one item is held in VWM and the task does not require that multiple items be maintained in an active, attention-guiding state.
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An oculomotor signature as a fraud-resistant tool for biometric verification
Instant access to personal data is a double-edged sword and it has transformed society. It enhances convenience and interpersonal interactions through social media, while also making us all more vulnerable to identity theft and cybercrime. The need for hack-resistant biometric authentication is greater than ever. Previous studies have demonstrated that eye movements differ between individuals, so the characterization eye movements might provide a highly secure and convenient approach to personal identification, because eye movements are generated by the owner’s living brain in real-time and are therefore extremely difficult to imitate by hackers.
To study the potential of eye movements as a biometric tool, we characterized the eye movements of 18 participants. We examined an entire battery of oculomotor behaviors, including the unconscious eye movements that occur during ocular fixation; this resulted in a high precision oculomotor signature that can identify individuals. We show that one-versus-one machine learning classification, applied with a nearest neighbor statistic, yielded an accuracy of >99% based with ~25minute sessions, during which participants executed fixations, visual pursuits, free viewing of images, etc. Even if we just examine the ~3 minutes in which participants executed the fixation task by itself, discrimination accuracy was higher than 96%. When we further split the fixation data randomly into 30 sec chunks, we obtained a remarkably high accuracy of 92%.
Because eye-trackers provide improved spatial and temporal resolution with each new generation, we expect that both accuracy and the minimum sample duration necessary for reliable oculomotor biometric verification can be further optimized.
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- NSF-PAR ID:
- 10430782
- Date Published:
- Journal Name:
- Society for Neuroscience Annual Meeting 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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