TRF1: It Was the Best of Time(s)…

Giersch, Anne; Coull, Jennifer T.

doi:10.1163/22134468-00603001

Cited by 3 publications

(3 citation statements)

References 80 publications

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“…To estimate whether confidence distorts duration judgments, physically and mentally healthy individuals were tested. Verifying previous findings 15,16 , we demonstrated that the perceived duration of the monetary gain condition was perceived as longer than the neutral condition and that confidence, as the perceived likelihood of being correct, was higher in a monetary gain scenario than in neutral and loss scenarios. We found that individual differences in confidence influenced duration judgments.…”

Section: Introductionsupporting

confidence: 89%

“…However, it has also been reported that when there is a potential to win money, the duration is perceived as longer than in loss or neutral conditions. In other words, cuing a monetary reward prior to a duration judgment task distorts time, causing it to be overestimated compared to the reference duration 16 . Given the aforementioned different monetary reward conditions, a considerable body of literature generally associates attentional [17][18][19][20][21][22] or intentional [23][24][25][26] resources with monetary gain, which causes a longer perception of duration.…”

Section: Introductionmentioning

confidence: 99%

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Time is Confidence: Monetary Incentives Metacognitive Profile on Duration Judgment

Sarabi,

Zimmermann

2023

Preprint

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The question we addressed in the current study is whether the mere prospect of monetary reward affects subjective time perception. To test this question, we collected trail-based confidence reports in a task in which subjects made categorical decisions about probe durations relative to the reference duration. When there was a potential to gain monetary reward, the duration was perceived to be longer than in the neutral condition, and confidence, which reflects the perceived probability of being correct, was higher in the reward condition than in the neutral condition. We found that confidence influences the sense of time in different individuals: subjects with high-confidence reported that they perceived the duration signaled by the monetary gain condition as longer than subjects with low-confidence. Our results showed that only high-confidence individuals overestimated the monetary gain context. Finally, we found a negative relationship between confidence and time perception, and that confidence bias at the maximum uncertainty duration of 450 ms is predictive of time perception. Taken together, the current study demonstrates that subjective measure of the confidence profile caused overestimation of time rather than by the outcome valence of reward expectancy.

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Section: Introductionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Time is Confidence: Monetary Incentives Metacognitive Profile on Duration Judgment

Sarabi,

Zimmermann

2023

Preprint

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“…They achieved greater precision and accuracy of 92.06% by combining multimode magnetic resonance with the CNN core. Gunawardena et al in [26] utilized 1615 complete brain MRI scans for the diagnosis of AD. At first, they employed the existing vector carrier method for detecting AD with an accuracy of 84.4%.…”

Section: B Convolutional Neural Network Basedmentioning

confidence: 99%

A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection

et al. 2019

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Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.

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Creating a Home for Timing Researchers: Then, Now, and the Future

Vatakis,

Teki

2024

Advances in Experimental Medicine and Biology

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TRF1: It Was the Best of Time(s)…

Abstract: This is an open access article distributed under the terms of the prevailing CC-BY license at the time of publication.

Cited by 3 publications

References 80 publications

Time is Confidence: Monetary Incentives Metacognitive Profile on Duration Judgment

Time is Confidence: Monetary Incentives Metacognitive Profile on Duration Judgment

A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection

Creating a Home for Timing Researchers: Then, Now, and the Future

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