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Martins E Silva DC, Marinho V, Teixeira S, Teles G, Marques J, Escórcio A, Fernandes T, Freitas AC, Nunes M, Ayres M, Ayres C, Marques JB, Cagy M, Gupta DS, Bastos VH. Non-immersive 3D virtual stimulus alter the time production task performance and increase the EEG theta power in dorsolateral prefrontal cortex. Int J Neurosci 2020; 132:563-573. [PMID: 32962509 DOI: 10.1080/00207454.2020.1826945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
AIM The study investigated the cortical activity changes and time production task performance induced by changes in motion speed of a non-immersive 3D virtual stimulus. MATERIAL AND METHODS Twenty-one individuals were participated in the crossover study with the visual-time reproduction task under three-speed conditions: original, slow and fast virtual stimulus. In addition, the electroencephalographic analysis of the theta band power in the dorsolateral prefrontal cortex was done simultaneously with time production task execution. RESULTS The results demonstrated that in the slow speed condition, there is an increase in the error in the time production task after virtual reality (p < 0.05). There is also increased EEG theta power in the right dorsolateral prefrontal cortex in all speed conditions (p < 0.05). CONCLUSIONS We propose that the modulations of speed of virtual stimulus may underlie the accumulation of temporal pulses, which could be responsible for changes in the performance of the production task of the time intervals and a substantial increase in right dorsolateral prefrontal cortex activity related to attention and memory, acting in cognitive domains of supraseconds.
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Affiliation(s)
| | - Victor Marinho
- Neuro-innovation Technology & Brain Mapping Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,The Northeast Biotechnology Network (RENORBIO), Federal University of Piauí, Teresina, Brazil
| | - Silmar Teixeira
- Neuro-innovation Technology & Brain Mapping Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,The Northeast Biotechnology Network (RENORBIO), Federal University of Piauí, Teresina, Brazil
| | - Gabriela Teles
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - João Marques
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Anderson Escórcio
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Thayaná Fernandes
- Neuro-innovation Technology & Brain Mapping Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Ana Cláudia Freitas
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Monara Nunes
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,The Northeast Biotechnology Network (RENORBIO), Federal University of Piauí, Teresina, Brazil
| | - Marcos Ayres
- Neuro-innovation Technology & Brain Mapping Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,The Northeast Biotechnology Network (RENORBIO), Federal University of Piauí, Teresina, Brazil
| | - Carla Ayres
- Neuro-innovation Technology & Brain Mapping Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Juliana Bittencourt Marques
- Laboratory of Neurophysiology and Neuropsychology of Attention, Veiga de Almeida University, Cabo Frio, Brazil
| | - Maurício Cagy
- Masters and PhD Program in Biomedical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Daya S Gupta
- Department of Biology, Camden County College, Blackwood, NJ, USA
| | - Victor Hugo Bastos
- Brain Mapping and Functionality, Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,The Northeast Biotechnology Network (RENORBIO), Federal University of Piauí, Teresina, Brazil
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Li MS, Di Luca M. Musical Scales in Tone Sequences Improve Temporal Accuracy. Front Psychol 2018; 9:105. [PMID: 29467708 PMCID: PMC5808197 DOI: 10.3389/fpsyg.2018.00105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 01/22/2018] [Indexed: 11/21/2022] Open
Abstract
Predicting the time of stimulus onset is a key component in perception. Previous investigations of perceived timing have focused on the effect of stimulus properties such as rhythm and temporal irregularity, but the influence of non-temporal properties and their role in predicting stimulus timing has not been exhaustively considered. The present study aims to understand how a non-temporal pattern in a sequence of regularly timed stimuli could improve or bias the detection of temporal deviations. We presented interspersed sequences of 3, 4, 5, and 6 auditory tones where only the timing of the last stimulus could slightly deviate from isochrony. Participants reported whether the last tone was ‘earlier’ or ‘later’ relative to the expected regular timing. In two conditions, the tones composing the sequence were either organized into musical scales or they were random tones. In one experiment, all sequences ended with the same tone; in the other experiment, each sequence ended with a different tone. Results indicate higher discriminability of anisochrony with musical scales and with longer sequences, irrespective of the knowledge of the final tone. Such an outcome suggests that the predictability of non-temporal properties, as enabled by the musical scale pattern, can be a factor in determining the sensitivity of time judgments.
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Affiliation(s)
- Min S Li
- Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Massimiliano Di Luca
- Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Li MS, Rhodes D, Di Luca M. For the Last Time: Temporal Sensitivity and Perceived Timing of the Final Stimulus in an Isochronous Sequence. TIMING & TIME PERCEPTION 2016. [DOI: 10.1163/22134468-00002057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
An isochronous sequence is a series of repeating events with the same inter-onset-interval. A common finding is that as the length of a sequence increases, so does temporal sensitivity to irregularities — that is, the detection of deviations from isochrony is better with a longer sequence. Several theoretical accounts exist in the literature as to how the brain processes sequences for the detection of irregularities, yet there remains to be a systematic comparison of the predictions that such accounts make. To compare the predictions of these accounts, we asked participants to report whether the last stimulus of a regularly-timed sequence appeared ‘earlier’ or ‘later’ than expected. Such task allowed us to separately analyse bias and performance. Sequences lengths (3, 4, 5 or 6 beeps) were either randomly interleaved or presented in separate blocks. We replicate previous findings showing that temporal sensitivity increases with longer sequence in the interleaved condition but not in the blocked condition (where performance is higher overall). Results also indicate that there is a consistent bias in reporting whether the last stimulus is isochronous (irrespectively of how many stimuli the sequence is composed of). Such result is consistent with a perceptual acceleration of stimuli embedded in isochronous sequences. From the comparison of the models’ predictions we determine that the improvement in sensitivity is best captured by an averaging of successive estimates, but with an element that limits performance improvement below statistical optimality. None of the models considered, however, provides an exhaustive explanation for the pattern of results found.
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