Rhythmic sampling and competition of target and distractor in a motion detection task
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States
- Institute for Human Neuroscience, Boys Town National Research Hospital, United States
- Department of Psychological and Brain Science, Dartmouth College, United States
- Department of Psychology, University of Florida, United States
Figures
Figure 1
Experimental paradigm and general approach for electroencephalography (EEG) data analysis.
(A) Motion detection task. Randomly moving dots flickered at 4.29 Hz (target) were superimposed in International Affective Picture System (IAPS) images flickered at 6 Hz (distractor). Participants detected brief episodes of coherent motion. (B) Target-specific signals and distractor-specific signals were estimated and subjected to (1) whole trial analysis and (2) moving window analysis. MVPA decoding analysis was done using an ‘ERP’ decoding method. See Materials and methods for more details.
Figure 2
Steady-state visual evoked potential (SSVEP) analysis at the whole trial level.
(A) Grand average SSVEP at Oz. (B) Fourier spectrum of the data in A. (C) Target amplitude across all electrodes is significantly larger than distractor amplitude at p=2.6 × 10–4. (D) Topographical distributions of target and distractor amplitude. (E) Correlation between target SSVEP amplitude and task performance (left) and between distractor SSVEP amplitude and task performance (right). Both correlation values are not significant.
Figure 3
MVPA decoding analysis of distractor processing at the whole trial level.
(A) Pairwise decoding accuracies between pleasant vs neutral, unpleasant vs neutral, and pleasant vs unpleasant are 57.86% ± 9.86%, 55.14% ± 8.17%, and 59.45% ± 9.73%, respectively, which are all significantly above chance level of 50% (red dashed line) at p=3.2 × 10–4, p=3.0 × 10–3, and p=3.0 × 10–5. (B) Three-way decoding accuracy is 41.09% ± 6.25%, which is significantly higher than the chance level of 33% (red dashed line) at p=3.9 × 10–7. (C) Decoding accuracy vs task performance. The correlation of r=–0.0313 (p=0.8769) is not significant. (D) Distractor decoding accuracy vs distractor steady-state visual evoked potential (SSVEP) amplitude. The correlation of r=0.1531 (p=0.4458) is not significant.
Figure 4
Temporal dynamics of target and distractor processing.
(A) (i) Target amplitude time series from the moving window approach for a representative subject (left) and its Fourier spectrum (right). (A) (ii) The average target amplitude spectrum across 27 subjects. (B) (i) Distractor decoding accuracy time series from the moving window approach for a representative subject (left) and its Fourier spectrum (right). (B) (ii) The average distractor decoding accuracy spectrum across 27 subjects.
Figure 5
Target-distractor competition analysis.
(A) Phase polar histogram for the relative phase between target processing time series and distractor processing time series (1 Hz). The average relative phase is 0.51π. (B) Kolmogorov-Smirnov test showed that the relative phase distribution is not different from uniform distribution. (C) Temporal relationship between target processing and distractor processing for (i) a high performer (accuracy = 83.84%; relative phase = 0.877π) and (ii) a low performer (accuracy = 33.33%; relative phase = 0.053π). (D) Task performance vs 1 Hz relative phase. The significant positive correlation (r=0.6041, p=0.0008) indicated that the more separated the target and distractor sampling within the 1 Hz oscillation cycle, the better the behavioral performance. CDF: cumulative distribution function.
Appendix 1—figure 1
Simulation results.
(A) The signal containing a 4.29 Hz component and a 6 Hz component where the 6 Hz signal’s magnitude is about half that of the 4.29 Hz signal. The amplitude is modulated at 1 Hz. No noise is added. (B) Low level of noise is added to the signal in Appendix 1—figure 1A, where the signal-to-noise ratio (SNR) = 12.72 dB. Sidebands are still seen. (C) Middle level of noise is added to the signal in Appendix 1—figure 1A where the SNR = 5.38 dB. Sidebands become difficult to see. (D) High level of noise is added to the signal in Appendix 1—figure 1A where the SNR = 2.24 dB, sidebands become more indistinguishable from the noise floor. Red dots indicate the location of the main frequency components and the locations where the sidebands should appear.
Appendix 1—figure 2
Experimental data.
(A) The time course of the steady-state visual evoked potential (SSVEP) and its Fourier spectrum from a subject with high signal-to-noise ratio (SNR). The sidebands can be observed. (B) The time course and its Fourier spectrum from a subject with low SNR. The sidebands are indistinguishable from the noise floor. (C) The averaged Fourier spectrum from five highest SNR subjects and five lowest SNR subjects. Again, for subjects with high SNR, the sidebands are identifiable, whereas for subjects with low SNR, the sidebands are not identifiable.
Appendix 1—figure 3
Steady-state visual evoked potential (SSVEP) amplitude analysis at the whole trial level.
(A) Target amplitude vs distractor amplitude, where the correlation is r=0.7992 (p=0.000006), suggesting the 6 Hz signal amplitude is strongly influenced by the 4.29 Hz signal amplitude. (B) Target amplitude vs distractor decoding accuracy, where the correlation is r=0.0536 (p=0.7908), suggesting that the decoding accuracy as an index of distractor processing is not influenced by the 4.29 Hz target amplitude.
Appendix 1—figure 4
Moving window analysis.
(A) The relative phase between the target amplitude time series and the distractor amplitude time series. (B) Kolmogorov-Smirnov test showed that the relative phase distribution is significantly different from the uniform distribution. (C) Relative phase vs task performance. r=0.1940 (p=0.3322) means that there is no significant correlation between amplitude relative phase and task performance.
Appendix 1—figure 5
Temporal dynamics of target and distractor processing with 0.1 s window length and 0.05 s step size.
(A) (i) Target processing time series from the moving window approach for a representative subject (left) and its Fourier spectrum (right). (A) (ii) The average Fourier spectrum across 27 subjects. (B) (i) Distractor processing time series from the moving window approach for a representative subject (left) and its Fourier spectrum (right). (B) (ii) The average Fourier spectrum across 27 subjects.
Appendix 1—figure 6
Target-distractor competition analysis with 0.1 s window length and 0.05 s step size.
(A) Phase polar histogram for the relative phase between target process time series and distractor processing time series (1 Hz). The average relative phase is 0.44π. (B) Kolmogorov-Smirnov test showed that the relative phase distribution is not different from uniform distribution. (C) Temporal relationship between target processing and distractor processing for (i) a high performer (accuracy=83.84%; relative phase=0.9483π) and (ii) a low performer (accuracy=30.95%; relative phase=0.29π). (D) Task performance vs 1 Hz relative phase. The significant positive correlation (r=0.6343, p=0.0004) means that the more separated the target and distractor sampling within the 1 Hz oscillation cycle, the better the behavioral performance. CDF: cumulative distribution function
Appendix 1—figure 7
Temporal dynamics of target and distractor processing with Hilbert transformed target and distractor processing time series.
(A) (i) Target processing time series from a representative subject (left) and its Fourier spectrum (right). (A) (ii) The average spectrum across 27 subjects. (B) (i) Distractor processing time series for a representative subject (left) and its Fourier spectrum (right). (B) (ii) The average spectrum across 27 subjects.
Appendix 1—figure 8
Target-distractor competition analysis with Hilbert transformed target and distractor processing time series.
(A) Phase polar histogram for the relative phase between target process time series and distractor processing time series (1 Hz). The average relative phase is 0.63π. (B) Kolmogorov-Smirnov test showed that the relative phase distribution is not different from uniform distribution. (C) Temporal relationship between target processing and distractor processing for (i) a high performer (accuracy=83.84%; relative phase=0.9567π) and (ii) a low performer (accuracy=28.57%; relative phase=0.2247π). (D) Task performance vs 1 Hz relative phase. The significant positive correlation (r=0.4020, p=0.0376) means that the more separated the target and distractor sampling within the 1 Hz oscillation cycle, the better the behavioral performance. CDF: cumulative distribution function.
Appendix 1—figure 9
Comparison of actual decoding accuracy against the distribution of random permutation decoding accuracy.
Random permutation decoding accuracy from (A) pleasant vs neutral, (B) unpleasant vs neutral, (C) pleasant vs unpleasant, and (D) three-way. In all four conditions, the actual decoding accuracy is significantly above chance level at p<0.001.
Author response image 1
Appyling moving window analysis (0. 02s window duration and 0.01 step size) to a different EEG-fMRI dataset.
(A) The amplitude time series of the 4.29 Hz component and the Fourier spectrum. (B) The group level Fourier spectrum. At both individual and group level, no 1 Hz modulation is observed, suggesting that the 1 Hz modulation observed in our data is not introduced by the artifact removal procedure.
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Rhythmic sampling and competition of target and distractor in a motion detection task
eLife 14:RP106140.
https://doi.org/10.7554/eLife.106140.3
