Decoupling behavioral components and neural bases of the attentional blink.

(Left) Identifying the precise origin of accuracy deficits induced by the attentional blink. T2 identification accuracies at different inter-target (T1-T2) lags, in a conventional attentional blink task. x-axis: inter-target lag in milliseconds; y-axis: T2 identification accuracy (%). Red horizontal line: asymptotic T2 identification accuracy for long inter-target lags; red vertical arrows: accuracy deficit with T2 identification for short inter-target lags (attentional blink). (Right, top) The identification deficit could reflect impaired detection of T2’s presence which, in turn, could arise either from a detection sensitivity deficit (upper row) or a detection bias (criterion) deficit (lower row). Gray and white Gaussians: decision variable distributions corresponding to signal (target present) and noise (target absent), respectively. Black vertical line: criterion for deciding between target present and absent.

(Right, bottom) The identification deficit could also reflect impaired discrimination of T2’s features (e.g. orientation), which, again, could arise either from a discrimination sensitivity deficit (upper row) or a discrimination bias (criterion) deficit (lower row). Purple and orange Gaussians: decision variable distributions corresponding to a counterclockwise (CW) and clockwise (CCW) gratings, respectively. Black vertical line: criterion for deciding between target features (clockwise and counterclockwise orientation). Brain schematics (rightmost column): Distinct neural markers of each subcomponent – detection (top) or discrimination (bottom) -- of attentional blink deficits.

Novel task design to distinguish subcomponents of the attentional blink.

A. Schematic of the attentional blink task. Stimuli were presented in a rapid serial visual presentation (RSVP) paradigm at a 10 Hz rate (70 ms onset, 30 ms offset). Following fixation, plaid gratings appeared for a variable interval (200-1200 ms, geometrically distributed), followed by the first target (T1): a low spatial frequency grating (100 ms). After this a series of plaid gratings appeared for variable intervals (0, 200, 400, 600, and 800 ms; geometric distribution) followed by the appearance of the second target (T2): a high spatial frequency grating (100 ms). Following T2, plaid gratings were presented for a fixed interval (600 ms). Finally, in the response epoch, participants reported T1’s orientation as being closer to the cardinal or diagonal axes (two-alternative), and then reported T2’s orientation as being clockwise or counterclockwise of vertical, or absent (three-alternative).

B. Psychometric function of accuracy (% correct) for T2 detection with increasing inter target (T1-T2) lags, for trials in which T1 was reported correctly (n=24 participants). Filled circles and solid lines: average accuracy for high contrast T2 gratings; open circles and dashed lines: average accuracy for low contrast T2 gratings. Error bars: s.e.m. Asterisks: significance levels for comparing accuracies between short (100 and 300 ms) and long (700 and 900 ms) lag trials; solid and dashed brackets: comparisons for high and low contrast gratings respectively. *p<0.05, **p<0.01, ***p<0.001 and n.s.: not significant.

C. Same as in panel B, but showing the psychometric function of accuracy for T2 discrimination with increasing inter target (T1-T2) lags (n=24). Other conventions are the same as in panel B, except that markers and lines are depicted in orange colour.

D. Stimulus-response contingency table for the 3-alternative T2 decision. Rows represent the three possible T2 stimulus events: clockwise orientation (CW, orange), counterclockwise orientation (CCW, purple) or absent (none, gray). Columns represent three possible choices: clockwise (CW), counterclockwise (CCW) or absent (none). The table depicts the nine stimulus-response contingencies: two each of hit rates (H), misidentification rates (MI), miss rates (M), false alarm rates (FA) – one for each orientation (CW/CCW) – and one correct rejection rate (CR).

E. Same as in panel B, but showing psychometric function of average hit rates.

F. Same as in panel B, but showing psychometric function of average misidentification rates.

G. Same as in panel B, but showing psychometric function of average miss rates. (E-G). Other conventions are the same as in panel B except that markers and lines are denoted in black color.

H. Same as in panel B, but showing psychometric function correct rejection (filled circles) and false alarm (open circles) rates on T2 absent trials.

A novel psychophysical model decouples attentional blink subcomponents.

A. Signal detection model for estimating T2 sensitivity and bias. The decision variable is a bivariate Gaussian (Ψ) whose components, Ψdet (ordinate) and Ψdis (abscissa), encode sensory evidence for stimulus presence and orientation, respectively. Black circle: Ψ distribution for T2 absent trials (noise distribution), is centered on the origin. Orange and purple circles: Ψ distributions for clockwise and counterclockwise T2 orientations, respectively (signal distributions). Solid and dashed outlines: High and low contrast T2, respectively. Vertical gray and horizontal orange lines (double headed arrows): detection sensitivity (d’det) and discrimination sensitivity (d’dis) for high contrast T2, respectively. Dashed gray lines: Signal mean projections onto the detection and discrimination axes.

B. Decision surface with linear decision boundaries (thick black lines) demarcates 3 decision zones, one for each potential 3AFC choice: Clockwise T2 (CW, orange shading), counterclockwise T2 (CCW, purple shading), no T2 (None, gray shading). The decision surface is parameterized by: i) a discrimination criterion (cdis), governing the horizontal position of the decision surface (horizontal double arrowhead), ii) a detection threshold (tdet), governing the vertical position of the decision surface (vertical double arrowhead), and iii) the angle between the two oblique decision boundaries (b). Other conventions are the same as in panel A.

C. Psychophysical function of detection sensitivity (d’det) with increasing inter target (T1-T2) lags. Other conventions are the same as in Figure 2A.

D. Same as in panel C but showing the psychophysical function of discrimination sensitivity (d’det). Model selection yielded identical psychophysical functions for high and low contrast T2 (Methods). Other conventions are the same as in Figure 2B.

E. Same as in panel C but showing the psychophysical function of detection criterion (cdet). Dashed horizontal line: cdet=0.

F. Discrimination criterion (cdis). Model selection constrained cdis to be equal across lags (Methods). Other conventions are the same as in panels D-E.

G. Modulation index (MI) of the discrimination blink for low (open plot) and high (filled plot) detection blink MI blocks. Box plots, center line: median; box limits: upper and lower quartiles; whiskers: 1.5x the interquartile range. Violin plots: kernel density estimates.

(C-G) Asterisks: *p<0.05, **p<0.01, ***p<0.001; and n.s.: not significant.

N2p and P3 event related potentials signal detection sensitivity deficits.

A. (Left) The event related potential (ERP, n=18 participants) showing the N2p component in occipitoparietal electrodes (inset), locked to T2 onset (dashed vertical line). Bright green, dull green, dark green, light gray and black traces: Average ERPs for the five inter-target lags – 100, 300, 500, 700 and 900 ms – respectively. Shading: s.e.m. Gray vertical shading: Time epoch for N2p amplitude quantification. ERPs were computed by subtracting the average ERPs on correct rejection trials (Methods). (Right) Violin plots showing the distribution of the peak N2p amplitudes across participants separately for the short (green: 100+300 ms) and the long (gray: 700+900 ms) lag trials. Asterisks: *p<0.05, **p<0.01, and ***p<0.001. Other conventions are the same as in Figure 3G.

B. (Left) Same as in panel A (left) but showing the P3 ERP component in the parietal electrodes (inset). (Right) Same as in panel A (left) but showing the quantification of the P3 ERP. Other conventions are the same as in panel A.

C. Variation of detection sensitivity (d’det; y-axis) with N2p peak amplitude (x-axis) across inter-target lags (circles). r and p denote the robust correlation coefficient and permutation test p-value, respectively (Methods). Dashed line: linear fit. Error bars: s.e.m.

D. (Top) Partial correlation between N2p peak amplitude (x-axis, amplitude residual) and detection sensitivity (y-axis, d’det residual) while controlling for the discrimination sensitivity (d’dis). Each of the five shapes – filled triangle, diamond, square, pentagon, circle – represents one inter-target lag (legend). rp and p denote the partial correlation coefficient and permutation test p-value, respectively (Methods). Solid line: Linear fit; dashed curves: 95% confidence intervals. (Bottom) Same as in the top panel but showing partial correlation between N2p peak amplitude (x-axis, amplitude residual) and discrimination sensitivity (y-axis, d’dis residual) while controlling for detection sensitivity (d’det).

E. Same as in panel C but showing variation of discrimination sensitivity (d’dis, y-axis) with P3 peak amplitude (x-axis) across inter-target lags.

F. Same as in panel D but showing the partial correlation of P3 peak amplitude with d’det while controlling for d’dis (top), or, conversely, with d’dis while controlling for d’det (bottom). (E-F) Other conventions are the same as in panels C-D, respectively.

High-beta fronto-parietal coherence signals discrimination sensitivity deficits.

A. Coherence between frontal and parietal electrodes (Methods) as a function of time (x-axis) and frequency (y-axis) locked to T2 onset (t=0) (n=18 participants), normalized frequency-wise by a baseline mean (gray horizontal bar), and computed by subtracting the average coherograms for correct rejection trials. Cooler colors: higher coherence. Dashed horizontal line: low-beta (13 to 19 Hz) and high-beta (20 to 30 Hz) sub-bands. (Left and middle) Coherograms for the short (100+300 ms) and long (700+900 ms) lag trials, respectively. (Right) Difference coherogram. Bold black outline: statistically significant coherence difference between short and long lag trials (cluster forming threshold p<0.05).

B. Bilateral fronto-parietal coherence values in the high-beta band across participants, separately for the short (100+300 ms, green) and long (700+900 ms, gray) lag trials. Topoplot: schematic of electrode pairs (red circles and black arrows) used for computing bilateral frontoparietal coherence (Methods). Other conventions are the same as in Figure 3G.

C. (Left) Same as in panel A (right panel) but showing the difference coherogram for the left hemispheric frontoparietal electrodes. Other conventions are the same as in panel A. (Right) Same as in panel B but showing high-beta coherence values over the left hemispheric fronto-parietal electrodes. Other conventions are the same as in panel B and Figure 3G.

D. (Left and right) Same as in panel C but showing the difference coherogram for the right hemispheric fronto-parietal electrodes. Other conventions are the same as in panel C. (B-D). Asterisks: *p<0.05, **p<0.01, and ***p<0.001.

E. Same as Figure 4E but showing the variation of discrimination sensitivity (d’dis, y-axis) with left fronto-parietal high-beta coherence (x-axis) across inter-target lags. Other conventions are the same as in Figure 4E.

F. Same as in Figure 4F but showing the partial correlation of high-beta left fronto-parietal coherence with d’det while controlling for d’dis (top), or, conversely, with d’dis while controlling for d’det (bottom). Other conventions are the same as in Figure 4F.

Distinct neural dimensions encode détection and discrimination bottlenecks.

A. Schematic showing the “detection” dimension, hdet (y-axis): a linear dimension in a multidimensional electrode space along which neural activity encodes the presence versus absence of T2 (Methods). Gray, orange and purple dot clusters: distribution of EEG activity for T2 absent, T2 clockwise (CW) and counterclockwise (CCW) trials, respectively. Darker (lighter) shades denote longer (shorter) inter-target lags.

B. Same as in panel A but showing the “discrimination” dimension, hdis (x-axis): a linear dimension along which neural activity encodes the orientation of T2 (clockwise versus counterclockwise of vertical).

C. (Left) Time evolution of the average inter-class distance along the detection dimension (||hdet||) locked to T2 onset (n=18 participants). Values at each time point are plotted relative to the longest lag (900 ms). Bright green, dark green and black traces: short (100+300 ms) intermediate (500 ms) and long (700 ms) lags, respectively. Shading: s.e.m. Dashed vertical line: T2 onset. Gray shading: Time epoch for neural distance quantification and statistical comparison. (Right) Distribution of hdet inter-class distances across participants separately for the short (green, 100+300 ms) and the long (gray, 700+900 ms) lag trials. Asterisks: *p<0.05, **p<0.01, and ***p<0.001. Other conventions are the same as in Figure 3G.

D. Same as in panel C but showing the time evolution of the average neural inter-class distance along the discrimination dimension (||hdis||) (left), and the distribution of neural distances along the discrimination dimension (right). Other conventions are the same as in panel C.

E. Same as in Figure 4C, but showing variation of detection sensitivity (d’det, y-axis) with neural distance along the detection dimension (||hdet||, x-axis) across inter-target lags.

F. Same as in Figure 4D but showing the partial correlation of ||hdet|| with d’det while controlling for d’dis (top), or, conversely, with d’dis while controlling for d’det (bottom).

G. Same as in Figure 4E, but showing variation of discrimination sensitivity (d’dis, y-axis) with neural distance along the discrimination dimension (||hdis||, x-axis) across inter-target lags.

H. Same as in Figure 4F but showing the partial correlation of ||hdis|| with d’det while controlling for d’dis (top), or, conversely, with d’dis while controlling for d’det (bottom).

Neural dimensions of detection and discrimination deficits correlate with their respective EEG markers.

A. Same as in Figure 4C, but showing variation of the neural distance along the detection dimension (||hdet||, y-axis) with P3 amplitude (d’det, x-axis) across inter-target lags.

B. Same as in Figure 4D but showing the partial correlation of the P3 amplitude with ||hdet|| while controlling for ||hdis|| (top), or, conversely, with ||hdis|| while controlling for ||hdet|| (bottom). (A-B) Other conventions are the same as in Figures 4C-D.

C. Same as in Figure 4E, but showing variation of the neural distance along the discrimination dimension (||hdis||, y-axis) with left high beta frontoparietal coherence (x-axis) across the 5 inter-target lags.

D. Same as in Figure 4F but showing the partial correlation of the left high beta frontoparietal coherence with ||hdet|| while controlling for ||hdis|| (top), or, conversely, with ||hdis|| while controlling for ||hdet|| (bottom). (C-D) Other conventions are the same as in Figures 4E-F.

Distinct neural bases of subcomponents of the attentional blink

The attentional blink selectively impairs a specific component of attention – perceptual sensitivity (d’) – and produces both detection (top, left) and discrimination (top, right) deficits. Detection d’ deficits – deficits with distinguishing the presence versus absence of the second target (T2) – are correlated with reduced amplitudes of N2p and P3 ERPs (gray shading, left top). They are also accompanied by a representational collapse along the detection dimension (gray shading, left bottom). By contrast, discrimination d’ deficits – deficits with discriminating T2’s orientation – are evidenced by reduced left fronto-parietal beta coherence (red shading, left top) and a representational collapse along the discrimination dimension (red shading, left bottom).

Model comparison analysis.

A. Summary of the 5 models used in the analysis (see Methods for details).

B. Distribution of goodness of fit (p-values) with a randomization test based on the chi-squared statistic for all five models (model I – V, n=24 participants). Other conventions for the violin plot same as in Figure 3G (main text).

C. Distribution of Akaike information criterion (AIC) values for all five models. Other conventions are the same as in panel B.

D. Same as panel C but showing the distribution of Bayesian information criterion (BIC) values for all five models. Other conventions are the same as in panel C.

Correlation between detection and discrimination blink.

A. Correlation between the modulation indices (MI) of detection and discrimination sensitivity. The MI is calculated as MI-d’det = (d’detLL - d’detSL)/ (d’detLL + d’ SL) (SL: short lag, LL: long lag; Methods). Gray circles: individual participants. Dashed line: Linear fit. r and p values: correlation coefficient and its significance level based on robust correlations (Methods).

B. Same as in panel A except showing the correlation between MIs for detection and discrimination accuracy. Other conventions are the same as in panel A.

P1 ERP component in occipital electrodes.

(Left) The P1 event related potential (ERP) in the bilateral occipital electrodes (inset, topoplot), locked to T2 grating onset (n=18 participants). Bright green, dull green, dark green, light gray and black traces: Average ERPs for the five inter-target lags (100, 300, 500, 700 and 900 ms), respectively. Shading: s.e.m. Gray vertical bar: Time epoch considered for quantifying the P1 component amplitude. Dashed vertical black line: T2 onset. Here, and elsewhere, ERPs were computed by subtracting the average ERPs for correct rejection trials (Methods). (Right) Violin plots showing the distribution of the peak P1 amplitudes across participants separately for the short (green; 100+300 ms) and the long (gray, 700+900 ms) lag conditions. Asterisks denote significance differences: *p<0.05, **p<0.01, and ***p<0.001. Other conventions for the violin plot are the same as in Figure 3G.

Linking neural dimensions with neural markers of detection deficits – N2p peak amplitude.

A. Variation of the neural distance along the detection dimension (||ηdet||, y-axis) with N2p amplitude (d’det, x-axis) across the 5 inter-target lags (circles in distinct shades of gray). Dashed curve: linear fit. Error bars: s.e.m along the respective axis.

B. (Top) Partial correlation of the N2p peak amplitude (x-axis, amplitude residual) with ||ηdet|| (y-axis, detection distance residual) while controlling for ||ηdis||. The five shapes – filled triangle, diamond, square, pentagon, circle – represent the five inter-target lags – 100, 300, 500, 700 and 900 ms – respectively (data pooled across n=18 participants). rp and p denote the partial correlation value and p-value calculated with a permutation test (Methods). Solid line: Linear fit; black dashed curves: 95% confidence intervals. (Bottom) Same as in the top panel but showing the partial correlation between N2p peak amplitude (x-axis, amplitude residual) with ||ηdis|| (y-axis, discrimination distance residual) while controlling for ||ηdet||. Other conventions are the same as in the top panel.

Attentional blink effect on psychometric measures.

2-way ANOVA analysis with the psychometric measures as dependent variables and lags and contrasts as independent factors.

Partial correlations of ERP amplitudes with detection and discrimination sensitivity.

Magnitude of blink induced deficit in ERP amplitudes, and partial correlation between ERP amplitudes, and detection (discrimination) d’, while controlling for the confounding effect of discrimination (detection) d’ (Methods). Bold entries correspond to significant partial correlations.

Partial correlation of fronto-parietal coherence with detection and discrimination sensitivity.

Partial correlation between left and right frontoparietal beta coherence and detection and discrimination sensitivity. Other conventions are the same as in SI Table S2.

Partial correlation between neural measures and neural distances in the detection and discrimination dimensions.

Partial correlation between neural measures (N2P and P3 ERP amplitudes and left frontoparietal high-beta coherence) and neural distances in detection (discrimination) dimension while controlling for the confounding effect of the neural distance in the discrimination (detection) dimension.