Featural and temporal attention selectively enhance task-appropriate representations in human primary visual cortex

Abstract

Our perceptions are often shaped by focusing our attention towards specific features or periods of time irrespective of location. Here we explore the physiological bases of these non-spatial forms of attention by imaging brain activity while subjects perform a challenging change-detection task. The task employs a continuously varying visual stimulus that, for any moment in time, selectively activates functionally distinct subpopulations of primary visual cortex (V1) neurons. When subjects are cued to the timing and nature of the change, the mapping of orientation preference across V1 systematically shifts towards the cued stimulus just prior to its appearance. A simple linear model can explain this shift: attentional changes are selectively targeted towards neural subpopulations, representing the attended feature at the times the feature was anticipated. Our results suggest that featural attention is mediated by a linear change in the responses of task-appropriate neurons across cortex during appropriate periods of time.

1

Selective attention in a change detection task. A. Human subjects viewed a full-field, continuously rotating Gabor and responded by button press when the spatial frequency of the stimulus briefly changed (dashed outline). During attention conditions, these target events were more likely to occur at a single orientation (green, A45; violet, A135). Prior to each trial, a static grating indicated to the subject the orientation about which targets are likely to occur. In one control condition, the target probability is static over time (No-Cue, black). B. Mean event-related response to stimulus rotation (aligned to preferred phase, solid) and to target events (dashed, aligned to individual target events per condition) averaged across all voxels with significant orientation selectivity. The response to individual target events was negligible, but removed via linear regression in all future analyses. The mean global response increased with attention. C. Reaction time, indicated by radius, is fastest prior to the cued orientation when subjects anticipate target events. [uni03BC], orientation with the fastest mean response during each Attend condition; colored arc, full-width at half-maximum (FWHM) range of fastest reaction times (A45 FWHM 98°, A135 FWHM 92°).

2

Orientation-selectivity in single V1 voxels. A. Fourier and regression analysis (see Methods) provides the amplitude (A) and peak (O) of each voxel's orientation tuning curve. Peaks are offset to account for the hemodynamic lag between stimulus presentation and BOLD response. Shaded region shows a confidence interval of the fitted sine wave for this example voxel. Orientation-selectivity metrics for this voxel: coherence coefficient = 0.2131 (p=1.7·10⁻⁵), decoding accuracy ≈ 100%. B. Many individual voxels accurately discriminate between their preferred and anti-preferred orientations (mode at 50% represents chance performance). C. Many voxels have significant coherence at the signal frequency. Coherence coefficient of 0.0984 (arrow) is the threshold for statistical significance. B and C include all V1 voxels. D. Among orientation-selective voxels, attention recruited weakly orientation-selective voxels. E. Among orientation-selective voxels, the distribution of preferred orientations is biased toward the attended orientation.

3

Attention biases the orientation preference map. A. Preferred orientation from one subject, coded by color and measured during No Cue task, plotted on a medial view of occipital cortex. Inset is a flattened representation of the occipital pole. Greater color saturation indicates a higher certainty in preference estimate. Orientation selectivity was greatest along gyri, likely due to the use of a surface coil. (white scale bar: 2 mm. black line: calcarine sulcus). B, C. As A, with orientation preference measured during the attention conditions. Attention increased the extent of orientation-selective activity across the occipital cortex (top) and biased population orientation preferences at the hyper-columnar scale (bottom).

4

Attention selectively advances and amplifies orientation tuning curves. A. Voxels with an orientation preference near 79° respond sooner (have a less positive orientation preference) during A45 condition. Error bars show 99.9% confidence intervals of the mean. Note that 270° is equivalent to 90°. Inset histogram shows distribution of tuning preference shifts as distance from the identity line. (black line, identity line; [uni03BC]_O, center of range with significant tuning advance; [uni03BC]_S, mean tuning curve advance over all voxels). Color indicates individual voxels that are a significant (q_FDR<0.01, likelihood estimation) distance from the identity line. B. As A, showing an orthogonally-distributed tuning curve advance during A135. C. Direct comparison between A45 and A135 conditions reveals a strong and orthogonal relationship between preferred orientation and changes in tuning preference. D. As C, comparing tuning amplitude between A45 and A135. Amplitude increases for voxels with preference prior to the attended orientation (* significant change, von Mises (A,B,C) or t-test (D), variable DOF, p<0.001).

5

Attentional modulations are linear over V1. Normalized activity (see Methods) during No-Cue and Attend conditions is highly similar in Stay-On voxels (gray). Each point represents the mean normalized activity of all voxels with a set range of phase preferences over a single sample interval; preference and time are sampled to thirteen points each, providing 169 data points. The effect of attention over all points is well described by a linear fit (R² = 0.92, slope 0.96, y-intercept 0.11). Changes in tuning curves observed in Turn-On (red) and Turn-Off (blue) voxels are also relatively well-described by linear functions (Turn-On: R² = 0.61, slope 0.95, intercept 0.93; Turn-Off: R² = 0.86, slope 0.27, y-intercept 0.14).

6

A combination of featural and temporal mechanisms is required to explain attentional changes in orientation tuning. A. Normalized BOLD activity during No-Cue condition, averaged across all subjects and retinotopic locations, for the Stay-on voxel group. Each row represents the mean tuning curve (response over the stimulus cycle) for voxels that share a common orientation preference relative to the cue. Orientation and time are given relative to the cue, which occurs at 0º/0s. B. The No-Cue activity surface is enhanced by a linear transformation and phase advance (0.6 sec) in order to most closely approximate the corresponding Attend activity surface (C) defined from the same voxels with the same scaling. D. The residual difference between the observed (C) and predicted (B) Attend activity is small (R²= 0.99, $R_{adj}^2=0.82$ after removing variance due to global effects [see Methods]). The Bayesian information criterion is used to compare this full model with simpler submodels in order to determine which mechanisms of attention are most consistent with these data. E. The model in (B) is generated from the sum of four different attentional mechanisms (small images): a global term which modulates all data points equally, featural and temporal terms which act purely as a function of either the feature (row) or time (column) dimension, and an interaction term which acts with both featural and temporal specificity. All terms may incorporate both a multiplicative and additive component (i.e. are linear functions of the form y = mx+b, see Methods); however only additive effects are shown in this and in Fig. 7 as multiplicative modulations were not statistically justified. Each attentional mechanism alone is insufficient to explain the effects of attention, as shown by the patterned error surfaces produced when only one mechanism is modeled.

7

Attention model parameters from three separate groups of voxels are nearly identical. The optimal model (lowest BIC) is comprised of three attentional mechanisms: a feature-specific additive enhancement, a time-specific subtractive inhibition, and an inhibitory interaction term which gates the feature-attention to the relevant period of time. A. The empirical attention surface generated from Stay-On (SO) voxels, computed as the difference between the Attend and No-Cue activity surfaces. Variance that is explained by global changes in activity over all voxels (the trend line in Fig. 5) has been removed. The middle of this surface represents the cued orientation/time. B, C. As A, showing the attention surfaces generated from Turn-On (ON) and Turn-Off (OFF) voxels. All surfaces show an increase in activity within voxels that prefer an orientation just prior to the cue, and all surfaces show a global suppression after the stimulus passes the cued orientation. D. The best-fitting model surface for the SO voxel attention surface. Curves along the left and bottom sides show the modeled featurally and temporally specific modulations, while the black-and-white surface shows the feature-time interaction term (always suppressive). The sum of all three attentional mechanisms provides the colored model surface. E, F. As D, showing models derived for the ON and OFF voxels. All three models approximate their respective attention surfaces well ($R_{adj,SO}^2=0.82,R_{adj,ON}^2=0.80,R_{adj,OFF}^2=0.44$), and all three models agree as to when and where in V1 attentional modulations are found (featural attention peaks: SO -32°, ON -52°, OFF -34°; featural attention widths: SO 51°, ON 115°, OFF 30°; temporal attention peaks: SO 63°, ON 71°, OFF 66°; temporal attention widths: SO 47°, ON 64°, OFF 36°).