Attentional Changes in Either Criterion or Sensitivity Are Associated with Robust Modulations in Lateral Prefrontal Cortex

Abstract

Visual attention is associated with neuronal changes across the brain, and these widespread signals are generally assumed to underlie a unitary mechanism of attention. However, using signal detection theory, attention-related effects on performance can be partitioned into changes in either the subject's criterion or sensitivity. Neuronal modulations associated with only sensitivity changes were previously observed in visual cortex, raising questions about which structures mediate attention-related changes in criterion and whether individual neurons are involved in multiple components of attention. Here, we recorded from monkey lateral prefrontal cortex (LPFC) and found that, in contrast to visual cortex, neurons in LPFC changed their firing rates, pairwise correlation, and Fano factor when subjects changed either their criterion or their sensitivity. These results indicate that attention-related neuronal modulations in separate brain regions are not a monolithic signal and instead can be linked to distinct behavioral changes.

Keywords: attention; prefrontal cortex; signal detection theory; single-unit electrophysiology; visual cortex.

Figure 1. Visuospatial attention can be partitioned using signal detection theory

(A) In the signal detection model, each stimulus evokes a noisy internal signal. If the signal is stronger than the criterion (c), the stimulus is reported as a target. The distributions of internal signals evoked by the target and by the nontarget overlap, and the separation between these two distributions is indexed as sensitivity (d′). The subject’s response to each stimulus is categorized as a hit (H), miss (M), false alarm (FA), or correct rejection (CR), and the frequency of these responses are used to calculate c and d′. (B) An increase in hit rate, which is used to operationalize attention in many single-neuron studies, typically consists of a combination of changes in criterion and sensitivity (Luo and Maunsell, 2015). A typical behavioral change of this sort is shown (in gray), along with an isolated change in criterion (in orange) and an isolated change in sensitivity (in blue). The dashed orange line represents an isopleth along which d′ is constant and only c varies, and the dashed blue line represents the isopleth along which c is constant and only d′ varies. Any change in hit rate can be brought about by a change in criterion alone, a change in sensitivity alone, or both.

Figure 2. Isolation of attentional changes in criterion and sensitivity

(A) Monkeys had to detect an orientation change that occurred on either the first or second test stimulus and report it with a saccade. If no change occurred on the first test, monkeys had to wait for a change that would always occur on the second test. In a trial in which a change occurred on the first test, the outcome was categorized as either a hit (H) or a miss (M), and when no change occurred on the first test, the outcome was either a false alarm (FA) or a correct rejection (CR). (B) Average reward parameters and behavioral indices across sessions. Each session consists of two task conditions, and isolation was achieved by varying reward sizes between the two conditions. Criterion was controlled primarily using the ratio of reward for a H to a reward for a CR (“H:CR reward ratio”), and sensitivity was controlled primarily using the average reward across H and CR. The task condition (high d′, low d′, low c, or high c) indicates the animal’s performance at the contralateral location. (C) Reward contingencies in each daily session. Each marker refers to one of the two task conditions of each session, and the pair of markers representing the same session are connected by a line. The data are from 24 Δc isolation sessions (shown in orange) and 24 Δd′ isolation sessions (in blue). Diamonds and squares represent monkeys C and Y, respectively. (D) Hit rates and false alarm rates of each task condition of each daily session. (E) Criterion and sensitivity of each task condition of each daily session. (F) Criterion change and sensitivity change in each session. Crosshairs in D-F represent 95% confidence intervals. See also Figure S1.

Figure 3. A memory-guided saccade task was used to index spatially selective

(A) After acquiring fixation, a target (0.4° white square) appeared for 400 ms in either the left or right hemifield. After a delay of 750-1000 ms, the fixation point disappeared to cue animal to saccade to the remembered location of the target. A spatial selectivity index (SSI) was computed for each neuron during each of four task periods. The 100 ms time window with the maximal firing rate was selected separately for events in the contralateral and ipsilateral hemifield. Contralateral and ipsilateral firing rates were then compared using a receiver operating characteristic (ROC) analysis, and the resulting area under the ROC curve was the SSI. SSI > 0.5 indicates stronger activity at the contralateral location. Shaded regions indicate the range of time windows that can be selected for computing SSI. (B) The peri-event time histogram (PETH) of an example unit was computed by convolving spikes with a Gaussian with σ = 25 ms. Solid lines correspond to trials with a contralateral target and saccade. Shaded bars indicate the time windows used to compute the SSI during each period. Darker shading corresponds to contralateral events and lighter to ipsilateral events. The example was selected by picking the unit that had the lowest SSI_visual among those with SSI_visual > 0.75. (C) Distributions of SSI were centered close to 0.5, but SSI_visual was skewed more towards 1 than other SSIs. N = 1,088. (D) Partial correlations between each pair of SSI’s. No reliable correlation was detected between SSI_visual and either SSI_presac or SSI_postsac. N = 1,088. (E) Contra-selective visual units with SSI_visual > 0.75. To compute the population-averaged PETH, each unit was normalized by the maximal value in its PETH. The maximum of the y-axis is 0.75 times the average peak firing rate across the population. Shading indicates mean ± SEM.

Figure 4. Visual responses increased when the subject either lowered its criterion or increased its sensitivity

(A) PETH of units with SSI_visual > 0.75 during the attention task. Orange lines represent units recorded during Δc isolations, and blue lines represent units from Δd′ isolations. Spikes were convolved with a Gaussian with σ = 8 ms. The maxima of the y-axes are 0.75 times the average peak firing rate across the population. Shading indicates mean ± SEM. The bar near the x-axis indicates the sample period (80-480 ms after sample onset). Either lowering c or elevating d′ increased the firing rates of visual neurons throughout the sample period and into the test period. (B) Modulation index (MI) of firing rates during the period 80-480 ms after sample onset as function of the minimum SSI_visual of the units included for analysis. A Δc-related modulation index > 0 indicates stronger firing rates when the animal responded more frequently to the contralateral location. A Δd′-related MI > 0 indicates stronger firing rates when the animal was more accurate in discriminating between an orientation change and a match at the contralateral position. Shading indicates mean ± SEM across units. Modulation indices related to either Δc or Δd′ are greater than 0 regardless of the SSI_visual threshold. The sample sizes for minimum SSI_visual = 0.51 were 492 units from Δc isolations and 567 units from Δd′ isolations. (C) P-values were computed using the Wilcoxon signed-rank test for a zero median in the distribution of Δc-related modulation indices (orange) or Δd′-related indices (blue) and from the Wilcoxon rank-sum test of Δc- and Δd′-related indices having the same median (gray). See also Figure S4.

Figure 5. Firing rate modulations were correlated with spatial selectivity measured from multiple periods of the memory-guided saccade task

Partial correlation between firing rate MI and each SSI (among SSI_visual, SSI_delay, SSI_presac, and SSI_postsac) while controlling for the other three SSI. The left column of panels (orange) corresponds to Δc-related modulations and the right column (blue) to Δd′-related modulations. Only the 33/48 sessions in which behavior was isolated at both the ipsilateral and contralateral locations were included for this analysis. N = 559 units from Δc isolations and 667 units from Δd′ isolations. Vertical bars indicate 95% confidence interval. See also Table S1.

Figure 6. Pairwise correlation and Fano factor decreased when the animal either lowered its criterion or elevated its sensitivity

(A) Pairwise correlations between contra-selective visual units decreased when the animal performed with a lower criterion or a higher sensitivity at the contralateral hemifield. Correlations were computed using firing rates during the sample period (80-480 ms after sample onset). The sample sizes for minimum SSI_visual = 0.51 and 0.75 were 25273 and 988 pairs from Δc isolations and 26635 and 974 pairs from Δd′ isolations. (B) Changes in pairwise correlation were normalized by the population-averaged correlation in either high c contra condition (for Δc isolations) or the low d′ contra condition (for Δd′ isolations). (C) P-values were computed using the Wilcoxon signed-rank test for a zero median in the distribution of normalized changes related to either Δc (orange) or Δd′ (blue) and using the Wilcoxon rank-sum test of Δc- and Δd′-related correlation changes having the same median (gray). (D) Fano factor of contra-selective visual neurons also decreased when the animal performed with either a lower criterion or higher sensitivity. Fano factor was computed using spike counts during the sample period. The sample sizes for SSI_visual = 0.51 and 0.75 were 492 and 99 units from Δc isolations and 567 and 122 units from Δd′ isolations. (E) Normalized change in Fano factor. (F) P-values for normalized changes in Fano factor. See also Figure S5.

Figure 7. Neuronal modulations related to Δc were reliably observed in the population activity of LPFC but not of area V4

(A) Normalized change in firing rate, pairwise correlation, and Fano factor of neurons in LPFC and V4 with SSI_visual > 0.75. Error bars indicate mean ± SEM. P-values were computed using a Wilcoxon signed-rank test. (B) Maximum likelihood fits from model 1 (circles and dotted line) and from model 2 (squares and dashed lines). Model 1 assumes zero Δc-related modulations in V4, and model 2 assumes a multiplicative relationship between the modulations in V4 and LPFC. Each model has three parameters. The likelihood ratio and its bootstrapped 95% confidence intervals are indicated at the bottom of the panel. (C) The likelihood ratio of model 1 to model 2 across thresholds of SSI_visual. The likelihood ratio was greater than one (favoring model 1) for all thresholds of SSI_visual for all three measures. For firing rate and pairwise correlation, the bootstrapped 95% confidence interval (shading) of the likelihood ratio was greater than one for a broad range of SSI_visual. (D) The fraction of neurons whose firing rates were significantly modulated in association with either Δc (orange lines) or Δd′ (blue lines) plotted against the threshold for statistical significance. Solid lines indicate the fraction with significantly increased firing rates, and dashed lines indicate the fraction with a significant decrease. Black lines indicate the discovery rate at chance (p-value divided by two). (E) The mean firing rate change across all neurons with an individually significant increase or decrease in their firing rates.

Figure 8. Firing rate modulations related to Δc and Δd′ were correlated across neurons in LPFC

(A) Firing rate modulation indices (MI) from each pair of Δc-isolation session and Δd′-isolation session from each unit that was recorded in both sessions. Each marker indicates one pair of MI (576 pairs from 47 units that had a SSI_visual > 0.75). Both MIs of each pair belonged to the same unit. The slope of the regressed line (dashed, no intercept) was 1.72. (B) MIs from each pair of sessions with the same isolation from each unit that was recorded in both sessions. To combine MI pairs from Δc sessions (orange) and from Δd′ sessions (blue), MIs of each isolation type were z-scored (710 pairs from 79 units that had a SSI_visual > 0.75). (C) Correlations between Δc/Δd′ MI pairs and between same-isolation MI pairs. Correlations were calculated after distribution-matching the number of intervening sessions between each pair (Figure S7A-B). Shading indicates 95% confidence interval. (D) Normalized Δc/Δd′ correlations were computed by dividing Δc/Δd′ correlations by same-isolation correlations, and 95% confidence intervals were computed using a bootstrap procedure. Normalized correlations in LPFC were statistically indistinguishable from 1 and reliably different from normalized correlations in V4, which were indistinguishable from 0. See also Figure S7.