Faster Detection of “Darks” than “Brights” by Monkey Superior Colliculus Neurons

Diverse preferences for darks and brights across SC neurons

We first asked whether neurons tended to be more sensitive to darks or brights across the population. For each recorded neuron, we plotted firing rate as a function of time from stimulus onset, and we assessed the strength of the visual burst as a function of luminance contrast polarity. Figure 1A–C shows the responses of three example neurons (from the same monkey, A) to a 100% contrast stimulus. The black lines show responses to the negative polarity stimulus (darker than background), and the light gray lines show responses to the positive polarity stimulus (brighter than background). In all cases, the negative and positive polarity stimuli were of the same size and presented at the same location. They also had the same absolute Weber contrast, and their presentation sequence was randomly counterbalanced across trials. As can be seen, there was a diversity of neural preferences across the three neurons: neuron 1 (Fig. 1A) was more sensitive to the positive polarity stimulus than to the negative polarity stimulus; neuron 2 was, more or less, equally sensitive to the two stimuli (Fig. 1B); and neuron 3 was clearly more sensitive to the dark stimulus (Fig. 1C). We also plotted full contrast sensitivity curves for the same neurons (Fig. 1D–F) by relating peak visual response strength to stimulus contrast (Materials and Methods). Consistent with Figure 1A–C, there was a diversity of preferences for darks and brights across the three neurons in their full contrast sensitivity curves.

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Figure 1.

Diversity of preferences, in terms of visual burst strength, for dark and bright stimuli by SC neurons. A–C, Visual responses of three example neurons from monkey A to a 100% contrast stimulus appearing within their response fields (RFs). Black indicates that the stimulus was negative in luminance polarity (darker than the background); gray indicates that the stimulus was brighter than the background. Each row of tick marks indicates a single trial, and each tick mark indicates the time of an action potential. The firing rate plots below the raster plots summarize the neurons' firing rates. Neuron 1 had a higher sensitivity to bright stimuli, whereas neuron 2 was equally sensitive to dark and bright stimuli. Neuron 3, on the other hand, clearly preferred dark stimuli. Note that response latency (that is, when the stimulus-evoked action potentials first appeared) was always shorter for dark stimuli (see subsequent analyses). Error bars denote SEM across trials. D–F, For each neuron, we measured peak average firing rate after stimulus onset (individual symbols), and we plotted it as a function of stimulus Weber contrast. We also fit the data with continuous curves (Materials and Methods). Neuron 1 had higher contrast sensitivity for bright stimuli, evidenced by the higher plateau firing rate at maximal contrast. Neuron 2 plateaued at the same firing rate for both dark and bright stimuli, and neuron 3 was more sensitive to dark stimuli. Dashed vertical lines indicate C50, the semi-saturation contrast of each neuron (Materials and Methods).

These observations held across the population of 172 neurons that we analyzed. For each neuron, we fit a contrast sensitivity function (Eq. 1; see Fig. 1D–F for examples) by optimizing three parameters characterizing how the neuron altered its visual response with Weber contrast: R reflected the dynamic range of the response, C50 characterized the semi-saturation contrast of the neuron, and N characterized the steepness of the contrast sensitivity curve (slope parameter). We performed such a fit for either positive or negative luminance polarity stimuli. We then obtained a parameter modulation index, describing, for each neuron, to what extent each parameter of the fit was different between positive and negative luminance polarity stimuli. For example, for dynamic range (parameter R in Eq. 1), we obtained the R value for bright stimuli minus the R value for dark stimuli in each neuron, and we then divided this difference by the sum of R values for the two stimulus types (Materials and Methods). This gave us an index in which 1 meant that the neuron responded maximally only to bright stimuli and −1 meant that the neuron responded maximally only to dark stimuli. An R parameter modulation index value of 0, instead, indicated equal visual response dynamic ranges for bright and dark stimuli. We then plotted histograms of the parameter modulation indices across the population. As can be seen in Figure 2, all three modulation indices of the contrast sensitivity function fits had distributions straddling 0, and with large diversity across the population. Some neurons clearly preferred bright stimuli, others clearly preferred dark stimuli, and yet others were equally sensitive to darks and brights (near 0 in the histograms of Fig. 2). The vertical lines in Figure 2 indicate the mean (solid) and median (dashed) parameter modulation index values across neurons, and they were all close to 0. Approximately half of the neurons were more sensitive to bright stimuli (whether in terms of dynamic range, semi-saturation contrast, or slope of the contrast sensitivity function), and the other half were more sensitive to dark stimuli.

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Figure 2.

Diversity of contrast sensitivity curve parameters for dark and bright stimuli across SC neurons. A, For each neuron, we compared the neuron's visual response dynamic range (parameter R in Eq. 1; Materials and Methods) between dark and bright stimuli. A modulation index value of 1 indicates maximal responsiveness to only bright stimuli, and a value of −1 indicates maximal responsiveness only to dark stimuli. Most neurons responded to both stimuli (note the rarity of ±1 modulation index values), but with varying degrees of sensitivity; some neurons clearly preferred dark stimuli, whereas others clearly preferred bright stimuli (similar to the examples in Fig. 1). The vertical lines show mean (solid) and median (dashed) modulation index values across the population, and they were both close to 0: across the population, SC visual responses were equally sensitive to dark and bright stimuli. B, Similar observations for the semi-saturation contrast (C50) parameter. Neurons with positive modulation indices in this case are neurons with higher semi-saturation contrasts for bright stimuli (that is, they were less sensitive to bright than dark stimuli). Again, the population average and median semi-saturation contrasts were largely similar between darks and brights (vertical lines), but with large variability across individual neurons. C, Similar observations for the slope parameter of the contrast sensitivity curves. These results are consistent with the example neurons of Figure 1.

Therefore, in the SC, we noticed a substantial diversity of sensitivity preferences for darks and brights across the population (unlike in LGN and V1). This suggests that stimuli of both positive and negative luminance polarities can indeed be represented well by SC neural populations.

Earlier detection of darks by SC neurons, regardless of preference

Unlike response sensitivity, for which we saw diverse preferences for brights and darks (Figs. 1, 2), SC neurons exhibited systematically shorter visual response latencies for dark stimuli, independently of their visual response strengths at a given contrast. Consider, for example, the same three neurons of Figure 1A–C. In each of them, visual responses occurred earlier for the dark stimuli than for the bright stimuli, as can be visually assessed from the spike rasters and the firing rate density plots below them. This happened even for neuron 1, which preferred bright stimuli (Fig. 1A). It also happened at different contrast levels (Fig. 3), although stimulus contrast expectedly modulated the response strength and latency of each neuron. For example, at 20% contrast, all three neurons from Figure 1 still responded earlier to dark than bright stimuli, despite the weakened and delayed visual responses relative to the 100% contrast conditions. Thus, at each contrast level, there was an apparent dissociation between visual response sensitivity and visual response latency in these three neurons, not unlike what we recently observed when we presented different spatial frequencies to SC neurons (Chen et al., 2018).

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Figure 3.

Earlier visual bursts for dark than bright stimuli at different stimulus contrasts. A–C, Visual responses of the three example neurons of Figure 1 at each of our tested contrasts (different columns). Black curves indicate dark stimuli, and gray curves indicate bright stimuli. Reducing contrast expectedly weakened and delayed visual responses for both dark and bright stimuli (compare firing rates across columns; also see Fig. 1D–F). Note, however, that at each contrast level, visual bursts occurred slightly earlier for dark than bright stimuli, even for neuron 1, which was more sensitive to bright stimuli. Also note that at the weakest contrast level (5%), both neuron 1 (A) and neuron 3 (C) were more sensitive to light decrements than light increments. We quantify these observations in subsequent figures and analyses. Error bars denote SEM across trials.

To investigate this dissociation further, we estimated, for each neuron, the onset of the visual burst as the first time point at which the lower bound of the 95% confidence interval of the neuron's average firing rate was elevated for a prolonged period of time above its baseline activity (Materials and Methods). Although estimating neural response latencies from firing rate measures like we did might blur the actual absolute values of the response latencies, because of convolution kernels with spike times, this approach was still sufficient to capture the latency differences across luminance polarities that we were interested in (Materials and Methods). Therefore, for each contrast, we subtracted each neuron's visual response latency for dark stimuli from its visual response latency for bright stimuli, and we sorted the neurons according to this difference. An example of such sorting can be seen in the top panel of Figure 4A for the 100% contrast stimuli. Note how a majority of neurons (76.7%; 128 out of 167) had an earlier visual response for dark stimuli (evidenced by a positive latency difference in the figure). This is in contrast to the diversity of preferences for darks and brights seen in Figures 1–3. In fact, with the very same sorting of the neurons as in the top panel, we next plotted (Fig. 4A, bottom panel) the same neurons' differences in peak visual burst strengths between darks and brights (Materials and Methods). The neurons were no longer as properly ordered as in the top panel, suggesting that the latency effect in the top panel was not trivially explained by a systematic difference in response sensitivity between darks and brights. For example, both neurons 126 and 156 (highlighted in Fig. 4A with small diagonal arrows) possessed clearly stronger responses for bright stimuli than dark stimuli (positive difference in the bottom panel), but they both had a later response latency for bright stimuli (positive difference in the top panel). Therefore, visual responses to dark stimuli still occurred earlier than visual responses to bright stimuli even when neurons preferred bright stimuli.

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Figure 4.

Faster detection of darks than brights by monkey superior colliculus neurons. A, For the highest stimulus contrast, we estimated visual response latency (Materials and Methods) separately for dark and bright stimuli in each neuron. We then subtracted, for each neuron, the visual response latency for dark stimuli from the visual response latency for bright stimuli (positive would indicate earlier responses for dark stimuli). We then sorted all neurons based on this difference (top panel). In the bottom panel, we used the very same sorting, but we now plotted the difference in peak visual response strength between bright and dark stimuli (Materials and Methods). Most neurons had a shorter visual response latency for dark than bright stimuli (top panel; vertical line shows the sorted neuron index at which response latency differences flipped sign from negative to positive). This happened independently of response strength; the bottom panel (with the same sorting) did not show a systematic ordering. For example, the neurons highlighted with diagonal arrows preferred bright stimuli (bottom panel) but still detected dark stimuli earlier (top panel). B–E, Similar results for lower contrasts. Of course, with lower and lower contrasts, the earlier detection of darks was less and less prevalent (see the crossover points in the top row). However, this was because lower contrasts were already associated with delayed and weakened visual responses (Fig. 5). Also note that the lower row shows a decreasing likelihood of bright-preferring neurons as contrast level decreases, suggesting a contrast-dependent processing of darks and brights in SC neurons (Fig. 6).

We also made similar observations for the other stimulus contrasts that we tested (Fig. 4B–E). Note that for each panel in Figure 4, we indicated the total number of neurons included into each analysis, which varied across panels (that is, across contrast levels). This happened because some neurons may not have met our inclusion criteria for estimating visual response latencies, resulting in slightly different neuron counts across the different panels (Materials and Methods). For example, for the particularly low contrast stimuli (e.g., 5% and 10%), some neurons did not exhibit any significant visual bursts at all (Materials and Methods), so they were not included in the figure. Having said that, in all contrasts, there was a majority of neurons responding earlier to dark than bright stimuli (Fig. 4, top row in each panel) regardless of the relative strengths of their visual responses (Fig. 4, bottom row).

We confirmed this observation statistically with permutation tests, conducted on pairwise latency differences separately for each contrast level and for each monkey (Materials and Methods). In monkey M, there were significantly longer latencies for bright stimuli in all contrasts (100% and 50% contrasts: mean differences = 3.18 and 3.56 ms, respectively, Monte Carlo p < 0.0001; 20% contrast: mean difference = 1.62 ms, Monte Carlo p = 0.0448; 10% contrast: mean difference = 2.03 ms, Monte Carlo p = 0.0152; and 5% contrast: mean difference = 4.5 ms, Monte Carlo p < 0.001). In monkey A, latencies were significantly longer for bright stimuli in 100%, 50%, and 5% contrasts (mean differences = 2.97, 4.31, and 4.91 ms, respectively; Monte Carlo p < 0.0001, p < 0.0001, and p = 0.0059, respectively); no significant differences were found in 20% and 10% contrasts (mean differences = 1.39 and 0.27 ms, respectively; Monte Carlo p = 0.0548 and p = 0.7587, respectively), but the same trends were still there (also see Fig. 5E). Thus, faster detection of dark than bright stimulus contrasts is a general property of SC neurons.

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Figure 5.

Interaction between contrast and luminance polarity in SC visual response latencies. A, Visual responses of an example SC neuron from monkey M for high (100%) and low (5%) contrast bright stimuli (that is, with luminance higher than the background luminance). Each row of tick marks represents a single trial repetition, and each tick mark indicates the time of an action potential. The neuron responded earlier to the high contrast stimulus. B, In the same neuron, the visual responses to a dark 100% contrast stimulus (that is, with luminance darker than the background luminance) occurred even earlier than the responses to a bright 100% contrast stimulus. Thus, both contrast and luminance polarity affected the neuron's visual response latency. C, Cumulative histograms of our estimates of visual response latency across all of our neurons, for both stimulus contrast (different colors) and stimulus luminance contrast polarity (solid vs dashed lines). High contrasts were associated with earlier visual response latencies in the SC. In addition, at each contrast, dark stimuli were systematically associated with earlier visual response latencies. D, Average visual response latencies for brights and darks across contrast levels, demonstrating consistently faster responses for dark stimuli. E, Average differences in visual response latencies between responses for bright and dark stimuli per contrast level. All contrast levels were associated with faster detection of dark than bright stimuli. The effect increased in strength with increasing contrast from 10% to 100%. At the 5% contrast condition, the effect was the strongest, likely because there were more dark-preferring neurons than at higher contrasts (see the bottom row of Figs. 4 and 6J,O). Error bars in D, E denote SEM across neurons.

Of course, our results do not deny that high visual response sensitivity is normally associated with short visual response latencies. For example, with the sorting of neurons shown in Figure 4 based on their response latency differences (top row), there was still a hint of an additional trend: neurons with a smaller latency difference between dark and bright stimuli tended to be the neurons preferring bright stimuli (bottom row). For example, compare the first and last quartiles in the bottom panel of Figure 4A: more bright-preferring neurons occurred in the first quartile (having 0 or negative latency differences) than in the last quartile (having positive latency differences). This suggests that there were divergent forces influencing visual response latency: a neuron strongly preferring bright stimuli might have had its high response strength for bright stimuli (at a given contrast level) counterbalance the normally earlier detection of dark stimuli. Indeed, when we evaluated response latency as a function of both stimulus contrast (a proxy for visual response strength in the neurons) and luminance polarity, we found that both factors clearly influenced the neurons' visual response latencies. This is shown in Figure 5A,B for an example neuron, and in Figure 5C for the population. In Figure 5A, high contrast stimuli evoked stronger and, therefore, earlier visual responses than low contrast stimuli, as expected (Boehnke and Munoz, 2008; Marino et al., 2012, 2015; Hafed and Chen, 2016; Chen et al., 2018). With high contrast dark stimuli, the same neuron exhibited even earlier visual bursts than for high contrast bright stimuli (Fig. 5B). Across the population, cumulative histograms of estimated visual response latencies (Fig. 5C), as well as their averages and standard errors of the mean (Fig. 5D), revealed that increasing stimulus contrast systematically decreased response latencies, as expected (Boehnke and Munoz, 2008; Marino et al., 2012, 2015; Chen et al., 2018), but also that visual response latencies were always systematically shorter, at a given contrast, for dark than bright stimuli (consistent with Fig. 4; also see Fig. 6 below). This polarity effect on response latencies had an order of magnitude of a few milliseconds difference between dark and bright response latencies (Fig. 5E), similar to results in the cat LGN (Jin et al., 2011) and V1 (Komban et al., 2014). Therefore, both stimulus contrast (a proxy for response sensitivity) and stimulus polarity (conferring a temporal advantage for darks) dictated our SC neurons' visual response latencies.

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Figure 6.

Contrast-dependent SC processing of dark and bright stimuli. A–E, Scatterplots of response latencies to dark versus bright stimuli at each contrast level. The proportion of neurons responding faster to darks (above the unity line) increased with increasing contrast level. The proportion of neurons responding faster to darks was also substantially larger at the lowest contrast level (E). F–J, Scatterplots of peak visual responses to darks versus brights at each contrast level. With increasing contrast, the proportion of neurons responding stronger to brights (above the unity line) also increased. K–O, Correlations between differences in visual response latencies and differences in peak visual responses for brights and darks. The differences were obtained in the same way as in Figure 4: a positive response latency difference indicates a faster response to dark stimuli, and a positive peak response means higher sensitivity to bright stimuli. The gray quadrants indicate the regions where preferences in terms of sensitivity and latency coincided: neurons in the upper left quadrant responded both stronger and faster for brights, and neurons in the lower right quadrant responded both stronger and faster for darks. There were weak negative correlations between response latency and sensitivity differences across contrasts, consistent with a known relationship between response latency and sensitivity. Critically, however, earlier responses to darks were not dictated by sensitivity preferences; if this was the case, most neurons would have occupied the shaded gray quadrants. Instead, at the highest contrast levels (e.g., K–M), the majority of neurons, which responded earlier for darks, responded stronger for brights (occupying the upper right quadrants), suggesting that dark stimuli were potent in expediting visual bursts despite the bursts being nonpreferred by the neurons.

Interaction between stimulus contrast and the processing of darks and brights by SC neurons

The results of Figure 5E were particularly intriguing to us, in the sense that the lowest contrast stimuli (5%) were associated with a seemingly bigger effect of visual response latency difference between darks and brights than the more visible 10% and 20% contrast targets. One possibility could be that at 5% contrast, there were fewer bright-preferring neurons. We, therefore, next asked whether there was an interaction between luminance contrast level and the preference of neurons for darks or brights. To do so, we replotted the data above as scatter plots of visual response latency for brights versus darks in Figure 6A–E and as scatter plots of visual response sensitivity for brights versus darks in Figure 6F–J. The latency plots confirmed our previous observations that there was systematically faster detection of dark contrasts across all contrasts. For sensitivity, there was an interaction between contrast level and SC population preference. At high contrasts (Fig. 6F,G), the population was biased toward preferring bright stimuli (despite responding faster for dark stimuli), whereas at 5% contrast (Fig. 6J), this bias disappeared and tended to be in the opposite direction; neuron 1 in Figures 1D and 3A also demonstrates this effect: although the neuron preferred brights in its plateau firing rate of the contrast sensitivity curve, its (weak) response at 5% contrast was still higher for dark targets. Thus, there was an interaction between contrast level and sensitivity to darks in our SC neurons. While this pattern is different from natural image statistics (Cooper and Norcia, 2015) and cat V1 properties (Liu and Yao, 2014), in the sense that we found more bright-preferring than dark-preferring neurons at high contrast, it does suggest that the larger latency effect magnitude in Figure 5E at 5% contrast might have been driven by a larger number of dark-preferring neurons at this contrast level.

Statistically, we confirmed that there were contrast-dependent sensitivity preference differences between brights and darks. We applied the same pairwise latency difference procedure described above, but now to pairwise peak visual response differences (Materials and Methods). In monkey M, SC visual responses to brights were significantly stronger than responses to darks in the 100%, 50%, and 20% contrast conditions (mean differences = 11.69 spikes/s, 8.92 spikes/s, and 4.26 spikes/s, respectively; Monte Carlo p < 0.0001, p < 0.001, and p = 0.0187, respectively); the differences were not significant for 10% and 5% contrasts, and their trends were in the opposite direction (mean differences = −0.09 spikes/s and −2.35 spikes/s, respectively; Monte Carlo p = 0.9611 and p = 0.1712, respectively). In monkey A, the neurons were significantly more sensitive for brights in all but the lowest contrast (100%, 50%, 20%, 10%, and 5% contrasts: mean differences = 6.73 spikes/s, 7.35 spikes/s, 6.52 spikes/s, 6.92 spikes/s, and 0.81 spikes/s, respectively; Monte Carlo p = 0.0106, p = 0.002, p = 0.0028, p < 0.001, and p = 0.8652, respectively).

In the same vein, for each neuron, we plotted the visual response latency difference against the peak response difference in Figure 6K–O. We used the same conventions as in Figure 4: a positive response latency difference indicating a faster response to dark stimuli, and a positive peak response difference meaning higher sensitivity to bright stimuli. If the results of Figures 4 and 5 were solely determined by response sensitivity at each contrast level, then all neurons should have occupied the shaded quadrants of these plots. In contrast, only a minority of neurons occupied these quadrants, particularly at high contrast. For example, in Figure 6K, even neurons with >50 spikes/s difference in peak sensitivity in favor of bright stimuli were still significantly faster to detect dark stimuli. Interestingly, at 5% contrast, there were significantly fewer bright-preferring neurons, again providing a plausible explanation for the relatively large effect size in response latency seen in Figure 5E at 5% contrast versus 10% and 20% contrast.

Interaction between visual field location and the faster detection of darks by SC neurons

The above results indicate that there is faster detection of dark than bright stimuli by SC neurons, in general. However, it is also known that SC visual responses preferentially process the upper visual field (Hafed and Chen, 2016), consistent with the notion that eye movements support orienting toward or away from extrapersonal stimuli largely occupying the upper visual field (Previc, 1990). If that is indeed the case, then it might be expected that differential temporal processing of dark versus bright stimuli might be magnified in the SC's upper visual field representation. For example, birds of prey, or other threats, across a daylight sky would normally cast dark contrasts on retinal images, and they need to be detected efficiently by SC neurons. We, therefore, also asked whether the results of Figure 4 could depend on the visual field locations of our recorded neurons.

We repeated the analyses of Figure 4, but this time after separating neurons based on upper and lower visual field RF locations. The results are shown in Figure 7 (for the highest contrast stimuli only, for simplicity). There was indeed a larger latency difference between dark and bright stimulus responses in the upper visual field neurons than in the lower visual field neurons (top panel). That is, the latency advantage for dark stimuli was magnified in the case of upper visual field SC neurons. We tested this observation statistically by using a permutation test with 10,000 shuffles. Here, we should note that although we pooled the data of both monkeys for visualization purposes in Figure 7, we performed the statistical procedures only on data collected from monkey A. This was because there was a strongly unbalanced sampling of neurons in the upper and lower visual fields in monkey M (68 and 21 upper and lower visual field neurons in this monkey, versus a more balanced distribution of 24 and 33 neurons in monkey A). In monkey A, there was a significant difference between upper and lower visual field effects (mean difference = −4.09 ms, Monte Carlo p = 0.0099; Materials and Methods). Therefore, a known visual response latency advantage for the upper visual field in SC neurons (Hafed and Chen, 2016) was accompanied, at least in one monkey, by a larger difference between dark and bright stimulus responses. This result is consistent with the ecological likelihood of dark contrasts in natural environments (Liu and Yao, 2014; Cooper and Norcia, 2015), and also with the role of the SC's visual processing machinery in supporting the sampling of extrapersonal visual space by orienting eye movements (Previc, 1990; Hafed and Chen, 2016; Fracasso et al., 2022).

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Figure 7.

Magnification of response latency and sensitivity differences between dark and bright stimuli in the upper visual field. We repeated the same analyses of Figure 4A, but now separating the neurons according to whether they represented upper or lower visual field locations. The top panel shows that the faster detection of dark stimuli by SC neurons that we saw in Figure 4 was amplified for upper visual field neurons. The lower panel again shows that the effects of the top panel were dissociated from visual response sensitivity (all figure conventions are similar to Fig. 4). Interestingly, the lower panel shows that the absolute value of visual response sensitivity difference (between brights and darks) was also higher for upper visual field neurons than for lower visual field neurons (compare the dynamic ranges of the two curves). Therefore, both visual response latency and visual response sensitivity effects, in terms of luminance contrast polarity, were magnified in the upper visual field.

Note also that the same dissociation between response latency and response sensitivity occurred in Figure 7 as in Figure 4; Figure 7, bottom panel, shows that with the same ordering of the neurons as in the top panel, response sensitivity was not systematically ordered in either the upper or lower visual fields, consistent with the results of Figure 4. Interestingly, the absolute value of the difference in response strength between dark and bright stimuli was also higher in the upper visual field neurons than in the lower visual field neurons (monkey A; mean difference = −13.3 spikes/s; Monte Carlo p = 0.0166, permutation test). This suggests that both latency differences (top panel) and absolute values of sensitivity differences (bottom panel) between dark and bright stimuli were amplified in the upper visual field representation of the SC, adding to a growing body of evidence of visual field asymmetries in the primate SC (Hafed and Chen, 2016; Zhang et al., 2022; Fracasso et al., 2022).

Independence of the faster SC detection of darks from the luminance polarity at fixation

Finally, we wondered whether the black fixation spot at display center (Materials and Methods) might have dictated our results above. Such an effect would be unlikely because our neurons were extrafoveal; our stimuli were, therefore, generally far from the fixation spot (Materials and Methods). However, to unambiguously rule such an effect out, we repeated the same experiment with two slight modifications. First, the fixation spot was now white instead of black (Materials and Methods). If the black fixation spot was the reason for the faster detection of dark stimuli in the results of Figures 1, 3–7 above, then this effect should be altered with a white fixation spot. Second, the fixation spot was now removed at the same time as stimulus onset, allowing the monkeys to generate immediate, visually-guided saccades. We analyzed 213 neurons recorded with this variant of the task (81 were also recorded in the original fixation task). We will describe saccadic reaction times as a function of contrast and luminance polarity in more detail below. However, for now, our aim was to replicate the visual burst results shown above. For each neuron, we normalized the neuron's average firing rate by the peak visual response for 100% contrast stimuli in the interval 0–100 ms after stimulus onset (Materials and Methods). We then averaged all of the normalized firing rate curves of each monkey's neurons (Fig. 8A,B). We separated the neurons of each monkey in this analysis to demonstrate the repeatability of our results across the animals, and also because subsequent saccadic behavior later in the trials differed between them, as we clarify in more detail below.

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Figure 8.

Faster detection of darks than brights in the SC, independently of central fixation spot appearance. A, B, We repeated the same experiment as in Figures 1–7, but this time with a white fixation spot rather than a black fixation spot (Materials and Methods). Also, here, we removed the fixation spot at stimulus onset, to allow monkeys to generate visually-guided saccades to the appearing stimuli. In each panel, we averaged each monkey's stimulus-aligned firing rates (after normalizing each neuron's firing rate curve to its peak visual response at high contrast; Materials and Methods). The numbers of neurons are shown in each panel, and error bars denote SEM across neurons. As can be seen, visual responses still occurred earlier for dark stimuli than for bright stimuli in this task, and in both monkeys. Note that at around 100 ms from stimulus onset, there was an elevation of firing rates, which was the beginning of the saccade bursts for the triggered eye movements. Nonetheless, in monkey M, the elevation looked to be sharper than in monkey A, an observation that we discuss in more detail in Figures 9, 11 below with respect to saccadic reaction times. C–E, We replicated the analyses of Figure 4 for the three contrasts that we tested in this task variant. The same conclusions were reached. Most neurons detected dark stimuli earlier than bright stimuli in their visual response latencies (top row), and this effect was dissociated from individual neuron sensitivity to either darks or brights (bottom row). All other conventions are similar to Figure 4.

Both animals had clear visual responses in the task, consistent with the results of the fixation variant (Figs. 1, 3–7). Most importantly, these responses were also clearly still occurring earlier for dark stimuli than for bright stimuli (Fig. 8A,B). To summarize these results on an individual neuron basis, we replicated the same analyses of Figure 4 (Fig. 8C–E; we combined the neurons of both monkeys here because of how similarly they behaved in Fig. 8A,B). We did this for all three stimulus contrast levels that we tested in this variant of the task (Materials and Methods). For all contrasts, most neurons still detected dark contrasts earlier than bright contrasts, regardless of neural sensitivity (Fig. 8C–E), just as in Figures 1, 3–7. Permutation tests run on the latency differences confirmed this observation for all contrasts (in monkey M, 100% and 50% contrasts: mean differences = 4.97 and 4.42 ms, respectively, Monte Carlo p < 0.0001; 10% contrast: mean difference = 1.62 ms, Monte Carlo p = 0.0185; in monkey A, 100% and 50% contrasts: mean differences = 3.71 and 3.35 ms, respectively; Monte Carlo p < 0.0001; 10% contrasts: mean difference = 2.52 ms; Monte Carlo p < 0.001). Note that the effect sizes were also of the same order of magnitude as those shown in Figure 5E. Therefore, the results of Figures 1–7 were not trivially caused by the use of a black fixation spot at screen center. Moreover, the results still persisted in a more reflexive behavioral task, in which prolonged fixation was not enforced in the face of a salient eccentric stimulus onset.

Different temporal dynamics of firing rates in the sustained interval for darks and brights

The results so far have focused on initial visual bursts. However, with prolonged fixation (as in our primary task of Figs. 1–7), we also observed significant differences in SC neural response dynamics in the sustained interval (long after stimulus onset) for bright and dark stimuli. In particular, bright stimuli were generally associated with secondary elevations of firing rate above those of dark stimuli. To illustrate this, Figure 9A,B shows the responses of four example neurons to high contrast stimuli (100%). Two neurons are from monkey A (Fig. 9A), and two neurons are from monkey M (Fig. 9B). In the first three neurons (neurons 5–7 in Fig. 9A,B), after the initial visual bursts, bright stimuli evoked stronger sustained activity than dark stimuli (see pink intervals highlighting the sustained interval). The stimuli were still present within the RFs of the neurons in all cases, but there was an altered response dynamic after the initial visual bursts, particularly for bright stimuli. Even the fourth neuron (neuron 8 in Fig. 9B), which showed relatively weak sustained activity, still showed a subdued secondary peak in firing rate after the initial visual burst for bright stimuli (also see Fig. 11 below for more details on monkey M's secondary bursts for bright stimuli).

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Figure 9.

Preference for bright stimuli in later sustained intervals of visual neural SC responses. A, Two example neurons from monkey A showing how later sustained responses (after the initial visual bursts) were elevated for bright more than dark stimuli. Responses for 100% contrast stimuli are shown, but similar observations were also made across contrasts (see C). B, Two example neurons from monkey M showing similar observations. Note how neuron 8 had a weaker sustained response for bright stimuli, but it still exhibited a secondary burst (of small amplitude) for bright stimuli (also see Fig. 11D for this monkey's population response summary in the sustained interval). The pink rectangles denote our interval of choice when analyzing sustained visual responses. C, In such interval, we obtained millisecond-by-millisecond fits of contrast sensitivity curves for bright and dark stimuli. The dynamic range parameter of Equation 1, R, showed a clear and significant elevation for bright stimuli relative to dark stimuli across the population (top panel). This was also the case in each monkey individually. The middle and lower panels show that the thresholds (middle panel) and slopes (lower panel) of contrast sensitivity curves were getting progressively worse in the sustained interval (relative to initial visual bursts), as expected, but with little differences between dark and bright stimuli. Error bars in all panels denote SEM (across trials in A, B, and across neurons in C).

To characterize this altered dynamic of neural responses as a function of time in more detail, we took each firing rate curve after 80 ms from stimulus onset (that is, after the initial visual bursts). We then estimated contrast sensitivity curves at each time point. Each time sample of a firing rate curve is already a kind of average over some discrete measurement interval (because of the convolution of spike times with a Gaussian kernel to generate firing rates). Therefore, we took each sample of the firing rate curve of a neuron in the sustained interval, and we used it to fit contrast sensitivity curves from Equation 1 at each time point. This gave us a series of contrast sensitivity curves as a function of time. We then plotted the time courses of the parameters R, C50, and N of the fits during the sustained interval, and we did this for either bright or dark stimuli. The results across the entire population of neurons are shown in Figure 9C. As can be seen, all parameters were varying differently between darks and brights in the interval around ∼100–200 ms after stimulus onset (that is, during sustained visual response intervals), consistent with the example neurons of Figure 9A,B. The biggest effect was in the R parameter, which was stronger for brights than darks, suggesting higher sustained firing rates for brights after the ends of the initial visual bursts. Both C50 and N gradually changed in a manner that was consistent with higher thresholds and shallower contrast sensitivity functions in the sustained interval. That is, the contrast sensitivity of the neurons was generally the highest in the initial visual burst intervals, and it gradually degraded in sustained intervals (other than the R parameter elevation for bright stimuli). This makes sense given that sustained intervals were generally associated with much lower firing rates than in the initial visual burst intervals (and were therefore less likely to be strongly differentially modulated by stimulus properties). In any case, during the sustained interval, and unlike in the initial phases of SC visual responses, there was a generalized elevation of firing rates for bright stimuli compared with dark stimuli for all contrasts. As we show later, this effect was strong enough in monkey M, to the extent that it appeared to dominate this monkey's saccadic reaction time patterns in the immediate, visually-guided saccade version of the task.

Earlier detection of dark stimuli also by inhibited SC neurons

In all of the above analyses, we focused solely on neurons exhibiting positive visual responses (that is, increases in firing rates above baseline). However, with our offline neuron sorting pipelines (Materials and Methods), we also isolated a fewer number of neurons that exhibited transient decreases in activity after stimulus onset rather than increases. These neurons were obtained from similar recording sites to those from which we isolated neurons with visual bursts (we used linear electrode arrays primarily orthogonal to the SC surface; Materials and Methods). The neurons were, therefore, from similar topographic locations to those associated with the neurons reported in Figures 1–9. When we analyzed these inhibited neurons in more detail, we found that their transient, stimulus-induced decreases in firing rates were still sensitive to luminance polarity. For example, in Figure 10A,B, we show two example neurons from one of our electrode penetrations in monkey M. The two neurons showed classic visual responses (to black stimuli); moreover, their RFs (shown in the insets for data collected with the presentation of black small spots during fixation) were spatially localized and overlapping with each other. From the very same electrode penetration, Figure 10C shows a third sample neuron that was recorded simultaneously with the two other neurons; it was thus in the same SC topographic region as the two neurons of Figure 10A,B. The neuron of Figure 10C was inhibited instead. Most interestingly, this neuron clearly “responded” to a high contrast dark stimulus earlier than to a bright stimulus of the same contrast, with the only difference from the results of Figures 1–9 being that the response in this case was a transient reduction from baseline activity rather than an increase. Across the population of such inhibited neurons (n = 15 neurons), we repeated the same latency analyses of Figure 4 above. That is, we assessed the relative time of “response” between bright and dark contrasts (Materials and Methods). As can be seen from Figure 10D, the majority of such inhibited neurons also reacted to dark stimuli earlier than to bright stimuli, just like with the neurons possessing visual bursts. Similar observations were also made for the lower contrast stimuli. Interestingly, all 15 inhibited neurons had their “response” to stimulus onset slightly later than classic visual bursts in other neurons (compare the visual bursts in Fig. 10A,B to the inhibition time in Fig. 10C; the inhibition occurred slightly later than the bursts). Therefore, even inhibited neurons in the SC detected dark contrasts faster than bright contrasts.

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Figure 10.

Faster detection of darks than brights even in SC neurons inhibited by stimulus onset. A, B, Firing rates of two example neurons from a single linear electrode array penetration into the SC of monkey M. Both neurons had visual responses to dark stimuli in the upper left quadrant, with their RFs (obtained by presenting small black spots at different locations; insets) being well localized in space, consistent with the SC topographic representation (Robinson, 1972; Chen et al., 2019). C, A third neuron recorded simultaneously with the neurons in A, B. This neuron was inhibited by stimulus onset (also see the RF map in the inset). Nonetheless, the inhibition was still stimulus dependent: there was earlier inhibition for dark than bright stimuli, consistent with our earlier results (Figs. 1, 4–8). Error bars denote SEM across trials in A–C. D, Replication of the analysis of Figure 4A, top, for all neurons that were inhibited by stimulus onset. Now, we estimated visual response latency by checking when the neural activity was significantly decreased from baseline. Most neurons were still modulated earlier by dark than bright stimuli, consistent with our earlier results for visual bursts (Figs. 1, 4–8). All other conventions are similar to Figure 4A.

Saccadic reaction times can be significantly shorter for dark stimuli

Finally, prior work has demonstrated a tight relationship between SC visual response properties and saccadic reaction times (Boehnke and Munoz, 2008; Marino et al., 2012, 2015; Hafed and Chen, 2016; Chen et al., 2018). Specifically, both visual response sensitivity (Boehnke and Munoz, 2008; Marino et al., 2012, 2015; Hafed and Chen, 2016; Chen et al., 2018) and visual response latency (Chen et al., 2018) can predict such reaction times. Therefore, given the faster response latencies of SC neurons for dark stimuli that we found, we wondered whether this effect was sufficient to be associated with faster saccadic reaction times to such stimuli (even when response sensitivity was, on average, similar for darks and brights, as shown in Fig. 2; or even slightly favoring brights at high contrasts, as shown in Fig. 6). We tested our two monkeys and a third one on the immediate visually-guided saccade task; we used the same sessions as in Figure 8 for monkeys M and A, and we ran separate behavior-only sessions for monkey F. The monkeys simply generated a saccade as soon as the target appeared (the fixation spot also disappeared at target onset, as mentioned above for Fig. 8 and in Materials and Methods). We measured saccadic reaction times and plotted them as a function of stimulus contrast and stimulus luminance polarity.

All monkeys showed faster reaction times for higher contrast stimuli, as expected (Marino et al., 2012, 2015; p < 3 × 10⁻¹⁰³ in each monkey individually, Kruskal–Wallis test exploring the effect of contrast on reaction time, when collapsing across luminance polarities). Interestingly, two out of the three monkeys (A and F) also showed consistently faster reaction times for the darker stimuli, like with the SC visual bursts. These results are shown in Figure 11A,C,E; monkeys A and F were faster to react to dark stimuli at all contrasts. Monkey M, on the other hand, had faster reaction times for the bright stimuli (Fig. 11C). All of these results (of luminance polarity effects on reaction times) were significant (p < 2.5 × 10^–8 in each monkey individually, Kruskal–Wallis test exploring the relationship between luminance polarity and reaction time, when collapsing across contrasts); the effect sizes (shown for each condition in Fig. 11A,C,E) were also substantial. In addition, the effect sizes in monkeys A and F were of the same order of magnitude as the effect sizes of the visual response latency differences between darks and brights seen in Figure 5E.

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Figure 11.

Relationship between saccadic reaction times and SC visual response properties for darks and brights in the SC. A, Saccadic reaction times as a function of stimulus contrast (x-axis) and luminance polarity (different lines) in monkey A from the saccade version of our task. Error bars denote SEM across trials. Reaction times were significantly shorter for dark than bright stimuli at all contrasts, suggesting a potential role for the earlier SC visual responses for dark stimuli in triggering earlier saccades for such stimuli. B, This monkey's neural responses during the fixation task showed clearly earlier visual bursts for dark stimuli, with equal visual burst strengths for darks and brights (consistent with Fig. 2). The figure was obtained similarly to Figure 8A,B. Note also that the population sustained response (from the peak of the visual burst onward) was larger for brights than darks (consistent with Fig. 9). Error bars denote SEM across neurons. C, Monkey M showed the opposite reaction time effects from Monkey A. D, In this monkey's neurons, the secondary burst for bright stimuli in the fixation variant of the task was particularly prominent (compare to the monkey A neural responses). This suggests that in this monkey, this secondary preference for bright stimuli might have dominated the monkey's reaction times in the saccade task. E, We tested a third monkey behaviorally, and we replicated the monkey A results. Therefore, in two out of the three monkeys, saccadic reaction times were earlier for dark than bright stimuli, consistent with the neural results of Figures 1, 4–8, 10.

We were particularly intrigued by the discrepancy in the reaction times of monkey M with respect to dark and bright stimuli. On the one hand, it might suggest that SC visual response latency (Figs. 4, 5) is not the only determinant of saccadic reaction times, which is indeed plausible. For example, we earlier found that SC visual response latency and visual response sensitivity together provided a better correlate of reaction times than either parameter alone (Chen et al., 2018). Therefore, since about half of the neurons in our population were more sensitive to bright stimuli anyway (Fig. 2), despite the faster detection of darks, it could be that this particular monkey's reaction times were more dictated by SC visual response sensitivity than by visual response latency. On the other hand, it could additionally be the case that the later elevation of responses for bright stimuli that we saw in Figure 9 was more pronounced in this monkey, potentially suggesting stronger top-down control for bright stimuli. In that case, bright stimuli could be preferentially processed by this monkey. Indeed, in a previous behavioral study in which we investigated the properties of saccadic inhibition as a function of luminance contrast polarity, this monkey reacted differently to full field white versus black visual flashes from the two other monkeys in the very initial oculomotor response to flash onset, again reacting faster for bright than dark flashes (Malevich et al., 2021, see their Fig. 3). Therefore, we decided to check how this monkey's neurons, in particular, reacted to white stimuli long after the initial visual bursts, and we were able to do so from our fixation variant of the task.

We plotted each monkey's population visual responses for dark and bright stimuli in the fixation variant of the task, allowing us to explore the longer sustained interval. These results are shown in Figure 11B,D. Although monkey M's neurons still detected dark stimuli earlier than bright stimuli in the initial visual response period (consistent with all of our results shown earlier), this monkey's elevation of sustained visual activity for the bright stimuli (Fig. 9) was particularly pronounced when compared with monkey A (note the secondary peak in population firing rate for bright stimuli in Fig. 11D for monkey M, which was stronger than the same peak in monkey A). We also even saw hints of this secondary elevation in Figure 8B in the immediate, visually-guided saccade variant of the task, with a sharper elevation for bright stimuli right after the initial visual burst and leading up to the saccade-related burst; however, of course, in this task, this sharper elevation for brights was harder to properly analyze in the saccade task because of how quickly the motor burst came.

Therefore, the results of Figure 11 suggest that saccadic reaction times can indeed be faster for dark than bright stimuli, consistent with the faster detection of dark stimuli by SC neurons, and that even violations of such an observation (as in the case of monkey M) are still related to the SC visual responses (in this case, the sustained responses after the initial visual bursts subside).

In all, our results in this study indicate that SC neurons robustly detect dark stimuli faster than bright stimuli; that sustained visual responses in the SC instead favor bright stimuli; and that saccadic reaction times can reflect the faster detection of dark stimuli in the SC's initial visual bursts and/or the later elevation for bright stimuli.