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• W2804571081 abstract "•Advantageous perception of dark compared to light temporally varying stimuli•Dark advantage occurs at different flicker frequency for human and tree shrew•Tree shrew V1 neurons show similar advantage for processing dark visual flicker•Distinct frequency specialization might be related to lifestyle of different species Despite well-known privileged perception of dark over light stimuli, it is unknown to what extent this dark dominance is maintained when visual transients occur in rapid succession, for example, during perception of moving stimuli. Here, we address this question using dark and light transients presented at different flicker frequencies. Although both human participants and tree shrews exhibited dark dominance for temporally modulated transients, these occurred at different flicker frequencies, namely, at 11 Hz in humans and 40 Hz and higher in tree shrews. Tree shrew V1 neuronal activity confirmed that differences between light and dark flicker were maximal at 40 Hz, corresponding closely to behavioral findings. These findings suggest large differences in flicker perception between humans and tree shrews, which may be related to the lifestyle of these species. A specialization for detecting dark transients at high temporal frequencies may thus be adaptive for tree shrews, which are particularly fast-moving small mammals. Despite well-known privileged perception of dark over light stimuli, it is unknown to what extent this dark dominance is maintained when visual transients occur in rapid succession, for example, during perception of moving stimuli. Here, we address this question using dark and light transients presented at different flicker frequencies. Although both human participants and tree shrews exhibited dark dominance for temporally modulated transients, these occurred at different flicker frequencies, namely, at 11 Hz in humans and 40 Hz and higher in tree shrews. Tree shrew V1 neuronal activity confirmed that differences between light and dark flicker were maximal at 40 Hz, corresponding closely to behavioral findings. These findings suggest large differences in flicker perception between humans and tree shrews, which may be related to the lifestyle of these species. A specialization for detecting dark transients at high temporal frequencies may thus be adaptive for tree shrews, which are particularly fast-moving small mammals. Converging studies have demonstrated that ON and OFF dominant neurons in the early visual system, as well as the primary visual cortex (V1), exhibit an asymmetric pattern of activity in terms of response gain and response latency (Jin et al., 2011Jin J. Wang Y. Lashgari R. Swadlow H.A. Alonso J.M. Faster thalamocortical processing for dark than light visual targets.J. Neurosci. 2011; 31: 17471-17479Crossref PubMed Scopus (49) Google Scholar, Veit et al., 2011Veit J. Bhattacharyya A. Kretz R. Rainer G. Neural response dynamics of spiking and local field potential activity depend on CRT monitor refresh rate in the tree shrew primary visual cortex.J. Neurophysiol. 2011; 106: 2303-2313Crossref PubMed Scopus (18) Google Scholar, Yeh et al., 2009Yeh C.I. Xing D. Shapley R.M. “Black” responses dominate macaque primary visual cortex v1.J. Neurosci. 2009; 29: 11753-11760Crossref PubMed Scopus (89) Google Scholar). Behaviorally, the differential spatial visual resolution of light and dark stimuli has long been known to physicists and astronomers and has been documented by Galilei, 1632Galilei, G. (1632). Dialogue Concerning the Two Chief World Systems, Ptolemaic and Copernican.Google Scholar, who reported the observation that a dark patch on a light background seems smaller than a same-sized light patch on a dark background. This observation, named the irradiation illusion by von Helmholtz, 1867von Helmholtz H. Handbuch der Physiologischen Optik. Voss, 1867Google Scholar, has been the basis for many studies examining the differences in the perception of light and dark stimuli. These studies have largely focused on spatial aspects of differences between light and dark stimuli at the level of behavioral perception (Blackwell, 1946Blackwell H.R. Contrast thresholds of the human eye.J. Opt. Soc. Am. 1946; 36: 624-643Crossref PubMed Scopus (531) Google Scholar, Buchner and Baumgartner, 2007Buchner A. Baumgartner N. Text—background polarity affects performance irrespective of ambient illumination and colour contrast.Ergonomics. 2007; 50: 1036-1063Crossref PubMed Scopus (59) Google Scholar, Komban et al., 2011Komban S.J. Alonso J.M. Zaidi Q. Darks are processed faster than lights.J. Neurosci. 2011; 31: 8654-8658Crossref PubMed Scopus (48) Google Scholar, Lu and Sperling, 2012Lu Z.L. Sperling G. Black-white asymmetry in visual perception.J. Vis. 2012; 12: 8Crossref PubMed Scopus (30) Google Scholar) and evoked neural activity (Kremkow et al., 2014Kremkow J. Jin J. Komban S.J. Wang Y. Lashgari R. Li X. Jansen M. Zaidi Q. Alonso J.M. Neuronal nonlinearity explains greater visual spatial resolution for darks than lights.Proc. Natl. Acad. Sci. USA. 2014; 111: 3170-3175Crossref PubMed Scopus (67) Google Scholar, Liu and Yao, 2014Liu K. Yao H. Contrast-dependent OFF-dominance in cat primary visual cortex facilitates discrimination of stimuli with natural contrast statistics.Eur. J. Neurosci. 2014; 39: 2060-2070Crossref PubMed Scopus (14) Google Scholar, Zaghloul et al., 2003Zaghloul K.A. Boahen K. Demb J.B. Different circuits for ON and OFF retinal ganglion cells cause different contrast sensitivities.J. Neurosci. 2003; 23: 2645-2654Crossref PubMed Google Scholar, Zemon et al., 1988Zemon V. Gordon J. Welch J. Asymmetries in ON and OFF visual pathways of humans revealed using contrast-evoked cortical potentials.Vis. Neurosci. 1988; 1: 145-150Crossref PubMed Scopus (85) Google Scholar) and suggested that neuronal nonlinearity is the underlying mechanism for the greater visual spatial resolution for dark stimuli than light stimuli (Kremkow et al., 2014Kremkow J. Jin J. Komban S.J. Wang Y. Lashgari R. Li X. Jansen M. Zaidi Q. Alonso J.M. Neuronal nonlinearity explains greater visual spatial resolution for darks than lights.Proc. Natl. Acad. Sci. USA. 2014; 111: 3170-3175Crossref PubMed Scopus (67) Google Scholar, Ratliff et al., 2010Ratliff C.P. Borghuis B.G. Kao Y.H. Sterling P. Balasubramanian V. Retina is structured to process an excess of darkness in natural scenes.Proc. Natl. Acad. Sci. USA. 2010; 107: 17368-17373Crossref PubMed Scopus (113) Google Scholar). However, temporal aspects of the behavioral and neuronal differences in the processing of light and dark stimuli have been relatively less the focus of research. These studies have generally been limited to the dynamics and time course of behavioral (Komban et al., 2014Komban S.J. Kremkow J. Jin J. Wang Y. Lashgari R. Li X. Zaidi Q. Alonso J.M. Neuronal and perceptual differences in the temporal processing of darks and lights.Neuron. 2014; 82: 224-234Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar) and neuronal responses to light and dark stimuli (Gollisch and Meister, 2008Gollisch T. Meister M. Rapid neural coding in the retina with relative spike latencies.Science. 2008; 319: 1108-1111Crossref PubMed Scopus (385) Google Scholar, Jin et al., 2011Jin J. Wang Y. Lashgari R. Swadlow H.A. Alonso J.M. Faster thalamocortical processing for dark than light visual targets.J. Neurosci. 2011; 31: 17471-17479Crossref PubMed Scopus (49) Google Scholar, Komban et al., 2014Komban S.J. Kremkow J. Jin J. Wang Y. Lashgari R. Li X. Zaidi Q. Alonso J.M. Neuronal and perceptual differences in the temporal processing of darks and lights.Neuron. 2014; 82: 224-234Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar, Rekauzke et al., 2016Rekauzke S. Nortmann N. Staadt R. Hock H.S. Schöner G. Jancke D. Temporal Asymmetry in Dark-Bright Processing Initiates Propagating Activity across Primary Visual Cortex.J. Neurosci. 2016; 36: 1902-1913Crossref PubMed Scopus (22) Google Scholar). However, natural visual scenes comprise a range of both spatial and temporal frequency information, and the temporal resolution of ON/OFF neuronal channels of a species has direct implications for the efficiency of perception of moving targets. However, unlike differential visual spatial resolution of light and dark stimuli, whether such ON/OFF asymmetry induces differential visual perception and neuronal activity in response to temporally varying visual stimuli is unknown. We address this question in the current study using flickering stimuli with luminance increments or decrements at different frequencies in human participants and tree shrews. Tree shrews (Tupaia belangeri) are close relatives of primates and are slender, small, day-active, and fast-moving animals with a well-developed visual system (Callahan and Petry, 2000Callahan T.L. Petry H.M. Psychophysical measurement of temporal modulation sensitivity in the tree shrew (Tupaia belangeri).Vision Res. 2000; 40: 455-458Crossref PubMed Scopus (21) Google Scholar, Fitzpatrick, 1996Fitzpatrick D. The functional organization of local circuits in visual cortex: insights from the study of tree shrew striate cortex.Cereb. Cortex. 1996; 6: 329-341Crossref PubMed Scopus (156) Google Scholar, Martin, 1968Martin R.D. Towards a new definition of primates.Man. 1968; 3: 377-401Crossref Google Scholar, Schafer, 1969Schafer D. Experiments on physiology of eye of tree shrew Tupaia glis (Diard, 1820).J. Comp. Physiol. 1969; 63: 204-226Google Scholar). Sinusoidally modulated flickering stimuli have traditionally been used to investigate visual temporal resolution and to estimate temporal contrast sensitivity functions in different species (for a review, see Jarvis et al., 2003Jarvis J.R. Prescott N.B. Wathes C.M. A mechanistic inter-species comparison of flicker sensitivity.Vision Res. 2003; 43: 1723-1734Crossref PubMed Scopus (25) Google Scholar). Sinusoidally modulated stimuli span luminance increments and decrements around a mean luminance. Taking advantage of flickering stimuli to separately study behavioral and neuronal responses to temporally varying light and dark stimuli, we generated distinct light and dark flickering stimuli by implementing brief impulses of luminance deviations from an intermediate gray background. Our results demonstrate a differential visual temporal resolution for temporally varying light and dark stimuli in human participants and tree shrews, albeit at different frequencies. We also demonstrate a neural correlate for such differences in the V1 of tree shrews. We used impulses of transient increments or decrements of luminance from a midgray background to generate visual flickering stimuli (range tested, 7.5 to 60 Hz) (Figure 1). This approach allowed us to estimate behavioral and neuronal visual temporal resolution separately for both contrast change polarities (increments and decrements) at different flicker frequencies. Participants performed a 3-alternative forced-choice (3AFC) flicker detection task. On every trial, a flickering stimulus, with particular values of three independent variables (flicker frequency, modulation depth, and polarity), was presented at a randomly selected location (the target location) and two equi-luminant distractors were presented at the remaining two locations. The thresholds at 67% correct performance were estimated from psychometric fits (Figure S1A). The estimated thresholds of individual participants were used for statistical testing. We observed lower thresholds and therefore greater sensitivity for luminance decrements than luminance increments, particularly in the lower range of flicker frequencies. Human participants were most sensitive (indicated by the inverse of threshold) to flicker around 15 Hz for both dark and light stimuli (Figure 2A), which is in the range of previously reported values of about 10–15 Hz for sinusoidal flicker (Jarvis et al., 2003Jarvis J.R. Prescott N.B. Wathes C.M. A mechanistic inter-species comparison of flicker sensitivity.Vision Res. 2003; 43: 1723-1734Crossref PubMed Scopus (25) Google Scholar). Two-way repeated-measures ANOVA revealed a significant main effect of frequency (F4, 32 = 31.53; p < 0.0001) but not polarity (p > 0.1) on participants’ thresholds. However, the same analysis revealed a significant frequency-polarity interaction (F4, 32 = 2.85; p < 0.05), and post hoc tests revealed that the only significant difference between the two polarities occurred at the flicker frequency of 10.9 Hz. As an alternative analysis, bootstrapping estimates of thresholds from psychometric fits on the averaged data confirmed a significant difference (p < 0.05) solely at the flicker frequency of 10.9 Hz. Threshold is a temporal sensitivity measure representing the midway of a critical range of modulation depth in which the sharpest changes of perception take place. We also analyzed an alternative measure by summing average performance at each of six modulation depths tested. This measure is independent of fitting psychometric functions and produces values that are equivalent to the area under the performance versus modulation depth curve. As shown in Figure 2B, the temporal sensitivity patterns estimated by this measure were similar to those drawn from the psychometric functions, exhibiting main effects of frequency (F4, 32 = 30.20; p < 0.0001) and polarity (F1, 8 = 4.64; p < 0.05), as well as a significant difference between two polarities exclusively at the flicker frequency of 10.9 Hz. Furthermore, we conducted two-way repeated-measures ANOVAs on suprathreshold performances (three, two, and one highest modulation depths) (Figure S2) and showed a significant main effect of frequency (p < 0.001) but no significant difference between dark and light stimuli (p > 0.35) and no significant polarity × frequency interaction (p > 0.1). We then examined reaction times (RTs) (Figure 3). RT data are usually not normally distributed, as was the case for our data, so we employed non-parametric statistical tests. First, we used the aligned rank transform for non-parametric factorial analyses (Wobbrock et al., 2011Wobbrock, J.O., Findlater, L., Gergle, D., and Higgins, J.J. (2011). The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, ed.Google Scholar), which revealed significant main effects of frequency and modulation, as well as frequency × polarity, modulation × polarity, and frequency × modulation interactions (p < 0.001 and p < 0.001, as well as p < 0.05, p < 0.01, and p < 0.01, respectively). This test was complemented by non-parametric Wilcoxson signed rank tests on average RTs of light and dark stimuli at different frequencies and modulation levels. As expected, there is a clear reduction in response time as the modulation depth increases (Figure 3A). Our results suggest that participants could detect dark flicker faster than light flicker across tested flicker frequencies except 10.9 Hz (Figures 3A and 3B). This was exactly the flicker frequency for which there was a significant difference between two polarities in response accuracy. This suggests a general advantage of the human visual system in detecting dark transients across flicker frequencies, with a trade-off between response speed and accuracy. We trained tree shrews extensively on an analogous flicker detection task (Figures 1C and 1D). Due to the higher flicker fusion frequency of tree shrews, an additional level of frequency (60 Hz) was added to the five levels of frequency tested in human participants. The performance pattern of tree shrews resembled an initial sharp rise followed by a plateau or a slow increase. After extensive testing, we found that linear regression of the initial part of the data, anchored to the chance level, produced the most reliable fits (Figure S1B). We estimated thresholds using this method and generated average temporal sensitivity functions separately for light and dark stimuli. For the light flicker, temporal sensitivity was highest at the intermediate frequencies (between 24 and 40 Hz), while the sensitivity remained at peak values at 60 Hz for the dark flicker (Figure 2C). This was the maximum frequency we could test with our experimental apparatus and is markedly higher than the sensitivity peak obtained previously for tree shrews using sinusoidal flicker (Callahan and Petry, 2000Callahan T.L. Petry H.M. Psychophysical measurement of temporal modulation sensitivity in the tree shrew (Tupaia belangeri).Vision Res. 2000; 40: 455-458Crossref PubMed Scopus (21) Google Scholar). A two-way repeated-measures ANOVA revealed a significant main effect of frequency (F5, 25 = 4.35; p < 0.001), a significant main effect of polarity (F1, 5 = 7.54; p < 0.05), and a significant frequency × polarity interaction (F5, 25 = 2.61; p < 0.05), with post hoc tests demonstrating a significant difference between the two polarities at 60 Hz (p < 0.01). To obtain a model-free measure of temporal modulation sensitivity, we also analyzed sums of average performances at each of the six modulation depths tested. This was especially important as a corroborative analysis, because the linear fits for tree shrews were generally not as high quality as the psychometric fits for human participants. Figure 2D shows that the result of this analysis closely resembled the threshold-based estimates. Two-way repeated-measures ANOVA revealed a significant main effect of frequency (F5, 25 = 6.05; p < 0.001), a significant main effect of polarity (F1, 5 = 9.39; p < 0.01), and a significant interaction (F5, 25 = 2.80; p < 0.05). Post hoc analyses revealed a significant difference between two polarities at a flicker frequency of 60 Hz (p < 0.0001). Neuronal activity results reported here are based on 91 units recorded from the V1 of six tree shrews. Receptive fields (RFs) were mapped (see Supplemental Information for details) using the sparse noise paradigm. Figure S3A shows an example RF mapped using this paradigm. Figures S3B–S3F and S4 display RF properties and transient-sustained index distribution of the population of neurons, respectively. We have recorded mostly ON-OFF neurons, because most of our neurons were from supragranular layers (51) and infragranular layers (23). We had also 13 neurons recorded from the layer IV and 2 neurons with uncertainty about their histologic localization. During RF mapping, we classified the neurons as OFF-dominated and ON-dominated neurons based on the polarity dominance measure (see Supplemental Information for details). As shown in Figure S3C, most neurons (∼86%) were OFF dominated, consistent with the previous findings from our lab (Veit et al., 2014Veit J. Bhattacharyya A. Kretz R. Rainer G. On the relation between receptive field structure and stimulus selectivity in the tree shrew primary visual cortex.Cereb. Cortex. 2014; 24: 2761-2771Crossref PubMed Scopus (18) Google Scholar) demonstrating that tree shrew striate cortex is dominated by OFF neurons. Spikes of individual neurons during each trial were converted to firing rate, and repetitions of trials with the two highest modulation depths (i.e., 70% and 100%) were taken and tested for statistical differences. Two-way repeated-measures ANOVAs revealed a significant main effect of frequency in 66 (72.5%) neurons, a significant main effect of polarity in 24 (26.4%) neurons and a significant frequency × polarity interaction in 24 (26.4%) neurons. The union of neurons with significant main effect of polarity and/or significant interaction (i.e., polarity-sensitive neurons) composed 44% (40) of the neurons. Figure 4 shows the average firing rate of polarity-sensitive neurons. As evident in this figure, the difference between the two polarities becomes larger as the stimulus frequency increases. Although the statistical analyses at the level of individual neurons demonstrate that a sizable number of neurons respond to stimulus frequency and polarity, the population response average might cancel out significant effects if the neurons have opposing preferences. Two-way repeated-measures ANOVA on mean spike rate averaged across the population showed a significant main effect of frequency (F4, 360 = 59.8; p < 0.001), a significant main effect of polarity (F1, 90 = 25.5; p < 0.001), and a significant frequency × polarity interaction (F4, 360 = 4.4; p < 0.002). Therefore, both at the level of individual neurons and total variance explained, these neurons show significant differences in response to different frequencies and polarities. Post hoc analyses showed that the significant differences between two polarities exist at frequencies higher than 15 Hz (i.e., 15, 24, and 40 Hz), and the most robust difference is at 40 Hz. Subsequently, to see the effect of modulation depth in conjunction with frequency and polarity, we averaged repetitions of firing rates separately for all stimulus conditions including all modulation depths using a three-factor within-subject ANOVA. The analysis revealed significant main effects of all three factors (frequency, p < 0.001; modulation, p < 0.001; and polarity, p < 0.001) and significant two-way interactions among them (frequency × modulation, p < 0.001; frequency × polarity, p < 0.01; and modulation × polarity, p < 0.001). Figure 4B depicts how the interaction of all three factors influences the average neuronal firing rate. The larger differences in neuronal firing rate between dark and light flicker at higher temporal frequencies might result from the higher number of luminance impulses at these frequencies. Therefore, we wanted to see whether there are differences in neural responses (peri-stimulus time histograms [PSTHs]) to a single impulse of transient change in luminance as a function of flicker frequency and polarity. Figure 5A shows raster plots and spike histograms of a representative neuron at three frequencies and both polarities (see Figure S5 for other example units). To quantify the response of neurons to each transient impulse, we selected a segment of response starting at the timestamp of the respective transient impulse (Figure 5B) and identified the first two consecutive time bins in which neural activity surpassed the threshold. Subsequently, we found the closest peak or peaks near these two points. Within a 13 ms segment encompassing the peak or peaks, any timestamp that passed the threshold was considered a response to the respective transient impulse of the visual flicker. These points were summed, excluding the baseline value, and were subjected for statistical analyses. The 13 ms segment is approximately the half-duration of the shortest cycle among the frequencies tested. Rayleigh test for circular uniformity on spike times of individual units at different frequencies and polarities confirmed that the peaks selected through the previously mentioned procedure for further analyses are specific responses to transient impulses of luminance rather than general response fluctuations. Figure 5C depicts circular distribution of spike times of the same example neuron as in Figures 5A and 5B at the flicker frequency of 40 Hz. Differential directions (angles) of light and dark flicker emanate from distinct response latencies. Circular mean resultant vectors of all units (middle panel) and their Rayleigh distribution (right panel) are also shown in Figure 5C. Because different frequencies have different periods, mean directions of units (Figure 5D, left panel) were re-converted to time (Figure 5D, right panel) and were subjected for two-way repeated-measures ANOVA. These values represent the time lag from the stimulus onset until the peak response. The statistics revealed significant main effects of both frequency (p < 0.001) and polarity (p < 0.002) and a significant frequency × polarity interaction (p < 0.05). Table S1 summarizes the statistical analyses on different measures of neuronal signals and shows the contribution of each factor to the total variance explained. Subsequently, we examined neuronal responses to individual transients measured through the previously mentioned procedure (Figure 5B). We ran separate two-way repeated-measures ANOVAs on neural responses calculated from the PSTH of cycles 1 and 3 (first and third transient impulses). The analyses demonstrated that in neural responses to the first cycle of visual flicker (Figure 6A), there is neither a main effect of frequency or polarity nor a significant interaction between two factors. However, the statistical analysis on the neural responses to the third cycle (Figure 6B) revealed a significant main effect of frequency (F4, 236 = 16.00; p < 0.001) and a significant main effect of polarity (F1, 59 = 7.37; p < 0.01). As shown in Figure 6B, there is a clear advantage for dark visual flicker in the strength of spiking activity at the frequency of 40 Hz (p < 0.001). The results of similar analysis separately for OFF- and ON-dominated units are shown in Figures S6A and S6B. As seen in raster plots (Figures 5A and S5) and demonstrated by circular statistics (Figures 5C and 5D), neurons respond to transients stronger than the baseline. We compared pre-trial baseline activity and showed that there is no significant main effect of either polarity or frequency (Figure S5C). Because pre-trial baseline follows a random preceding trial, we also compared the last 25 ms of the interstimulus intervals (ISIs) based on the trials they follow. This analysis did not reveal any significant main effect of polarity or frequency, suggesting that differential light adaptation across conditions did not play a main role in our design (Figure S5D). To have a better picture of the spiking activity in response to single transients over time, we calculated the neuronal activity for all cycles throughout the trial duration. Figures 6C and 6D depict the development of spiking activity over time at different frequencies for light and dark visual flicker. This analysis showed that all frequencies start the activity at a similar level and then the amount of activity to subsequent transients decreases in all frequencies except 40 Hz, which exhibits an initial activity buildup during early cycles followed by a decrease in activity in subsequent cycles. We used four time points as measures of neural response latency to address the way temporally varying visual stimulus influences the latency. In both PSTHs and visual evoked potentials (VEPs), the timestamp of the first point of two consecutive points that passed the threshold was considered the response onset latency. In addition, in VEPs, the timestamp of the peak activity was considered a separate measure of the response latency (data not shown). The latency of peak spiking activity was also calculated through circular statistics (Figure 5D). Figure 7 shows response onset latencies of the PSTHs and VEPs for the first and third cycles of visual flicker. The graphs show that in both PSTHs and VEPs, the neural response to dark flicker emerges faster. Two-way repeated-measures ANOVAs revealed only a significant main effect of polarity for the first cycle (PSTH, p < 0.01; VEP, p < 0.001), but not a significant frequency or interaction effect. The frequency will have meaning over time, and during the first cycle, there is no presence of temporal frequency modulation. This has appropriately been reflected in the lack of significant effect of frequency on neural responses to and response latencies of the first cycle. However, the third cycle is when we expect main effects of both temporal frequency and polarity if they influence the timing of neural responses. Repeated-measures ANOVAs prove that both temporal frequency and polarity of temporal modulation influences the timing of neural responses. These tests demonstrated significant main effects of both frequency (PSTH, p < 0.05; VEP, p < 0.001) and polarity (PSTH, p < 0.002; VEP, p < 0.001), as well as significant interactions (PSTH, p < 0.05; VEP, p < 0.001). Post hoc analyses revealed that in the VEP, there is a significant difference between pairs of polarities at all frequencies. In PSTHs, the polarities had significantly different latencies at the frequencies of 7.5, 15, and 40 Hz. The comparison of response latencies of PSTHs separately for OFF- and ON-dominated units is shown in Figures S6C and S6D. The analysis on the latency of peak response in the VEPs showed similar results with an additional time offset. Our results demonstrate significant differences in the temporal visual resolution of temporally varying light and dark stimuli in human participants and tree shrews. Tree shrews exhibited a pronounced difference in the temporal resolution of light and dark stimuli at higher frequencies (∼60 Hz), whereas the difference was less pronounced in human participants and was manifested at lower frequencies (∼11 Hz). We also demonstrate a neural correlate for such differences in the V1 of tree shrews. V1 neurons fired more strongly in response to dark stimuli than light stimuli, and the difference was larger at higher frequencies than lower frequencies. Brief transients in flicker stimulus are reflected in brief response increases in neurons, which can form a temporal pattern in downstream areas. If a neuron merely showed elevated activity continuously, this would not be detected downstream as flicker. As shown in Figures 5C and 5D, we demonstrate that the responses are entrained to the flicker frequency and show a clear non-stationarity, suggesting that the responses are temporally patterned with reference to the temporal dynamics of transients in the stimulus and are not the result of random or irregular fluctuations in the response. Each unit exhibited stimulus-locked non-stationarity, and the population of neurons demonstrated a preferred phase for the response f" @default.
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• W2804571081 title "Distinct Frequency Specialization for Detecting Dark Transients in Humans and Tree Shrews" @default.
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