Visual masking occurs when perception of one stimulus, a target, is reduced by the presence of a second stimulus, a mask. The strength of masking is quantified as the reduction in the visibility of some aspect of the target (e.g., its shape). In forward masking the mask stimulus precedes the to-be-discriminated target; in backward masking the mask follows the to-be-discriminated target. Metacontrast is a specific case of backward masking in which spatially contiguous but non-overlapping stimuli are used. Because the target and mask do not overlap spatially, and thus do not stimulate the same retinal cells, any interaction between the target, and the mask must occur primarily at the cortical level. In line with this idea, target visibility is strongly impaired under dichoptic presentation conditions in which target and mask stimuli are presented separately to each eye (Kolers and Rosner, 1960; Schiller and Smith, 1968; Weisstein, 1971; Breitmeyer and Kersey, 1981). Here, the visual signals from each stimulus can only interact at post-retinal levels.

By varying the stimulus onset asynchrony (SOA) between the target and the mask one can track the change in the visibility of the target with respect to the onset of the mask. Metacontrast typically yields U-shaped target visibility functions (see Figure 1). The exact SOAs of optimal masking vary from study to study due to differences in experimental parameters and criterion the subject is asked to adapt (Kahneman, 1968; Breitmeyer and Öǧmen, 2006). In general, however, metacontrast masking produces the strongest suppression of target visibility at approximately a 50 ms SOA while preserving stimulus visibility at the earlier SOAs (e.g., Breitmeyer, 1978; Enns and Di Lollo, 1997; Breitmeyer et al., 2008; for reviews see Breitmeyer and Öǧmen, 2000, 2006; Figure 1). This pattern of results indicates that the strongest interaction between target and mask neural events occurs when they are separated by several tens of milliseconds rather than immediately.

The non-monotonic, U-shaped visibility of target function differs markedly from integration masking in which masking is most effective at an SOA of 0 ms and decreases in effectiveness monotonically with increasing SOAs (e.g., Hellige et al., 1977). This pattern of results suggests (nearly) immediate interactions between neural target and mask signals rather than after some delay. In other words, the very same SOAs that produce maximal integration masking (typically obtained when the target and mask spatially overlap) produce little or no masking during metacontrast (when target and mask do not spatially overlap). Whereas integration masking is largest when the mask is near in time to the feedforward signal of the target, metacontrast masking specifically circumvents that form of masking and instead affects some later processes beyond the initial feedforward signal.

Metacontrast and integration masking are also thought to differ in terms of where in the visual processing stream they occur. Integration masking is thought to occur when the target and mask stimulate common retinal and early cortical cells, resulting in an integration of target and mask signals starting at the earliest stages of processing (Weisstein, 1972; Breitmeyer, 1978; Hellige et al., 1979; Michaels and Turvey, 1979; Breitmeyer and Öǧmen, 2006). Metacontrast masking, on the other hand, is designed to minimize retinal integration and is thought to primarily reflect cortical interactions. It has been argued, however, that even these non-spatially overlapping stimuli may in fact be integrated (as opposed to interrupted, as it is sometimes described), although presumably at later levels of the visual system than the classic “integration masking” effects (Herzog and Koch, 2001; Otto et al., 2006; Plomp et al., 2009; Scharnowski et al., 2009; Rüter et al., 2010). In general, when masking methods are used to investigate awareness, care should be taken regarding the assumptions about the mechanism by which stimuli were suppressed from awareness. With this in mind, whenever relevant to our discussion we will only draw on evidence from masking studies with overlapping stimuli if cortical interactions between target and mask stimuli can be inferred (also see Breitmeyer and Öǧmen, 2006, for a further discussion on this point). Also, note that the latency differences between magnocellular and parvocellular channels are accommodated within the range of retinocortical transmission times discussed below, and thus we do not separate out the contributions of these pathways to visual masking effects and awareness.

Another way in which to bypass sensory signal interactions in areas before V1 is to directly stimulate visual cortex with TMS. The rapid changes in the magnetic field generated by a TMS coil can induce electrical activity in the brain through the scalp and skull, thus non-invasively stimulating the brain area under the coil. In their seminal paper, Amassian et al. (1989) applied TMS to the occipital pole at varying SOAs relative to the stimulus onset. Reminiscent of metacontrast masking results, they found that occipital TMS reduced stimulus visibility when applied 80–100 ms after stimulus onset. Since then further research has confirmed that single-pulse TMS reduces target visibility in distinct temporal epochs when applied to the occipital pole, stimulation of which has since been shown to include V1, V2, and even V3 (Kammer et al., 2005b; McKeefry et al., 2009; Thielscher et al., 2010; Salminen-Vaparanta et al., 2012). This suppression of visibility effect has been more consistently reported as a window centered at a post-stimulus SOA of 100 ms (Amassian et al., 1989; Paulus et al., 1999; Corthout et al., 1999a,b, 2002; Heinen et al., 2005; Kammer et al., 2005b; Kammer, 2007a; Sack et al., 2009; Camprodon et al., 2010; de Graaf et al., 2011a, 2012a; Koivisto et al., 2011b; Railo and Koivisto, 2012; Emmanouil et al., 2013; Allen et al., 2014; Tapia et al., 2014; Figure 1). Several studies have also reported an additional and very late post-stimulus TMS effect at 200 to 280 ms after the onset of the stimulus (Allen et al., 2014), some in tasks that required animal categorization and relied on figure-ground segmentation processes (Heinen et al., 2005; Camprodon et al., 2010; Koivisto et al., 2011a).

Unlike in visual masking, where both target and mask enter the visual system via the retina, in TMS masking studies an externally induced TMS event applied directly over EVC affects an internal event in EVC that was initiated at the retina. Given that the retinocortical transmission time varies from 55 to 70 ms and averages around 60 ms (Wilson et al., 1983; Baseler and Sutter, 1997; Vanni et al., 2001; Foxe and Simpson, 2002; Boehler et al., 2008), any effect of TMS must first account for this retinocortical transmission time. For example, if we assume that it takes approximately 60 ms after the stimulus onset before TMS can directly interact with the incoming visual signal, then an effect of TMS on visibility at the 100 ms SOA would mean that TMS had its effect on the visual information approximately 40 ms after the sensory signal reached EVC.

After a retinocortical transmission adjustment is made, we can begin to compare SOAs in visual and TMS masking studies (see also Railo and Koivisto, 2012). Figure 1 shows target visibility functions in metacontrast and post-stimulus TMS studies with the 60 ms adjustment. The 50 ms backward masking effect dovetails with post-stimulus TMS effects at an EVC-adjusted SOA of 40 ms (i.e., 100 ms post-stimulus onset). In other words, visual and TMS masking paradigms impair stimulus visibility at comparable time windows, suggesting that they affect the same visual process(es) that are critical for visual awareness (Breitmeyer et al., 2004a; Lamme, 2006b; Breitmeyer, 2007).

Data regarding the neural effects of backward masking is consistent with the idea that the mask interferes with feedback rather than an initial feedforward signal of the target. Single cell responses during backward masking have been recorded from a number of different primate brain areas involved in visual processing. In area V1 metacontrast masks suppress spike activity in a later time window (post-100 ms) while the early neural response is generally unaffected (Bridgeman, 1980; Macknik and Livingstone, 1998). Similarly, masking specifically disrupts signals responsible for differentiating figure from ground which occur in a later time window (post-100 ms), whereas it has no effect on the early signals (pre-100 ms) which reflect orientation differences of the elements comprising the figure-ground stimuli (Lamme et al., 2002). Because figure-ground segmentation depends on feedback from extrastriate areas to V1 (Lamme et al., 1998), these results suggest that the mask interferes with the feedback processing of the target. Furthermore, only the late, but not the early component of V1 responses correlates with behavioral reports of stimulus visibility, with higher neural activation observed for seen stimuli (Bridgeman, 1980; Lamme et al., 2000; Supèr et al., 2001).

Electroencephalography recordings with humans reveal similar findings to those of single-cell recording studies. Fahrenfort et al. (2007) presented texture-defined target stimuli and partially overlapping backward masks to human observers during a target detection task. In trials where the target was seen (i.e., not masked), three stages of visual processing indicated the presence of a figure (target) against a background. The first stage, which occurred during the first 110 ms after stimulus onset and was apparent in occipito-temporal electrode activity, was interpreted to reflect an initial feedforward activation of the visual system extending into the ventral stream. A second stage, which started at 110 ms after stimulus onset and was apparent in occipital electrodes, was interpreted as reflecting a reactivation of occipital regions due to recurrent feedback. A distinct third stage, which occurred around 200 to 300 ms and was apparent in occipito-temporal electrodes activity, was interpreted as reflecting an additional wave of recurrent activity. Importantly, the authors found that in trials where target was masked (i.e., not seen), both the second and third stages were no longer apparent; only the first stage of processing was unaffected by masking. In other words, the stages indicative of recurrent feedback were affected most, supporting the position that backward masks interrupt feedback processing of the target stimuli in the visual cortex. Together, single cell recording and human neuroimaging studies suggest that backward masking interrupts the later but not the initial neural response; these two stages of processing have been attributed to feedback and feedforward processing, respectively, (Fahrenfort et al., 2007, 2008; Boehler et al., 2008).

With this mechanism of masking in mind let us return briefly to studies that employ overlapping target and mask stimuli to make inferences about feedforward and feedback processes and their contribution to visual awareness. As discussed earlier, stimuli that overlap in space activate identical cells along the visual processing pathway allowing masking to occur due to sensory signal integration at pre-cortical levels. Because these studies typically employ pattern masks, successful discrimination of natural stimuli under ultra-rapid presentation conditions (i.e., target-to-mask SOAs of less than 100 ms) likely indicate poor integration masking rather than scene processing that does not require feedback; that is, fairly high target accuracy even at the shortest SOAs (Bacon-Macé et al., 2005; Walther et al., 2009; Loschky et al., 2010) may indicate that the masks used to interfere with natural scenes are simply in effectively integrated with the feedforward signal of the target. Indeed, both faces and scenes are most effectively masked by face and scene stimuli, respectively, (Loffler et al., 2005; Loschky et al., 2010), suggesting that other masks (e.g., noise) may be too simple or may not be effectively integrated with the rich feedforward signal from natural images. With these methodological considerations in mind we argue that data obtained using ultra-rapid presentation paradigms do not strictly preclude recurrent processing and thus they cannot be taken as strong evidence against the idea that awareness depends on feedback processes (see also Koivisto et al., 2014a). In fact, as mentioned earlier, some evidence exists to suggest that TMS over the occipital pole might interfere with feedback signals from scene stimuli in a 100 ms and later time window (Camprodon et al., 2010; Koivisto et al., 2011a).

The neural mechanisms behind TMS-induced reduction of stimulus visibility are less clear, due primarily to unknown variation in retinocortical transmission time. If we accept a retinocortical transmission time of 60 ms (Railo and Koivisto, 2012), then the timing of TMS-induced masking is comparable to that of backward visual masking. Thus, the classic post-stimulus TMS effect at 100 ms, like visual masking, could be attributed to interference with feedback processing in the EVC (Corthout et al., 1999a,b; Breitmeyer et al., 2004a; Lamme, 2006b; Breitmeyer, 2007; Allen et al., 2014). Even given some variability in retinocortical transmission time, it has been argued that such a late effect is unlikely to be due to TMS interference with the initial feedforward sweep (Celebrini et al., 1993; Merigan and Maunsell, 1993; Nowak et al., 1995; Schmolesky et al., 1998; Lamme and Roelfsema, 2000) and instead is likely due to interference with feedback processes (Breitmeyer et al., 2004a; Lamme, 2006b; Breitmeyer, 2007). The idea that feedback to EVC is required for visual awareness is supported by joint backward masking and TMS studies. Here in trials where target’s visibility is suppressed by a visual mask, if TMS is applied at around 100 ms after the onset of the mask, target stimulus visibility greatly increases while that of the mask is suppressed by TMS. Such unmasking of the target and masking of the mask by TMS has been taken as evidence of recurrent feedback processing in EVC (Amassian et al., 1993; Ro et al., 2003).

To directly test similarities and differences between metacontrast and TMS, Railo and Koivisto (2012) compared masking effects using identical target stimuli with the same group of participants. Both methods impaired subjective target visibility ratings in comparable time windows; the optimal masking SOA was 33 ms in metacontrast and 75 ms in TMS trials. Assuming a retinocortical transmission time of 60 ms, Railo and Koivisto (2012) suggest that TMS interference with visual processing occurs slightly earlier than with metacontrast masks. Interestingly, neither TMS nor metacontrast impaired subjects’ ability to simply detect the target in a 2-alternative forced choice location task. These data suggest that while feedback may be necessary for subjective reports of awareness, the intact feedforward signal may be sufficient to enable above chance detection in a forced-choice location task (VanRullen and Koch, 2003; VanRullen, 2007).

Although the timing of the TMS and visual masking effects obtained by Railo and Koivisto (2012) can be accommodated in the common framework of interference with feedback processes in EVC, the fact that the TMS effect occurs slightly earlier (depending on actual retinocortical transmission time) than visual masking raises an alternative view. It has been suggested that TMS in this time window may interrupt (some of the) feedforward in addition to feedback activity (Sack et al., 2009; Koivisto et al., 2011b; de Graaf et al., 2012a, 2014; Miyawaki et al., 2012; Railo and Koivisto, 2012). There are several reasons to consider this hypothesis. The range of SOAs that are lumped into the classical TMS suppression window not only vary from 60 to 140 ms but can extend over tens of milliseconds (Amassian et al., 1989; Corthout et al., 1999a,b, 2002; Kammer et al., 2005b; Sack et al., 2009; Camprodon et al., 2010; de Graaf et al., 2011b, 2012a; Koivisto et al., 2011a,b; Jacobs et al., 2012b; Railo and Koivisto, 2012; Allen et al., 2014; Tapia et al., 2014). This suggests that the earliest effects might be occurring as sensory information is just arriving in EVC and raises a possibility that TMS interferes with multiple visual processes in a wide window (Koivisto et al., 2011b; de Graaf et al., 2014).

The hypothesis that the 100 ms TMS suppression window may reflect interference with (some of the) feedforward in addition to (multiple recurrent) feedback processes (Camprodon et al., 2010; Koivisto et al., 2011b; de Graaf et al., 2012a, 2014; Miyawaki et al., 2012; Railo and Koivisto, 2012) is only now being considered in empirical investigations. Here, paradigms that allow differentiating between feedforward and feedback processes are especially useful. The framework of visual awareness we have adapted for this review specifically states that feedback is required for awareness, while feedforward processes are sufficient to “prime” selective motor responses (Lamme and Roelfsema, 2000; Breitmeyer, 2007). This view is supported by masked priming studies (Klotz and Wolff, 1995; Klotz and Neumann, 1999; Vorberg et al., 2003). Here, a visual masking paradigm is adapted to measure priming by varying the similarity between the target-prime and mask-probe. Responses to the mask-probe are faster when the two stimuli match on a to-be-discriminated feature (e.g., color) as compared to when they do not. Interestingly, the target-prime affects responses to the mask-probe both in trials when the target-prime is seen and when its visibility is suppressed by the subsequent mask-probe. We and others (e.g., Lamme and Roelfsema, 2000; Chen and Treisman, 2009; Tapia and Breitmeyer, 2011) have interpreted this pattern of results as indicating that the feedforward sweep of activity elicited by the target-prime is sufficient to produce priming even though the target-prime fails to reach awareness due to the interruption of its feedback processes by the mask-probe. Masked priming effects have been consistently reported for various stimuli in a metacontrast paradigm (e.g., Klotz and Neumann, 1999; Schmidt, 2002; Vorberg et al., 2003; Breitmeyer et al., 2004b, 2005, 2007; Enns and Oriet, 2007; Breitmeyer and Hanif, 2008; Kentridge et al., 2008; Ro et al., 2009; Schmidt and Schmidt, 2010; Tapia et al., 2010, 2011, 2013; Tapia and Breitmeyer, 2011).

If post-stimulus TMS over EVC at around 100 ms interrupts solely feedback processes, we would also expect similar (i.e., feedforward-supported) effects in TMS paradigms; that is, we would expect to obtain priming from a TMS-masked target. Alternatively, if post-stimulus TMS also interferes with (some of the) feedforward processes during this time window, performance on tasks that rely on feedforward activity should be impaired; that is, we would expect priming to be diminished when the target is suppressed by TMS. Only a handful of studies have investigated this hypothesis to date, but they all show that priming is either reduced (Sack et al., 2009; Railo et al., 2012) or entirely absent (Jacobs et al., 2012a; Persuh and Ro, 2013) at TMS SOAs ranging from 60 to 100 ms post-stimulus, suggesting that TMS in this range might be interfering with feedforward processes.

It is difficult to draw clear conclusions about feedforward and feedback processes from these studies not only because there are only a few of them, but also because the experimental procedures used differ among the studies. TMS stimulation parameters (e.g., intensity, shape of coil) should be systematically explored in the priming task as they have been shown to differentially affect performance in other paradigms (Beckers and Hömberg, 1991; Kammer et al., 2005a; Hallett, 2007; de Graaf et al., 2011a; Reichenbach et al., 2011). Additionally, differences between behavioral tasks should also be examined. For example, it is not known whether priming effects differ between paradigms in which TMS is applied either between presentation of target-prime and mask-probe (Persuh and Ro, 2013), during (Railo et al., 2012) or at the end of this stimulus sequence (Sack et al., 2009; Jacobs et al., 2012a; Railo et al., 2012). Moreover, assessing target-prime visibility during the same (e.g., Persuh and Ro, 2013) or separate sets of trials (e.g., Jacobs et al., 2012a) might also lead to different results (Lin and Murray, 2014). A combination of any of these factors might influence when and if priming with TMS is observed. Nonetheless, we believe that this line of inquiry will yield significant insights into feedforward and feedback contributions to visual awareness once these experimental procedures are fine-tuned.

In summary, although it has been previously argued that the 100 ms post-stimulus window reflects TMS interference with feedback processing in EVC, data from the newest studies suggest that the classical TMS window of suppression might in fact encompass both the feedforward and feedback processes, with lower SOAs tapping into the feedforward and later SOAs tapping into the feedback activity (Koivisto et al., 2011b; de Graaf et al., 2012a, 2014; Miyawaki et al., 2012). Hence, while there is ample evidence to suggest that metacontrast and TMS interfere with feedback processes, future research should inform the effects of TMS on feedforward activity as well as how and why this method of masking differs from that of metacontrast.

Given that feedforward processes precede and initiate feedback processes, it should also be possible to interfere with the initial feedforward sweep of activity and reduce stimulus visibility. Paracontrast is thought to be one such example. It is a specific case of forward masking that, like metacontrast masking, involves spatially non-overlapping target and mask stimuli. Unlike in metacontrast masking, here the mask stimulus precedes the target, and is thus thought to interfere with feedforward processing of the target. As in backward masking, varying the SOA between the target and the mask reveals distinct windows of visual suppression (Figure 2). In paracontrast strongest suppression of target visibility is obtained at SOAs around -170 to -100 ms and -10 ms (Cavonius and Reeves, 1983; Kaitz et al., 1985; Öǧmen et al., 2003; Breitmeyer et al., 2006).

As in forward visual masking paradigms, TMS can also be applied before the onset of a visual stimulus. Pre-stimulus TMS impairs target visibility at a range of SOAs spanning from -80 to -10 ms (Corthout et al., 1999b, 2002, 2003; Laycock et al., 2007; de Graaf et al., 2011a; Jacobs et al., 2012b, 2014). Interestingly, pre-stimulus TMS at -20 ms shows retinotopically specific effects, that is reducing stimulus visibility only in specific locations, while TMS at -50 ms exhibits a global reduction of stimulus visibility regardless of where the stimulus was presented (Jacobs et al., 2014). Post-stimulus TMS effects shortly after the onset of the target (SOA effects at 0, 10, 20–60 ms; Beckers and Hömberg, 1991; Paulus et al., 1999; Corthout et al., 1999a,b, 2002, 2003) have also been reported (Figure 2).

Behavioral and especially neural effects of paracontrast are much less studied than those of metacontrast. This is likely due in part to the fact that paracontrast produces much weaker suppression than metacontrast (Alpern, 1953; Weisstein, 1972). Single-cell recordings in primate V1 reveal that paracontrast reduces the initial neural activity associated with the onset response of the target and slightly reduces subsequent excitatory after-discharge due to target offset (Macknik and Livingstone, 1998). Thus, it has been proposed that paracontrast masks interfere primarily with the incoming feedforward activity of the target (Breitmeyer and Öǧmen, 2006; Breitmeyer, 2007).

The neural consequence of the pre- and early post-stimulus TMS reduction of visibility is under question. Post-stimulus effects of SOAs up to 60 ms have been difficult to replicate consistently (Kammer, 2007a,b). Moreover, the SOAs of optimal suppression in pre-stimulus TMS studies, like in paracontrast, are quite varied (Beckers and Hömberg, 1991; Paulus et al., 1999; Corthout et al., 1999a,b, 2002, 2003; Laycock et al., 2007; de Graaf et al., 2011a; Jacobs et al., 2012b, 2014). In fact, some pre-stimulus TMS effects have been attributed primarily to eye-blink artifacts (Corthout et al., 1999b, 2011) raising questions as to whether it is a neural effect at all. More recent work, however, has shown that in trials where no eye-blinks occurred stimulus visibility was still significantly impaired with pre-stimulus TMS at -80 and -60 ms SOAs (Jacobs et al., 2012b, 2014).

Even with the masking effect localized to the cortex, the relationship between the pre-stimulus TMS effects and para- and metacontrast masking is still under debate. The early post-stimulus TMS effects at SOAs up to 60 ms have been explicitly attributed to TMS interruption of the feedforward sweep (Corthout et al., 1999a). However, given retinocortical transmission time, TMS applied earlier than that would presumably affect the visual cortex before the visual signal arrives there. Hence, it has been suggested that the pre-stimulus and early post stimulus TMS effects reflect changes in pre-stimulus brain activity, such as changes in alpha power or phase, rather than a direct effect on the evoked activity of the target (de Graaf et al., 2011a; Jacobs et al., 2012b, 2014). Such suggestions also raise the possibility that paracontrast masks also change brain states, such as those related to alpha power and phase. Moreover, although paracontrast and pre-stimulus and early post stimulus effects on stimulus visibility (when TMS SOAs have been adjusted for retinocortical delay) overlap in time, they do not align as nicely as with backward visual and TMS masks (see Figure 2). Of course, these conclusions critically depend on actual retinocortical transmission time. If one accepts that the backward masking effects of TMS and visual masks are comparable, then it is tempting to conclude that paracontrast and forward masking effects with TMS tap into the same visual mechanism of processing, this time interference with the feedforward signal (Corthout et al., 1999a,b; Breitmeyer et al., 2004a; Breitmeyer, 2007). As we have argued above, the post-stimulus TMS window likely reflects TMS interference with the incoming feedforward signal. If pre-stimulus TMS also affects the feedforward sweep, it may do this indirectly, by altering the brain state. Clearly, more research is needed on this issue.