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This is especially important given that changes in the target or mask type, not only in terms of low-level parameters e. For example, in two studies A. Although attentional effects are very well established with various visual tasks, there is no consensus about its mechanistic basis. Based on psychophysical, neurophysiological, and neuroimaging data, many computational models of attention have been proposed.

Modulating contrast and response gains have been associated with endogenous i. What do our results imply in terms of signal and noise modulation by attention and masking? Our data suggest that masking reduces the target signal-to-noise ratio SNR whereas decreasing attentional load increases it and their effects simply add up. A simple interpretation of our results is that the metacontrast mask reduces the strength of the target signal thereby reducing SNR whereas attention enhances signal strength, given that our target is presented under low noise conditions.

Given the lack of interactions between metacontrast and attention, these signal enhancement and reduction modulations by masking and attention take place as independent additive effects. According to this framework, three distinct mechanisms of attention can be differentiated experimentally by adding varying levels of noise to the visual stimuli.

The Perceptual Template Model PTM consists of four stages and incorporates both additive and multiplicative noise sources. This stage filters out some of the external noise that accompanies the desired signal. In the second stage, the output of the first stage is rectified and fed into a multiplicative Gaussian noise source with zero mean and a standard deviation proportional to the signal strength i. In the third stage, an independent Gaussian noise with zero mean and a constant standard deviation is added.

The last stage is a standard signal detection i. PTM can differentiate three distinct attention mechanisms each of which leads to a signature behavioral improvement in perceptual tasks.

THE DYNAMICS OF INFORMATION PROCESSING

These mechanisms are i stimulus enhancement, ii external noise exclusion, and iii multiplicative noise reduction. There are both physiological and behavioral evidence in support of these mechanisms. There are two broad categories of spatial cueing, namely central and peripheral cueing. Central cues are generally presented at the locus of fixation and signal the location of the target stimulus in a way that requires interpretation.

For example, when an arrow is used, the observer has to interpret the direction of the arrow to infer the cued location. Central cueing activates voluntary, or endogenous, attention mechanisms. Peripheral cues are generally presented at or close to the spatial location of the stimulus and hence they indicate the location of the stimulus directly in spatial representations without necessitating interpretive processes. These cues activate the reflexive, or exogenous, attention mechanisms.

Lu and Dosher found that endogenous attention works by external noise exclusion whereas exogenous attention invokes both external noise exclusion and signal enhancement mechanisms. We will consider whether PTM can explain our findings. In our experiment, we have manipulated set-size to control attention. Increased set-size can potentially affect both endogenous and exogenous attention. PTM predicts that external-noise reduction is the mechanism underlying endogenous attention effects whereas both external-noise reduction and signal enhancement underlie exogenous attention effects.

Under the external-noise reduction scenario, PTM predicts large attentional effects when external noise is large. Accordingly, the effect of attention should be strong when masking is strong, and weak when masking is weak. Therefore, there should be interactions between attention and masking. This does not agree with our results. According to PTM, signal enhancement is most effective when external noise is low.

Several studies reported that cuing improves sensitivity in simple detection tasks when stimuli are presented with masks but not when stimuli are presented in the absence of masks e. Crucially, ISM incorporates interacting masking and attention mechanisms and predicts larger attentional benefits when a stimulus is masked compared to when it is unmasked.

Likewise, the stronger the masking is, the larger the attentional effects will be. Hence, both the aforementioned empirical findings and the predictions of ISM appear to be at odds with our findings: Our baseline data, which correspond to no mask conditions, show clear effects of attention and we found no interactions between attention and masking. However, it is important to point out that the experimental paradigms leading to different results are fundamentally different: Lack of attentional effects for unmasked stimuli were found for simple detection tasks or equivalently for easy discrimination tasks, such as horizontal vs.

Visual Masking - 1st Edition

This is clearly not the case in our study, wherein observers are required to report as accurately as possible the orientation of the target line. Hence, we found the classical set-size effect in our no-mask baseline conditions, in agreement with other studies e. Simple detection and easy discrimination tasks can be carried out by both transient and sustained mechanisms, whereas difficult fine-discrimination tasks are likely to necessitate sustained mechanisms.

Hence, both task difficulty and the contrast level are expected to influence the mechanistic criterion contents , i. Given that attention is also known to influence transient and sustained mechanisms in different ways, the interaction effects that emerge from data may be due to changes in criterion contents.

Lecture 11: Visual Attention and Consciousness

In fact, this is a major challenge for any study, including ours, seeking to analyze interactions of masking with other processes. In order to mitigate this issue, in this study we sought to analyze interactions based on a complete type-B metacontrast function comparing identical masking conditions i. There is an ongoing conflict regarding the relationship between attention and consciousness e. Attention has been proposed as a gateway or mechanism of consciousness Posner, This can be interpreted in two ways: i Attention selects and amplifies the contents of consciousness, or ii attention itself gives rise to consciousness Breitmeyer, According to the first view, attention and masking operate independently because whether and how masking controls the contents of consciousness is not affected by attention.

Attention can modulate only whatever is already registered into consciousness. However, the second view suggests that attention and masking operate at the same stage, and hence, their effects may interact. There are theoretical and empirical evidence for both views e. Our study gives support to the first view by providing evidence that attention and masking operate independently. To summarize, masking and attention are both involved in information processing and transfer at multiple stages of visual processing.

Determining their relationships can help us reach a richer and more integrated understanding of visual information processing. Previous studies showed significant interactions between different types of masking and attention e. Here, we investigated the relationship between metacontrast masking and attention based on two performance measures: i mean absolute response-errors empirical , and ii distribution of signed response-errors modeling.

We found strong evidence against interactions between attention and metacontrast masking for both performance measures. As mentioned above, neither masking nor attention is a unitary phenomenon, and hence additional studies are needed to establish firmly the relations between types of masking and attention. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Metacontrast masking and attention do not interact.

Article First Online: 31 March Participants Seven observers participated in this study. Any one of the bars could potentially be the target stimulus, and the target was specified by the mask location, which was a non-overlapping ring having 1. In other words, only one mask stimulus was presented and its location cued which oriented bar is the target. The other bars will be referred to as distractors from here on in. The task of the observers was to report the orientation of the target bar.

Figure 1 illustrates the stimuli and procedures. Within the same block, a small square rather than a mask indicated the location of the target bar in some of the trials. The trials in which a small square was presented were considered as baseline trials. The luminance of the target and mask was adjusted individually for each observer to avoid floor and ceiling effects.

Once the target-mask sequence was presented and turned off, a randomly oriented response bar was displayed at the center of the screen, and observers adjusted its orientation illustrated by red arrows in Fig. The response bar stayed on the display until observers were satisfied with their responses and the next trial began with another button press.

In a separate blocks, we presented an array with two or six oriented bars. Varying the set size allowed us to determine the effect of attention and its interaction, if any, with masking. Open image in new window. We defined response errors as the difference between the actual and reported orientations. We obtained masking functions after transforming response errors to a probability-like measure such that performance values of 0.

We calculated transformed performance Ogmen et al. Before the actual experiments, we first trained the observers with two or three blocks of trials with all conditions to make sure that they became familiar with the experiment and the setup so as to stabilize their performance and minimize changes due to learning. In order to avoid the ceiling and floor effects described above, we adjusted two parameters: the target luminance and the mask luminance. The criteria that we used to obtain the target and mask luminance values were as follows: C1 The maximum performance with masking must be significantly lower than the baseline performance the ceiling when set size is two.

Based on pilot experiments and our previous studies on metacontrast masking, we carried out a power analysis to select the number of trials per SOA for masking and baseline i. Power analysis is necessary to assess Type-II errors i. Therefore, we determined the number of trials required to reject the null hypothesis i.

Visual Masking: Time Slices Through Conscious and Unconscious Vision

In a previous study, we found that the average standard deviation of transformed-performance across observers is roughly 0. We also found small-to-moderate effect sizes defined as the difference between two conditions divided by the standard deviation of one group Cohen, between baseline and weak masking conditions.

Therefore, we assumed a small effect size i. This analysis yielded roughly trials per SOA value in total. Therefore, each observer ran masking trials and 75 baseline trials per SOA. Table 1 summarizes the target-mask luminance pairs for all observers as well as the results of statistical tests to verify that the aforementioned criteria C1 and C2 are met. Once we established a set of parameters, which satisfied all the criteria given above, we analyzed transformed-performance of each observer separately within-subject analysis.

Table 2 lists all regression models used to fit the data. Both metrics resulted in similar, if not identical, model selections for all observers. Both metrics penalize the models with more free parameters. Absolute values of BICs are not meaningful, therefore one needs to look at differences between BICs from different models.

Therefore, the smaller the BIC, the better the model performs. Table 2 The regression models used to fit transformed performances and the winning model parameters. Since the stimulus display consists of multiple oriented bars, observers may report the orientation of one of the non-target bars, e. The contribution of this incorrect identity binding error can be captured by another Gaussian term in the model. In consequence, the PDF of response errors can be written as a weighted sum of the target Gaussian, non-target Gaussian, and a uniform component Eq.

Considering these priors, Eq. Psychophysics Figure 2 shows results from all observers. The vertical axes represent the transformed performance while the horizontal axes represent SOA between the target and mask or cue in the baseline conditions stimuli. Open and filled symbols correspond to the baseline and masking conditions, respectively.

Circles and squares plot the results for set-size of two and six, respectively. Consider first the baseline data. For each observer, we performed a two-sample t-test between baseline and masking conditions at an SOA value where minimum masking occurs e. In addition to ceiling effects, we have also checked our data for floor effects criterion C2, see Methods by performing a one-sample t-test against the chance level i. Table 1 lists the target-mask luminance pairs which allowed us to avoid ceiling and floor artefacts for each observer, as well as the results of the t-tests. Taken together, these results show that our masking data are free of ceiling and floor effects.

Next, we examined the distribution of response errors of each observer by using the BMC technique see Methods. Figure 3 the leftmost column shows BMC differences between every combination of model pairs for each observer. Among the four models tested, the GU model was the winning model for all observers; it has the highest BMC value. Therefore, further analyses were done on model parameters of the GU model. The weight of the uniform component in the GU model showed an inverse U-shaped pattern which was consistent across observers.

In fact, regressions of the weight of uniform based on adjusted R 2 metric revealed that M16 is the best regression model for all observers but CBK and EK. Besides, qualitatively, interaction between SOA and set size is not very apparent for these observers. Therefore, we conclude that, although there is some evidence for interactions between masking and attention when the analysis is carried out through the weight of the uniform distribution in the GU model, the evidence for this interaction is neither consistent across observers, nor strong.

Hence, in the light of the analysis carried out directly on transformed performance, we conclude that attention and metacontrast masking do not interact. Table 4 summarizes the best regression models in capturing the change in model parameters as a function SOA and set size, for each observer.

Table 4 The winning regression model for each parameter and observer. Another way to understand how model parameters and masking functions are related is to compute the correlation between each model parameter and masking functions. Figure 4 shows individual correlation coefficients as well as the average across observers. We will discuss these findings in the next section. Effects of attention and masking on signal and noise Although attentional effects are very well established with various visual tasks, there is no consensus about its mechanistic basis.

See Appendix for the regression analysis of the baseline data. Agaoglu, S. A statistical perspective to visual masking. Vision Research, , 23— Effects of central and peripheral pre-cueing on metacontrast masking. Google Scholar. Argyropoulos, I. Set size and mask duration do not interact in object-substitution masking.

PubMed Google Scholar. Atkinson, R. The control of short-term memory. Scientific American, 2 , 82— Averbach, E. Short-term momory in vision. Bell System Technical Journal, 40 1 , — CrossRef Google Scholar. Short-term storage of information in vision. Cherry Ed. London: Butterworth. Bachmann, T. The process of perceptual retouch: Nonspecific afferent activation dynamics in explaining visual masking.

Psychophysiology of visual masking: the fine structure of conscious experience. Commack, NY: Nova Science. Attention as a process of selection, perception as a process of representation, and phenomenal experience as the resulting process of perception being modulated by a dedicated consciousness mechanism. Frontiers in Psychology. Bays, P. The precision of visual working memory is set by allocation of a shared resource.

Journal of Vision, 9 10 , 1— Breitmeyer, B. The visual un conscious and its dis contents: A microtemporal approach. UK: Oxford University Press. Implications of sustained and transient channels for theories of visual pattern masking, saccadic suppression, and information processing. Psychological Review, 83 1 , 1— Visual masking: time slices through conscious and unconscious vision 2nd ed.

Oxford: Oxford University Press. Bridgeman, B. Metacontrast and lateral inhibition. Psychological Review, 78 6 , — Carrasco, M. Visual attention: The past 25 years. Vision Research, 51, — However, P has also been frequently found to mark conscious experience e. Intriguingly, it is possible that there are two separate processes— 1 perceptual microgenesis, where conscious experience emerges fast and decays fast possibly equal to iconic-memory decay and 2 immediate memory-based microgenesis, where conscious experience of the same target forms a bit slower than perceptual microgenesis and decays much later than iconic delay e.

This idea fits with the distinction between phenomenal and reflective consciousness, which are thought to depend on different types of attention e. Now, an intriguing theoretical question appears: if one and the same stimulus-event is related to both, perceptual and immediate-memory microgenetic processes with concomitant two sets of NCC, should we then regard these NCC as different aspects of one NCC or a principally different, two, NCCs Bachmann, ?

How does the time course of subjective microgenesis relate to the time course of representational content development obtained with neural decoding and representational similarity analysis e. What are the relative roles of feedforward and re-entrant neural processes in perceptual microgenesis?

This crucial prediction was recently confirmed Campana et al. Are the nature and regularities of microgenesis the same when an external stimulus is becoming microgenetically formed and when a memory-image of the same stimulus is evoked and formed?

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More generally: are the curves of formation and disformation similar for all the transitions occurring at the threshold of consciousness? There are many examples of transitions in and out of consciousness. Can one benefit from the knowledge gathered while studying brief visual stimuli to understand processes that unfold with other types of stimuli? Is in all of these cases the emergence of conscious content gradually evolving over time? There are also many examples for the transition out of consciousness: fading of the conscious percept; fading of iconic memory; loss of thought or imagery content; loss of explicitly experienced WM content; loss of conscious awareness of a stimulus after refocusing attention elsewhere; loss of a certain aspect feature, attribute, quality of conscious perception of a discontinued stimulation while other aspect s sustain; loss of conscious perception of a binocular-rivalry stimulus when it becomes suppressed; fading of perceptual content e.

Is the fading of conscious content gradually evolving over time in all of these cases? Gathering such a list leads yet again to interesting questions. For example, which transitions are reversible and which ones not? What could this small set of non-reversibility tell us about the neural mechanisms of consciousness? The list of these phenomena seems to suggest that conscious experience is heavily influenced by prior knowledge—once insightful knowledge about a particular stimulus is established, it is hard or even impossible to remove it.

More importantly, the variety of examples leads to the question whether there are general mechanisms and regularities underlying all of these phenomena. Is it possible or even meaningful to try to do it with other types of transitions? We do not have definitive experimental approaches, but we consider these questions to be important to put forth and to explore. We hope that some of these ideas and concepts are beneficial for unraveling the neural mechanisms of consciousness.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. National Center for Biotechnology Information , U. Journal List Front Psychol v. Front Psychol. Published online Feb 2. Author information Article notes Copyright and License information Disclaimer. This article was submitted to Consciousness Research, a section of the journal Frontiers in Psychology.

Received Oct 25; Accepted Jan Keywords: consciousness, NCC, microgenesis, time course of consciousness, transitions of consciousness. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms. Open in a separate window. Figure 1. Author contributions TB conceived the initial ideas, JA expanded them, both JA and TB discussed the ideas and contributed to writing the manuscript Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References Aru J. Distilling the neural correlates of consciousness. Early effects of previous experience on conscious perception. This was motivated by our recent study on masking where we found that the mean of the Gaussian is not significantly different from zero Agaoglu et al.

Therefore, in the following analyses, the target Gaussians were centered on target orientations i. The reciprocal of standard deviation represents how precisely the stimulus falling onto the retina is encoded by the visual system. In other words, decreased stimulus encoding precision is reflected by the increased variability of behavioral responses. Note that in a separate analysis, we used step sizes of 0.

After selecting the best fitting model, we sought to find how different model parameters change with SOA and set size. The motivation behind this analysis was to understand whether and how masking and attention affect the statistics of observer responses. After determining the winning model, we created different data sets for each observer separately by resampling the response errors by replacement, and fitted the winning model to these data sets.

We present here the means and standard errors for model parameters obtained from this bootstrap analysis. Next, we fitted the regression models listed in Table 2 to see the contributions of SOA, set size, and their interactions to model parameters. In order to determine whether masking strength and different model parameters are related or not, we also quantified the correlation between model parameters and masking function for each set-size by calculating Pearson R coefficients. A strong correlation would suggest a critical role for that parameter in accounting for masking effects, and a change in correlation with set size would suggest an interaction between attention and masking.

The left column shows transformed performance for each observer against stimulus onset asynchrony SOA. The red open and filled circle symbols represent performance in baseline and metacontrast masking, respectively, when set-size is two, whereas the blue open and filled square symbols represent performance in baseline and metacontrast masking, respectively, with set-size six.

With the same color convention, solid and dashed lines represent the best regression fits for metacontrast masking and baseline conditions, respectively. Chance level is 0. The right column shows pairwise Bayesian Information Criterion BIC differences between regression models listed in Table 2 in explaining transformed performances. A square with coordinates x,y on each plot represents the BIC difference between model x and y i. The smaller the BIC, the better the model performs, therefore negative values i. Model M16 was the best model for all observers.

Note that adding more terms to model M16 does not improve model performance, which is evident by dark blue bands formed in the lower left quadrant of each plot. Figure 2 the right column shows pairwise model comparison results based on the BIC metric. Greenish colors represent equivalent performances whereas blue and red colors represent better and worse model performances, respectively. As evident from Fig. This is to be expected since the U-shape of type-B functions is better captured by a quadratic term than a linear term. The key aspect of this analysis was to determine whether models with interaction terms would perform better than those without interaction terms.

The model M16 was the best model for each and every observer who participated in the present study. This model consists of linear SOA and set size terms as well as a quadratic SOA term but does not have any interaction term. Therefore, our analysis indicates that SOA i. The first column from left represents the Bayesian Model Comparison BMC differences between every combination of model pairs. In order to have the same color notation i.

The second and third columns show the parameters of the winning GU model. The second column shows the standard deviation of the Gaussian in the GU model as a function of stimulus onset asynchrony SOA , and the third column shows the weight of the Uniform component in the GU model. The red lines represent set size two condition whereas the blue lines represent set size six condition. Error bars represent standard errors obtained by bootstrapping see Methods.

The fourth and fifth columns show Bayesian Information Criterion BIC differences between pairs of regression models listed in Table 2 for standard deviation of the Gaussian and the weight of the Uniform in the GU model, respectively. All color conventions are the same as in Fig. Figure 3 also shows the model parameters for the winning GU model for all observers the second and third columns. There is no discernable pattern that is consistent across all observers in the dependence of standard deviations on SOA and set-size Fig. On the other hand, the weight of the uniform component has clear and consistent pattern in all observers Fig.

The weight parameter changes as a function of SOA following an inverse-U function, which reflects the shape of Type-B metacontrast functions.

These inverse-U functions appear to be shifted vertically as a function of set-size, mirroring attentional affects found in the transformed performance data. In order to quantify these informal observations, we fitted a series of regression models listed in Table 2 see Methods for details. Pairwise comparison results of all regression models are given in the two rightmost columns of Fig. For observer MNA, the model M8 appeared to be the best of all, suggesting significant roles for SOA and set size as well as their first order interaction.

In sum, these findings support the aforementioned informal observations that there is no clear or consistent trend across observers in the dependence of the standard deviation parameter on SOA and set-size. In our previous work Agaoglu et al. The correlation of the weight parameter was higher than the correlation of the standard deviation.

In the current study, the best regression model for the standard deviation had a main factor of SOA in five out of seven observers, which suggests a significant role for standard deviation of the Gaussian term in explaining metacontrast masking, consistent with the previous finding. However, this dependence did not show a consistent pattern across observers and hence may be related to individual observer-dependent variations. On the other hand, as we mentioned above and discuss below in more details, the weight parameter appears to reflect a more general property that is common across all observers.

The correlation between model parameters and masking functions for each set size condition. The correlation coefficients for individual observers as well as average across observers are shown. The red and blue bars represent set size two and six conditions, respectively.

The visual system constantly receives an overwhelming amount of information. These attentional effects can be quantified experimentally with tasks that require the observer to detect, discriminate, or recognize a given object. In spatial cueing paradigms, attentional resources are directed to specific spatial locations and performance at cued and uncued locations are compared. Visual masking has also been shown to control the quantity and quality of information transfer from sensory memory to short-term memory.

An intuitive question is whether these two processes that control the transfer of information from sensory memory to short-term memory operate independently or interact. In this study, we asked observers to report the orientation of a target bar randomly selected from a set of bars presented in the display. Since the target bar was indicated by a metacontrast mask or a peripheral post-cue, we assumed that by increasing the set size, observers spread their attention to more locations thereby reducing attentional benefits at individual locations. We found strong evidence against interactions between metacontrast masking and attentional mechanisms.

Our results showed that mean absolute response-errors in orientation judgments are independently influenced by masking strength a function of SOA and attentional load a function of set size. Three issues need to be addressed in considering the generality of our results. Given this, we cannot rule out masking-attention interactions with stimuli with very high or very low luminance contrast values.

Second, Maksimov and colleagues have shown genetically-based individual variations in metacontrast masking Maksimov, et al. Since we have not genotyped our subjects, we cannot generalize our results across all genotypes. A priori, it is not clear whether our results would hold for other manipulations. To address this issue, in a separate study, we investigated the attention-metacontrast relationship by presenting either central or peripheral pre-cues in different blocks Agaoglu, Breitmeyer, Ogmen, in preparation; Ogmen, Agaoglu, Breitmeyer, We kept set-size fixed and varied the time delay between the cue onset and the target onset, as well as the SOA between the target and mask arrays.

Our results are in agreement with the results of this study, i. As mentioned in the Introduction section, while some models of masking view attention as an integral component of masking effects, others view it as an independent add-on process. In particular, the object-substitution model of masking, which was derived from the common-onset masking experimental paradigm, posited interactions between masking and attention and provided empirical evidence in support of this prediction.

In the light of this finding, we now discuss previous studies that reported interactions between these two processes. Ramachandran and Cobb used a row of three disks central one being the target and a column of four flanking disks two above and two below the target disk. They asked observers to give a visibility rating for the target disk on a scale of 0 to 5. They found stronger masking when observers attended the column of disks which constituted the mask compared to when they attended the row of disks that included the target.

The authors interpreted this finding as an interaction between attention and backward masking. Tata reported similar findings and interpretations with metacontrast masking. He used elements similar to Landolt Cs and asked observers to report the orientation of the masked one. He varied set-size to control the attentional load and found significant interactions between set-size and masking.

In inattentional blindness studies, meaningful stimuli were found to be more resistant to inattentional blindness than neutral stimuli. This was interpreted as meaningful stimuli automatically attracting additional attentional resources compared to neutral stimuli. Following this logic, Shelley-Tremblay and Mack manipulated attention by using meaningful happy-face icon, individual name versus neutral stimuli inverted face icon, scrambled face icon, neutral words, annulus.

The interpretation of these data in favor of interactions is subject to two important caveats: First, baseline performance for each type of stimulus i. Second, in the experimental design, target or mask type covaries with attentional manipulation. This is especially important given that changes in the target or mask type, not only in terms of low-level parameters e. For example, in two studies A. Although attentional effects are very well established with various visual tasks, there is no consensus about its mechanistic basis.

Based on psychophysical, neurophysiological, and neuroimaging data, many computational models of attention have been proposed. Modulating contrast and response gains have been associated with endogenous i. What do our results imply in terms of signal and noise modulation by attention and masking? Our data suggest that masking reduces the target signal-to-noise ratio SNR whereas decreasing attentional load increases it and their effects simply add up.

A simple interpretation of our results is that the metacontrast mask reduces the strength of the target signal thereby reducing SNR whereas attention enhances signal strength, given that our target is presented under low noise conditions. Given the lack of interactions between metacontrast and attention, these signal enhancement and reduction modulations by masking and attention take place as independent additive effects.

According to this framework, three distinct mechanisms of attention can be differentiated experimentally by adding varying levels of noise to the visual stimuli. The Perceptual Template Model PTM consists of four stages and incorporates both additive and multiplicative noise sources. This stage filters out some of the external noise that accompanies the desired signal.

In the second stage, the output of the first stage is rectified and fed into a multiplicative Gaussian noise source with zero mean and a standard deviation proportional to the signal strength i. In the third stage, an independent Gaussian noise with zero mean and a constant standard deviation is added. The last stage is a standard signal detection i. PTM can differentiate three distinct attention mechanisms each of which leads to a signature behavioral improvement in perceptual tasks. These mechanisms are i stimulus enhancement, ii external noise exclusion, and iii multiplicative noise reduction.

There are both physiological and behavioral evidence in support of these mechanisms. There are two broad categories of spatial cueing, namely central and peripheral cueing. Central cues are generally presented at the locus of fixation and signal the location of the target stimulus in a way that requires interpretation. For example, when an arrow is used, the observer has to interpret the direction of the arrow to infer the cued location.

Central cueing activates voluntary, or endogenous, attention mechanisms. Peripheral cues are generally presented at or close to the spatial location of the stimulus and hence they indicate the location of the stimulus directly in spatial representations without necessitating interpretive processes.

These cues activate the reflexive, or exogenous, attention mechanisms. Lu and Dosher found that endogenous attention works by external noise exclusion whereas exogenous attention invokes both external noise exclusion and signal enhancement mechanisms. We will consider whether PTM can explain our findings.

In our experiment, we have manipulated set-size to control attention. Increased set-size can potentially affect both endogenous and exogenous attention.

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PTM predicts that external-noise reduction is the mechanism underlying endogenous attention effects whereas both external-noise reduction and signal enhancement underlie exogenous attention effects. Under the external-noise reduction scenario, PTM predicts large attentional effects when external noise is large. Accordingly, the effect of attention should be strong when masking is strong, and weak when masking is weak.

Therefore, there should be interactions between attention and masking. This does not agree with our results. According to PTM, signal enhancement is most effective when external noise is low. Several studies reported that cuing improves sensitivity in simple detection tasks when stimuli are presented with masks but not when stimuli are presented in the absence of masks e. Crucially, ISM incorporates interacting masking and attention mechanisms and predicts larger attentional benefits when a stimulus is masked compared to when it is unmasked. Likewise, the stronger the masking is, the larger the attentional effects will be.

Hence, both the aforementioned empirical findings and the predictions of ISM appear to be at odds with our findings: Our baseline data, which correspond to no mask conditions, show clear effects of attention and we found no interactions between attention and masking.

Special Newsletter issue on consciousness research

However, it is important to point out that the experimental paradigms leading to different results are fundamentally different: Lack of attentional effects for unmasked stimuli were found for simple detection tasks or equivalently for easy discrimination tasks, such as horizontal vs. This is clearly not the case in our study, wherein observers are required to report as accurately as possible the orientation of the target line. Hence, we found the classical set-size effect in our no-mask baseline conditions, in agreement with other studies e.

Simple detection and easy discrimination tasks can be carried out by both transient and sustained mechanisms, whereas difficult fine-discrimination tasks are likely to necessitate sustained mechanisms. Hence, both task difficulty and the contrast level are expected to influence the mechanistic criterion contents , i. Given that attention is also known to influence transient and sustained mechanisms in different ways, the interaction effects that emerge from data may be due to changes in criterion contents. In fact, this is a major challenge for any study, including ours, seeking to analyze interactions of masking with other processes.

In order to mitigate this issue, in this study we sought to analyze interactions based on a complete type-B metacontrast function comparing identical masking conditions i. There is an ongoing conflict regarding the relationship between attention and consciousness e. Attention has been proposed as a gateway or mechanism of consciousness Posner, This can be interpreted in two ways: i Attention selects and amplifies the contents of consciousness, or ii attention itself gives rise to consciousness Breitmeyer, According to the first view, attention and masking operate independently because whether and how masking controls the contents of consciousness is not affected by attention.

Attention can modulate only whatever is already registered into consciousness. However, the second view suggests that attention and masking operate at the same stage, and hence, their effects may interact. There are theoretical and empirical evidence for both views e. Our study gives support to the first view by providing evidence that attention and masking operate independently. To summarize, masking and attention are both involved in information processing and transfer at multiple stages of visual processing.

Determining their relationships can help us reach a richer and more integrated understanding of visual information processing. Previous studies showed significant interactions between different types of masking and attention e. Here, we investigated the relationship between metacontrast masking and attention based on two performance measures: i mean absolute response-errors empirical , and ii distribution of signed response-errors modeling.

We found strong evidence against interactions between attention and metacontrast masking for both performance measures. As mentioned above, neither masking nor attention is a unitary phenomenon, and hence additional studies are needed to establish firmly the relations between types of masking and attention. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Metacontrast masking and attention do not interact. Article First Online: 31 March Participants Seven observers participated in this study. Any one of the bars could potentially be the target stimulus, and the target was specified by the mask location, which was a non-overlapping ring having 1.

In other words, only one mask stimulus was presented and its location cued which oriented bar is the target. The other bars will be referred to as distractors from here on in. The task of the observers was to report the orientation of the target bar. Figure 1 illustrates the stimuli and procedures. Within the same block, a small square rather than a mask indicated the location of the target bar in some of the trials. The trials in which a small square was presented were considered as baseline trials.

The luminance of the target and mask was adjusted individually for each observer to avoid floor and ceiling effects. Once the target-mask sequence was presented and turned off, a randomly oriented response bar was displayed at the center of the screen, and observers adjusted its orientation illustrated by red arrows in Fig. The response bar stayed on the display until observers were satisfied with their responses and the next trial began with another button press. In a separate blocks, we presented an array with two or six oriented bars.

Varying the set size allowed us to determine the effect of attention and its interaction, if any, with masking. Open image in new window. We defined response errors as the difference between the actual and reported orientations. We obtained masking functions after transforming response errors to a probability-like measure such that performance values of 0. We calculated transformed performance Ogmen et al.

Before the actual experiments, we first trained the observers with two or three blocks of trials with all conditions to make sure that they became familiar with the experiment and the setup so as to stabilize their performance and minimize changes due to learning. In order to avoid the ceiling and floor effects described above, we adjusted two parameters: the target luminance and the mask luminance.