Université Pierre-Mendès-France (Grenoble II)UFR Sciences de l’Homme et de la Société

Laboratoire de Psychologie et NeurocognitionMaster 1 de Psychologie Cognitive et Sociale

Can We Get a Boosted Bounce?

Spatial Frequencies Play a Role in the Attentional Blink.

Perrier MickaëlResearch directed by Pr. Mermillod Martial

Jointly directed by Beffara Brice, Ph.D. studentJune 2015

Acknowledgements

I would like to thank:

Cédric Batailler for his remarks in the writing of this paper and his help in statistical analyses.

3D4Medical, Dublin, for their authorization in using their brain textures in the Figure 3.

Martial Mermillod for his trust, his good mood, and for the autonomy he gave me.

Brice Beffara for his patience, his availability, his support, and his comments.

Louise Kauffmann for allowing us to use these stimuli and for her help.

And because nothing is worthy without a quote within:

« Consider an adult human brain (with the rest of the adult human attached in the usual way)

in a visual neuroscience laboratory. Visual stimuli are presented on a computer screen and

the resulting eye movements are recorded. Simple stimuli, like a bright spot, can evoke

an extremely rapid saccade. More complex stimuli, like landscapes or faces, lead to

slower responses. What is happening in between to produce these differences? »

— Kathleen Taylor, Brainwashing: The science of thought control, p. 170

Running Head: CAN WE GET A BOOSTED BOUNCE? !3

Abstract 4

Introduction 5

The Bounced Target: An Attentional Blink Phenomenon 5

Prediction in Visual Perception: The Predictive Brain Hypothesis 7

Is Anticipation the Root of Consciousness? 10

Method 11

Participants and design 11

Apparatus 11

Stimuli 12

Procedure 13

Data analysis 15

Results 17

Discussion 18

References 22

Appendices 28

Appendix 1. Normality tests 28

Appendix 2. Analysis of variance (ANOVA) 30

Appendix 3. t-Tests 32

Appendix 4. Accuracy of T1 report 33

Appendix 5. Accuracy of T2|T1 report 33

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Abstract

During a rapid serial visual presentation (RSVP), when a second target is presented between

200 ms and 500 ms after a first target, the second is often to be missed: a phenomenon called

‘attention blink’. Coarse-to-fine models of vision suppose that vision begins with a first wave

of fast but coarse information which could enhance further visual processing. In order to test

if such anticipation of the visual world can modulate the emergence or the collapse of

consciousness, forty-four undergraduate students were recruited to participate in our

experiment in which they had to detect and report two indoor scenes presented among

outdoor scenes (all presented for 83 ms) during an RSVP. The second target could appear

either at lag 1, lag 3, or lag 8, and the distractors presented between the targets were either

non-filtered scenes (NF), low-spatial frequency scenes (LSF), high-spatial frequency scenes

(HSF), or masks. As expected, HSF distractors produced a lesser blink than NF or LSF

distractors on lag 3, indicating that the precocious modulation of the visuo-attentional system

by the LSFs plays a role in the emergence of consciousness. Such results are explained in

light of the boost and bounce theory (Olivers & Meeter, 2008), and a mechanism underlying

the direction of attention by working memory is proposed.

Keywords: attentional blink, spatial frequencies, visual attention, visual working memory

consciousness

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Introduction

Because of the quanta of information the human eye receives every single second and

because of information-processing limitations, the brain has to select which datum is relevant

and which is not in accordance with the purposes of the organism. The attentional blink (AB)

phenomenon could be the witness of such limitations. This effect—first described by

Broadbent and Broadbent (1987) and later termed the ‘attentional blink’ by Raymond,

Shapiro, and Arnell (1992)—can be observed during a rapid serial visual presentation

(RSVP). In this paradigm the participant is presented with a bunch of images streaming

sequentially at rates of about 10 items per second (i.e., 100 ms/item), the task being to detect

and report one or more targets per trial. The fact is that when a first target (T1) is consciously

perceived, the second target (T2), depending on the interval between the two (approximately

200-500 ms) and on the distractors between them (Martens & Valchev, 2009), is likely to be

processed without reaching conscious report (Luck, Vogel, & Shapiro, 1996). Explanations

about the processes underlying such a failure of the visual system still abound, going on with

this paper. Our study investigated the role of spatial frequencies in visuo-attentional

mechanisms and how consciousness could emerge from anticipatory mechanisms.

The Bounced Target: An Attentional Blink Phenomenon

A major model accounting for the blink is the boost and bounce theory (Olivers &

Meeter, 2008), which has been proposed to diverge from the previous models as it is not

relying on capacity limitations to explain the phenomenon (for reviews see Dux & Marois,

2009; Martens & Wyble, 2010). This model asserts that the template (also called ‘attentional

set’ or ‘gating’) stored in working memory—and defining ‘the representations involved in the

selection of task-relevant stimuli’ (Corbetta & Schulman, 2002, p. 202)—modulates the

bottom-up visual input through top-down reentrant connections according to the relevance of

the visual information. For example, if the visual representation being processed matches the

template, this low-level representation will be boosted through excitatory feedbacks, whereas

in case it is irrelevant the representation shall be bounced via inhibitory feedbacks. Olivers

and Meeter (2008) assumed that the bulk of this feedback activity would be reached around

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100 ms after the stimulus onset. This delay implies firstly that the stimulus may be long gone

when this peak is reached, and secondly, that the following stimuli could be modulated by

this very activity.

During the RSVP task until a target is presented only distractors are encountered,

hence, the system remains in a ‘stable inhibitory state’. Once a target (T1) is captured by the

visual system, as it matches the template, this one would trigger excitatory feedbacks from

the gate neurons in working memory back to the sensory stages in which it is processed,

enhancing its identification. With a stimulus onset asynchrony (SOA) of 100 ms, which is the

putative time necessary for the feedback activity to reach its apogee, the distractor following

T1 would profit the boost engendered by this latter. Consequently, the bounce (i.e., inhibitory

feedback) of the distractor, due to its irrelevancy, would be boosted since higher areas would

receive a strong signal from sensory areas that does not fit the template. Exactly the same

thing would occur when T2 appears: A strong inhibitory feedback generated by the boosted

bounce of the distractor would hit the early visuals areas, resulting in an insufficient

activation from T2 to the higher levels. To sum up, the attentional blink relies, according to

this theory, on temporal dynamics of excitatory and inhibitory feedbacks; it is mostly time-

based, and in fact, distractors are necessary for the occurrence of this phenomenon (see

Olivers & Meeter, 2008).

Whereas the authors declare the attentional blink is not occurring because of

processing limitations, this phenomenon still relies on the limitations of our brain (statement

that is not denied by Olivers and Meeter). Without these limitations, there wouldn’t be any

need to enhance information through visual attention as each information could be processed

equivalently. The relevant information could not be ignored and visual attention would be

unnecessary. Studies conducted through event-related potentials (ERPs) paradigms brought

out that spatial attention freezes during the attentional blink (Dell’Acqua, Sessa, Jolicœur, &

Robitaille, 2006), and that this freeze corresponds to the failure of attentional allocation to

targets (Pomerleau, et al., 2014). These results fit entirely the boost and bounce theory. Still,

this model needs to be compared to neuro-functional evidences about visual attention, visual

working memory, or visual perception more generally, to consider it as being a plausible

theory of temporal attention and to determine the mechanisms underlying conscious access.

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Prediction in Visual Perception: The Predictive Brain Hypothesis

Bar (2009b) proposed to comprehend visual perception as a quest of recognition

instead of identification. This view approaches the concept of embodied cognition (Barsalou,

2008) which discourses the notion of perception as processing sensory information thanks to

existing processing networks. As a matter of fact, there seems to be ‘a universal principle in

the operation of the human brain’: predictions (Bar, 2009a). Thus, instead of asking ‘what is

this I am seeing?’, the question would be ‘among what I already know, what does this look

like?’. Economically speaking, this seems to be beneficial since there is no need to process

the entire visual information in a bottom-up manner before being able to know what is being

perceived. The question is: how are these predictions generated?

Two decades ago, Goodale and Milner (1992) published an article describing the

ventral and the dorsal pathways, conveying preferentially high spatial frequencies (HSFs) and

low spatial frequencies (LSFs) respectively. According to several authors (e.g., Bar, 2003;

Bullier, 2001; Peyrin, et al., 2010), vision is a ‘coarse-to-fine’ processing. The LSFs,

conducted mainly from the occipital lobe to the frontal lobe via the dorsal pathway, convey

fast and coarse information; an ideal for the orbital frontal cortex (OFC) to quickly generate

first hypotheses about the object identity, non solely on the b asis of its visual appearance but

also from the general context in which it appears and from the non-contextual knowledges

about the object (Bar, 2003). Despite the fact that LSFs permits predictions, it is necessary for

the OFC to back project its guesses to compare them with the latent and fine visual

information conveyed by the HSFs, allowing conscious recognition of the scene, mainly

along the ventral pathway. Nonetheless, as we said earlier, the brain has to weigh the

importance of visual objects a priori to choose which ones deserve the most to be consciously

perceived.

Attention is the process by which something relevant is enhanced while something

irrelevant is merely ignored or reduced. We can define this function by saying it is a selective

mechanism emphasizing specific stimulations probably relevant (Chun, Golomb, & Turk-

Browne, 2011; Hollingworth & Maxcey-Richard, 2013). Although the mechanisms

underlying visual attention are not engraved in stone, many papers gave a rough idea about

the dynamic of it. If we take a look at this literature, endogenous top-down attention is

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believed to be engaged by a dorsal frontoparietal network involving the intraparietal sulcus

(IPS) and the frontal eye field (FEF), along with other discussed areas including the lateral

prefrontal cortex (LPFC) and the superior colliculi (SC) (see Fig. 3; Bisley, 2011; Noudoost,

Chang Steinmetz, & Moore, 2010; Raffone, Srinivasan, & van Leeuwen, 2014; Ruff, 2013).

According to the theory of attention and consciousness (Raffone, Srinivasan, & van

Leeuwen, 2014) the function of the IPS could be to accumulate evidences about the identity

of visual objects. This same area is believed to be involved in an inhibition-activation relation

with the FEF. That is, when a distractor is perceived the accumulation about its identity

would cause the IPS to inhibit the FEF, whereas on the opposite a pertinent target would

cause the IPS to excite the FEF which role could be to amplify the features of the target in

visual areas via top-down circuitry (Moore, Armstrong, & Fallah, 2003; Raffone, et al.,

2014).

Since the fovea situated near the centre of the retina covers only 1° to 2° of visual

angle, and as the cones coding for HSFs are mostly present within the fovea, the brain needs

to analyze peripheral information as much as possible and needs to quickly orient attention in

case something relevant bursts into periphery (Carretié, Ríos, Periáñez, Kessel, & Álvarez-

Linera, 2012). Surprisingly, IPS, FEF, and LPFC happen to process visual information in a

retinotopic manner (Saygin & Sereno, 2008; Silver & Kastner, 2009). Therefore, we can

suspect that visual attention is oriented via spatial frequencies, and this seems to be the case.

In their study, Carretié et al. (2012) showed that exogenous attention (i.e., bottom-up

attention) is preferentially captured by biologically salient stimuli filtered in LSFs. There are

reasons to believe that endogenous attention (i.e., top-down attention) can be oriented by

LSFs too as several cortical areas are common with exogenous attention, including FEF

(Mayer, Dorflinger, Rao, & Seidenberg, 2004) which is preferentially engaged when the first

information about the world is LSF rather than when it is HSF (Peyrin, et al., 2010).

Congruently, participants when primed with images filtered LSF were faster to direct their

attention and to identify the same image in a forced choice compared to when they were

primed with HSF images (Patai, Buckley, & Nobre, 2013). If we assume that the brain uses

fast LSFs to make first assumptions about the scenes that will modulate the visual input as

Bar (2003) stated and that the visuo-attentional system could also be directed by LSFs, then

these processes should occur early in the visual processing.

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Silver, Ress, and Heeger (2007), via fMRI, observed sustained visual attention

occurring in V1 (see also Kastner, Pinsk, De Weerd, Desimone, & Ungerleider, 1999), that is,

attention in the absence of any visual stimulation. This study (i.e., Silver, et al., 2007) allows

to point out the interesting current debate about the dissociation between visual attention and

visual working memory (VWM). Chun and colleagues (Chun, 2011; Chun, Golomb, & Turk-

Browne, 2011) hypothesized that VWM is the maintenance of visual attention on a limited

number of internal representations in the absence of sensory input. Several papers support

this idea by bringing data showing that VWM content is maintained within the visual cortex,

such that colors (Serences, Ester, Vogel, & Awh, 2009) and orientations (Harrison & Tong,

2009) are represented in V1, or even that objects (Han, Berg, Oh, Samaras, & Leung, 2013),

faces (Lee, Kravitz, & Baker, 2013), and scenes (Sreenivasan, Vytlacil, & D’Esposito, 2014)

are maintained in occipito-temporal cortices (reviewed in D’Esposito & Postle, 2015).

It is now widespread that representations held in VWM allow direction of attention to

visual areas (e.g., Chun, et al., 2011; Corbetta & Schulman, 2002; de Fockert, Rees, Frith, &

Lavie, 2001; Hollingworth, 2012; Hollingworth, Matsukura, & Luck, 2013; Luck & Vogel,

2013; Ruff, 2013; Soto, Hodsoll, Rotshtein, & Humphreys, 2008; Woodman, Carlisle, &

Reinhart, 2013), although the mechanisms by which this is managed are still discussed.

Silvanto and Cattaneo (2010) conducted a transcranial magnetic stimulation (TMS) study in

which they induced phosphenes to participants retaining the motion of a dot. Depending on

the congruency with the content held in working memory, the induced phosphene was either

likely to be enhanced or to be reduced. These are important data since they bring direct

support to the postulate of the boost and bounce theory stating that congruent information is

boosted whereas incongruent information is bounced.

Now we may wonder what are the regions involved in top-down selection of the

content in visual working memory. Actually, they happen to be very close to those involved

in visual attention, including FEF, IPS, and the LPFC (Corbetta & Shulman, 2002; Ruff,

2013). A paper by Chao and Knight (1998) supported the still-famous idea that the prefrontal

cortex (PFC) plays a role in top-down processes as lesions in this area disrupt the suppression

of distractors. Numerous studies since permitted to theorize that the LPFC could be the

storage of high-level representations providing such top-down signals allowing to guide

activity in other brain regions (for review see D’Esposito & Postle, 2015).

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Is Anticipation the Root of Consciousness?

Endogenous attention is somehow anticipation of the visual world led by the

organism’s goals, including the content maintained by the VWM (Summerfield & Egner,

2009), whereas recognition as envisaged by Bar (2009b) is anticipation of the forthcoming

data on the basis of an initial guesswork led by LSFs. Still, these two anticipatory

mechanisms must be to dissociate. On the one hand, attention is believed to emphasize the

sensory input, while, on the other hand, predictions seem to dim the input (e.g., Backlemore,

Frith, & Wolpert, 1998). More specifically, expectations might decrease the signals of cortical

areas but increase the quantity of information carried by these signals (den Ouden, Kok, & de

Lange, 2012). Therefore, generation of predictions by LSFs permits the visuo-attentional

system to distribute attentional resources efficiently toward unpredicted stimuli rather than

investing resources to explore something that was predicted (Bar, 2009b). Nonetheless, some

behavioral results bring support to a bayesian model stating that attention reverses the effects

of prediction (Kok, Rahnev, Jehee, Lau, & de Lange, 2011). That is, an unattended and

unpredicted stimulus provokes more cortical activation than an unattended predicted

stimulus, but an attended predicted stimulus will trigger more activation than if it was

unpredicted. Prediction and attention could then act contingently.

By synthesizing what we reported so far, we can suppose that the more the visual

system gathers information about the scene, the more it can anticipate the next information.

Predictions might be driven (at least partially) by LSFs, whereas visual attention might be

driven precociously by LSFs since the dorsal frontoparietal network processes the visual

input in a retinotopic manner. Visual working memory, which content is represented in visual

areas, can bias visual attention toward relevant stimuli; we can therefore imagine that this

bias operates directly in primary visual areas as soon as LSFs are processed. By following the

underlying logic of the boost and bounce theory—stating that the boosted bounce of inter-

targets distractor(s) results in the bounce of the second target—we investigated the

consequences of frequency filtering of distractors on conscious perception of targets during

an RSVP task. We hypothesized that anticipation of the visual scenes would modulate the

attentional blink in such a way that distractors filtered in low-pass would bring stronger

evidence of irrelevancy to the visuo-attentional system than would do distractors filtered in

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high-pass. Consequently, we predicted a deeper blink with LSF than with HSF at lag 3—as

the intensity of the bounce depends on the dissimilarity between the distractors and on the

template (Müsch, Engel, & Schneider, 2012; Raymond, Shapiro, & Arnell, 1995)—in case

visual perception and visual attention are directed by LSFs. This hypothesis also led us to

predict that there would not be any difference between the NF and the LSF conditions at any

lag. Nevertheless, we did not expect any difference between the conditions at lag 8 as T2

report is usually back to normal at this stage. The mask condition was expected to produce

the highest accuracy of T2 report, that is, equal or higher than HSF at lag 3 and 8. Finally, no

difference was meant to be found at lag 1 since there was no distractors between the targets.

Method

Participants and design

Forty-four Université Pierre-Mendès-France undergraduates (38 women, mean age =

19.50 years, SD = 1.22 ; 6 men, mean age = 20.50 years, SD = 1.76) participated in our

experiment for course credits. All had normal or corrected-to-normal vision. The study was

introduced as investigating consciousness and attention, and corresponded to a 4 (type of

distractor: low band-pass filtered (LSF), high band-pass filtered (HSF), non-filtered (NF), or

mask) × 3 (lag: 1, 3, or 8) fully within-subjects design.

Apparatus

The experiment ran using the E-Prime 2 software (E-Prime Psychology Software

Tools Inc., Pittsburg, PA) on a Windows XP computer plugged to a 17” monitor (model Dell

P791; 16” VIS) set at a resolution of 1024 × 768 pixels (aspect ratio, 4:3) and at a refresh rate

of 60 Hz. Participants were tested with a viewing distance of 70 cm, by laying their head on a

chin-rest, and were prompted to give answers by pressing keys on a desktop-computer

keyboard.

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Stimuli

The stimuli were taken from Kauffmann, Ramanoël, Guyader, Chauvin, and Peyrin

(2015) and were elaborated using the MATLAB image processing toolbox (Mathworks Inc.,

Sherborn, MA, USA). We selected 20 black and white outdoor scenes to be used as

distractors and 20 black and white indoor scenes to be used as targets (256-level grey-scales,

1024 × 768 pixels, 24 × 18 degrees of visual angle) in such a way that there were no similar

pictures. However, all photographs from both categories were similar on the basis of their

amplitude spectrum and on the basis of visual cluttering (Subband Entropy measures). Also,

within both categories, exemplars showed equivalent distribution of energy in spatial

frequencies and in dominant orientations. Outdoor scenes could be presented under three

forms: non-filtered (NF), low-spatial frequency (LSF), and high-spatial frequency (HSF). The

spatial frequency content of scenes was filtered by multiplying the Fourier transformation of

original images by Gaussian filters. For LSF stimuli, spatial frequencies above 0.5 cycles per

degree (cpd; i.e. low-pass cut-offs of 12 cycles per image) were removed, whereas for HSF

stimuli, was removed the spatial frequency content below 3 cpd (i.e. high-pass cut-offs of 71

cycles per image). We chose these values in order to include as much information as possible

in the HSF images and as less as possible in the LSF ones. Luminance of these filtered scenes

was normalized to 0.5 on a scale of 0 to 1. We also introduced a mask, as a control condition,

matching the amplitude spectrum of visual scenes (i.e., 1/f) but presenting a random phase

spectrum. As no pertinent information is available in this image we didn’t expect any effect

from it.

We used the same pseudo-randomized list of stimuli for all participants. The 20

outdoor scenes were randomly ordered for each trial so none of them could appear twice in a

trial. The 20 indoor scenes were randomly ordered for each condition and for each of the two

targets so none of them could appear twice in the RSVP, in one condition, and at one place

(e.g., the first indoor scene appeared only once as T1 at lag 1 in the NF condition, only once

as T2 at lag 1 in the NF condition, and so forth). We also rearranged manually the indoor

scenes to make sure that none of them could appear several times in the same trial as six

different images were necessary for each trial (see the procedure below).

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Procedure

Participants were tested individually in a darkened experimental box for 20-minutes

sessions and wore a noise-canceling headset. They were instructed orally and by reading to

detect two indoor scenes (targets) among outdoor scenes (distractors) presented sequentially

at quick pace. The experiment started with a training session during which the experimenter

remained in the room. The training phase consisted of 10 trials split in two conditions absent

from the experimental session: a ‘no-T2-and-no-distractors’ condition and a ‘lag-7-with-

mask-distractors’ condition. In this session the participants were trained to give their answers

properly (see the instructions below). Each trial started with a fixation cross lasting 1000 ms

followed by a 13- to 22-images-long RSVP presented at a rate of about ~12 Hz (image

duration: 83 ms)(see Fig. 1). T1 was presented after four or six non-filtered distractors, then,

there could be nought, two, or seven distractors between the targets (corresponding to lag 1,

3, and 8 respectively). All distractors within each trial belonged to the same type (i.e., NF,

LSF, HSF, mask). Once T2 appeared, the number of distractors left in the RSVP was fixed to

five in order to diminish the variance in the degradation of working memory before T2

retrieval.

At the end of each trial, participants were first prompted to identify T1 presented

among two other possible targets by pressing key ‘1’, ‘2’, or ‘3’ (Bach, Schmidt-Daffy, &

Dolan, 2014). These three potential targets were displayed horizontally at the top of the

screen with a digit above each of them indicating the corresponding key to press. A similar

screen was displayed for the retrieval of T2, however this time the targets were presented at

the bottom of the screen, still with a digit above them. This was established to ensure that

participants would notice the screen had changed in case no response would have been

registered and that screen change had occurred during an eye saccade. Indeed, participants

were given 4 sec to give their answer on both of the answer screens. In case no target was

perceived or recognized participants were instructed to press the ‘space’ key instead of

answering hazardously as we were not interested in unconscious perception by the dorsal

pathway or in any knowledge that it could retrieve (Goodale, 2008).

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!

Figure 1. Example of a trial in the lag 3 condition with masks as inter-targets

distractors. Green edges indicate the first target (T1) and the slide where it should

be retrieved. Purple edges indicate the second target (T2) and the slide where it

should be retrieved. The lag corresponds to the delay between T1’s onset (also

called ‘Lag 0’) and T2’s onset (here, ‘Lag 3’). T1 could appear at the seventh of

the ninth position in the rapid serial visual paradigm (RSVP), whereas for T2, the

number of distractors following it was fixed to five.

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Data analysis

Usual statistical analyses for attentional blink studies are conducted on T1 report and

on T2 responses when T1 was correctly reported (e.g., Martens & Wyble, 2010; Sergent &

Dehaene, 2004; Vermeulen, Godefroid, & Mermillod, 2009). Errors for ‘T2-if-T1’ data were

not normally distributed and, unfortunately, no transformation could fix this issue.

Nevertheless, as we obtained a similar pattern of results for T2 whether T1 had been correctly

reported or not (see Appendix 5), we chose to run our analyses on global T2 performances in

order to gain statistical power. The Shapiro-Wilk test of normality, which was preferred to the

Kolmogorov-Smirnov test (Ghasemi & Zahediasl, 2012), emphasized that several

distributions of residuals didn’t fit a normal distribution. Box-plots of data allowed to detect

three potential outliers, one of which was determined significantly deviant thanks to the

Grubb’s test (α = .05, p = .001) and was then excluded from analyses. After this

manipulation, the distribution of errors remained significantly different from a gaussian only

in the NF condition at lag 8 (p = .025).

A repeated measures analysis of variance (ANOVA) was conducted on participants’

global performances as residuals for each lag and for each condition were normally

distributed. In fact, calculation of the main effects in the ANOVA is based on modalities

means (El Methni, n.d., pp. 2-3) and on the global mean of data; only the interaction effect

needs to consider the mean of a modality coupled with another modality (lag × distractor).

Moreover, the NF condition was more of a control than a real modality of interest and the

non-normality problem occurred at lag 8, that is, when the attentional blink is not supposed to

be present anymore; justifying our choice to use parametric analyses instead of using the non-

parametric Friedman’s test (which reports only one p-value). Nevertheless, the values

reported for the interaction effect are to be considered with a reservation. Effect sizes were

reported as eta-squared (η2 < 0.1 = small effect size; 0.1 < η2 < 0.25 = medium effect size; η2

> 0.25 = large effect size; Müsch, et al., 2012). Four post hoc t-tests for paired samples were

then conducted: (1) effect of LSF vs. HSF for lag 3; (2) effect of LSF vs. HSF for lag 8; (3)

effect of NF vs. LSF for lag 3; (4) effect of HSF vs. mask for lag 8. Note that no analysis was

conducted on the NF condition at lag 8.

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!

Figure 2. Mean accuracy of T2 report for Lag 1, 3, and 8 when the distractors

between the targets were non-filtered scenes (NF; black), low-spatial frequency

scenes (LSF; dark grey), high-spatial frequency scenes (HSF; medium grey), or a

mask (light grey).

T2 re

triev

al %

0 %

25 %

50 %

75 %

Lag 1 Lag 3 Lag 8NF LSF HSF Mask

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Results

First of all, a lag-1 sparing effect couldn’t be observed since T2 report (M = .43; SD

= .11) didn’t exceed that of T1 (M = .41; SD = .13) at lag 1. Overall accuracy of T2 report

(Fig. 2; M = .24; SD = .14) seemed inferior to T1 report (M = .68; SD = .16) at lag 3, but also

at lag 8 (T2: M = .50; SD = .19; T1: M = .70; SD = .17). Mauchly’s test indicated that the

assumption of sphericity was violated for the lag effect, χ2(2) = 12.8, p = .002, ε = .814,

therefore we applied the Huynh-Feldt’s correction over the degrees of freedom since ε > .75

(see Girden, 1992). An ANOVA with repeated measures was conducted and showed a main

effect of the lag, F(1.63, 68.38) = 110.04, p < .001, η2 = .724, 1−β = 1.0, confirming an

attentional blink, a main effect of distractors was also obtained, F(3, 126) = 13.97, p < .001,

η2 = .25, 1−β = 1.0, and an interaction effect was reported, F(6, 252) = 7.09, p < .001, η2 = .

144, 1−β = 1.0. In an effort to explore the effects of distractors and to test our original

assumptions, we performed four paired samples t-tests. The first means comparison reported

a significant difference between the LSF and the HSF conditions at lag 3 [T2 report with

LSF: 23%; T2 report with HSF: 31%; t(42) = −3.85, p < .001], confirming that LSF produced

a stronger blink than HSF did. The second paired samples t-test indicated that LSF

engendered a better report of T2 than HSF at lag 8 [T2 report with LSF: 56%; T2 report with

HSF: 49%; t(42) = 3.57, p < .001]. The third t-test demonstrated that there was no significant

difference between NF and LSF at lag 3 [T2 report with NF: 21%; T2 report with LSF: 23%;

t(42) = −1.34, p = .19]. Finally, the fourth paired samples t-test reported a significant

difference between the mask and the HSF conditions at lag 8 [T2 report with mask: 44%; T2

report with HSF: 49%; t(42) = 2.29, p = .027], indicating a weaker report of T2 in the mask

condition than in the HSF condition.

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Discussion

Consistent with previous studies (e.g, Bach, Schmidt-Daffy, & Dolan, 2014; Sergent

& Dehaene, 2004) an AB was elicited on T2 at lag 3, as shown on Figure 1 and as indicated

by the ANOVA. Our main hypothesis was confirmed since T2 retrieval in the LSF condition

was significantly lower than in the HSF condition at lag 3, plus, no difference was observed

between the LSF and the NF conditions. This can be explained conjointly by a predictive

brain hypothesis (Bar, 2009b) and the boost and bounce theory (Olivers & Meeter, 2008).

Assuming that visual perception begins with the processing of rapid coarse information—via

the magnocellular pathway up to the frontal lobe in which an (unconscious) initial guess that

will settle a top-down control over the visual input is created—then a first possibility

accounting for our results is that LSFs could trigger the template-matching of working

memory in the frontal lobe (as formulated by Olivers & Meeter, 2008) earlier and stronger

than HSFs could do it, boosting or bouncing the visual input in a precocious way. This

hypothesis follows very closely the boost and bounce model and juxtaposes only the idea of a

coarse-to-fine visual processing. If we seek to enrich this model and to take account of the

numerous data from the fields of visual attention and working memory as reviewed in the

introduction, we can propose another hypothesis accounting for these results. We can

speculate that VWM—which content is represented in the areas processing the visual

information during perception and which is mainly controlled by the PFC, but also by the

FEF and the LIP—could filter the visual input as soon as during the bottom-up wave of

information, that is, as soon as the visual input reaches the visual areas. Visual information

would pass to the higher levels anyway, but in case it is incongruent, it would be delayed and

diminished (see Gazzaley, Cooney, McEvoy, Knight, and D’Esposito, 2005). The LIP and the

FEF would then be recruited and serve as an attentional top-down modulation of the visual

input (Bisley, 2011). Finally, prefrontal regions, namely the LPFC (D’Esposito & Postle,

2015; Noudoost, et al., 2010) and the OFC (Bar, 2009b), would trigger top-down signals to

occipito-temporal regions processing the visual representations (see Fig. 3). Since LSFs

travel faster than HSFs, it seems logical that the attentional dorsal prefrontal network would

be fed with information issued from LSFs in the first place, and then later by HSFs, after this

information had been gated by the content in VWM. There would still be a delay between the

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moment the visual input reaches the low-level visual areas during the bottom-up wave and

the moment it reaches the low-level visual areas but originating from the higher areas (a

delay putatively accounting for the blink). This proposition only suggests that VWM plays a

precocious role of modulation instead of a late top-down control responsible for the blink,

and that this blink is due instead to a late attentional modulation of the information. Although

the way VWM modulates visual processing is still quite undocumented, if templates are

represented in visual areas this sustained activity is susceptible to play a role in modulating

oncoming input. Note that this is only speculation and that nothing in our data challenges this

hypothesis.

Figure 3. Regions putatively underlying the attentional blink. Green items

represent the visuo-attentional network. Purple items depict the network involved

in visual working memory (VWM). Blue items indicate some of the regions

supposedly involved in anticipatory recognition of visual scenes and visual

objects as stated in Bar’s model (2003). Abbreviations: OC, occipital cortex; IPS,

intraparietal sulcus; FEF, frontal eye field; DLPFC, dorsolateral prefrontal cortex;

OFC, orbital frontal cortex; IT, inferior temporal cortex. (Image taken with

courtesy from 3D4Medical’s Essential Anatomy 5 app)

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Unintended results are to be reported though. Indeed, at lag 8, T2 report in the LSF

condition overwhelms the one in the HSF condition (and maybe also the one in the NF

condition). This pattern could be explained by the temporal dynamic of spatial-frequency

processing. In fact, Schyns and Oliva (1994) gathered data during a scene recognition task

with hybrid images, composed of a highway low-pass filtered (LSF) and of a city high-pass

filtered (HSF) for instance, indicating that scenes were likely to be categorized according to

the LSF content of the images when they were presented with short durations (30 ms) and

that categorization was more based on HSFs when presented with long durations (150 ms).

We can suppose that the visuo-attentional system not only uses LSF information, but also

uses HSFs in later stages. Therefore, the attentional blink could occur later when distractors

are high-passed than when they are low-passed since the time necessary for the HSFs to reach

the visual system is longer than for the LSFs. This hypothesis cannot be verified here as not

enough lags were used, still, this can account for the fact that HSF exceeds LSF at lag 3 and

for the fact that LSF exceeds both HSF and NF at lag 8. In non-filtered images all frequencies

are available, hence, we can suspect that NF equals LSF at lag 3 since scene recognition is

preferentially guided coarse information with short SOAs, and that NF equals HSF at lag 8

since scene recognition is more based on fine information with longer SOAs. Nonetheless,

this assumption is to consider with caution as the data obtained in the NF condition at lag 8

were not normally distributed.

Another unexpected result is that T2 report in the mask condition was the lowest of all

conditions. We didn’t expect this as no frequency information is available in the mask we

used. Nevertheless, this image respects the amplitude spectrum of natural scenes. It is

plausible that this amplitude influenced the top-down control of visual processes since

categorization seems to be amplitude-based (Guyader, Chauvin, Peyrin, Hérault, &

Marendaz, 2004). This mask may have provided ambiguous information, disrupting the

categorization process, and resulting in a stronger blink.

Several methodological problems encountered in this study are to be noticed. Firstly,

it would be advised in further experiments to increase the number of participants and to

decrease the difficulty of the task in order to avoid non-normality problems. In fact, the

global accuracy is relatively low compared to other studies (e.g., Vermeulen, Godefroid, &

Mermillod, 2009) and performances of T2-if-T1 report tended most of the time toward zero;

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although, in most of the experiments the participants were asked to answer even if they did

not perceive either of the targets. In this situation they could at least approach the chance

level. Secondly, we used only one mask. During the RSVP, when multiple distractors were

placed between the targets, the mask was displayed multiple times for 166 ms (lag 3) or 581

ms (lag 8). This could have given an impression of disruption in the rhythm of the RSVP.

Several masks should be used and chosen with care in light of the characteristics of the

images used in the paradigm. Finally, participants reported they had seen grey images that

were actually HSF images perceived grey because of the luminance normalization and of the

fine information barely visible at such speed. Whereas we can argue that spatial frequency

processing is an automatic process, this argument is lessened if participants notice that we are

manipulating the type of image. The lesser participants are aware of, the better. It could be

interesting to use images from which we subtracted the spatial frequencies of interest in order

to minimize such effect of normalization.

This study shows that the attentional blink can be modulated by frequency

information carried by the distractors, emphasizing again their importance in the production

of the blink. The results bring support to the idea that anticipatory processes play a role in the

emergence of consciousness. Moreover, we formulated a hypothesized role of the VWM

describing how this function could contribute to the modulation of the visual input. Our next

experiments should investigate the possibility of a shifted blink in the HSF condition by

including more lags. Besides this, in order to test explanations (e.g., Raffone, et al., 2014)

based on the neuronal global workspace (Dehaene, Sergent, & Changeux, 2003; but see

Dehaene, Kerzberg, & Changeux, 1998), future experiments could investigate the possibility

of a cross-modal attentional blink. In other words: Is an auditory target to be blinked after a

visual target? Indeed, global workspace theories state that the inability to report T2 is due to a

monopolization of the system by T1. If this is true, we should be able not only to suppress a

peripheral visual target appearing at the onset of T2 after T1 being presented in central vision,

but also should us be able to conceal an auditory T2 after a visual T1.

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Appendices

Appendix 1. Normality tests

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Appendix 2. Analysis of variance (ANOVA)

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Appendix 3. t-Tests

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Appendix 4. Accuracy of T1 report

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Appendix 5. Accuracy of T2|T1 report

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Mean accuracy of T1 report

T1 re

triev

al %

0 %

25 %

50 %

75 %

100 %

Lag 1 Lag 3 Lag 8

NF BSF HSF Mask

Mean T2 report if T1

T2 r

etriev

al %

0 %

25 %

50 %

75 %

Lag 1 Lag 3 Lag 8

NF BSF HSF MASK

Mean T2 report if not T1

0 %

25 %

50 %

75 %

Lag 1 Lag 3 Lag 8

NF BSF HSF MASK