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The independence of endogenous attentional orienting and object individuation

Stephanie C. Goodhew

Research School of Psychology, The Australian National University

Word count: (Main text): 6,908

Corresponding Author: Stephanie C. Goodhew

Address: Research School of Psychology (Building 39)

The Australian National University, Canberra, 2601

Email: stephanie.goodhew@anu.edu.au

Running head: Endogenous attention and object individuation

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Abstract

Object individuation is the process whereby the brain infers that dynamic input reflects

multiple discrete objects, rather than a single, continuing object over time. Object substitution

masking is popular method for operationalising object individuation inferences in the

laboratory. While object substitution masking was historically thought to interact with

attentional processes, an emerging body of literature indicates that this form of visual

masking is impervious to some attentional manipulations. However, one form of attention

that has not been systematically studied in relation to object-substitution masking is

endogenous attentional orienting. This is important because in other domains, endogenous

attentional orienting has been found have qualitatively distinct effects from other forms of

attention, including impacting visual perception when other forms of attention do not.

Therefore, if attention does interact with object individuation processes, then endogenous

attentional orienting is the most likely candidate mechanism for such a relationship. Here,

therefore, the impact of endogenous attentional on object-substitution masking was tested.

Across two experiments, while endogenous attentional orienting impacted overall target

perception, it had no impact on object substitution masking. This implies that object

individuation inferences are indeed independent of attention.

Public Significance Statement: This manuscript investigates two important visual processes

and determines whether they interact or are independent. One is voluntary visual attention,

the process of strategically selecting certain aspects of visual scene for privileged perceptual

processing, and the other is object-individuation, the process whereby the human brain

determines that dynamic visual input reflects multiple objects rather than a single object

continuing over time. Here, it was found that these processes, while each having a strong

impact on perception in their own right, operate completely independently of one another.

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Keywords: object individuation; object-substitution masking; object perception; visual

masking; attention; visual attention; endogenous attention; attentional orienting

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Imagine that you are driving and glance in the wing mirror of your car, and see a car driving

directly behind you. In a few moments’ times, you see a car in the lane beside you. Is this the

same car? If your brain decides that this is the same object that has changed relative location

over time, then an inference of object-correspondence is said to have occurred. In contrast, if

your brain determines that it is in fact a distinct object and that the first car is still behind you

(e.g., now in your blindspot), then an inference of object-individuation is said to have been

reached. While such object-individuation inferences can be conceptualised as a judgement,

like much of cognitive processing, they would typically occur pre-consciously, influencing

the contents of conscious perception while the decision-process itself remaining outside of

this sphere. Such inferences can have dramatic consequences for our perception of and

interaction with the world around us (Goodhew, 2017).

Now consider another core cognitive process: attention. In the visual domain, when

looking at a scene there is typically far too much information for our capacity-limited

resources to fully process. Visual attention, therefore, serves as a triaging mechanism,

prioritising the processing of salient and relevant information while filtering out other

information (Broadbent, 1982). For example, when driving, an effective use of attentional

resources would be to focus on other vehicles, road signs, and potential hazards (e.g.

pedestrians approaching the road), while filtering out the roadside trees and advertising signs.

Visual attention can be allocated in different ways, including being involuntarily captured to

a given location, stimulus, or feature due to its raw physical salience, even when it is not

necessarily helpful to the task at hand (e.g., a flashing billboard capturing attention while

driving; exogenous attentional orienting) (Awh, Belopsolsky, & Theeuwes, 2012; Chica,

Bartolomeo, & Lupianez, 2013; Eimer & Kiss, 2007; Jonides & Yantis, 1988; Theeuwes &

Godijn, 2002; Yeshurun & Levy, 2003). Visual attention can also be strategically applied to a

location or stimulus based on useful sources of information in the environment (e.g.,

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voluntarily applying attention to an upcoming intersection to check for cross-traffic;

endogenous attentional orienting) (Abrams, Barbot, & Carrasco, 2010; Addleman, Tao,

Remington, & Jiang, 2018; Awh et al., 2012; Becker, 2010; Chica et al., 2013; Hein, Rolke,

& Ulrich, 2006; Most et al., 2001; Sharp, Melcher, & Hickey, 2018; Vromen, Lipp, &

Remington, 2015), and even on the basis of reward or previous selection goals (e.g., attention

being captured by a red car because you were previously scanning for a red traffic light;

selection history) (Awh et al., 2012; Scalf, Ahn, Beck, & Lleras, 2014). The key question

addressed in this present study is: to what extent do endogenous visual-attentional and object-

individuation mechanisms interact?

A well-established method for operationalising the cognitive process of object

individuation is object-substitution masking (OSM). In OSM, a sparse mask (e.g., four-dots),

that onsets at the same time but temporally-trails after the target interferes with the perception

of the target, via object-individuation mechanisms (Goodhew, 2017; Lleras & Moore, 2003).

Masking magnitude is quantified as the difference in target perception (gauged via

identification or detection accuracy) for these delayed mask-offset trials subtracted from

baseline performance when the target and mask offset simultaneously (simultaneous offset

condition). It is now widely understood that the masking reflects the inference that the

dynamic display reflects a single, continuing object, and therefore the conscious percept is of

the end-state (four-dots alone). Correspondingly, when the original target array and trailing

four-dot mask array are treated as distinct objects (i.e., inference of object-individuation),

then masking is reduced or eliminated (Goodhew, 2017; Goodhew, Boal, & Edwards, 2014;

Goodhew, Edwards, Boal, & Bell, 2015; Goodhew, Gozli, Ferber, & Pratt, 2013; Goodhew,

Greenwood, & Edwards, 2016; Guest, Gellatly, & Pilling, 2012; Lleras & Moore, 2003;

Moore & Lleras, 2005; Pilling & Gellatly, 2010).

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In contrast, early accounts of OSM posited that the preventing focussed attention on

the target was a necessary condition for masking to occur (Di Lollo, Enns, & Rensink, 2000;

Enns & Di Lollo, 1997). Specifically, it was proposed that the target representation is

consolidated and ultimately perceived via a series of iterative re-entrant processing loops that

liaise between broad-brushstroke perceptual representations in frontal regions and localised

high-resolution perceptual details in early visual areas. However, the presentation parameters

of the delayed mask offset in OSM interferes with this process, creating a conflict between an

initial partial or incomplete representation of the target and incoming high-fidelity input of

the mask alone. Masking occurs when the mask ‘wins’ the competition for consciousness,

due to greater perceptual weight than the target, and/or an inability to finalise the rapidly-

decaying target representation (Di Lollo et al., 2000). This object-substitution model is

explicit with respect to the role of attention, stating that non-target items in the display are

involuntarily processed, meaning that the higher the number of non-target stimuli, the greater

the number of iterative loops required to resolve the target (and therefore the greater the

chance that the mask will win the competition and masking will result) (Di Lollo et al.,

2000). If so, then if attention is applied to the location of the target in advance, then masking

should be mitigated or eliminated.

Consistent with this, historically, it was thought that OSM did not occur for a single

item in a display, but instead it only occurred when there were distractors preventing

focussed attention being applied to the target. Moreover, it was thought that attention and

OSM interacted, such that the magnitude of masking increased concomitantly with the

number of distractors or other attentional manipulation (Carlson, Rauschenberger, &

Verstraten, 2007; Di Lollo et al., 2000; Dux, Visser, Goodhew, & Lipp, 2010; Goodhew,

Dux, Lipp, & Visser, 2012; Kotsoni, Csibra, Mareschal, & Johnson, 2007; Tata & Giaschi,

2004; Weidner, Shah, & Fink, 2006; Woodman & Luck, 2003). However, a more recently

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emerging corpus of evidence indicates that this conclusion stemmed from methodological

artefacts in these earlier studies, such as ceiling effects artificially reducing the effect of

attentional manipulations on masking magnitude when attention is more strongly focussed

(e.g., at smaller set-sizes). This work has shown that when these issues are corrected, while

attention has an overall impact on target perception, masking magnitude (i.e., difference in

accuracy between simultaneous and delayed mask offset conditions) appears unaffected, and

OSM can indeed occur for single-item presentations with attention focussed on them

(Argyropoulos, Gellatly, Pilling, & Carter, 2013; Camp, Pilling, Argyropoulos, & Gellatly,

2015; Filmer, Mattingley, & Dux, 2014, 2015; Filmer, Wells-Peris, & Dux, 2017; Goodhew

& Edwards, 2016; Pilling, Gellatly, Argyropoulos, & Skarratt, 2014). More specifically,

OSM magnitude is unchanged irrespective of: the number of distractors presented

concurrently with the target (Argyropoulos et al., 2013; Filmer et al., 2014), exogenous

attentional orienting to the target location (Pilling et al., 2014), the size of the attended region

for a centrally presented target (Goodhew & Edwards, 2016), and even executive rather than

spatial attentional manipulations (Filmer et al., 2017). These findings challenge the object-

substitution account of OSM. However, none of these studies directly tested the impact of

endogenous shifts of attention. This is important because some aspects of perception are only

modulated by endogenous shifts of attention (Prinzmetal, McCool, & Park, 2005; Prinzmetal,

Zvinyatskovskiy, Gutierrez, & Dilem, 2009; Rohenkohl, Coull, & Nobre, 2011). This means

that it may be that attention does modulate OSM and consequently that the object-substitution

account can be rescued – but only by endogenous attentional orienting.

Endogenous attentional orienting, in contrast to its exogenous counterpart, is where a

spatial shift in attention occurs voluntarily or with volition – a strategic response to an

informative rather than a salient cue. In the lab, this is typically operationalised as a centrally-

presented stimulus that has above-chance level predictiveness of the location of the

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subsequent target. This form of cueing also operates on longer timescales than its exogenous

counterpart (Carrasco, 2011; Chica et al., 2013; Jonides, 1981; Posner, 1980). Endogenous

attentional orienting is a crucial aspect to consider, because endogenous attentional orienting

has been found to have qualitatively different effects on perception, with some perceptual

benefits emerging exclusively under endogenous attentional orienting conditions. In

particular, endogenous shifts of attention consistently facilitate perceptual processes such as

temporal acuity and texture segmentation, whereas exogenous shifts do not (Hein et al., 2006;

Yeshurun & Carrasco, 1998; Yeshurun & Hein, 2011; Yeshurun & Levy, 2003; Yeshurun,

Montagna, & Carrasco, 2008). More broadly, it has been suggested that only endogenous

attentional orienting is able to improve perception, rather than just facilitate response

efficiency, which is the outcome of exogenous attentional orienting (Prinzmetal et al., 2005;

Prinzmetal et al., 2009). This highlights the possibility that endogenous attention may

uniquely be able to facilitate the perceptual process that is object-individuation, thereby

reducing masking.

Therefore, in the present study, the effect of endogenous attentional orienting on

object-individuation was examined. A standard OSM array was used, and to operationalise

endogenous attentional orienting, a centrally-presented cue that was 75% predictive of the

location of the target was employed. In Experiment 1, this was an arrow pointing to the left or

right, and in Experiment 2, a colour cue was used instead. Masking magnitude was compared

on for valid trials (where the cue correctly predicted the location of the target) versus invalid

trials (where the cue did not predict the location of the target). If endogenous attention and

object-individuation processes interact, then masking magnitude should be reduced for the

valid compared with the invalid trials. In contrast, if the endogenous attention and object-

individuation processes do not interact, then masking magnitude should be equivalent for the

valid and invalid trials.

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Experiment 1

Method

Participants. Thirty participants were recruited for the experiment. This sample size

was pre-determined on the basis of a power analysis that corrects for publication bias

(Anderson, Kelley, & Maxwell, 2017). This was done using the two-factor repeated-measures

ANOVA Shiny App calculator that accompanies the article by Anderson et al. (2017). This

calculator requires the following information: (a) an observed F-value from a previous study

from (b) a given sample size, for a design with (c) specified number of levels for each factor

in the design, (d) alpha level for the previous study, (e) alpha level for planned study, and (f)

assurance value, and (g) desired level of statistical power for the planned study (see Anderson

et al., 2017, for more information). Here, the F value and N (39) were entered from a

previous study that examined the interaction between attentional breadth and OSM, which

had two factors each with two levels (Goodhew & Edwards, 2016), like the current study.

This previous study showed significant main effects of both the attended-region size and

mask duration factors, and to adopt a conservative approach to ensure adequate power, the

smaller of these two F-values was selected (12.1) for the power calculations here. Alpha level

for both the previous and current study were set at .05. Assurance was set to 0.5 (which

corrects for any publication bias in previously-obtained effect sizes) and power to 0.8.

From this, it was calculated that a sample size of 30 was required to have 80% power to

detect the smaller of the two main effects in Goodhew and Edward’s (2016) Experiment 1.

All participants in this and the following experiment provided written informed consent prior

to participation. The experimental protocol was approved by ANU’s Human Research Ethics

Committee (protocol number 2017/565).

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Demographic data was not available for one participant. For the remaining participants,

the mean age was 21.6 years (SD = 2.3). Eighteen participants reported their gender as

female, 11 as male. Twenty-five reported being right-handed, 3 left-handed, and 1

ambidextrous. Ten participants were born in China, eight in Australia, four in Malaysia, two

in India, and one in each of: New Zealand, Indonesia, Japan, South Korea, and Russia1.

Stimuli & Apparatus. The experiment was run on a 21.5” screen iMac computer with a

refresh rate of 60Hz (and so all stimulus durations are in intervals of 16.67ms). Viewing

distance was fixed at approximately 60cm by means of a chin-rest. The stimuli were

generated and displayed via the Psychtoolbox in Matlab (Brainard, 1997). The cue was a

double-headed arrow presented in the centre of the screen, pointing either to the left or right

(<< or >>). The target was the capital letter ‘E’ or ‘F’, presented approximately 3 of visual

angle to the left or right of the centre of the screen (see Figure 1). Both the cue and target

were presented in size 20 Helvetica font. Each individual dot of the four-dot mask subtended

about 0.3, and they were arranged in a square centered on the target letter, where each side of

the square was about 1. The cues were black [0 0 0], the targets and four-dot mask were dark

grey [55 55 55] while the background of the screen was set to mid-grey [128 128 128].

Procedure. Participants were tested individually and completed two conditions:

Cueing Only Condition, and the Cueing and OSM Condition (order counterbalanced across

participants). The former was included as a check that cueing effects were obtained in a

standard cueing arrangement, whereas the latter directly assessed the interaction between

cueing and OSM.

In the Cueing Only Condition, on each trial, the arrow cue was presented for 300ms.

Next, the target appeared until a response was registered. Participants’ task was to identify

the target letter (as E or F) by pressing the corresponding key on the keyboard. Participants

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were instructed to respond as quickly and accurately as possible. Accuracy was recorded to

ensure compliance with task instructions, but it was expected that response time on correct

trials (RT) would be the primary source of variation.

In the Cueing and OSM condition, on each trial, the arrow cue was presented for 300ms.

Next, the target appeared, surrounded by the four-dots mask, was presented for the target

duration. Then the cue and target disappeared. On simultaneous mask offset trials, the mask

also disappeared at the same time as the cue and target, and the screen was then blank until a

response was registered, whereas on the delayed mask offset trials, the mask alone was

presented for 200ms before this happened. Participants were instructed to identify the target

letter (as E or F) by pressing the corresponding key on the keyboard. Accuracy of response

was emphasised (not speed). Here, therefore, accuracy was the dependent variable.

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Figure 1. An illustration (not to scale) the trial structure in the Cueing and OSM condition in

Experiment 1. This represents a valid trial, since the direction of the arrow correctly predicts

the location of the target. Target duration was determined by participant performance during

practice (see below). This depicts a delayed-mask offset trial, on a simultaneous offset trial,

the screen would be blank after the target disappeared (i.e., 0 ms trailing mask).

For both the Cueing Only and the Cueing and OSM conditions, the validity of the cue

was randomised such that there was a 75% probability of it being valid on a given trial. The

two levels of each of the variables of target identity (E or F), target side (left or right), and for

the Cueing and OSM condition, mask offset condition (simultaneous or delayed mask offset)

occurred on exactly 50% of trials, and were fully crossed and randomly intermixed. Whether

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the cue was pointing left or right was constrained on each trial by the validity and target side

variables. Both conditions had a blank inter-trial interval of 1000ms. The Cueing Only

condition consisted of 200 trials (100 per validity), whereas the Cueing and OSM condition

consisted of 400 trials (100 per combination of validity and mask offset condition).

Participant-paced rest breaks were offered to the participant at 25%, 50%, and 75% of the

way through the number of trials in each condition.

For both conditions, participants first completed a practice block to familiarise them with

the task, where on-screen accuracy of the response was provided on each trial (whereas it was

not in the main experiment). For the Cueing Only condition, the practice block consisted of

four trials, and participants were required to respond correctly on three of the four trials to

progress to the experiment (instructions clarified and practice repeated, if required). For the

Cueing and OSM condition, the purpose of the practice was not only task-familiarity, but also

to gauge an appropriate target presentation time tailored to each individual participant to

mitigate against floor or ceiling effects contaminating the data. The practice block for this

condition therefore contained 50 trials. To facilitate familiarity with the more briefly-

presented stimuli, the target presentation time began at 10-times the final presentation

duration (i.e., 330ms) for trials 1 and 2, 5-times the final presentation duration (i.e., 165ms)

for trials 3 and 4, 2-times the final presentation duration (i.e., 66ms) for trials 5 and 6, and

then 33ms from trial 7 through to trial 50. In a two-alternative forced-choice task as per this

experiment, the level of performance most sensitive to detecting changes (i.e., least affected

by floor/ceiling) is 75%. Hence, if after 50 trials, participants scored greater than or equal to

85% accuracy, they were assigned to a quicker target duration (17ms) for the experiment

proper. If they scored less than 85% but greater than or equal to 65%, they remained at the

33ms target duration. If they scored less than 65%, they were assigned to a 50ms target

duration.

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Results & Discussion

Raw data for both experiments is publicly available (see https://osf.io/gdpxy/). Trials

were excluded as invalid where either: (a) participants pressed a key other than one of the

designated response keys in either condition, or (b) response time in the Cueing Only block

was too rapid to reflect genuine responding (<100ms) or too slow to reflect compliance with

the instruction to respond as quickly and accurately as possible (>2.5 SDs above participant’s

mean RT). This led to an average of less than 3% of trials being excluded in the Cueing Only

block, and less than 1% in the Cueing and OSM condition.

In the Cueing Only condition, participants’ mean accuracy was high, and did not

significantly differ between the Valid (93%) and Invalid (94%) conditions, as revealed by a

repeated measures t-test (t < 1). Participants’ mean RT on correct-response trials was

significantly faster in the Valid (499ms) than the Invalid condition (520ms), t(29) = 3.89, p

= .001. This shows that the cueing procedure was effective in a standard RT-based cueing

paradigm. These results were unchanged (i.e., results stayed significant / non-significant)

irrespective of whether or not a participant who performed below chance-level accuracy in

the Cueing Only condition was included.

Average accuracy in the Cueing and OSM condition was 72%. As can be seen in

Figure 2, there was a clear validity effect, whereby valid trials (left two bars) produced higher

accuracy than invalid trials (right two bars). There were also masking effects, whereby

accuracy was higher in the simultaneous offset condition (yellow bars) than the delayed mask

offset conditions (blue bars). This was true for both valid and invalid conditions, and

crucially, it appears that the masking effect was equivalent for the valid versus invalid

conditions (i.e., no interaction).

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The statistics confirm the pattern apparent in Figure 2. The accuracy data was

submitted to a 2 (Validity: Valid, Invalid) x 2 (Mask Offset Condition: Simultaneous,

Delayed) repeated-measures ANOVA. This revealed a significant main effect of Validity,

F(1, 29) = 16.31, p < .001, p2 = .360, such that accuracy was greater in the Valid (74%)

versus the Invalid condition (69%). This illustrates that participants oriented their attention in

response to the cue, and that this impacted their overall perceptual performance. There was

also a significant main effect of Mask Offset condition, F(1, 29) = 9.84, p = .004, p2 = .253,

such that accuracy was higher in the Simultaneous Offset Condition (73%) compared with

the Delayed Mask Offset Condition (70%). This reveals that OSM was present. The

interaction between Validity and Mask Offset Condition was not significant, F(1, 29) = .024,

p = .877, p2 = .001. This means that despite the fact that endogenous attentional orienting

and masking each impacted accuracy in their own right, these effects were impervious to one

another. That is, the magnitude of masking did not change as a function of whether

endogenous attention was applied to the target or not, in fact, the effect size for the

interaction approached zero.

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Figure 2. An illustration of the results of Experiment 1. Error bars are standard errors

corrected for within-subjects designs (Cousineau, 2005).

For the Cueing and OSM condition, it is important that any potential interaction

between an attentional manipulation and OSM does not reflect an artefact of floor or ceiling

effects. While titrating target exposure duration tailored to each participant was the primary

means of ensuring this, five participants’ datasets were in the range where accuracy was

potentially approaching floor (four with <60% average accuracy) and ceiling (one with >90%

average accuracy). Therefore, the above analysis was repeated with these five datasets

excluded. For the remaining 25 participants, mean accuracy for the group in the Cueing and

OSM condition was 74% - right in the most sensitive range, away from floor (50%) and

ceiling (100%). For this group, the above results replicated (i.e., significant main effects of

Validity and Mask Offset condition, and no interaction (F<1)). This demonstrates that the

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absence of an interaction was not artifactually induced by the constraints of floor or ceiling

effects.

In this experiment, there was a notable lack of interaction – the effect size approached

zero. However, to check that the smallish masking effect (~3%) had not constrained the

power of the interaction, an additional analysis was performed in which participants with

masking effects (difference in accuracy between Simultaneous and Delayed Mask Offset

Conditions) at or below zero were excluded. This left 22 datasets for analysis. This sample

had an average masking effect twice as large (6%). The same repeated-measures ANOVA as

above revealed a significant main effect of Validity, F(1, 21) = 9.56, p = .006, p2 = .313, and

a highly significant main effect of Mask Offset Condition, F(1, 21) = 52.27, p < .001, p2

= .713. However, the interaction was still not significant, F(1, 21) = 2.38, p = .138, p2 = .102.

Since the key result here is an absence of an interaction between two variables, a

Bayesian analysis was also performed to determine whether there was evidence in favour of

the null hypothesis. That is, these 22 datasets were subjected to a Bayesian analysis with the

default priors in JASP (2018). This revealed a BF10 >20 for Validity, a BF10 >670 for Mask

Offset Condition, and BF10 < 1 for the interaction. These factors represent a ratio of the

amount of evidence in favour of the alternative hypothesis versus the null hypothesis.

Therefore, these can be interpreted as strong and very strong evidence for the alternative

hypothesis for the two main effects respectively, and no evidence in favour of the alternate

hypothesis for the interaction. In fact, since the BF10 <1 for the interaction, this analysis

suggests that there is more evidence in favour of the null hypothesis for the interaction

(Jarosz & Wiley, 2014). In other words, the Bayesian analysis also supports the absence of an

interaction between Validity and Mask Offset condition.

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These results suggest that endogenous attentional orienting and OSM are independent

of one another, rather than interactive. However, before this conclusion can be accepted, an

outstanding issue requires addressing. That is, while historically arrow cues have been

employed as prototypical endogenous cues (Jonides, 1981; Muller & Rabbitt, 1989), it has

since been suggested that there may be some reflexive components (Kuhn & Kingstone,

2009). There are several reasons to think that this was not occurring in Experiment 1, since

(1) the cue duration used in Experiment 1 is sufficiently long to likely preclude exogenous

components, and (2) accuracy was impacted by the cueing procedure in the combined Cueing

and OSM block, whereas it has been suggested that exogenous attentional orienting with

valid and invalid cues only affects RT, whereas endogenous cueing can impact accuracy

(Prinzmetal et al., 2005). However, it is already known that exogenous attention and OSM do

not interact (Pilling et al., 2014), and it is therefore possible that in Experiment 1, that any

such a reflexive component may have overwhelmed the endogenous component to arrow

cues and not allowed for a clean test of the interaction between endogenous attentional

orienting and OSM. Therefore, to be sure, in Experiment 2, a “pure” endogenous attentional

cue was used.

Experiment 2

Here in Experiment 2, a colour cue was used instead of an arrow cue. Since a colour cue,

whose relationship to a spatial location is arbitrary and requires interpretation (unlike an

arrow) would likely take longer to process, a longer cue duration was also employed.

Furthermore, in order to increase masking magnitude, the mask dots were changed from

circles to squares, to match the angularness of the E/F letter targets. This is because OSM is

modulated by the similarity between the target and mask stimuli with respect to colour,

luminance, orientation, and spatial frequency content (Goodhew et al., 2015; Luiga &

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Bachmann, 2008; Moore & Lleras, 2005). It was therefore thought that masking might also

be increased by this form similarity.

Method

Participants. Thirty-two participants were recruited for the experiment. As per

Experiment 1, 30 was calculated as the minimum sample size for power, and 32 were

recruited to finish counterbalancing (with the introduction of an additional variable – which

cue colour was valid to the counterbalancing scheme compared with Experiment 1, please see

Procedure section for more information). Participants’ mean age was 23.1 years (SD = 3.8).

Eleven reported their gender as male, 21 as female. Eight participants were born in Australia,

eight in China, four in India, three in Singapore, two in Malaysia, and one in each of:

Myanmar, Greece, Philippines, Pakistan, South Korea, Russia, and Indonesia.

Stimuli & Apparatus. The stimuli and apparatus for Experiment 2 were identical to

Experiment 1, with the following exceptions. The central cue was now a box, coloured either

orange or purple. It subtended about 1.15. The mask dots were squares rather than circles.

Procedure. The procedure for Experiment 2 was identical to Experiment 1, with the

following exceptions. For both conditions, participants were instructed to orient their

attention based on the colour of the cue. For half of participants, orange indicated left and

purple right, whereas for the other half of participants, purple indicated left and orange

indicated right (counterbalanced across participants, and fully crossed counterbalancing with

order of condition completion). Cue exposure duration was increased from 300ms to 600ms.

For the Cueing and OSM condition, target exposure duration during practice was increased to

50ms. This was done because there were more cases affected by potential floor than ceiling

effects in Experiment 1, suggesting that sensitivity could benefit from a slight increase in

performance. To facilitate this, target duration was increased by one monitor refresh. If

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participants scored greater than or equal to 85% accuracy, they were assigned to the quickest

target duration (33ms) for the experiment proper. If they scored less than 85% but greater

than or equal to 65%, they remained at the 50ms target duration. If they scored less than 65%,

they were assigned to a 67ms target duration.

Results & Discussion

Less than 4% of trials were excluded as invalid from the Cueing Only condition and

less than 0.2% from the Cueing and OSM condition.

In the Cueing Only condition, accuracy was high, and did not significantly differ

between the Valid (96%) and Invalid conditions, (96%), (t < 1). RT also did not reliably

differ between the Valid (515ms) and Invalid conditions (519ms), t(31) = 1.20, p = .241. That

is, cueing did not appear to be evident in the Cueing Only condition. These results were

unchanged by the exclusion of one dataset where there were more than 50% invalid

responses. However, endogenous attentional orienting can produce effects where accuracy

rather than speed is the primary dependent variable, and so the effect of the cue was

examined in the Cueing and OSM condition where accuracy was the dependent variable. (Of

course, there is an accuracy measure here in the speeded block, but this is unlikely to be

modulated due to (by design) ceiling effects on accuracy for speeded task. Instead, accuracy

is only likely to be informative in unspeeded tasks, such as the Cueing and OSM condition).

In the Cueing and OSM condition, average accuracy across conditions was 72%.

Figure 3 illustrates the presence of a validity effect, whereby accuracy was higher for valid

than for invalid trials, and a masking effect, such that accuracy was greater for the

simultaneous mask offset compared with the delayed mask offset conditions. Moreover, the

masking effect was present and equivalent for the valid versus invalid conditions. Consistent

with this, a 2 (Validity: Valid, Invalid) x 2 (Mask Offset Condition: Simultaneous, Delayed)

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repeated-measures ANOVA on target identification accuracy revealed a significant main

effect of Validity, F(1, 31) = 25.25, p < .001, p2 = .449, such that accuracy was higher in the

Valid (78%) than the Invalid (66%) condition. Here, therefore, there is clear evidence that

participants did orient their attention in response to the cue, in contrast to the Cueing Only

condition. There was a significant main effect of Mask Offset Condition, F(1, 31) = 19.45, p

< .001, p2 = .385, such that accuracy was greater for the Simultaneous (74%) versus the

Delayed mask Offset condition (70%). There was no significant interaction between Validity

and Mask Offset Condition, F(1, 31) = 0.33, p = .571, p2 = .010. That is, despite clear

evidence that Validity and Mask Offset condition reliably impacted accuracy in their own

right, they were independent rather than interactive in their effects. Furthermore, even when

cases where average accuracy was <60% or >90% were removed, the main effects remained

significant (ps < .001), and the interaction remained non-significant (F<1).

22

Figure 3. An illustration of the results of Experiment 2. Error bars are standard errors

corrected for within-subjects designs (Cousineau, 2005).

Moreover, an additional analysis was performed in which only participants with

masking effects greater than zero were included (N = 26). This revealed a significant main

effect of Validity, F(1, 25) = 16.45, p < .001, p2 = .397, and a significant main effect of

Mask Offset Condition, F(1, 25) = 42.59, p < .001, p2 = .630. The interaction, however, was

still clearly non-significant, F(1, 25) = 1.07, p = .310, p2 = .041. Finally, a Bayesian analysis

indicated that the BF10 > 400,000 for Validity, BF10 >3 for Mask Offset Condition, and BF10

<1 for the interaction. These can be interpreted as very strong, positive, and no evidence for

the alternative hypothesis respectively (Jarosz & Wiley, 2014). Indeed, the Bayesian analysis

implies greater evidence for the null hypothesis for the interaction. In other words, the

Bayesian analysis supports the conclusion that Validity and Mask Offset condition do not

interact.

General Discussion

Across two experiments it was shown that endogenous attentional orienting and

object-individuation processes were independent of one another. That is, despite clear and

demonstrable evidence that an endogenous cue on the one hand and the OSM manipulation

on the other each impacted perceptual performance in their own right, these effects did not

interact. This provides converging evidence with the existing literature that OSM is

independent of exogenous attentional orienting (Pilling et al., 2014), the number of

distractors in the display (Argyropoulos et al., 2013), the distribution of attention across space

(Goodhew & Edwards, 2016), and executive attentional manipulations (Filmer et al., 2017).

Since in other domains endogenous attentional orienting has produced qualitatively different

patterns of effects compared to exogenous attentional orienting, it was entirely possible that

23

this form of attention would interact with OSM despite the other forms being shown not to.

However, here, this was clearly not the case. This was not a conclusion erroneously

stemming from insufficient power – the current experiments were well powered, and the

effect sizes for the interaction were so minuscule that they approximated zero. Moreover, the

Bayesian analysis provided direct evidence in favour of the null hypothesis for the interaction

term. This indicates that the interaction really was not there to be found.

Two previous studies have attempted to understand the role of endogenous attention

in OSM. The first, Luiga and Bachmann (2007), sought to differentiate exogenous and

endogenous attention and their effects of OSM. Their results appeared to suggest that

endogenous attentional orienting did not modulate OSM. However, there are several

methodological issues that cloud a clear interpretation of this data. One issue is that the

endogenous attentional cue was a central arrow, which has since then been shown to

potentially reflect some exogenous components (Kuhn & Kingstone, 2009). Another issue is

that the key comparison was between a present central cue, versus an absent one. The

presence of the central cue may therefore have provided a temporal alerting/vigilance cue,

independent of any spatial-attentional effect. A more appropriate baseline is the comparison

of two conditions where the cue is present, one where it validly predicts the location of the

target, and the other where it does not. Finally, the small sample size (six participants), which

may have underpowered the study’s ability to find reliable results.

Moreover, the conclusion from Luiga and Bachmann (2007) conflicts with that of a

subsequent study, which claimed that endogenous cueing does modulate OSM (Germeys,

Pomianowska, De Graef, Zaenen, & Verfaillie, 2010). However, the evidence to support this

claim was that the impact of the trailing mask on performance was modulated by the interval

of time between the appearance of an arrow cue and the appearance of the target. However,

once again, any benefit here is likely to have reflected a general alerting effect, rather than a

24

spatial attentional effect. In order to support their claim, the appropriate comparison would

have been between valid versus invalid cueing trials. Moreover, the small sample size (six

participants) again may have underpowered the study, undermining its ability to find reliable

results, and finally, the use of an arrow cue may have conflated exogenous and endogenous

attentional orienting components. In contrast, the present appropriately-powered study

directly compared two conditions where the alerting cues were constant because a cue was

always present, but the cue either did or did not predict the location of the target, thereby

actually manipulating spatial attention, unlike the previous studies (Germeys et al., 2010;

Luiga & Bachmann, 2007). Moreover, critically, a pure endogenous attentional orienting

manipulation, completely separated from any exogenous components, was employed. This

manipulation had a strong effect on visual perceptual performance in general, however, the

results clearly demonstrated that this endogenous attentional orienting did not modulate

OSM.

The absence of an attentional cueing effect on OSM demonstrated in the present study

represents the strongest call for, at a minimum, a revision to the object-substitution account of

OSM. According to this theoretical model, preventing focussed attention on the target was

espoused to be key to how masking arises (Di Lollo et al., 2000). Here, however, it was

shown that even the most powerful and perceptually-impactful form of attention –

endogenous attentional orienting – had no influence on OSM. This poses a serious challenge

to the object-substitution theory. Furthermore, one could even argue that since the role of

attention was central to the object-substitution account, that the present evidence altogether

undermines the merit of the account, to the extent that it should be discarded. In contrast, the

object-individuation account of OSM does not propose a critical role for attention. The

object-individuation account also has an undeniably strong evidence base in its own right (for

25

a review, see Goodhew, 2017). The current study could be taken as evidence to suggest that it

should be considered the sole viable account for the mechanisms underlying OSM.

However, from an alternative perspective, while attention was espoused as a key

mechanism to the object-substitution account, perhaps this aspect of the model can be revised

while the other components remain. The other main aspects of the model are the feedback

mechanism in which re-entrant activity disrupts visual representation, and reduced reliance

on local, inhibitory interactions (relative to other forms of masking, such as metacontrast

masking). If we assume that multiple re-entrant processing loops are required in order to

identify a target even when it is attended, then this mechanism could still potentially explain

OSM for attended targets. However, when the evidence is critically reviewed, re-entrant

processing is specific to neither OSM nor the theory of object-substitution. Notably, there

was a zeitgeist of burgeoning appreciation for re-entrant processing and its importance that

coincided with when the object-substitution account was proposed (Lamme & Roelfsema,

2000; Lamme, Super, & Spekreijse, 1998), which means that the emphasis on this new-found

mechanism was understandable. However, since then, a consensus of evidence informs us

that re-entrant processes are integral to visual processing and visual awareness (Ahissar &

Hochstein, 2004; Bar, 2003; Bullier, 2001; Kveraga, Boshyan, & Bar, 2007; Lamme, 2001;

Laycock, Crewther, & Crewther, 2007; Pascual-Leone & Walsh, 2001; Ro, Breitmeyer,

Burton, Singhal, & Lane, 2003; Sabatinelli, Lang, Keil, & Bradley, 2007; Sillito, Cudeiro, &

Jones, 2006; Silvanto, Lavie, & Walsh, 2005; Tapia & Beck, 2014; Wyatte, Curran, &

O'Reilly, 2012; Zeki, 2001). Indeed, it is now uncontroversial to be the point of being

incontrovertible that re-entrant processing is implicated in visual perception of the target in

OSM, because re-entrant processing is implicated in virtually every aspect of visual

perception, including forms of masking that the proponents of object-substitution have been

at pains to differentiate from OSM, such as metacontrast masking (Ro et al., 2003). It is

26

therefore in no way specific to OSM, but rather a general property of conscious visual

perception. The fact that neural re-entrant processing occurs, therefore, does not provide any

evidential weight to any particular theory of OSM.

Of course, it is worth noting that some recent studies have claimed to support the

object-substitution account, but while this evidence is consistent with the model, it is also

consistent with other models – that it, it is not theory-diagnostic. For example, it has been

found that OSM operates on an all-or-none rather than a graded perceptual basis (Pilling,

Guest, & Andrews, 2019). This could be considered consistent with the object-substitution

account, but such a finding is in no way exclusive to the object-substitution account. For

instance, it is equally true that object-individuation could operate on this basis. This evidence,

therefore, while interesting, does not adjudicate between different theoretical accounts.

The final aspect of the original object-substitution model is more a consequence of the

hypothesised attentional and re-entrant processing mechanisms – that OSM is more

impervious to low-level local inhibitory interactions, compared with more traditional forms

of masking such as metacontrast masking. The major issue with this, however, is that even if

it is true, it does not necessarily derive from other aspects of the theory. That is, many,

diverse hypothesised mechanisms other than that proposed in the theory (e.g., object

individuation processes) could also result in relative insensitivity to such low-level effects.

Therefore, altogether we have some evidence that, while consistent with object-substitution,

does do not give reason to favour this over many other theories, since it is equally consistent

with other models. On the other hand, we have some evidence which directly undermines the

object-substitution account, such as the complete independence of attention and OSM, and

some evidence which directly supports other accounts and not object substitution, such as

featural and episodic similarity between the target and mask modulating masking magnitude

27

(for a review, see Goodhew, 2017). In this light, object-substitution theory frankly does not

offer us much.

Finally, visual attention and object-individuation are both such fundamental visual-

cognitive processes, that it may seem surprising that they do not interact with one another,

especially since visual attention impacts such a diverse array of perceptual processes.

However, there may be functional utility in a system keeping these processes separate. That

is, object correspondence versus object inferences would occur so pervasively in real-world

visual perception – indeed it is an inference that needs to made for virtually every object in a

visual scene. Attentional resources are limited and cannot be focussed on all objects

simultaneously. Therefore, it could be problematic to have object individuation inferences

change according to whether attention is applied an object. Moreover, whether attention is

applied to an object at a given location at a particular point in time may not be informative

about whether it reflects a continuing or a new object in the scene. On the one hand, attention

can track objects over time (Flombaum, Scholl, & Pylyshyn, 2008), but equally voluntary

attention can be applied to features such as onsets indicating new objects appearing (Folk,

Remington, & Wright, 1994). The fact that an object is attended therefore does not diagnose

whether an inference of object correspondence versus object individuation should be reached.

In conclusion, here it was shown that endogenous attentional orienting and inferences

of object-individuation do not interact, but are instead independent of one another. More

specifically, neither an arrow nor a colour endogenous cue modulated OSM magnitude. This

represents the final piece of evidence required to conclude that object-individuation truly is

separable from attentional processes. This, in conjunction with a critical review of decades of

research on different forms of masking and the neural processes that support visual

awareness, raises some serious doubts about the ongoing contribution of object-substitution

theory to our understanding of the dynamics of object perception.

28

29

Notes

1. Previous research has indicated that there may be differences in attentional breadth between

individuals born in East Asia versus Western countries (see supplementary material for

Goodhew & Plummer, 2019; McKone et al., 2010). The effectiveness of attentional cueing

could be compromised if both possible target regions fell within a person’s attentional

breadth. Therefore, in both experiments, results were analysed as a function of participants’

country of birth, which was classified into a dichotomous variable of Eastern (e.g., China)

versus Western (e.g., Australia) country of birth. The main results were unchanged when this

between-subjects variable was included in the analysis, and this variable did not interact with

either main effect or the interaction.

30

Acknowledgements

This research was supported by an Australian Research Council (ARC) Future Fellowship

(FT170100021) awarded to S.C.G. I thank Rani Gupta and Nicholas Wyche for assistance with the

data collection. Correspondence regarding this study should be addressed to Stephanie Goodhew

(stephanie.goodhew@anu.edu.au), Research School of Psychology, The Australian National

University.

31

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