bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

A decisional space account of saccadic reaction times towards
personally familiar faces

Meike Ramon*, Nayla Sokhn* & Roberto Caldara

Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg,
Switzerland* Both authors contributed equally to this work

Corresponding author:
Meike Ramon
University of Fribourg
Visual and Social Neuroscience
Department of Psychology
Faucigny 21700 Fribourg
Switzerland
meike.ramon@gmail.com+41 26 300 7666

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Abstract
Manual and saccadic reaction times (SRTs) have been used to determine the minimum time requiredfor different types of visual categorizations. Such studies have demonstrated that faces can bedetected within natural scenes within as little as 100ms (Crouzet, Kirchner & Thorpe, 2010), while
increasingly complex decisions require longer processing times (Besson, Barragan-Jason, Thorpe,
Fabre-Thorpe, Puma et al., 2017). Following the notion that facial representations stored in memory
facilitate perceptual processing (Ramon & Gobbini, 2018), a recent study reported 180ms as thefastest speed at which “familiar face detection” based on expressed choice saccades (Visconti di
Ollegio Castello & Gobbini, 2015). At first glance, these findings seem incompatible with the earliest
neural markers of familiarity reported in electrophysiological studies (Barragan-Jason, Cauchoix &Barbeau, 2015; Caharel, Ramon & Rossion, 2014; Huang, Wu, Hu, Wang, Ding & Qu et al., 2017),

which should temporally precede any overtly observed behavioral (oculomotor or manual)
categorization. Here, we reason that this apparent discrepancy could be accounted for in terms ofdecisional space constraints, which modulate both manual RTs observed for different levels of visual
processing (Besson et al., 2017), as well as saccadic RTs (SRTs) in both healthy observers andneurological patients (Ramon, in press; Ramon, Sokhn, Lao & Caldara, in press). In the present
study, over 70 observers completed three different SRT experiments in which decisional space wasmanipulated through task demands and stimulus probability. Subjects performed a gendercategorization task, or one of two familiar face “recognition” tasks, which differed with respect to thenumber of personally familiar identities presented (3 vs. 7). We observe an inverse relationshipbetween visual categorization proficiency and decisional space. Observers were most accurate forcategorization of gender, which could be achieved in as little as 140ms. Categorization of highly
predictable targets was more error-prone and required an additional ~100ms processing time. Ourfindings add to increasing evidence that pre-activation of identity-information can modulate earlyvisual processing in a top-down manner. They also emphasize the importance of considering
procedural aspects as well as terminology when aiming to characterize cognitive processes.
Keywords
Face processing; visual categorization; minimum saccadic reaction times; personal familiarity; task
demands; stimulus predictability

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Introduction
The human visual system can rapidly perform numerous tasks across large variations in stimulus
input, and with extremely high efficiency. For instance, categorization of animals presented in imagesof natural scenes presented for as little as 20ms can be reliably achieved in under 300 milliseconds(ms) (e.g., Thorpe, Fize & Marlot, 1996; Rousselet, Fabre-Thorpe & Thorpe, 2002; Macé, Thorpe,
Fabre-Thorpe, 2005). Among animate objects, human faces appear to have a special status: they
can be rapidly categorized (detected within natural scenes) within as little as 100-110ms (Crouzet,
Kirchner & Thorpe, 2010). Such rapid categorical responses have been used to study thecharacteristics of visual processing. Specifically, the time required to perform such accurate visual
categorizations provide a valuable source of information that can be levered to constrain theories ofvisual processing (for a review see e.g., Fabre-Thorpe, 2011).
In attempting to maximize the precision estimates of visual processing speed, different
paradigms have been developed (for a direct comparison of paradigms, see Bacon-Macê, Kirchner,
Fabre-Thorpe & Thorpe, 2007). Estimates of visual categorizations have been derived fromverification and naming tasks, which typically involve longer reaction times (RTs) (Tanaka & Taylor,1991) compared to manual forced choice paradigms, where subjects press (or release) one of two
buttons to distinguish between stimulus categories (e.g., Hasbroucq, Mouret, Seal, & Akamatsu,
1995). To reduce response times, more sensitive Go/no-go paradigms have been developed that
require subjects to respond to a predefined target category by finger-lift from an infra-red sensor(e.g., Thorpe et al., 1996; VanRullen & Thorpe 2001; Rousselet et al., 2002; Bacon-Macé, Macé,
Fabre-Thorpe & Thorpe, 2005). Finally, other paradigms exploiting the very rapid responses fromoculo-motor, as compared to manual, effectors have been developed to provide a more precise
description of the lower bound of speeded categorizations (Kirchner & Thorpe, 2006; Crouzet,
Joubert, Thorpe & Fabre-Thorpe, 2009; Crouzet et al., 2010). Aside from deriving mean or medianRTs, these paradigms have aimed to determine the (manual or saccadic) minimum reaction time(minRT). The minRT is defined as the first time-bin for which correct responses significantly
outnumber incorrect ones (Fabre-Thorpe, Richard & Thorpe, 1998; VanRullen & Thorpe, 2001), andis considered to reflect the minimal processing time required for reliable responses (Rousselet, Macè& Fabre-Thorpe, 2003).
Visual processing speed of faces is, however, not only determined by the paradigm opted for,but also depends on the nature of visual categorization performed (Barragan-Jason, Lachat &Barbeau, 2012), which impacts upon information diagnosticity (Schyns, 1998), as does priorfamiliarity (Ramon, Caharel & Rossion, 2011; for a review see Ramon & Gobbini, 2018). For
example, superordinate human vs. animal decisions can be performed manually with high precision(98%) in as little as 285ms, while famous vs. unfamiliar manual decisions are more error-prone andslower (75%, 468ms); gender categorization on the other hand can be performed with high fidelity

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and intermediate minRTs (94%; ~310ms) (Barragan-Jason et al., 2012). Such findings have led tothe suggestion that visual processing involves initial feed-forward propagation of activity throughoccipito-temporal regions (Thorpe et al., 1996), providing coarse magnocellular representations,which require additional processing and refinement before higher-level categorization can beachieved (Fabre-Thorpe, Delorme, Marlot & Thorpe, 2001; Macé, Joubert, Nespoulous & FabreThorpe, 2009; Fabre-Thorpe, 2011). In short, this evidence suggests that face detection (i.e.,
ascertaining the presence or location of a face) occurs before establishing the familiarity of a face.
The idea that face detection precedes familiarity recognition is also supported by a recent
carefully conducted study reported by Besson and colleagues (Besson, Barragan-Jason, Thorpe,
Fabre-Thorpe, Puma et al., 2017). This study involved a Speed and Accuracy Boosting procedure –a variant of the manual go/no-go paradigm that includes a response deadline and performance
feedback designed to constrain subjects to use their fastest strategy. Using this procedure in
combination with extremely large stimulus sets to avoid image repetitions, the authors reported
manual minRTs that differed as a function of task. Both human (vs. animal) face recognition andindividual face recognition (i.e., searching for a single predefined target identity) were performed ina highly accurate and extremely fast manner (min RTs: ~240ms and ~260ms). On the other hand,
deciding whether a face belonged to a large pool of famous individuals (“famous face recognition”)
was more error prone and required more time (~380ms), in line with findings from speeded go/nogo paradigms with personally familiar faces (Ramon et al., 2011). Together these findings support
the idea that 380ms may represent the lower bound for speeded manual familiarity decisions that
occur ca. 180ms after the earliest neural marker of familiarity (e.g., Caharel, Ramon & Rossion,
2014; Barragan-Jason, Cauchoix & Barbeau, 2015; Huang, Wu, Hu, Wang, Ding & Qu et al., 2017).
One recent study seems incompatible with these independent lines of evidence. Measuring
the minimum speed of choice saccades expressed towards personally familiar (PF) faces presented
simultaneously with unfamiliar face (UF) distractors, Visconti di Oleggio Castello and Gobbini (2015)
reported remarkably fast personally familiar face “detection” in 180ms. Naturally, overtly expressedsaccadic behavior would require prior successful neural discrimination. However, the minimum
saccadic reaction times (minSRTs) reported by Visconti di Oleggio Castello and Gobbini (2015)
concur with or precede the earliest familiarity-dependent, differential electrophysiological response
to faces reported to date (~140-200ms; e.g. Caharel et al., 2014; Barragan-Jason et al., 2015).
We reasoned that procedural aspects could account for these apparently discrepant findings.
On the surface, Visconti di Oleggio Castello and Gobbini’s (2015) task seems comparable to thefamous face recognition reported by Besson et al. (2017) and Ramon et al. (2011): all required
categorical, i.e. binomial decisions of familiarity stimuli on a trial-by-trial basis. However, an important(and in our opinion neglected) aspect concerns the number of stimuli comprising, and thereby
constraining observers decisional space, which can dramatically affect observers categorization

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bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

proficiency (Besson et al., 2017; Ramon, Sokhn & Caldara, 2017; Ramon, Sokhn, Lao & Caldara, inpress). A fundamental difference between the aforementioned electrophysiological and behavioral
studies concerns the number of identities based on which observers’ performed their binary
decisions. Visconti di Oleggio Castello and Gobbini’s (2015) group of seven observers performed
choice saccades towards a total of three PF target identities. Despite the several hundreds ofrepetitions of each stimulus over the course of their experiment1, their observers accomplished thistask with modest performance accuracy (average: 62%; range: 49-69%), in as little as 180m 2. They
concluded that “detectors for diagnostic features associated with overlearned familiar faces” (p.2),“allow rapid detection that precedes explicit recognition of identity” (p.1). Using a comparable SRTparadigm, Mathey and colleagues (Mathey, Besson, Barragan-Jason, Garderes, Barbeau & Thorpe2012) reported even shorter minSRTs of <140ms, along with comparatively higher performanceaccuracy (range: 60-75%). Interestingly, their observers performed an “individual face recognition”
task, in which a single famous identity served as the sole target. Based on their findings, the authors
suggested that “information about identity can impact behavior much faster than had been previously
suspected” (p.166).
Thus, both studies involved SRT target search paradigms (Visconti di Oleggio Castello &Gobbini, 2015; Mathey et al., 2012) and reported extremely rapid (S)RTs executed towards familiar
faces. Comparing the reported results, it is appears that the decrease the number of target items (3targets vs. 1 target) could account for the observed performance difference (lower vs. higher
accuracy; slower vs. shorter minSRTs), which results from an effective narrowing of observers’decisional space. Interestingly, both studies draw vastly different conclusions based on theirconvergent findings. While Mathey et al. (2012) suggest that “information about identity can impact
behavior much faster than had been previously suspected” (p.166), Visconti di Oleggio Castello andGobbini (2015) concluded that “detection […] precedes explicit recognition of identity” (Visconti diOleggio Castello & Gobbini, 2015; p.1). In our opinion, the latter conclusion is not warranted, as theirstudy involved 1-of-3 target search task (cf. Besson et al., 2017; Ramon et al., 2017), which involves
different task demands than a detection task. This stresses the importance of considering procedural
aspects in concert with the appropriateness of the adopted terminology (cf. Ramon, in press).

1 Note that observers responded to different target stimuli (identities personally familiar to one subject served as unfamiliar

faces to others), and therefore different visual information. The authors state that “[f]or three subjects one of the target
familiar faces had a darker skin color than the other faces. We rejected all the trials containing images of this individual(both as a target and as a distractor) to avoid any bias due to a skin color difference.” (Visconti di Oleggio Castello &Gobbini, 2015; p.5). In our opinion, undesired effects of input-dependent biases cannot be abolished through exclusion ofindividual responses.
2

To our knowledge, Visconti di Oleggio Castello and Gobbini’s (2015) study is the only one to compute SRTs based oncorrect responses from all subjects – including those who performed at/near chance level for choice saccades directed
towards PF faces (see Table S6, Visconti di Oleggio Castello & Gobbini, 2015). Min(S)RT estimation in the aforementioned
studies involved exclusion of data from observers, who were unable to perform the task (e.g. Besson et al., 2017; Ramonet al., 2011; Ramon et al., 2017; Ramon et al., in press).

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bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Expanding on these previous findings, the present study sought to determine the lower
boundary for fast visual categorization of personally familiar faces, and ascertain the effect ofstimulus expectations on processing speed. Observers completed three different experiments
involving binary decisions by performing two alternative forced-choice saccades: a gendercategorization task, as well as two familiarity decision tasks (comparable to those reported
previously; Ramon et al., 2011; Visconti di Oleggigo Castello & Gobbini, 2015; Besson et al., 2017;
Ramon et al., 2017). The latter two tasks differed in terms of the number of personally (un)familiar
identities presented – observers performed a 1-of-n-target search task. This effectively manipulates
observers’ expectation: both familiarity decision tasks differed in that their decisional space waseither comparatively broad or narrow.
We anticipated that performance would be highest and SRTs fastest in the gendercategorization task, which did not require identity individuation, as subjects were instructed tosaccade towards target stimuli belonging to one of two categories (female faces). Although thefamiliarity tasks also involved binary decisions (saccades towards PF faces), we anticipated that
performance would depend on the target / distractor category set size used. Specifically, we
expected higher performance and faster SRTs for a narrow decisional space – when target identities
were highly predictable, given a smaller sample of PF faces to detect. In keeping with Besson et al(2017), but contrary to Visconti di Oleggio Castello and Gobbini (2015), we considered (i) data fromsubjects whose performance exceeded chance level exclusively, and (ii) potential effects of stimulus
repetition on RTs that have been reported previously (Lewis & Ellis, 2000; Ramon et al., 2011) during
estimation of processing speed based on SRTs.
To summarize, in this study we varied the probability of targets’ occurrence to determine theextent to which measured processing speed is influenced by stimulus predictability and repetition.
To anticipate our findings, very rapid minSRTs are observed under conditions of high predictability,
where the search space is confined to a binary, unambiguous category (gender decision task), or avery small number of target items (familiarity decision with few identities). These findings indicatethat “detection of personal familiarity” per se requires significantly more time than previously
reported, unless “detection” refers to responses towards an extremely restricted number of target
identities (Visconti di Oleggio Castello & Gobbini, 2015), which is terminologically more accurately
described as target identification (Besson et al., 2017).
Methods
Participants
We tested three groups of subjects: the first (n1=8; 4 females, mean age: 31, CI [28, 34]) comprised
group members from the iBMLab; group members were highly familiar with the target identities, whowere their colleagues for several years. The second and third group (n2=36; 31 females, mean age:6bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

21, CI [21, 22]; n3=27; 24 females, mean age: 22, CI [21, 22]) comprised students from the
Department of Psychology who knew the members of Department depicted in the stimulus material
through their teaching and mentoring activities. All participants provided written informed consent;
all procedures were approved by the internal ethics committee of the Department of Psychology at
the University of Fribourg, Switzerland and are in accordance with the Code of Ethics of the World
Medical Association (Declaration of Helsinki).
Stimuli
The full stimulus set comprised natural (uncropped, color) images of 14 facial identities (7 un/familiar)
taken from three different viewpoints (frontal, left, right). For each PF identity, images of acorresponding unfamiliar identity carefully matched for age, gender, and appearance (hair color andstyle, eye color) were taken. Image processing included placement on a uniform grey background(630 x 630 pixels) and correction for low-level properties (luminance, contrast) using the SHINE
toolbox (Willenbockel, Sadr, Fiset et al., 2010), as well as additional ones kindly provided by V.
Willenbockel to allow for equation of color stimuli.
Procedures
Prior to completing the experiments, subjects completed familiarity ratings to determine their level offamiliarity with each identity (PF and unfamiliar) presented. Each item of the familiarity questionnaire
consisted of an image of each stimulus identity taken under varied, natural conditions. Lab members’
images were taken from professional websites, unfamiliar identities’ images were taken from social
media. Observers had to indicate their self-reported degree of familiarity with each individual on ascale from 1 (not at all familiar) to 5 (highly familiar). For all experiments stimuli were presented on
a 1920 x 1080 pixel VIEWPixx monitor. Subjects’ oculo-motor behavior was recorded at a sampling
rate of 1000 Hz with an SR Research Desktop-Mount EyeLink 2K eye tracker (with a chin andforehead rest; average gaze position error ~.5, spatial resolution: ~.01). The eye-tracker had a linear
output over the range of the monitor used. Although viewing was binocular, only the left eye wastracked; given the fully balanced stimulus presentation across visual fields, inter-individual
differences in ocular dominance were considered irrelevant. The experiment was implemented in
Matlab (R2009b, The MathWorks, Natick, MA), using the Psychophysics toolbox (PTB-3) (Kleiner,
Brainard, & Pelli, 2007; Pelli, 1997) and EyeLink Toolbox extensions (Cornelissen, Peters, & Palmer,
2002; Kleiner et al., 2007). Calibrations of eye fixations were conducted at the beginning of theexperiment using a nine-point fixation procedure as implemented in the EyeLink API (see EyeLink
Manual) and using Matlab software. Afterwards, calibrations were validated with the EyeLink
software and repeated when necessary until reaching an optimal calibration criterion. Drift correction
was performed on each trial via central cross fixation.

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In the gender categorization task, subjects were instructed to perform choice saccades
towards female faces. In this task three images (viewpoint changes) for each PF individual (6identities, 3 females) and their unfamiliar counterparts were presented. A trial began with a central
fixation cross displayed between 800 and 1600ms, followed by a 200ms blank and subsequent
presentation of the target/distractor pair presented for 600ms. After a saccade was registered, thenext trial was presented after a 1000ms blank inter-trial interval. Stimuli subtended 14˚x14° (averageface height was 11°), and stimulus eccentricity was 8.6˚ of visual angle. With all possiblecombinations and equal number of presentations per identity and visual field, the total number oftrials was 216; subjects took self-paced breaks after each block of 54 trials.
The two familiarity categorization tasks differed in terms of the number of identities depicted,
but both required observers to perform choice saccades towards personally familiar identities
presented with unfamiliar distractors. The low and high predictability variants involved presentationof 7 PF (3 females), or 3 PF (all male) identities, respectively, as well as an equal number of wellmatched UF distractors. Presentation parameters were identical to those described for the gendercategorization task (see above). The procedural parameters paralleled those used by Visconti diOleggio Castello and Gobbini (2015), with exception of stimulus presentation duration (600ms
instead of 400ms), as initial pilot testing revealed slightly longer presentation durations werenecessary for acceptable performance levels. On each trial, a PF identity was paired with a samegender, same-orientation distractor and appeared with equal probability in either visual field. The
total number of trials for the low predictability familiarity categorization task amounted to 150. To
achieve a comparable number of trials in the high predictability variant for comparison with the low
predictability variant, each unique stimulus x visual field combination was presented three times
leading to a total 162 trials over three blocks; these trials / blocks were doubled to further determine
potential effects of repetition in the high predictability categorization task (see Analyses, section iii.).
Subjects took self-paced breaks after each block of 50 or 54 trials (low or high predictability variant),
respectively.
Analyses
Preprocessing
We applied the adaptive velocity based algorithm developped by Nystrom and Holmqvist (2010) to
find the onset of the first saccade (if any) within each trial. We discarded trials in which the onset ofthe first saccade was lower than 80 ms (Visconti di Oleggio Castello & Gobbini, 2015), as these wereconsidered anticipatory saccades.

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Statistical analyses
As mentioned above, across all experiments we considered only data from subjects whose
performance exceeded chance level. For the gender categorization task this led to exclusion of one
departmental member (n1=7/8); all student participants performed above chance and wereconsidered (n2=36/36). For the familiarity categorization tasks, only data from subjects who
performed reliably across both low and high predictability variants were considered (n1=4/8,
n3=14/27). Analyses performed to determine the effect of stimulus repetition were conducted on datafrom subjects who performed above chance across all blocks of the high predictability familiarity
categorization task (n1=7/8, n3=20/27). Analyses of accuracy and mean SRTs were performed in R(version 3.2.4; R Core Team, 2013) using the lme4 package (Bates, Maechler, Bolker & Walker,
2014) and the lmerTest (Kuznetsova, Brockhoff & Christensen, 2015) to obtain p-values of the fixed
predictors of the fitted models. Note that given our research question, we were not interested in
between-group differences, but rather those related to stimulus predictability, i.e. those observed
within groups.
i.

Gender categorization

Accuracy, mean and minSRTs are reported descriptively for the gender categorization task for allparticipants (n1, n2), as this task served only as a baseline to demonstrate subjects’ ability to performthe SRT task.
ii.

Personal familiarity categorization: low vs. high stimulus predictability

Accuracy. To investigate the effect of stimulus predictability on subjects’ accuracy, we performed
generalized linear mixed models with a binomial family (Jaeger, 2008) for the data obtained in theexperiments characterized by lower (7 identities), or higher stimulus predictability (3 identities),
respectively. This was done separately per group tested given the unequal sample sizes available.
In this model, the main predictor is the variable ‘predictability’ (low and high for larger and smaller
number of identities presented) and the variable participant is a random factor. We performed the
Log-likelihood Ratio Test to compare the null and full model, and assess the significance of thepredictor.
Mean SRTs. To investigate the effect of stimulus predictability on SRTs, we performed a linear mixed
model for the data obtained in the experiments characterized by lower (7 identities), or higher
stimulus predictability (3 identities), respectively, considering only the correct trials. 3 In this model,
the main predictor is the variable ‘predictability’ and the variable participant is a random factor. As

3 To account for the different number of trials across experiments, note that we considered all trials (150) from the low, andthose of first 3 blocks (162) for the low and high predictability variants of the familiarity categorization tasks.

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for accuracy scores, we performed the Log-likelihood Ratio Test to compare the null and full model,
and assess the significance of the predictor.
Minimum SRTs. We estimated minSRTs in two different ways 4. First, across subjects’ trials (i.e.,group minSRTs), we performed a chi-square test using 10ms time bins across trials. We considered
the first bin where the number of correct trials outperforms statistically the number of incorrect trials(p<.05), followed by at least 3 significant consecutive bins (Besson et al. 2017). Second, we
determined individuals’ minSRTs. To this end, following the procedure reported by Besson et al.
(2017), we (i) considered only RTs of participants who performed above chance level, and (ii) opted
for 40ms time bins using the Fisher’s exact test (p<.05). Using this procedure, some participants’
individual minSRT could not be computed (due to proximal in/correct trial distributions). Finally, we
assessed the effect of stimulus set size for individual subjects of n3 using the Wilcoxon signed-rank
test (p<.05). Note that the comparison of minSRTs as a function of stimulus set size was notconducted for n1 due to the insufficient statistical power, as only four participants’ data wereconsidered.
iii. Effect of stimulus repetition under conditions of high stimulus predictability
To determine the effect of stimulus repetition, we used the above described procedure for all
behavioral measures, however, taking into account all correct trials (from all 6 blocks of the highpredictability familiarity categorization experiment; cf. above). We compared RT associated with thefirst presentation of a stimulus with each subsequent presentation.
Results
Table 1 summarizes the results obtained for all subjects for gender categorization, and personallyfamiliar face recognition under low and high predictability conditions, respectively. Individual
subjects’ data are reported in Table S 1 and Table S 2; Figure 1 shows individuals’ minSRTs plottedagainst accuracy scores across all experiments.

4 Here, as for mean SRTs, only the first 3 blocks were considered.

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Table 1. Accuracy (in %) and minimum saccadic reaction times (in ms) obtained across experiments for all
subjects tested, whose performance was above chance level
Gender categorization

Accuracy
minSRT
Mean SRTMedian SRT
CI

Department
members (n1)81 [71, 90]
200ms

Students (n2)84 [81, 86]
140ms

276
262
[272, 281]252
236[251, 254]

Department
members (n1)Students (n3)Accuracy
minSRT
Mean SRTMedian SRTCI

74 [64, 81]
360
373
390
[370, 388]

68 [64, 72]
260
334
334
[331, 340]

Accuracy
minSRT
Mean SRTMedian SRT

82 [74, 89]
260
345
353

76 [71, 79]
240
345
339

CI

[343, 357]

[343, 350]

Personal familiarity categorization

Low predictability

High predictability

95% confidence intervals are provided in brackets.

Figure 1. Individual subjects’ minSRTs plotted against performance accuracy. Note that for 3 student
subjects minSRTs could not be computed (cf. Table S 2).

i.

Gender categorization

Both departmental members and student participants could reliably perform the gendercategorization task, achieving 81% and 84% on average, and exhibiting minimum SRTs of 200ms
and 140ms, respectively (see Figure 2).

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ii. Personal familiarity categorization: lower vs. higher stimulus predictability
Accuracy. The groups’ SRT distributions for the low and high predictability familiarity categorization
tasks are illustrated in Figure 3. For departmental members’ accuracy scores, the fitted model(considering the total of 1248 trials) revealed a significant main effect of stimulus set size (i.e.,
number of identities depicted; X2(1)=13.73, p=.0002); performance given 7 target identities was
significantly lower than when 3 target identities were presented (74% vs. 82%; z=3.69, p=0.0002).
For students’ accuracy scores, the results from the fitted model (based on a total of 4361 trials) also
showed a significant main effect of stimulus set size X2(1)=33.53, p<.0001); again, performance forthe lower as compared to higher predictability task variant was significantly lower (68% vs. 76%;z=5.78, p<.0001). For parameter estimates of the fixed effects for the generalized linear mixed modelwith the binomial family for each group tested see Table S 3.
Mean SRTs. For departmental members’ SRTs the fitted model (considering the total of 967 trials)
revealed a significant main effect of stimulus set size (X2 (1)=29.70, p<.0001). Mean (373 vs 345;
t=-5.49, p<.0001). For students’ SRTs the results from the fitted model (based on a total of 3142
trials) showed a significant main effect of stimulus set size (X2(1)=14.67, p=.0001). Contrary to thedepartmental members, the mean of RTs in Experiment 1 was significantly faster than in Experiment
2 (334 vs 345; t=3.83, p=.0001). For parameter estimates of the fixed effects for the Linear Mixed
Model for each group tested see Table S 3.

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Figure 2. Distributions of participants’ SRTs expressed during the gender categorization task. Hits and false alarms per time bin are indicated as thick and thinlines, respectively. Vertical lines indicate each group’s min SRTs; along with average accuracy.

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Figure 3. Distributions of participants’ SRTs expressed during the personal familiarity categorization task with low (blue) and high (red) predictability. Hits
and false alarms per time bin are indicated as thick and thin lines, respectively.

14

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

MinSRTs. The Wilcoxon rank sum test revealed no main effect of stimulus set size on the individual
minSRTS (see Table S 2) for student participants in n3 (p=.11).
iii. Effect of stimulus repetition under conditions of high stimulus predictability
Accuracy. The fitted model (considering the total of 1944 trials) revealed no significant effect ofrepetition on subjects accuracy scores for n1 (X2(5)=2.14, p=.83). However a significant main effectof repetition was shown for n2 (X2(5)=16.22, p<.05; based on 6480 trials in total). Performance in thefirst image presentation (73%) was significantly lower than for the fourth image presentation (z=2.61,
p<.05) and the fifth image presentation (z=3.38, p<.001) with a performance of 78% and 79%,
respectively (see Table 2). For parameter estimates of the fixed effects for the generalized linear
mixed model with the binomial family for each group tested see Table S 5.
Mean SRTs. The fitted model revealed a significant effect of repetition on mean RTs for n1(X2(5)=32.18, p<.0001) and n2 (X2(5)=126, p<.0001) (based on a total 1411 and 4834 trials,
respectively). Both groups were slower in the first image presentation compared to the five otherimage presentations (see Table 2). For parameter estimates of the fixed effects for the Linear Mixed
Model for each group tested see Table S 6.
Table 2. Accuracy, mean and min SRTs for choice saccades towards personally familiar faces as a function of
stimulus repetition in Experiment 2 (personal familiarity categorization with high predictability).
Department members (n1)
Block
1st
2nd
3rd
4th
5th
6th

Accuracy
in %
77
78
75
76
74
74

Mean
SRTs
331
307
306
293
310
306

CI

minSRTs[277; 386]
[239; 375]
[238; 375]
[225; 361]
[241; 379]
[237; 374]

250
300
340
290
260
290

Students (n2)Accuracy
in %
73
74
76
78
79
74

Mean
SRTs
350
330
326
312
318
319

CI

minSRTs[325; 374]
[298;361][294;357][280;343][286;350][287;351]

280
260
240
220
220
240

Note that per block each stimulus was shown 3 times.

Discussion
Previous work has demonstrated that the primate brain can visually categorize faces and animals ina highly proficient and rapid manner (e.g. Thorpe et al., 1996; Rousselet et al., 2003; Crouzet et al.,2010; Fabre-Thorpe, 2011; Kirchner & Thorpe, 2006). Visual categorization paradigms, which areconsidered to involve activation of representations that impact observers’ responses in terms of
maximal “presetting” of the visual system for the task at hand (Thorpe et al., 2001), have been
deployed to constrain theories of visual processing (for a review see e.g., Fabre-Thorpe, 2011),
focusing on the role of stimulus category and information on visual processing.
One body of studies has honed in on visual categorization of a single stimulus category:faces. Independent lines of research have reported that both level of processing (e.g., Besson et al.,15

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

2017; Barragan-Jason et al., 2012), as well as stimulus familiarity (e.g., Visconti di Oleggio Castello& Gobbini, 2015; Ramon et al., 2011; Ramon & Gobbini, 2018) can affect overtly observed faceprocessing proficiency. Besson et al. (2017) reported that manual minRTs increased systematicallyas a function of task performed, with higher proficiency exhibited for human face (vs. animal)
categorization (~240ms) and search for a predefined target identity (“individual face recognition”,~260ms). Famous face recognition on the other hand, i.e. deciding whether a given identity of many
possible ones was famous or not, took substantially longer (~380ms). Collectively, these findings
suggest that detecting the presence of a face precedes determining its familiarity, or identity (Besson
et al., 2017; Barragan-Jason et al., 2012).
Other behavioral findings, however, challenge this view. Visconti di Oleggio Castello andGobbini (2015) recently reported that observers could perform SRTs towards personally familiar
faces in as little as 180ms; even faster SRTs to famous face images were reported by Mathey et al.
(2014; <140ms). Based on their findings, Visconti di Oleggio Castello and Gobbini (2015) suggested
that processing of familiarity is rapidly detected at this latency, “prior to explicit recognition of identity”.
This notion is incompatible with the earliest reported neural markers of familiarity recognition(>170ms; Barragan-Jason et al., 2015; Caharel et al., 2014; Huang et al., 2017).
In the present study we reasoned that these apparently conflicting findings can be reconciledby careful consideration of the effect that both task demands and expectations exert on visual
categorization, as well as the terminology adopted to communicate findings (Ramon, in press).
Specifically, we proposed that procedural aspects effectively constrain the decisional space within
which observers perform a task, which in turn determines measured behavior (Ramon, in press;
Ramon et al., in press; see also Ramon & Rossion, 2010; Ramon, Busigny, Gosselin & Rossion,
2017; Ruffieux, Ramon, Lao, Colombo, Stacchi et al., 2017). To test this assumption, we measured
observers’ saccadic RTs across three experiments. Each experiment required observers to performbinary decisions, expressed through choice saccades towards a predefined target category. Thesetarget categories varied across tasks: female faces in the gender decision task, personally familiar
face in the familiarity decision tasks. Importantly, we deployed two familiarity decision tasks, which
differed in terms of the total number of possible target identities (3 vs. 7), thereby manipulating
stimulus predictability, and hence breadth of decisional space. Importantly, all observers were
presented the exact same target identities (i.e., stimuli considered as PF and UF faces), and wedetermined potential effects of stimulus repetition on processing speed.
The findings obtained for the gender categorization task reveal that visual processing can be
extremely efficient and fast when operating within a narrow decisional space. Student participants
exhibited high performance accuracy (84%) and very rapid minSRTs when performing choicesaccades towards female faces (~140ms). Department members exhibited slightly lower accuracy(81%) and somewhat longer minSRTs (200ms), which we attribute to the smaller hit-to-false alarm16

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

ratio given the smaller sample size (6 vs. 36 participants) and hence number of data points available. In the context of familiarity categorization tasks, we found that participants’ performance wasmodulated by the number of PF identities presented. Accuracy increased and RTs decreased withfewer target identities presented (7 vs. 3 PF identities), and high target predictability was associatedwith minSRTs of ~250ms.
Reconciling discrepancies in minSRTs reported for personally familiar face categorization
We believe that procedural differences may at least partially account for the difference in minSRTs
for familiarity decisions observed here and elsewhere (180ms; Visconti di Oleggio Castello &Gobbini, 2015). First, following others’ adopted procedures (e.g., Besson et al., 2017; Ramon et al.,2011; Ramon et al., 2017b; Ramon et al., in press) we included only data of subjects who exhibited
reliably high performance, as opposed to also including correct trials from individuals who were at ornear chance level (cf. Visconti di Oleggio Castello & Gobbini, 2015). Although we did not find asystematic relationship between accuracy and minSRTs among the subjects considered for ouranalyses (see Figure 1), we note that lab members’ RTs decreased by 100ms between the low andhigh predictability familiarity decision experiments. In comparison, students achieved lower accuracy
in general (68% and 76%) relative to lab members (73% and 82%), and across tasks did not exhibit
the decrease in minSRTs observed for lab members. That is, students performed at maximum speed
during the low predictability experiment, but could increase their performance accuracy due to anarrowing of the decisional space given less target identities. Regardless of whether groups differedregarding which behavioral measure improved by predictability, we believe that the comparable
minSRTs exhibited across groups (lab members: 260ms, students: 240ms) reveals the fastest
processing speed for a 1-of-three target identity categorization scenario when estimates are basedon highly reliable performance.
Moreover, in our study PF and UF stimuli were identical to all subjects (cf. e.g., Besson et al.,2017; Mathey et al., 2012; Ramon et al., 2011; Ramon et al., 2017a, b; Caharel et al., 2015). This isfundamentally different from Visconti di Oleggio Castello and Gobbini’s (2015) study, in which one
subjects’ PF stimuli served as UF stimuli for another. We suggest that the rapid minSRTs of 180ms
these authors reported are thus more likely to reflect effective top-down “presetting” towards
diagnostic features of specific images, which can lead to a significant decrease in minSRTs(Delorme, Rousselet, Macé & Fabre-Thorpe, 2004). Therefore, we suggest that not necessarily the
number of distractor items, but rather the overall number of potential targets to verify determines
visual processing speed in familiarity decisions.
The repetition effects reported here and elsewhere using stimulus set sizes ranging from one
famous (Lewis & Ellis, 2000), to 27 PF identities (Ramon et al., 2011) lend further support to the ideathat RTs decrease due to activation of image features; previously observed task-learning effects (cf.

17bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Visconti di Oleggio Castello & Gobbini, 2015) can also further contribute to a systematic decrease
in recorded RTs. Additional support for this claim stems from SRT differences between studies thathave both employed time sensitive go/no-go paradigms. Ramon et al. (2011) reported minSRTs andmean SRTs of 380ms and 510ms. We attribute the shorter manual response latencies compared tothose reported by Barragon-Jason et al. (2012: 467ms and 581ms; 2015: 460ms and 635ms) to thedifference in stimulus set size and hence target predictability. Collectively, our findings stress theimportance of considering the task’s decisional space and stimulus repetition, as both significantly
affect the speed of familiarity categorizations.
A formal framework of saccadic reaction times to face stimuli
In the present study, we focused on the impact of stimulus probability, or decisional space, andstimulus repetition as determinants of subjects’ minimum processing speed during visual
categorization. However, an additional factor which can further modulate expressed behavior, which
we did not systematically control here, is visual information. This point can be demonstrated byconsidering for example visual categorization of facial expression. Similar to the present study, thenumber of categories (i.e. facial expressions) presented will determine the breadth of decisional
space, and thus likely modulate subjects’ performance. Moreover, however, the type of information
based on which categorical decisions are performed will also play an important role. Inclusion of
facial expressions that are more likely confused by subjects (e.g., Jack, Blais C, Scheepers, Schyns& Caldara, 2009; Jack, Garrod, Yu, Caldara & Schyns, 2012; Rodger, Vizioli, Ouyang & Caldara,2015) will lead to an increase in task difficulty and hence decrease in accuracy and/or delayed RTs.
Beyond the objectively available visual information, inter-individual anatomical differences that
may affect information processing efficiency would also require consideration. One could speculatethat anatomical differences in early visual cortices (cortical thickness, surface area) reported to affectperceptual discrimination and mental imagery (Bergmann, Genc, Kohler, Singer & Pearson, 2016;
Song, Schwarzkopf, Kanai & Rees, 2015) may affect processing of the parafoveally presented visual
information subject to categorical responses in SRT paradigms. A comprehensive account ofprocessing speed based on which a predictive model of saccadic choices for a particular task canbe elaborated will require consideration of stimulus probability, as well as visual information andanatomically constrained information processing proficiency.
Implications beyond visual categorization measured behaviorally: neuroimaging of facerecognition
We suggest that effects of stimulus predictability may also account for seemingly contradictory
neuroimaging findings reported previously. Electrophysiological studies determining the influence offamiliarity have consistently reported familiarity-dependent modulation of later (>400ms)
components, while effects on earlier (<200ms) components are more variable (for reviews see

18

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Huang, et al., 2017; Ramon & Gobbini, 2018). For example, some studies suggest that the N170 /M170 – the earliest face-sensitive component electrical signal recorded from the scalp (Bentin &Deouell, 2000; Eimer, 2000) is sensitive to familiarity and task (Rossion et al., 1999) –. These studies
have, however, reported enhanced (Caharel, Courtay, Bernard, Lalonde & Rebai, 2005; Caharel,
Fiori, Bernard, Lalonde & Rebai, 2006; Kloth, Dobel, Schweinberger, Zwitserlood, Bolte et al., 2006;
Wild-Wall, Dimigen & Sommer, 2008), as well as decreased N170 amplitude (Todd, Lewis, Meusel& Zelazo, 2008). Of note is that active, identity-related tasks generally lead to modulation of early
components, compared to passive viewing or orthogonal tasks, for which later familiarity effects havebeen more consistently reported (Huang et al., 2017; Taylor, Shezad & McCarthy, 2016).
As reviewed by Ramon and Gobbini (2018), whether or not familiarity modulation can be foundfor early electrophysiological components may depend on pre-activation of associative knowledge.
Studies reporting early modulation for PF faces have in common that participants usually know inadvance the identities presented (e.g. Caharel et al., 2014). Similarly, studies reporting early
modulation for famous faces often involve stimulus selection based on subjects’ previously assessed
level of familiarity with various famous individuals (Huang et al., 2017). Lastly, studies using
experimentally learned faces have also reported familiarity-dependent modulation of early
components (Taylor et al., 2016). In all of these studies pre-activation of identity information can be
regarded as defining the decisional space from which to-be-presented identities are drawn. Basedon our findings, and reports of expectation-related N170 modulation (e.g. Johnston, Overell,
Kaufman, Robinson & Young, 2016), we hypothesize that the likelihood of observing familiaritydependent modulation of early electrophysiological components, and neural responses measured
with functional magnetic neuroimaging (Ramon & Gobbini, 2018), will increase with predictability offamiliar exemplars presented.
Conclusions
Based on the present and previous findings we emphasize the importance of adopting a terminologythat takes into consideration more than merely the stimulus being processed. The process involved,
which depends on the opted-for paradigm, crucially impacts processing speed (cf. Besson et al.,2017). For instance, the term “face recognition” or “detection” is oftentimes used across a range oftasks and without sufficient consideration of differences in specific procedures implemented, leading
to at times seemingly contradictory conclusions (Besson et al., 2017; Ramon, in press). Our findingsthat target predictability and repetition affect processing speed in binary forced-choice paradigms
emphasize the importance of simultaneously considering task constraints and procedural aspects
when attempting to determine processing speed to constrain theories of visual processing. At thevery least we suggest that previous findings of SRT modulation attributed to personal familiarity
should be revisited: the minSRTs reported by Visconti di Oleggio Castello and Gobbini (2015) do notreflect facilitated familiar face detection per se, but rather demonstrate how powerful top-down

19bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

predictions can expedite rapid visual orienting towards specific, expected images. These findings
also provide a basis to account for seemingly contradicting findings regarding familiarity-related
modulation of responses to faces recorded with neuroimaging techniques, which are likely affectedby the operational decisional space and “presetting” due to subjects’ expectations.
Acknowledgements
We thank our previous Bachelor students Nour Al-Khodairy, Morgane Bulliard, Aurelie Christinaz,and Lysiane Constantin for their invaluable help during data collection.
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bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplementary Material
Table S 1. Subjects’ accuracy (in %), mean and minimum saccadic reaction times (in ms, with 95 confidence
intervals) obtained for the gender categorization task.
Accuracy

minSRT

Mean SRT

Department members (n1)
1

92

200

279 [272; 288]

2

94

200

250 [244; 255]

3

65

200

177 [170; 183]

4

82

240

344 [330; 358]

5

78

200

248 [239; 257]

6

96

240

346 [338; 354]

7

63

240

237 [229; 244]

1

91

200

286 [280; 294]

2

89

160

264 [256; 273]

3

82

120

174 [168; 179]

4

88

120

205 [199; 212]

5

86

160

233 [225; 241]

6

90

160

328 [315; 341]

7

70

120

176 [170; 185]

8

78

160

188 [181; 196]

9

68

160

224 [215; 232]

10

90

160

231 [224; 238]

11

91

160

253 [245; 263]

12

88

160

230 [224; 235]

13

82

200

381 [364; 399]

14

87

160

267 [257; 279]

15

91

120

216 [210; 224]

16

85

160

251 [241; 264]

17

71

160

282 [267; 298]

18

84

160

197 [193; 202]

19

81

120

254 [243; 265]

20

83

120

297 [281; 312]

21

72

200

268 [256; 281]

22

89

160

226 [220; 232]

23

88

200

272 [265; 279]

24

88

120

178 [173; 182]

25

91

160

202 [195; 208]

26

89

120

232 [224; 239]

27

82

120

224 [220; 229]

28

88

200

281 [274; 288]

29

86

200

275 [270; 281]

30

90

160

310 [297; 323]

31

70

200

319 [307; 333]

32

78

160

260 [252; 268]

33

68

200

266 [257; 274]

34

90

200

250 [245; 256]

35

91

160

237 [232; 243]

36

88

160

290 [282; 298]

Students (n2)bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Table S 2. Subjects’ accuracy (in %), mean and minimum saccadic reaction times (in ms, with 95 confidence
intervals) for Experiment 1 and Experiment 2.
Experiment 1
Accuracy

Experiment 2

minSRT

Mean SRT

Accuracy

minSRT

Mean SRT

Department members (n1)
1

84

280

375 [362; 389]

87

280

364 [354; 373]

2

61

280

286 [272; 302]

72

240

281 [272; 292]

3

72

440

408 [382; 430]

77

240

329 [313; 346]

4

75

360

424 [412; 437]

91

320

405 [397; 415]

1

66

320

328 [309; 349]

78

280

355 [342; 369]

2

75

280

340 [325; 353]

82

320

370 [358; 380]

3

67

280

363 [348; 380]

62

320

409 [388; 428]

4

65

320

319 [302; 334]

73

280

318 [307; 330]

5

58

--

253 [232; 276]

77

320

356 [337; 374]

6

78

360

418 [405; 444]

81

280

361 [349; 373]

7

62

--

315 [303; 328]

57

320

287 [276; 299]

8

57

--

301 [280; 324]

78

280

382 [367; 398]

9

68

240

304 [291; 319]

80

240

305 [294; 317]

10

67

280

277 [268; 287]

79

240

299 [292; 308]

11

58

320

355 [339; 372]

73

240

358 [341; 375]

12

81

280

353 [342; 365]

82

240

304 [294; 314]

13

69

280

323 [308; 336]

83

320

366 [356; 377]

14

77

360

394 [379; 408]

75

280

352 [342; 362]

Students (n2)Table S 3. Parameter estimates of the fixed effects for the generalized linear mixed model with the binomial
family on accuracy
Fixed Effects
Lab Members
Interceptlog(Odds)

SE

CI

Odds

Odds CIz-valuep-value

1.03.22[.48; 1.6]

2.80

[1.61 4.95]

4.57<.0001high predictability

.51

.13

[.24; 0.78]

1.66[1.27; 2.19]

3.69

.0002

Students
Intercept

.77

.09

[.59; .94]

2.16

[1.80; 2.58 ]8.74<.0001high predictability

.40

.07

[.26; .53]

1.49

[1.30; 1.70]

5.78<.000126

bioRxiv preprint first posted online Apr. 3, 2018; doi: http://dx.doi.org/10.1101/292656. The copyright holder for this preprint(which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Table S 4. Parameter estimates of the fixed effects for the Linear Mixed-Effects Model on SRTs.
Fixed Effects
Lab Members
Intercept
high predictability
Students
Intercept(low predictability)
high predictability

Estimate

SE

CI

df

t-valuep-value

373

27.23

[314; 433]

3.1

13.71

<.0001-28

5.16[-39; -18]

961.6

-5.49

<.0001334

8.56[316; 351]

13.9

38.95

<.000111

2.88[5; 17]3126.8

3.83

.0001

Table S 5. Parameter estimates of the fixed effects for the generalized linear mixed model with the binomial
familyl on accuracy
Fixed Effects
Lab Members
Intercept
Block2Block3Block4Block5Block6Students
Intercept
Block2Block3Block4Block5Block6log(Odds)

SE

CI

Odds

Odds CIz-valuep-value

1.21
.05
-.10
-.03
-.13
-.17

.37
.19
.18
.19
.18
.18

[.37; 2.06][-.32; .42][-.47; .26][-.40; .33][-.50; .22][-.53; .19]

3.35
1.05
.90
.97
.88
.84

[1.44; 7.84][.73; 1.53][ .63; 1.30][.67; 1.39][.61; 1.25][.59; 1.21]

3.24
.28
-.56
-.19
-.74
-.92

.001
.77
.58
.85
.46
.36

.97
.06
.16
.26
.34
.09

.12
.10
.10
.10
.10
.10

[.73; 1.22][-.13; .25][-.03; .36][.07; .46][.15; .54][-.10; .29]

2.64
1.06
1.17
1.30
1.40
1.10

[2.07; 3.39][.88; 1.29][.97; 1.43][1.07; 1.58][1.16; 1.72][.90; 1.34]

7.98
.64
1.64
2.61
3.38
.94

<.0001
.52
.10
<.05
<.01
.35

Table S 6. Parameter estimates of the fixed effects for the Linear Mixed-Effects Model on SRTs.
Fixed Effects
Lab Members
Intercept
Block2Block3Block4Block5Block6Students
Intercept
Block2Block3Block4Block5Block6

Estimate

SE

CI

df

t-valuep-value

331
-24
-25
-39
-21
-26

26
6.94
7
6.98
7.03
7.04

[277; 386][-38; -11][-39; -11][-52; -25][-35; -7.2][-39;-12]

5
1400
1400
1400
1400
1400

12.81
-.3.49-.3.55
-3.55
-2.99
-3.64

<.0001<.0001<.0001<.0001
.002
.0002

350
-20
-24
-38
-32
-30

12.27
3.74
3.72
3.70
3.68
3.73

[325; 374][-27; -13][-31; -17][-45; 31][-39; -24][-38; -23]

21
4807
4807
4807
4807
4807

28.51
-5.31
-6.50
-10.22-8.58-8.15<.0001<.0001<.0001<.0001<.0001<.000127