Over-Selectivity is Related to Autism Quotient and Empathizing, But not to Systematizing
Over-Selectivity is Related to Autism Quotient and Empathizing, But not to Systematizing
0 Department of Psychology, Swansea University , Singleton Park, Swansea SA2 8PP , UK
The relationships of autism quotient (AQ), systematizing (SQ), and empathizing (EQ), with overselectivity were explored to assess whether over-selectivity is implicated in complex social skills, which has been assumed, but not experimentally examined. Eighty participants (aged 18-60) were trained on a simultaneous discrimination task (AB+CD−), and tested in extinction on the degree to which they had learned about both elements of the reinforced (AB) compound. Higher AQ and lower EQ scorers demonstrated greater over-selectivity, but there was no relationship between SQ and over-selectivity. These results imply that high AQ scorers perform similarly to individuals with ASD on this cognitive task, and that overselectivity may be related to some complex social skills, like empathy.
Over-selectivity; Autism quotient; Empathizing quotient; Systematizing quotient; Broad autistic phenotype
The concept of a broad autistic phenotype implies that
traits associated with autism spectrum disorder (ASD) are
distributed throughout the population to vary degrees and
with varying severities (e.g., Constantino and Todd 2003;
Folstein and Rutter 1977; Plomin et al. 2009). For example,
it has been suggested that close relatives of those with ASD
will show some of the characteristics of those with ASD
(e.g., Piven et al. 1997; Micali et al. 2004). Moreover, it has
also been suggested that those scoring highly on
psychometrically-defined measures of traits related to ASD should
exhibit similar cognitive and behavioral characteristics to
those demonstrated by individuals in clinical ASD samples
(Baron-Cohen et al. 2001).
A number of different psychometric scales are
commonly employed to assess the degree to which
individuals might possess various autistic-like traits. The Autism
Quotient (AQ) scale assesses individuals along a number
of dimensions related to ASD: social skill, attention
switching, attention to detail, communication, and imagination
(Baron-Cohen et al. 2001). Relative to those with low
psychometrically-defined autism traits (AQ), those who
score highly on AQ have been shown to display greater
self-focused attention (Lombardo et al. 2007), local rather
than global processing (Grinter et al. 2009), have difficulty
inferring others’ mental state from the eyes (Baron-Cohen
et al. 2001) or attentional cueing from gaze (Bayliss and
Tipper 2005), as well as narrowed visual search patterns
(Reed et al. 2011). These findings of similarities between
performance of those with high autism traits in nonclinical
samples and those with clinically-defined ASD give
support to the notion of a broad autistic phenotype.
One aim of the current study was to assess the degree
to which AQ scores in a nonclinical population predict
the existence of over-selective type responding by an
individual. Over-selectivity refers to the phenomenon whereby
behavior is controlled by one element of the
environment at the expense of other equally salient stimuli (e.g.,
Leader et al. 2009; Lovaas et al. 1971; Reed et al. 2009).
For example, understanding speech has been taken by some
to involve not only understanding the sounds of the words
but also interpreting the facial expressions that go with the
words, and interference with the ability to attend to both
inputs, such as is described by over-selective responding,
can disrupt understanding speech (e.g., Jordan et al. 2000).
It has been suggested that such over-selective responding
could be implicated in a range of skills noted to be
problematic for those with ASD, often involving social
interactions of various kinds (Cumming and Berryman 1965;
Lovaas et al. 1979; Schreibman and Lovaas 1973), but
there has been very little experimental investigation of this
Experimentally, over-selectivity has been studied using a
simultaneous discrimination task in which participants
initially are reinforced for selecting one compound stimulus
(AB) in preference to another (CD). During a subsequent
non-reinforced test, they are given a choice between
individual elements of the previously reinforced stimulus and
those from the previously non-reinforced compound (e.g.,
A v C, B v C, etc.). Participants displaying over-selectivity
choose one element from the previously reinforced
compound (e.g., A) in preference to elements from the
previously non-reinforced compound (C and D), to a greater
extent than they select the other previously reinforced
element (e.g., B) in preference to C or D.
Although over-selectivity is a common problem for
individuals with ASD (see Dube 2009; Ploog 2010, for
reviews), its existence in those with high AQ scores has not
been established, and this basic finding would extend the
range over which the performance of individuals with high
AQ is similar to that of those with ASD. Over-selectivity
has been noted in a typically-developing population lacking
any neurological damage, but tends to be seen more
readily when an additional cognitive load is employed
concurrently with the discrimination task (e.g., Reed and Gibson
2005; Reynolds and Reed 2011). The current study did not
use such a load in order to see if higher AQ scores would
be associated with greater over-selectivity in the absence of
such a procedure designed to induce its presence.
However, the current study had further goals in
addition to exploring this aspect of cognitive performance for
high AQ scorers. In assessing the BAP, other scales have
been developed to determine the degree to which
individuals might display other cognitive styles associated with
ASD; especially, systematizing (Baron-Cohen et al. 2003)
and empathizing (Baron-Cohen and Wheelwright 2004).
‘Systemizing’ (measured by the systematizing quotient;
SQ) reflects an individual’s drive to analyze the variables
in a (usually inanimate) system, in order to understand the
rules and mechanisms that govern that system; whereas
‘empathizing’ (measured by the empathizing quotient; EQ)
assesses the degree to which individuals understand the
emotions of others (Baron-Cohen and Wheelwright 2004).
The latter two scales have been developed based on a
particular view of sexual dimorphism in ASD, as involving
a bias towards systems—often characterized as the ‘male
brain’ (Baron-Cohen et al. 2002). The impact of these
psychometrically-defined traits has not been widely explored
in terms of their effect on the cognitive processing
abilities that are typically associated with ASD—such as
overselective responding, which would extend knowledge about
It might be expected, to the extent that all of these scales
are associated with the BAP, they may all be associated
with performance on any number of cognitive tasks.
However, while it is not unique in this regard, over-selective
responding does allow a number of theoretical and practical
implications of high scores on each of the scales separately
to be unpacked. Initially, it was suggested that low EQ and
high SQ scores combine to produce a cluster of symptoms
typically seen in those with clinical ASD (see Wheelwright
et al. 2006). As a consequence, it might be predicted that
AQ should be strongly positively related to SQ and
negatively related to EQ. If this is the case, then, to the extent
that AQ scores are associated with over-selectivity, both
high SQ and low EQ would also be related to
over-selectivity. However, the evidence relating to the associations
between AQ, EQ, and SQ, and as they relate to the broad
autistic phenotype, is somewhat mixed (see Barbeau et al.
2009), and all three scales do not always predict
performance on tasks known to be impacted in those with ASD
(see Voracek and Dressler 2006). Given this, it is not
certain that this simple theoretical prediction would be borne
In contrast to the above, it may be that only one, rather
than both, of the SQ and EQ scales might predict
overselectivity, and the nature of these specific relationships
with over-selectivity might help to illuminate the nature
of these psychometric ASD-related constructs. As noted
previously, over-selective responding has been linked with
higher-order ASD problem behaviors, involving social
interactions of various kinds (Cumming and Berryman
1965; Lovaas et al. 1979; Schreibman and Lovaas 1973)
that extend beyond low-level cognitive deficits such as
attention (Dube 2009) or retrieval (Leader et al. 2009). For
example, the ability to attend to multiple stimuli is
implicated in the formation of many complex social abilities
(Cumming and Berryman 1965), understanding of speech
(Jordan et al. 2000; Lovaas et al. 1979), and social
interactions (Reed and Steed 2015; Schreibman and Lovaas
1973). To the extent that empathizing is related to these
social abilities, and the presence of over-selectivity reduces
these abilities, it might be that EQ but not SQ, which is
typically thought to relate to inanimate systems, is related
to over-selective responding (low EQ predicting high
overselectivity). In contrast, if over-selectivity relates purely to
low-level cognition and processing of stimulus input, then it
might relate to high-order social skills (assumed to be
associated with EQ), but over-selectivity could be associated
with SQ scores—the latter being a stronger index of the
ability to integrate information about inanimate systems.
This latter view actually suggests a number of possible
relationships between SQ and over-selectivity that depend
on how the abilities clustering under
psychometricallydefined SQ are conceptualized. If these abilities require
parallel processing of information to arrive at a mechanistic
account of a system, then high SQ should predict less
overselectivity. However, if high SQ requires the ability to
process and integrate information in series, then SQ may not
be impacted by over-selectivity, which is associated with
processing multiple sources of information simultaneously.
Given the above unexplored possibilities, the current
study examined the relationships between AQ, SQ, and EQ
and over-selectivity. This would help extend the range of
tasks over which AQ has been explored, allow investigation
of whether AQ, SQ, and EQ are all related to such tasks,
which may shed light on whether over-selectivity might
be implicated in complex social skills in addition to
simple low-level processing, which has been assumed but not
Eighty participants (30 female, 50 male) were recruited
from the general public through advertisement. The study
was advertised as an investigation into personality and
learning. The participants were not paid, or given any
reward, for their contribution. The sample had a mean age
of 37.13 (±14.79, range = 19–60) years. G-power
calculations suggested that for a medium effect size (f = 0.25),
using a significance criterion of p< .05, in order to obtain
95% power, the size of the total sample should be 54. There
were a number of exclusion criteria applied to the study. In
the advert it was specified that that study was only
recruiting volunteers who were between 18 and 60 years old, the
upper criterion was adopted as it is known that age impacts
over-selectivity (Kelly et al. 2015; McHugh and Reed
2007), and who had no history of psychiatric problems or
developmental or intellectual issues. Only two volunteers
reported a history of mental health problems, and they were
excluded from the study. Individuals with an AQ score
of above 32 were excluded (as it is possibly that they had
clinical ASD), and those with an IQ of below 80 were also
excluded, as IQ is also known to impact over-selectivity
(see Kelly et al. 2015). No exclusions were made on these
Apparatus and Materials
Wechsler Abbreviated Scale of Intelligence (WASI, Sattler
1998) measures intellectual ability, and is suitable for ages
6 to 89 years. It comprises four subtests, two assessing
language (vocabulary and similarities), and two performance
measures (block design and matrix reasoning). Thus, the
WASI generates two scores of abilities, verbal and
performance scores, and a full score of intellectual functioning.
Test reliability has been stated at 0.87–0.92.
Autistic Spectrum Quotient Questionnaire (AQ;
BaronCohen et al. 2001) measures the level of autistic traits that
an individual may possess. This questionnaire consists of
50 questions, with a score of 32 generally being suggested
as indicating high functioning ASD. The test–retest
reliability of the scale is 0.70 (Baron-Cohen et al. 2001), and
the internal consistency (Cronbach alpha) of the AQ is 0.82
(Austin 2005). There are sub-scales to the AQ, however,
there is some debate about the appropriate factor solution
for the AQ, and the reliabilities of the sub-scales are
uncertain (see Austin 2005; Hurst et al. 2007). Given these
concerns, only the overall AQ score was employed.
Systemizing Quotient (SQ; Baron-Cohen et al. 2003)
assess interest in systems across a range of different classes
of system. It comprises of 60 questions, 40 assessing
systemizing and 20 distractor (control) items. It produces
a maximum score of 80, and has an internal reliability of
0.78 (Auyeung et al. 2009).
Empathy Quotient (EQ; Baron-Cohen and Wheelwright
2004) measures empathy, and comprises 40 items. It
produces a maximum score of 80, and has an internal
reliability of 0.93 (Auyeung et al. 2009).
Over-selectivity stimuli. Stimuli used during the
procedure included eight abstract pictorial symbols taken from
various fonts from Microsoft Word 2010 (Wingdings,
Wingdings 2 and Symbol). Stimuli were either presented as
a compound for training, or an elemental stimulus during
testing. In all phases, each symbol appeared in black and
measured approx. 5 cm × 5 cm (see Fig. 1).
All participants were tested individually in a small quiet
laboratory cubicle containing a desk, a chair, and a
computer. The over-selectivity procedure was automated on a
Dell Latitude E6540 laptop (display size 15.5″).
Training phase Participants initially were presented with
the instructions: “Please select one of the two stimuli
presented as soon as ‘respond now’ appears on the screen. You
will be given feedback indicating whether you selected the
correct or incorrect stimulus. Your aim is select the correct
Fig. 1 An example of the compound stimuli used during the training
phase, followed by an example of the elemental stimuli used during
the testing phase
All participants were then presented with a simple
discrimination task consisting of the compound stimuli (AB vs
CD). The compound stimulus AB appeared on the screen
paired with compound stimulus CD; for half of the trials
AB was on the right of the screen, and for the other half
of the trials it was on the left (determined randomly). All
participants received different symbols for each stimulus to
control for the effects of intrinsic salience of the elements.
Participants selected one of the compounds when
‘Respond Now’ appeared on the screen by clicking the
mouse cursor on one of the compounds. The instruction
was presented 2 s after the stimuli were presented. If the
Table 1 Mean (standard deviations) for the autism quotient (AQ),
systematizing quotient (SQ), empathizing quotient (EQ), and IQ
(WASI) scores, as well as the correlations between the variables
*p < .05; **p < .01; ***p < .001
participant selected the target compound stimulus (AB),
then ‘Correct’ appeared on the screen; but if they selected
the non-target compound (CD), then ‘Incorrect’ appeared
on the screen. Feedback was presented immediately after
a response, and the next trial commenced immediately.
Thus, AB was always reinforced, and CD was never
reinforced. If participants did not respond within 1.5 s, the next
trial commenced. Training continued until the participant
selected the correct compound consecutively ten times.
Test Phase After completing the training phase, the
test phase instructions were presented. Participants were
instructed: “Please select one of the two stimuli presented.
The computer will not tell you whether you are correct
or incorrect”. Participants were then presented with one
stimulus from the previously reinforced compound (e.g., A
or B) paired with a stimulus from the previously
non-reinforced compound (e.g., C or D). Each combination (A vs
C, A vs D, B vs C, B vs D) was presented five times; thus,
there were 40 trials in total. Participants were required to
select one of the cards using the mouse cursor. They were
provided with no feedback and each trial appeared on the
screen immediately after a response had been given.
Subsequent to completing the experiment, participants
were asked to complete three questionnaires (AQ, SQ, EQ),
and were given the WASI assessment.
Table 1 shows the mean (standard deviations) for the autism
quotient (AQ), systematizing quotient (SQ), empathizing
quotient (EQ), and IQ (WASI) scores, as well as the
correlations between the variables [Pearson’s, except for those
involving gender which were point biserial (positive =
correlation with male)]. These data revealed a significant
negative correlation between the autism quotient and empathy
quotient, small positive correlations between being male
and both autism quotient and systematizing quotient, a
strong relationship between being female and the empathy
quotient and a strong relationship between being older and
the empathy quotient.
[Pearson’s, except for those involving gender which were point
biserial (positive = correlation with male)]
The sample was split at the mean for each of the three
scales to create a lower and higher scoring group for
each, as has been done for previous examinations of the
impact of AQ on cognitive functioning (Grinter et al.
2009; Reed et al. 2011): AQ lower-scoring group (n = 48;
mean = 9.10 ± 3.10; range = 2–13), and AQ
higher-scoring group (n = 32; mean = 19.43 ± 3.89; range = 14–26);
SQ lower-scoring group (n = 40; mean = 39.52 ± 7.24;
range = 18–53), and SQ higher-scoring group (n = 40;
mean = 67.42 ± 12.30; range = 54–114); EQ lower-scoring
group (n = 47; mean = 42.40 ± 6.40; range = 22–52), and
EQ higher-scoring group (n = 33; mean = 62.57 ± 5.87;
range = 53–73).
Figure 2 shows the results from the test phase of the
experiment for both groups for each of the three scales.
The percentage times that each element from the
previously reinforced compound (AB) was chosen at test was
calculated, and the percentage times that the most- and
least-selected elements were chosen for each participant
noted. Over-selectivity is indicated to the degree that one
of the stimuli was chosen more often than the other at test.
Inspection of Fig. 2 for the AQ scale reveals little
difference between the most- and least-selected items for the
lower-scoring group, but a large difference between the
stimuli for the higher-scoring group. A two-factor
mixedmodel analysis of covariance (ANCOVA) with group
(lower vs higher) as a between-subject factor, and stimulus
(most vs least) as a within-subject factor, was conducted
on these data; systematizing (SQ) and empathizing (EQ),
IQ, as well as age and gender, were employed as
covariates. In addition, the effect size (and its 95% confidence
limits) was computed, as well as the Bayes factor for the
null hypothesis (BF0) and the probabilities of the
hypothesis (null and alternate) being true given the obtained data.
The latter statistics were employed to determine whether
Fig. 2 Number of stimuli chosen at test from previously reinforced
stimulus, for lower and higher scoring autism (AQ),
systematizing (SQ), and empathizing (EQ) groups. Error bars 95% confidence
any conclusions that depended on a null result were likely
due to power issues. This analysis revealed significant main
effects of stimulus, F(1,73) = 5.41, p < .05, η2p = 0.069
[95% CI = 0.069: 0.199]; BF0 = 0.513, p(Ho/D) = 0.339,
p(H1/D) = 0.661, and group, F(1,73) = 10.95, p < .001;
η2p = 0.130 [0.021–0.275]; BF0 = 0.033, p(Ho/D) = 0.032,
p(H1/D) = 0.976, and a significant interaction between the
factors, F(1,73) = 7.95, p < .01; η2p = 0.098 [0.008–0.237];
BF0 = 0.143, p(Ho/D) = 0.125, p(H1/D) = 0.874.
Simple effect analyses between the two stimuli for the
lowerscoring group revealed no significant difference between
the stimuli, F < 1; η2p = 0.001 [0.000–0.003]; BF0 = 5.51,
p(Ho/D) = 0.846, p(H1/D) = 0.153, but a significant
difference between the stimuli for the higher-scoring group,
F(1,73) = 7.42, p < .01; η2p = 0.092 [0.006–0.229];
BF0 = 0.184, p(Ho/D) = 0.156, p(H1/D) = 0.844.
Inspection of these data for the SQ scale again reveals
a difference between the most- and least-selected items
at test for both lower- and higher-scoring groups. A
twofactor mixed-model ANCOVA (group × stimulus; with
AQ, EQ, IQ, age, and gender as covariates) revealed a
significant main effect of stimulus, F(1,73) = 5.57, p < .05;
η2p =0.071 [0.001–0.201]; BF0 = 0.471, p(Ho/D) = 0.321,
p(H1/D) = 0.679, but there was no significant main
effect of group, F < 1; η2p = 0.002 [0.000–0.062];
BF0 = 8.38, p(Ho/D) = 0.893, p(H1/D) = 0.106, or
interaction, F(1,73) = 1.03, p > 0.30; η2p = 0.014 [0.000–0.106];
BF0 = 5.06, p(Ho/D) = 0.835, p(H1/D) = 0.165.
Inspection of these data for the EQ scale reveals that
the difference between the most- and least-selected items
for the lower-scoring group was greater than that for
the higher-scoring group. A two-factor mixed-model
ANCOVA (group x stimulus; with AQ, SQ, IQ, age, and
gender as covariates) revealed no significant main effects of
stimulus, F(1,73) = 2.50, p > .10; η2p = 0.033 [0.000–0.144];
BF0 = 2.32, p(Ho/D) = 0.699, p(H1/D) = 0.300, or group,
F < 1, η2p = 0.009, η2p = 0.008 [0.000–0.092]; BF0 = 6.30,
p(Ho/D) = 0.863, p(H1/D) = 0.137, but the
interaction between the factors was significant, F(1,73) = 5.48,
p < .05; η2p = 0.070 [0.001–0.200]; BF0 = 0.494,
p(Ho/D) = 0.330, p(H1/D) = 0.669. Simple effect analyses
revealed a significant difference between the stimuli for
the lower-scoring empathy group, F(1,73) = 4.89, p < .05;
η2p = 0.107 [0.000–0.147]; BF0 = 0.343, p(Ho/D) = 0.255,
p(H1/D) = 0.744, but no significant difference between
the stimuli for the higher-scoring group, F < 1;
η2p = 0.001 [0.000–0.074]; BF0 = 6.47, p(Ho/D) = 0.866,
p(H1/D) = 0.133.
A multiple regression was performed to see if any of
the potential predictors (AQ, SQ, EQ, IQ, age, and gender)
were related to the level of over-selectivity, as measured by
the difference between the most- and least-selected stimuli
at test. This analysis revealed a significant overall model,
F(6,73) = 4.86, p < .001, R2 = 0.285, with AQ (β= 0.134,
p < .01) and EQ (β = −0.066, p < .05) being the only
independently significant predictors of the difference (gender
β = −0.888, p > .10; age β = −0.005, p > .70; SQ β = 0.006,
p > .70; and IQ β = −0.027, p > .40).
In addition, a logistic regression was performed to
determine if any of the values (AQ, SQ, EQ, age or gender)
predicted over-selectivity. In the absence of any a priori
method of determining the level of difference between the
most- and least-selected stimuli at test needed for
overselectivity, the procedure recommended by Reynolds and
Reed (2011) was adopted. The mean probability of
choosing A and B was first calculated. Given this probability,
the binomial equation was used to obtain the probability of
choosing all possible combinations of A and B over C or D
on ten trials. The probability of choosing a reinforced
compound stimulus was set at the mean probability of
choosing A and B stimuli in a particular condition. Then, the
probability of obtaining 10 A, and zero to 10 B; the
probability of obtaining 9 A, and 0–10 B; etc., were calculated,
and put in a 10 × 10 contingency table. The contents of this
table were then multiplied by a 10 × 10 table that contained
the absolute A minus B difference score for each
combination. The resulting 10 × 10 table contained the expected
frequency of obtaining each possible A minus B difference
resulting from all possible combinations of A and B
frequencies. The sum of the values in this table (multiplied by
10) provided an estimate of the most minus least selected
difference, in percentage terms, expected by random
variation of selection of A and B stimuli. This gave a critical
value of 21.3% difference between the stimuli to show
over-selective responding (rounded to 30%). The
overall regression produced a significant result, X2(5) = 23.50,
p < .001, −2LL = 86.15, Nagelkerke R2 = 0.341. In terms
of the predictors, this analysis revealed that AQ (odds
ratio = 1.19, p < .01), and EQ (odds ratio = 0.978, p < .05)
were significant predictors of over-selectivity (SQ odds
ratio = 1.010, p > .60; IQ odds ratio = 0.948, p > .10; age
odds ratio = 0.985, p > .40; gender odds ratio = 0.355,
p > .10).
The current study demonstrated that an individual’s AQ
score was associated with over-selective responding; those
with higher AQ demonstrating greater over-selectivity.
This finding has been shown for individuals with clinical
ASD in comparison to typically-developing individuals
(e.g., Leader et al. 2009; Reed et al. 2009), but not for those
with high AQ scores. It should also be noted that, while
the over-selectivity effect has previously been observed in
typically-developing individuals (Reed and Gibson 2005;
Reynolds and Reed 2011), this has only occurred when
there has been a concurrent cognitive load task. The
current study found the effect in high-scoring AQ individuals
without such a cognitive load, and suggests that
over-selective responding does not have to be induced in this
population. This finding adds to the literature that suggests that
those with high-scoring AQ show a similar cognitive style
to those with ASD (e.g., Lombardo et al. 2007; Reed et al.
The results also demonstrated that an individual’s score
on the EQ scale was related to the level of over-selectivity
that they demonstrated—those with low EQ showed greater
over-selectivity. This corroborates what has been suggested
by a number of authors; namely, that complex social skills,
such as empathy, require the processing of multiple stimuli,
and that individuals who do not display strong abilities in
these areas may also show over-selective responding
(Cumming and Berryman 1965; Lovaas et al. 1979; Reed and
Steed 2015). It has been previously suggested that
situations in which over-selective responding might be seen
are those in which individuals with ASD display impaired
abilities, such as understanding speech (Jordan et al. 2000)
or facial emotion recognition (Reed and Steed 2015), but
there have previously been no demonstrations of a direct
relationship between any higher-order social ability, such as
empathizing, and over-selectivity.
However, there was no suggestion that
systematizing (SQ) was associated with over-selective responding.
Although it was the case that there were numerical
differences in the over-selective responding (i.e. those with
higher SQ showed more over-selective responding), this
difference did not reach statistical significance, nor was
this factor significant in any regression analysis. It should
be noted that the power of the current tests were sufficient
to produce a difference, and the Bayes statistics calculated
suggested that was extremely unlikely that these
differences were reliable. These findings also support the view
that SQ and EQ are separable traits, and do not always
predict performance together (e.g., Grove et al. 2013). There
are a number of possibilities regarding the lack of
association between SQ and over-selectivity in the current study. It
may be that systematizing tendencies were not engaged for
the current task, as it has been suggested that, while
empathizing is automatic, systematizing is a controlled process
only required in certain situations (Brosnan et al. 2014).
Alternatively, it may be that systematizing reflects ability
to process information in series, rather than in parallel. If
this were the case, then it would not be necessary to attend
to two cues at once, and SQ would not be related to
overselectivity. Clearly further studies are required to unpack
The other aspects of the current data support the view
that there are some sex differences in relation to AQ, SQ,
and EQ (Baron-Cohen et al. 2002; Wheelwright et al.
2006), but that only the latter (EQ) scale produced very
strong differences in this regard in the current study. This
finding is in line with the results reported by Wheelwright
et al. (2006), who also noted larger sized effects for EQ
compared to the other scales when comparing males and
females. Also of note in the current data was the strong
relationship between age and empathizing quotient, which has
not previously been noted. This latter finding may depend
on the way in which empathy is measured as Eysenck et al.
(1985) noted no such relationship between empathy and
age in their study using the Eysenck Personality
Questionnaire. It may also be noted that it is unclear whether this
relationship would hold if older individuals were included
in the study. The current experiment excluded those aged
over 60, due to concerns that age in itself can predict
overselective responding in older individuals (>70 years; e.g.,
Kelly et al. 2015). Further research might explore this
older population as it might be that a bitonic relationship
between EQ and age emerges if the age range is extended—
this would certainly be predicted if over-selectivity and
empathizing are related, as older people have been shown
to shoe more over-selective responding (McHugh and Reed
2007; Kelly et al. 2015).
In addition to the specific relationships between
overselectivity and the various questionnaires employed in the
current study, these results also have some implications
for a number of theories of ASD that might be further
discussed and explored. Of course, the current data was
focused on exploring effects in the BAP, which may or may
not be replicated within a clinical ASD population. This
means that any such extrapolation should be made with
caution and with the support of additional empirical
evidence. Notwithstanding this proviso, the current data seems
to bear on two theoretical views of ASD. Over-selective
responding is clearly predicted from views of ASD such as
weak central coherence (Happé and Frith 2006). However,
it is unclear that such a view would predict the lack of
relationship between systematizing and over-selective
responding. According to the weak central coherence view, the
same mechanisms are responsible for performance deficits
in some situations and advantages in others. If enhanced
systematizing is such a ‘double-edged’ mechanism that is
tied to weak central coherence, then this view might have
predicted high SQ scores would be related to over-selective
responding. On the other hand, the theory of mind view of
ASD (Baron-Cohen et al. 1985) may fare batter with these
data, although the challenge for this view is why
overselectivity occurs across a range of non-social situations.
It is important to note that, although the relationship
between over-selectivity and EQ suggests a role for the
former in the disruption of empathizing, the use of one
selfreport measure of empathy (i.e., the EQ) does not answer
the question of whether over-selectivity is implicated in
other complex social skills (although see Cumming and
Berryman 1965; Lovaas et al. 1979; Reed and Steed 2015;
Schreibman and Lovaas 1973). Further studies using
multiple tools, assessing multiple complex social skill domains,
are needed to fully address the relationship between these
skills and over-selectivity and the three psychometric scales
employed. Indeed, such complex social skills may not be
captured fully by any one or set of questionnaires, and
ecologically valid studies might be usefully conducted to
further this link to everyday social functioning. In addition, a
limitation of the current study was that the sample was not
specifically screened for psychiatric or neurological
problems, other than by their own self-report. Although this is a
common procedure, it may be useful to use a wider battery
of tests in the future in regards to this issue. Whether
overselectivity is implicated in problems with social skills is an
important question, particularly for informing interventions
for those with ASD, and further work extending the current
findings will be needed for a fuller answer to this question.
In summary, these results imply that those with higher
psychometrically-defined AQ scores perform similarly to
individuals with clinical ASD on this over-selectivity task.
Furthermore, they give the suggestion that the relationship
between over-selectivity and complex social skills, such as
empathizing, may be an important one that could open a
potential fruitful line of further study.
PR as sole author did everything for the
Open Access This article is distributed under the terms of the
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