Space, time and number: common coding mechanisms and interactions between domains

Abstract

Space, time and number are key dimensions that underlie how we perceive, identify and act within the environment. They are interconnected in our behaviour and brain. In this study, we examined interdependencies between these dimensions. To this end, left- and right-handed participants performed an object collision task that required space–time processing and arithmetic tests that involved number processing. Handedness of the participants influenced collision detection with left-handers being more accurate than right-handers, which is in line with the premise that hand preference guides individual differences as a result of sensorimotor experiences and distinct interhemispheric integration patterns. The data further showed that successful collision detection was a predictor for arithmetic achievement, at least in right-handers. These findings suggest that handedness plays a mediating role in binding information processing across domains, likely due to selective connectivity properties within the sensorimotor system that is guided by hemispheric lateralisation patterns.

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Psychological Research (2022) 86:364–374
https://doi.org/10.1007/s00426-021-01503-8
1 3
ORIGINAL ARTICLE
Space, time andnumber: common coding mechanisms
andinteractions betweendomains
DeborahJ.Serrien1 · MichielM.Spapé2
Received: 22 August 2020 / Accepted: 5 March 2021 / Published online: 23 March 2021
© The Author(s) 2021
Abstract
Space, time and number are key dimensions that underlie how we perceive, identify and act within the environment. They
are interconnected in our behaviour and brain. In this study, we examined interdependencies between these dimensions. To
this end, left- and right-handed participants performed an object collision task that required space–time processing and arith-
metic tests that involved number processing. Handedness of the participants influenced collision detection with left-handers
being more accurate than right-handers, which is in line with the premise that hand preference guides individual differences
as a result of sensorimotor experiences and distinct interhemispheric integration patterns. The data further showed that suc-
cessful collision detection was a predictor for arithmetic achievement, at least in right-handers. These findings suggest that
handedness plays a mediating role in binding information processing across domains, likely due to selective connectivity
properties within the sensorimotor system that is guided by hemispheric lateralisation patterns.
Introduction
In everyday life, we often interact with moving objects, such
as catching a ball or crossing a road. Crucial to these sen-
sorimotor activities is the ability to predict the trajectory of
the moving objects and the changes of their position over
time (Enns & Lleras, 2008; Senot, etal. 2003). To imple-
ment these predictions, the brain uses a range of quantitative
inputs, such as spatial, temporal and numerical information.
Moreover, space–time–number represent essential dimen-
sions that can be encoded through all sensory modalities
(Burr, etal. 2010). These dimensions further demonstrate
associations, such as the SNARC effect, that captures num-
ber–space interactions with faster responses occurring to
smaller/larger numbers on the left/right side of space due to
a representation of increasing numerical value from left to
right (Dehaene, etal. 1993).
To account for these interdependencies, Walsh (2003)
argued for a magnitude system that involves processing of
dimensional magnitudes and their interactions (Bonato,
etal. 2012; Burr, etal. 2010; Dehaene & Brannon, 2001;
Fabbri, etal. 2013; Hayashi, etal. 2013). Furthermore, the
proposed ATOM model (A Theory of Magnitude) underlines
that actions are instrumental in establishing the magnitude
system, with parietal circuitry providing a neural platform
to exchange information (Walsh, 2003). That is, it is through
actions that associations between magnitudes are learned for
example that larger objects tend to be heavier than smaller
ones. Thus, the magnitude system ties interactions between
dimensions such that ‘more’ in a dimension couples with
‘more’ in another dimension. The origin of these interactions
is that they reflect innate mappings or developmental pro-
cesses, although both types of mechanisms could influence
one another with innate pathways being influenced by early
experiences and learned processes by innate constraints (De
Hevia, etal. 2014; Stanescu-Cosson, etal. 2000; Walsh,
2003). Besides innate and developmental systems, atten-
tional processes also play an important role. For example,
attention can be directed towards specific task features, such
as a location in space or a moment in time, which accord-
ingly supports behavioural performance (Coull & Nobre,
1998; Dehaene, etal. 2003).
According to current viewpoints, a dimension could
emerge from another, resulting in functional similarities and
dependencies in computational and neural mechanisms. One
particular hypothesis is that space serves as a foundation for
the dimensions that are conceptually more abstract, such as
* Deborah J. Serrien
deborah.serrien@nottingham.ac.uk
1 School ofPsychology, University ofNottingham,
Nottingham, UK
2 Department ofPsychology andLogopedics, University
ofHelsinki, Helsinki, Finland
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365Psychological Research (2022) 86:364–374
1 3
time and number (Bonn & Cantlon, 2012). Thus, magnitude
representations with a common code could evolve due to
the substrates that already exist for the space dimension. Of
note, however, is that the existence of dimensional interac-
tions does not imply that these are equal in strength. For
example, there is evidence that space–time interactions are
strongest due to the specialisation of the magnitude system
for sensorimotor actions, followed by space–number and
time–number interactions (Bueti & Walsh, 2009).
Taking into account that the magnitude system is estab-
lished through actions, the question emerges about the effect
of sensorimotor experiences. That is, it can be argued that
magnitude representations would differ if individuals inter-
act in different ways with the environment due to inherent
biases. Handedness is such a characteristic that captures
asymmetry of movement control and expresses how indi-
viduals use their hands during manual activities. Research
has shown that handedness not only affects the sensorimo-
tor control mechanisms (Klöppel, etal. 2007; Pool, etal.
2014; Reid & Serrien, 2014; Serrien, etal. 2012) but also
influences how individuals attend to and respond to the envi-
ronment. In other words, handedness influences a range of
abilities that involves visuospatial functions (Bareham, etal.
2015; Hécaen & Sauguet, 1971; O’Regan & Serrien, 2018;
Vogel, etal. 2003), attentional regulation in space and time
(Buckingham etal., 2009; O’Regan, etal. 2017) and visual
processing in perihand space (Le Bigot & Grosjean, 2012).
Combined, these findings illustrate that handedness has a
widespread impact on the processing requirements of space
and time for meeting behavioural goals.
The aim of the present experiment is to examine how
space–time processing naturally connects with number pro-
cessing, based on the proposed interdependencies between
the dimensions of space–time–number. In this respect, a
valuable experimental approach is to study a functional
effect at the level of the behavioural outputs. First, we use
an object collision task that requires participants to predict
whether moving objects will collide with one another or
not at a specific moment in time. To be successful, an accu-
rate estimation of the moving objects over time is required
(O’Reilly, etal. 2008; Proffitt & Gilden, 1989). It involves
information about the path of the objects in space which is
strongly linked with spatial coordinates, whereas the veloc-
ity with which the objects move implies position changes
in temporal coordinates. From a neural viewpoint, previous
work has shown that collision detection associates with the
left inferior parietal cortex (Assmuss, etal. 2003). Second,
we include arithmetic tests that require the use of numerical
information processing established by operator-dependent
rules (Friedrich & Friederici, 2009). We use tests with dif-
ferent types of arithmetic operations; additions, subtrac-
tions, and multiplications. Neurally, research has shown
that tasks that involve numbers and calculations involve
bilateral inferior parietal activity as a function of the arith-
metic operation (Arsalidou & Taylor, 2011), albeit with
a key involvement of the left hemisphere (Dehaene, etal.
1993). Third, we study left- and right-handers based on the
premise that handedness introduces distinct sensorimotor
experiences that affect the processing demands. We also
conduct an evaluation of the participants’ personality traits
as a relationship between handedness and negative affect has
been proposed due to an underlying influence of the right
hemisphere (Sutton & Davidson, 1997) with left- as com-
pared to right-handers obtaining higher self-reported levels
of behavioural inhibition (Hardie & Wright, 2014).
In the present work, we argue that the space–time calcula-
tions of the object collision task associate with arithmetic
computations due to shared neural mechanisms. We further
hypothesise that the participants’ handedness guides the
processing demands as a result of their sensorimotor expe-
riences and interactions with objects. Combined, insights
into individual differences of handedness and interdependen-
cies across space–time–number processing will be valuable
to increase our understanding of common mechanisms that
underlie our behaviour.
Methods
Participants
There were 37 participants in this study (Mage = 20.7years,
SEage = 0.6), including 19 left-handers and 18 right-hand-
ers. They reported no history of neurological or psychiatric
conditions as evaluated by a standardised questionnaire,
and had normal or corrected-to-normal vision. Participants
gave written consent prior to the start of the experiment in
accordance with the Declaration of Helsinki. The study was
approved by the School of Psychology Ethics Committee.
Handedness questionnaire
To characterise handedness, participants completed a
15-item handedness questionnaire that measured hand pref-
erence for manipulation tasks (i.e., write a letter, use spoon,
use toothbrush, throw ball to hit target, use a comb, hold
racquet, hold needle when sewing, draw a picture, use com-
puter mouse, open lid from can, hold knife to cut, peel an
apple, use scissors, deal cards, use eraser).
The handedness questionnaire used a 5-point Likert scale
that varied between always left and always right. The score
per item was calculated with a value of 0 (always left), 1
(usually left), 2 (both equally), 3 (usually right) or 4 (always
right). For each participant, the scores of the items were
summed, divided by the maximum score of the question-
naire, and multiplied by 100. This provided a laterality index
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366 Psychological Research (2022) 86:364–374
1 3
of handedness that varied between 0 (extreme left-handed-
ness) and 100 (extreme right-handedness). The writing hand
was also included as a condition for handedness as most
people will categorise their handedness on the basis of their
writing hand (Perelle & Ehrman, 2005).
Reinforcement sensitivity theory personality
questionnaire (RST‑PQ)
To capture personality traits, participants completed subtests
of the RST-PQ (Corr & Cooper, 2016), which rely on the
premise that individual differences emerge from neurobio-
logical systems that are specialised in detecting, process-
ing, and responding to stimuli. In particular, the Behav-
ioural Inhibition System (BIS) engages risk assessment and
inhibits behaviour in response to goal conflict, resulting in
anxiety and behavioural avoidance. In contrast, the Behav-
ioural Approach System (BAS) facilitates goal-directed
activity and positive emotions, leading to optimistic mood
and achieved goals. The questionnaire covered BIS activity
with 23 items and BAS activity with 32 items; i.e., biased
attention towards reward interest (BAS-RI with 7 items),
goal drive persistence (BAS-GDR with 7 items), impulsiv-
ity (BAS-IMP with 8 items) and reward reactivity (BAS-RR
with 10 items).
The questionnaire used a 4-point Likert scale for accuracy
of statements, ranging between 1 (not at all) and 4 (highly
accurate). For each sub-test, the ratings were summed
across items to provide a total score. High scores indicated
increased sensitivity of a given neurobiological system.
Object collision task
Participants were seated at a viewing distance of 70cm from
a computer monitor. The trial presentation is illustrated in
Fig.1. Each trial started with the presentation of a fixation
cross that lasted 1000ms followed by the appearance of
a black and white object, either 3.6º on the upper, lower,
left or right side of the screen’s centre. Thereafter, the per-
pendicular presented objects with a diameter of 0.4° would
start to move in straight lines with a constant speed of 2.8
or 5.6°/s towards the screen’s centre, resulting in collision
and non-collision events. As soon as the objects started to
move, the participants were required to decide whether the
objects would collide (target hit) or not collide (target miss)
behind a mask that had a height and width of 3.6°. This point
of collision which occurred 1300ms after onset is not shown
to the participants as the mask would hide the final trajecto-
ries of the objects from 1200ms after onset. After another
700ms or until the participants made a response, a blank
screen occurred that marked the end of the trial. In 33% of
the trials, a third grey object (distractor) would move with a
similar speed alongside the black object towards the mask.
These trials were included to influence attentional selection
to the relevant objects. The performance conditions (without
distractor vs. with distractor) and type of collision (target
hit vs. target miss) were randomised. There were 32 trials
per performance condition, resulting in a total of 128 trials.
Participants were asked to respond as fast and as accurate
as possible in their decision-making using keys allocated to
the index and middle fingers of the left or right hand (coun-
terbalanced). Before the start of the experiment, a training
session with feedback was provided, and there were short
breaks throughout the experiment. The trial sequence and
data collection were implemented using e-Prime.
The measurements of the task were collision detection
time (ms) and accuracy (%). The collision detection time
comprised the time period between initiation of the moving
objects and key press responses whereas the collision detec-
tion accuracy referred to correctly confirmed collisions on
contact trials and correctly rejected collisions on no contact
trials, and represented a key measurement that captures the
ability to predict the collision event at a precise moment
in time. We also calculated the balanced integration score
to obtain a composite evaluation of both measurements.
This index integrates reaction time and accuracy with equal
weighting and is considered beneficial as compared to other
methods that assess speed-accuracy trade-offs (Liesefeld
Fig. 1 Collision task without and with distractor. Left side: after dis-
appearance of the fixation cross, the task starts with the black and
white objects moving towards the centre. Right side: after 1200ms,
these objects disappear behind a mask while their final trajectories
are hidden from view. In the collision task with distractor, the grey
object (distractor) moves along the black object on the side nearest to
the white object
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367Psychological Research (2022) 86:364–374
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& Janczyk, 2019; Vandierendonck, 2017). The balanced
integration score is calculated by independently standardis-
ing the reaction times and percentage correct responses to
bring them onto the same scale, and then subtracting one
standardised score from the other. Its interpretation is in
terms of performance above or below average, and there-
fore measures relative performance—for example, whether
one group of participants is more successful than another
group, or, whether one condition is more difficult than
another condition.
Arithmetic tests
Participants were asked to answer a series of arithmetic
operations, i.e., additions, subtractions, multiplications using
pen and paper. There were two lists consisting of 10 prob-
lems for each arithmetic operation, which were presented
separately. For additions and subtractions, the problems
involved double- and single-digit operands (64–7) or two
double-digit operands (69 + 15) whereas for multiplications,
the problems involved single-digit operands (7 × 4) or dou-
ble- and single-digit operands (92 × 3), excluding 0 and 1 as
one of the operands. Across the arithmetic tests, the prob-
lems required a combination of memory retrieval and com-
putation. Participants were asked to answer the problems
as fast and as accurate as possible. As a control condition,
a number copying tasks were conducted. This consisted of
copying a list of 20 numbers (four lists of five numbers)
that involved double- or triple-digit operands. Short breaks
between the tests were provided.
The measurements of the task were arithmetic perfor-
mance time (s) and accuracy (%) for each arithmetic opera-
tion in addition to the number copying time (s) per list of
five numbers.
Analysis
The object collision measurements were analysed by means
of 2 × 2 mixed-design ANOVAs (Handedness Group;left-
vs. right-handers and Distractor Presence; with vs. without
distractor). Secondary analyses assessing the start posi-
tion of the objects or their speed did not show any sig-
nificant effects, p > 0.05. The arithmetic measurements
were analysed by means of 2 × 3 mixed-design ANOVAs
(Handedness Group; left- vs. right-handers and Arithmetic
Operation; additions vs. subtractions vs. multiplications).
Frequency analyses were conducted by means of chi-square
tests. The number copying measurement and the personality
questionnaire scores were analysed by means of independ-
ent t tests on Handedness Group. A simple linear regression
analysis was conducted to assess whether space–time detec-
tion of object collision predicted mathematical achievement.
Initial checks showed that the Durbin–Watson test indicated
no concern for autocorrelation, with data homoscedasticity
and a normal probability plot of the residuals. Mean ± SE
is reported. Bonferroni correction was made for multiple
comparisons, where appropriate.
Results
Handedness questionnaire
The laterality index obtained from the handedness question-
naire was used to classify the participants, resulting into 19
left-handers (LI = 19 ± 3%, age = 23 ± 1y, 18 females) and
18 right-handers (LI = 93 ± 2%, age = 19 ± 1y, 11 females).
Personality questionnaire
The total RST-PQ scores showed no significant difference
between left-handers (M = 141.5 ± 3.7) and right-handers
(M = 138.1 ± 3.2), p > 0.05. Additional analyses for the
subtests revealed no significant differences between left-
and right-handers for BIS (M = 60.1 ± 2.6 and 57.2 ± 2.4),
BAS-RI (M = 17.4 ± 1.0 and 17.4 ± 0.8), BAS-GDR
(M = 19.5 ± 0.6 and 17.9 ± 0.7), BAS-IMP (M = 16.9 ± 1.0
and 18.6 ± 1.3), BAS-RR (M = 27.5 ± 1.3 and 26.9 ± 1.0),
all p > 0.05. In previous work, differences in negative affect
have been associated with handedness (Hardie & Wright,
2014). However, we observed no indication of a significant
shift in our sample, as shown in Fig.2 which illustrates the
participants’ BIS scores alongside their laterality index.
Fig. 2 Scatter plot of the BIS scores obtained from the personality
questionnaire (RST-PQ) as a function of the participants’ laterality
index from the handedness questionnaire. The laterality index varied
between 0 (extreme left-handedness) and 100 (extreme right-handed-
ness). The middle line exemplifies a score of 50 (ambidextrous)
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368 Psychological Research (2022) 86:364–374
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Object collision task
For collision detection time, the ANOVA analysis demon-
strated a significant main effect of Distractor Presence, F(1,
35) = 28.1, p < 0.01, ηp2 = 0.44, indicating that decision time
was longer in the presence of distractors (M = 1159 ± 42ms)
than without distractors (M = 1096 ± 36ms). No other effects
were significant, p > 0.05.
For collision detection accuracy, the ANOVA analysis
pointed to a significant main effect of Handedness Group,
F(1, 35) = 13.5, p < 0.01, ηp2 = 0.28, showing that left-hand-
ers (M = 69 ± 2%) detected object collisions more accurately
than right-handers (M = 60 ± 2%). No other effects were sig-
nificant, p > 0.05. Figure3 provides details about the colli-
sion detection accuracy of the participants alongside their
laterality index, and shows that left-handers were more accu-
rate performers than right-handers.
To portray more in detail the collision task for both hand-
edness groups, Fig.4 presents a categorisation of frequency
counts of the participants according to three performance
intervals: superior (i.e., fast/accurate performers), interme-
diate (in-between performers), and inferior (slow/inaccurate
performers). For the collision detection time (left-sided pan-
els), the data revealed that there were no significant group
differences for the performance intervals, p > 0.05. Of note
is that distractor presence did not impact the performance
intervals (p > 0.05).
For collision detection accuracy (right-sided panels), the
data indicated that both groups performed distinctively for
the inferior and intermediate intervals, χ21,N=37 = 11.45,
p < 0.001; and χ21, N=37 = 14.58, p < 0.0001. There was no
difference for the superior interval, p > 0.05. Performing
with or without distractor did not affect any of the intervals
(p > 0.05).
The balanced integration score revealed a significant
main effect of Handedness Group, F(1, 35) = 4.2, p < 0.05,
ηp2 = 0.11 with left-handers (0.28 ± 0.16) being more suc-
cessful than right-handers (− 0.33 ± 0.28). There was
also a significant main effect of Distractor Presence. F(1,
35) = 5.8, p < 0.05, ηp2 = 0.14, indicating a stronger perfor-
mance without distractor (0.12 ± 0.17) than with distractor
(− 0.16 ± 0.16).
Arithmetic tests
For arithmetic performance time, the ANOVA analysis
revealed a significant main effect of Arithmetic Opera-
tion, F(2, 70) = 19.9 p < 0.01, ηp2 = 0.36, demonstrating
that additions (M = 80 ± 5s) were performed fastest fol-
lowed by subtractions (M = 117 ± 9s) and multiplications
(M = 151 ± 15s). Post hoc comparisons demonstrated that
all tests differed from one another, p < 0.01.
For arithmetic performance accuracy, the ANOVA
analysis showed a significant main effect of Arithmetic
Operation, F(2, 70) = 19.3, p < 0.01, ηp2 = 0.35, revealing
that additions (M = 95 ± 1%) obtained highest accuracy
Fig. 3 Scatter plot of the collision detection accuracy scores as a
function of the participants’ laterality index from the handedness
questionnaire. The accuracy scores represent the combined collision
conditions. The laterality index varied between 0 (extreme left-hand-
edness) and 100 (extreme right-handedness). The middle line exem-
plifies a score of 50 (ambidextrous)
Fig. 4 Categorisation of the collision detection times (left side) and
accuracy scores (right side) for the left- and right-handers. The col-
our coding across the measurements indicates the performance level:
superior (white), intermediate (grey) and inferior (black)
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369Psychological Research (2022) 86:364–374
1 3
followed by subtractions (M = 90 ± 1%) and multiplications
(M = 85 ± 2%). Post hoc comparisons demonstrated that all
tests differed from one another, p < 0.01.
To illustrate the arithmetic tests in more detail, Fig.5 pre-
sents a frequency count for the arithmetic operations accord-
ing to three performance intervals: superior (i.e., fast/accu-
rate performers), intermediate (in-between performers), and
inferior (slow/inaccurate performers). Across the measure-
ments of performance time (left-sided panels) and accuracy
(right-sided panels), the data illustrate that ± 30% of the par-
ticipants performed in the intermediate range whereas ± 60%
corresponded to fast/accurate performers and ± 10% were
slow/inaccurate performers.
For the number copying task, the analysis demon-
strated no significant difference between left-handers
(M = 7.7 ± 0.3 s) and right-handers (M = 7.5 ± 0.3 s),
p > 0.05.
Object collision task andarithmetic tests: regression
analysis
Regression analysis was conducted to determine how
handedness affected behaviour at the individual level
across both handedness groups. In assessing both tasks, we
observed that collision detection accuracy and arithmetic
accuracy showed a positive association for subtractions
(top panel, Fig.6) and multiplications (lower panel, Fig.6)
as a function of handedness. The regression analyses for
right-handers revealed significant outputs for subtrac-
tions, F(1, 35) = 6.2, p < 0.03, with β = 0.53 and R2 = 0.28,
suggesting that 28% of the variance can be explained by
the model (adjusted R2 = 0.24) and for multiplications,
F(1, 35) = 5.42, p < 0.05, with β = 0.50 and R2 = 0.25,
Fig. 5 Categorisation of the arithmetic performance times (left side)
and accuracy scores (right side) for additions, subtractions and mul-
tiplications. The colour coding across the measurements indicates the
performance level: superior (white), intermediate (grey) and inferior
(black)
Fig. 6 Scatter plot of the collision detection accuracy and arithme-
tic accuracy scores, illustrating a positive association. The accuracy
scores represent the collision conditions (with and without distractor)
and the arithmetic conditions of subtractions (top panel) and multipli-
cations (lower panel)
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370 Psychological Research (2022) 86:364–374
1 3
suggesting that 25% of the variance can be explained by
the model (adjusted R2 = 0.21). For left-handers, no sig-
nificant outputs were observed, p > 0.05. In addition, no
effects were observed for the collision detection time and
arithmetic accuracies, p > 0.05.
Discussion
Making predictions is an innate capability of the human
brain. In particular, the brain makes sense of the environ-
ment by predicting future events and by testing whether
these are in line with incoming sensory information and
previous experiences (Clark, 2013; Schubotz, 2007). To
form these predictions, the dimensions of space, time and
number are elementary. That is, a representation can be
created by knowing where, when and how many, enabling
us to respond to and learn about environmental regulari-
ties (Burr, etal. 2010; Lourenco & Longo, 2011; Winter,
etal. 2015).
Predictive behaviour, such as estimating collisions,
is crucial to our everyday activities, such as anticipat-
ing the course of moving objects and (de)synchronising
our actions with them (Enns & Lleras, 2008; Senot, etal.
2003). We tested this real-world scenario by asking partic-
ipants to detect whether collisions between moving objects
would occur or not; decisions that are made on the basis
of the use of spatial and temporal information from the
motion trajectories, guided by attention to space and time.
In addition, the participants completed arithmetic tests of
addition, subtraction and multiplication, which are com-
mon in daily life such as required for counting and formal
mathematics. In this study, we examine interdependencies
between space–time–number by assessing both tasks in a
group of left- and right-handers due to their distinct sen-
sorimotor experiences.
Handedness group profiles: space–time andnumber
processing
Handedness is a manifestation of brain lateralisation that
provides a representational index of the hands and that
captures preference for manual activities (Corballis &
Häberling, 2017). Our results from the collision detection
task revealed that both handedness groups showed distinct
behavioural performances, with left-handers being more
accurate than right-handers; a performance advantage that
could be due to a greater range of sensorimotor experiences
and space–time integration pathways between both hemi-
spheres (Assmus, etal. 2003; Cherbuin & Brinkman, 2006;
Serrien, etal. 2012). In this context, hand use and sensori-
motor competence are bi-directionally linked, shaping the
information processing and associations between sensori-
motor and attentional systems (Buckingham, etal. 2011; Le
Bigot & Grosjean, 2012). Moreover, handedness affects the
representation of extra-personal and peripersonal space with
left-handers showing bilateral hemispheric activity whereas
right-handers demonstrate an asymmetry of both hemi-
spheres (Colman, etal. 2017; O’Regan & Serrien, 2018).
Further differences between left- and right-handers have
been proposed with distinct neglect-like patterns as a result
of alertness-related modulations. In particular, Bareham
etal. (2015) observed that left-handers experienced a left-
ward hemispheric shift in attention with drowsiness whereas
right-handers have the opposite pattern, a distinction that
could be due to differences in the attentional mechanisms
that control alertness and direct attention to external stimuli
(Liu, etal. 2009). In this study, we modified the attentional
demands of the object collision task by means of distractors.
We observed that their presence slowed the detection time
across all participants, suggesting that difficult decisions
take more time than easier ones and engage more neural
circuitry for optimising behaviour (Assmus, etal. 2005;
Smout, etal. 2019; Spapé & Serrien, 2011).
To take into account differences due to personality traits,
we included the RST-PQ (Corr & Cooper, 2016); a question-
naire that associates personality with distinct brain systems
labelled as BIS that predicts an individual’s response to
anxiety and performance avoidance as opposed to BAS that
supports motivation and desired outcomes. Previous work
has shown that left-handers have higher BIS scores than
right-handers, which has been coupled with different levels
of negative affect (Beaton, etal. 2017; Hardie & Wright,
2014). However, we observed no significant differences
between left- and right-handers for any of the subtests, sug-
gesting that there were no distinct variations in personality
traits in our sample.
We noted no clear pattern of arithmetic performance dif-
ferences between both handedness groups; a topic that has
provided mixed claims throughout the literature (Annett
& Kilshaw, 1982; Cheyne, etal. 2010; Crow, etal. 1998).
While some studies have shown that left-handers are strong
in mathematics and consistent right-handers perform least,
others have suggested that mixed-handers are more disad-
vantaged. In a more recent study, Sala, etal. (2017) con-
cluded that the relationship between handedness group pro-
files and mathematical ability is complex and depends on
several factors, such as age, gender and type of task. This
conclusion is in line with evidence that changes in math-
ematical processing occur as a function of development, and
that arithmetic achievement depends on domain-general as
well as domain-specific knowledge (Arsalidou & Taylor,
2011).
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371Psychological Research (2022) 86:364–374
1 3
Object collisions, arithmetic calculations
andindividual differences
Theories that account for functional overlap of
space–time–number processing suggest the use of shared
resources, common formats or a reliance on cross-dimen-
sional mappings (Fias etal., 2003; Lakoff & Johnson, 1980;
Walsh, 2003). In this respect, parietal circuits take a central
role for the processing of space, time and number (Bjoer-
tomt, etal. 2002; Coull & Nobre, 1998; Eger etal., 2009) as
well as for the interactions between space–time (Magnani,
etal. 2010; Oliveri, etal. 2009), space–number (Hubbard,
etal. 2005; Oliveri, etal. 2004), and time-number (Burr,
etal. 2010; Hayashi, etal. 2013). Of note is, however, that
each hemisphere responds to specific task characteristics.
In particular, for space and time processing, there is greater
sensitivity of right inferior parietal circuitry for orienting
in space (O’Reilly, etal. 2008) versus left inferior parietal
circuitry for cueing in time and for space–time integration
(Assmus, etal. 2003; Coull & Nobre, 2008). For num-
ber processing and numerical operations, inferior parietal
regions are usually activated across both hemispheres, with
specificity according to the type and complexity of the prob-
lem (Arsalidou & Taylor, 2011). However, there is shared
circuitry across basic numerical and arithmetic tasks that is
located within the left hemisphere, i.e., intraparietal sulcus
alongside precentral areas. Therefore, a left fronto-parietal
circuit can be considered a core network of numerical knowl-
edge in adults (Pesenti, etal. 2000; Simon, etal. 2004; Zago,
etal. 2001). The relevance of this network is that it overlaps
with sensorimotor circuitry that is recruited for predictive
control related to own actions and external perceptual events
(Coull, etal. 2008; O’Reilly, etal. 2008; Schubotz, 2007).
Moreover, this type of prediction arises when the essential
information concerns dynamic forward change with coding
of transitions in space–time, and underscores a key role of
the sensorimotor system for the prediction of future states
within the adopted reference frame, be it the body or the
environment (Schubotz, 2007).
The regression analysis revealed a positive relationship
between the detection accuracy of object collisions and the
performance of arithmetic calculations, albeit as a function
of handedness. That is, an association was observed only in
right-handers, suggesting a connection between manual later-
alisation and arithmetic. Support for such a relationship comes
from finger counting, which represents a natural routine that
supports the acquisition of basic numerical and arithmetic
principles (Butterworth, 1999). In Western cultures, counting
involves a preferred starting-hand alongside a relative order
of finger counting within a single hand. Thus, finger count-
ing strategies that are shaped by sensorimotor experience and
developed during childhood may influence and steer how
numbers are represented and processed later in life (Fischer,
2008; Pesenti, etal., 2000). In adults, these hand-starting
preferences have been observed to be different for left- and
right-handers (Zago & Badets, 2016). That is, consistent left-
handers typically started counting with their left hand whereas
the opposite pattern was noted for consistent right-handers;
a manual preference that aligned with their dominant hand
for unimanual activities. Furthermore, an fMRI study demon-
strated that left-starters showed higher activation in the right
right-sided motor and premotor cortices when they perceived
small numbers whereas right-starters showed the reverse pat-
tern (Tschentscher, etal. 2012). Thus, handedness modulates
the structural arrangement of finger counting routines and
further influences the involvement of the motor-dominant
hemisphere for number processing (Artemenko, etal. 2020).
This reliance on effector-specific circuitry in left- and right-
handers has also been observed for skilled movements, such as
grasping (Martin, etal. 2011). Together, the findings suggest
that hemispheric lateralisation of key brain regions distinc-
tively guides the covariation of functions that cross cognitive
domains.
Besides handedness, we also noted that the type of arith-
metic operation played a role in the relationship between colli-
sion detection and calculation performances. In particular, the
results showed that stronger space–time computations resulted
in increased performances for subtraction and multiplication
calculations. No effect was observed for additions, which
could be due to the fewer demands on number processing as
compared to the other tasks which likely required additional
processing steps (Fehr, etal. 2007). In using basic arithmetic
operations (addition, subtraction, multiplication), we observed
increasingly longer response times and lower accuracy rates,
confirming changes in complexity requirements. In our study,
the results did not show that the collision detection time sig-
nificantly linked with arithmetic performance, which is in line
with research that has shown that people alter where rather
than when they would hit targets, if given the choice (Brenner,
etal. 2015).
In conclusion, space, time and number are key dimen-
sions that underlie how we perceive, identify and act within
the environment. In this study, we examined interdependen-
cies between these dimensions using an object collision task
that required space–time processing and arithmetic tests that
involved number processing in left- and right-handers. Hand-
edness of the participants influenced collision detection with
left-handers being more accurate than right-handers, which
is in line with the premise that hand preference guides indi-
vidual differences as a result of sensorimotor experiences and
distinct interhemispheric integration patterns. The data further
showed that successful collision detection was a predictor for
arithmetic achievement, at least in right-handers. These find-
ings suggest that handedness plays a mediating role in binding
information processing across domains, likely due to selective
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372 Psychological Research (2022) 86:364–374
1 3
connectivity properties within the sensorimotor system that are
guided by hemispheric lateralisation patterns.
Acknowledgements The work was supported by a research grant from
the BIAL foundation to DJS (no. 376/14). We thank Louise O’Regan
for assistance in the project.
Data availability The data have been stored in the Open Science Frame-
work data repository (https:// osf. io/ rq8nu/).
Declaration
Conflict of interest The authors declare that they have no conflict of
interest.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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