Humans share with many animals a primitive non-verbal number system encoding the number of objects in space and events in time (Dehaene, 2011). The capacity to estimate number rapidly but imperfectly is thought to be a primary perceptual attribute, often termed the number sense (Burr and Ross, 2008). The past few decades have accumulated much evidence for the existence of the number sense. Many studies show that the sense of number is truly general, transcending space and time, as well as modality and format (Arrighi et al., 2014; Burr et al., 2017; Togoli et al., 2021). More recently, numerosity has been shown to interact strongly with action, leading to the idea of a sensorimotor number system, incorporating action, and its interaction with sensory systems (Anobile et al., 2016; Anobile et al., 2021).

Both physiological and psychophysical studies support the existence of a sensorimotor number system. Adaptation studies provide strong evidence: participants first tap continuously with their index finger for a few seconds, either rapidly or slowly, then estimate the numerosity of a dot array presented near the adapted region: adaptation to fast tapping causes underestimation and slow tapping overestimation (Anobile et al., 2016; Anobile et al., 2020; Maldonado Moscoso et al., 2020). Motor adaptation induces similar effects on sequential visual and auditory stimuli (Anobile et al., 2016; Togoli et al., 2020), consistent with the existence of a generalized system linking motor and sensory signals to encode numerosity (Anobile et al., 2021; Burr et al., 2021).

Electrophysiological studies in primates also point to the existence of a specific neural substrate for counting a small set of actions. In a seminal paper, Sawamura et al., 2002 trained monkeys to repetitively make five identical movements, then switch to a different movement, in a cyclical fashion: neurons in the posterior parietal cortex showed selectivity to the number of self-generated actions, whatever the action. Kirschhock and Nieder, 2022 trained crows to peck a specific number of times (1–5). Neurons in the telencephalon were shown to be tuned to the impending number of self-generated actions, during the phase between the disappearance of the target to the onset of motor reproduction. The activity of these neurons predicted the behavioral performance and was independent of both stimulus format (dots or digits) and of the temporal characteristics of the motor responses. Each tuning function peaked at a given preferred numerosity, with activity scaling down with numerical distance. Overall, these cells in the crow brain could constitute the neural substrate subserving the transformation of sensory inputs into a given quantity of numerical actions, possibly a similar mechanism to that driving the motor number adaptation effects in humans (Anobile et al., 2016).

Although motor adaptation has revealed a clear link between action and numerosity perception, this is the only technique used so far to investigate the interaction in humans. The aim of the current study is therefore to expand and generalize our previous findings, using a different psychophysical technique that exploits individual differences in reproduction precision (Peterzell and Kennedy, 2016). This technique has been widely used to reveal visual channels for motion (Morrone et al., 1999), spatial frequency (Reynaud and Hess, 2017; Simpson and McFadden, 2005), contrast sensitivity (Peterzell et al., 1995; Peterzell and Teller, 1996), color (Peterzell et al., 2000; Peterzell and Teller, 2000), and duration (Rammsayer and Troche, 2014).

The rationale behind this technique is that performance measures of stimuli detected by the same mechanism should correlate more between individuals than stimuli detected by different mechanisms. Figure 1 illustrates the technique by simulation of results in a numerosity reproduction task if the task were mediated for 1, 2, or 4 numerosity-selective channels. It assumes intrinsic variability in reproduction precision across participants, perturbed by additive random noise. The top row simulates results if reproduction of all numerosities were governed by a single mechanism, rather than a range of numerosity-tuned mechanisms. The correlation matrix for all pairs of stimuli (Figure 1B) shows no systematic pattern, only random variations in correlations due to the general noisiness of the channel. Figure 1C plots the average correlation plotted as a function of numerosity ratio, showing no dependence on number ratio. Increasing or decreasing the added noise will change the average correlation, but not create any dependency on numerosity ratio. However, if there exist mechanisms selective for numerosity, the results are quite different. Assuming just two mechanisms tuned to low and high numbers (Figure 1D) yields a correlation matrix with higher correlations between similar numbers (near the diagonal) than dissimilar numbers. This leads to the clear dependency on numerosity ratio shown in Figure 1E. Similarly, for a range of numerosity-selective mechanisms like the four shown in Figure 1G, there will be a strong dependency on numerical distance (Figure 1I). The cases of two and four channels are difficult to distinguish if the amount of added noise is free to vary. Techniques such as cluster analysis and principal component analysis (PCA) can be also applied to study further the tuning of the channels.

Figure 1

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Here, we applied the interindividual covariance technique to study sensorimotor tuning in humans, using a number matching task similar to that used by Kirschhock and Nieder, 2022. We measured the precision with which 30 participants could press a key a given number of times (8-32) without counting, and then correlated performance across all pairs of numbers (like the simulation of Figure 1). The results clearly demonstrate the existence of sensorimotor mechanisms converting symbolic numbers into actions, with correlations decreasing with numerical distance, like Figure 1F, I.

Participants pressed a key a specific number of times (visually displayed as a digit) while repeatedly pronouncing the syllable 'ba' as fast as possible, to prevent counting. To gauge the generalizability across different motor actions, in separate sessions, participants were required to tap concurrently as fast as they could (fast tapping) or at a comfortable rate (slow tapping). As expected, the rate of tapping was higher in the fast tapping (average = 6.8 Hz, SD = 1) compared with the slow tapping condition (average = 3.3 Hz, SD = 1.6). In both conditions, participants were able to correctly perform the numerical matching task, with average number of reproduced actions scaling linearly with visual targets (both r=0.99, Figure 2A, C). Figure 2B, D shows precision (Weber fractions, Wfs) in matching target and reproduced numerosity as a function of target number. Average Wfs (across target number) in the fast and slow conditions were virtually identical (average = 0.17, SD = 0.01 and average = 0.17, SD = 0.007 for fast and slow tapping respectively) and within each condition, Wfs were roughly constant across targets, following Weber’s Law.

We then computed the correlations of Wfs across participants, for all target values (see Methods for details). Figure 3A, C show correlation matrices across all participants (like the simulations of Figure 1), with each cell representing the Pearson correlation coefficient between the two target numbers. The correlations were all positive, ranging from 0.1 to 0.75 in the fast condition and 0.3–0.88 in the slow condition. Importantly, the correlations were clearly not randomly distributed, but most of the higher correlations (reddish) lie near the diagonal, indicating that the motor matching performance was more similar for neighboring numbers, compared to higher numerical distance.

Figure 3B, D show average correlation coefficients, averaged into six bins as a function of numerical distance. There is a systematic decreasing trend in both fast and slow tapping conditions (linear fit on unbinned data shown by dashed lines in Figure 3B, D: slopes = –0.58±0.11,–0.57±0.07 for fast and slow respectively, both p<0.001), clearly different to that predicted by a random pattern of matching precision levels (curves with black filled symbols in Figure 3B, D, see Methods). Overall, these analyses confirm that the degree of performance similarity was systematically modulated by the numerical difference between targets, in line with the existence of underlying tuning functions.

As a preliminary analysis, we modeled the data with simulations like that of Figure 1, assuming one, two or four numerosity-selective channels. The average WFs for the participants was taken from the data, and channel width, variability, and measurement noise free parameters. The fits with a single channel were very poor, with R²=0.003 and 0.002 (essentially no better than the mean) for the fast and slow tapping respectively. However, the two-channel model gave much better fits, with R²=0.54 and 0.29. The four-channel model had similarly good fits, with R²=0.52 and 0.28. The parameters to yield best fits seemed reasonable, with channel width of 1.2 and 1.8oc taves, and channel variability between 0.051 and 0.106.

We then investigated the structure underlying the number sensorimotor translation system with a hierarchical clustering analysis (see Methods for details). Figure 4A, C shows the resulting dendrograms. In both the fast and slow tapping conditions, the analyses returned a two-cluster solution with one cluster aggregating low numbers (N8-13 and N8-14 in the fast and slow conditions) and the other aggregating high numbers (N>13 and N>14, respectively). This analysis corroborates the results obtained from the correlation analysis and add on this suggests the existence (at least) of two channels dealing with relatively different numerical targets. A control analysis on randomised data (see Methods) yielded clusters containing contiguous numbers only in 0.005% of cases (for both the fast and slow tapping conditions), suggesting that the results with the original dataset likely represent a structure genuinely organised as a function of the highest similarity between neighboring numbers.

To study the tuning of these two hypothetical channels, we then performed a factor analysis on Wfs. In both fast and slow tapping conditions, two factors emerged (see Methods), explaining a total of 68% and 81% of the total variance (for the fast and slow tapping conditions, respectively). Figure 4 shows the rotated component strength as a function of target number. The results revealed two bell-shaped tuning functions for both fast (panel B) and slow (panel D) tapping conditions, with factor strength distributions reasonably described by logGaussian functions (fast tapping: R²=0.6 and R²=0.65; slow tapping: R²=0.97, R²=0.94 for 1st and 2nd components). In the fast-tapping condition, the first component peaked at lower target numbers (N 11) and the second component at higher numbers (N 29). In the slow motor condition, the first component packed at higher target numbers (N 26) and the second component at lower numbers (N 8).

That fast and slow tapping conditions provided similar results despite the very different tapping rates suggests that temporal frequency of the tapping did not play a major role. However, as total response duration was positively correlated with target numbers (r=0.95, r=0.36, for fast and slow tapping, both p<0.001), participants might have used response duration instead of number of taps as a stop criterion. To test this possibility, a sub−sample of 9 participants were given a control task to match the duration of a tone (see Methods). If the number task was performed through duration, this latter must have an equal or lower precision level compared to that measured in the number task. Figure 5A, B shows that this was not the case with the precision level (Wfs) in the number task being clearly lower (higher precision) than the duration task, making the use of duration strategies during the number task unlikely (number: average = 0.16, SD = 0.03; duration: average = 0.24, SD = 0.04). A RM ANOVA with task (duration, number) and stimuli intensity (11 levels) confirmed the difference (F(1, 80) = 19.97, p=0.002).

Figure 5

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Using a motor reproduction task, we measured the precision with which number digits were translated into sequences of actions. Replicating previous evidence from both human and animal studies (Cordes et al., 2001; Kirschhock and Nieder, 2022; Whalen et al., 1999), precision followed Weber’s law, a well-established signature of the Approximate Number System (Anobile et al., 2014; Dehaene, 2011; Feigenson et al., 2004; Ross, 2003). As the focus of the current study was on the individual differences in the number-to-action transformation process, we used digits as sensory stimuli. Digits encoding, at least with educated adults, is noiseless, eliminating this source of variability but leaving that associated with the sensorimotor transformation process.

Importantly, by looking at the covariance structure of the motor reproduction precision index (Weber fraction) across participants, we found evidence for sensorimotor channels tuned to number. Precision for reproducing numerically similar targets was positively correlated (r~0.8) but for larger numerical distances, correlations decreased (r~0.1), suggesting the action of sensorimotor channels sharing the encoding of neighboring numbers. In line with this, cluster analyses identified two main clusters, one aggregating low numbers (from 8 to ~13) and the other higher numbers (from ~13 to 32), pointing to (at least) two mechanisms encoding low and higher numbers. Finally, Principal Component Analyses on Weber fractions confirmed this and went to describe two bell-shaped components, one peaking at relatively low numbers (~10) and the second at higher numbers (~27).

The covariance technique used here has been widely employed to reveal visual channels in several basic perceptual dimensions such as colour, motion, contrast and spatial frequency (Morrone et al., 1999; Peterzell et al., 1995; Peterzell et al., 2000; Peterzell and Kennedy, 2016; Peterzell and Teller, 1996; Rammsayer and Troche, 2014). The technique is based on the idea that interindividual variability conveys information that can reveal common sensory processes (Peterzell and Kennedy, 2016), as illustrated in Figure 1. In practice, this approach requires multiple and densely sampled measurements of different stimuli intensity, in the same sample. Regions of high intercorrelation between neighboring stimuli intensity can be interpreted to imply that sets of stimuli are processed by the same (shared) underlying channel. This channel, while responding relatively more to its preferred stimulus, will also be activated by neighboring stimuli that although slightly different from the preferred intensity, are nevertheless included in the same response distribution. Following this rationale, relatively lower correlations among more distant stimuli would indicate that these are processed by different, or at least partially independent, channels (Peterzell and Teller, 1996). The results obtained in this study are in line with those predicted by this technique, implying the existence of sensorimotor channels tuned to different numerical targets, with a rather wide response distribution. These channels probably reflect the activity of noisy neurons responsible for translating sensory information into the corresponding numerical motor output, such as those observed in crows (Kirschhock and Nieder, 2022).

The results reinforce adaptation studies showing number-selective adaptation between actions and perceived numerosity (Anobile et al., 2016; Maldonado Moscoso et al., 2020). Those studies provided strong evidence for the existence of at least two (but possibly more) sensorimotor mechanisms, tuned to high and low numbers. The results are also broadly in line with recent evidence from animal physiology showing number selective sensorimotor neurons in the crow brain, translating visual inputs (digits and dot arrays) into number of pecks (Kirschhock and Nieder, 2022). While the crow study found as many channels as the number tested (N1–5), we found evidence for only two channels (clusters and factors) over the range of 8–32. However, it is important to note that PCA aims to summarize the dataset with the minimal number of components (channels). We can therefore not exclude the possible existence of more than two (perhaps not fully independent) channels. The results should be not interpreted as evidence for only two sensorimotor channels, but rather as evidence that tuned mechanisms exist in the human brain, with at least two different tunings. This is also apparent from the simulations of Figure 1, showing that the predictions of two or multiple channels are very similar, difficult to distinguish with the current technique. Indeed, the fits to the data using two or four channels were very similar, but both almost infinitely better than the one-channel fit. In other words, the channels described here likely represent a coarse summary representation of several (probably sharper) underlying channels.

The results reported here are unlikely to be contaminated by strategies capitalizing on duration of motor responses and/or temporal frequency. Despite the very different tapping rates in the slow (~3 Hz) and fast motor conditions (~7 Hz), the overall pattern of results remained almost unchanged. The only difference was an inversion of the order of the principal components and factors, with the first component peaking at lower numbers for the fast-tapping condition and higher numbers for the slow tapping condition (and vice versa for the second component). This result suggests that for fast actions lower number targets show more variance than higher numbers, and vice versa for slower actions. Although we have no definitive explanation for this pattern of results, it could arise from different effects of the matching motor noise associated with different tapping rates across the number ranges. In any event, the results showed two covariance channels for both conditions, similarly distributed along the numerical targets, supporting the existence of at least two mechanisms, one tuned to the lower numbers and one to the higher (at least within this numerical range).

Even if total duration of responses were positively correlated with target number, and although participants were free to use duration as a viable stopping criterion, the exploitation of this non-numerical strategy seems unlikely. Duration could conceivably be useful proxy for numerosity, but only if the thresholds for duration were lower than those for numerosity: but the results show they are in fact higher. Once again, these results are in line with previous studies showing lower sensitivities for motor matching duration tasks compared with motor number matching tasks. The electrophysiological study on crows also found similar neural response curves across very different motor response timing conditions (Kirschhock and Nieder, 2022). In summary, the results seem to suggest that the channels found here, like those in crows, reflect a genuine number selective sensorimotor transformation processes.

What may be the adaptive value of this mechanism? The spontaneous use of the number of actions in the animal kingdom is now well-established. For example, desert ants rely on the number of steps to return to the nest (Wittlinger et al., 2006), some species of male frogs match or exceed the chucks of competitors to attract female partner (Rose, 2017), and some songbirds modulate the numbers of syllables in their calls to signal dangerousness of predators (Suzuki, 2016; Templeton et al., 2005). In all cases, the use of number-related motor strategies by animals has a clear ecological value for survival and species preservation.

This system may have been preserved in humans and repurposed for our species-specific needs. Some hints come from the literature on the role of visual numerosity perception (dot arrays). Although most animal species are capable of some form of numerosity estimation (Butterworth, 2022), it appears that in humans numerosity estimation may act as a non-symbolic precursor for the development of symbolic mathematical skills (Chen and Li, 2014; Decarli et al., 2023; Halberda et al., 2008; Mazzocco et al., 2011; Piazza, 2010; Piazza et al., 2010; Schneider et al., 2017). The sensorimotor system could act as an early tool to constantly update and calibrate motor and sensory systems for the encoding and active manipulation of quantities and objects, with a potential impact on the quality of formal mathematical skills in later development. In line with this idea, there are reports of a possible co-occurrence of motor impairments in children with mathematical learning disorders such as dyscalculia (Westendorp et al., 2011), and children with developmental coordination disorders (which impair gross and fine motor function) perform poorly on math and numerosity tasks (Gomez et al., 2015; Gomez et al., 2017). This is in line with the suggestive idea proposed by Walsh, 2003 of a sensory magnitude system, encoding abstract quantities such as space, time and number, to guide planning and execution of actions. It is also in line with more recent proposal suggesting that the acquisition of number meaning is deeply grounded in sensorimotor experiences (Ranzini et al., 2022; Sixtus et al., 2023). This is the first study showing sensorimotor channels for the transformation of numbers into action sequences in humans, and many questions remain open. Among these: is the channel preference (the peak) fixed on the absolute numerical value, or does actively recalibrate according to the numerical context (range)? Do these channels also integrate non-symbolic numerical information such as the numerosity of sets of elements and/or sequences of visual/acoustic events? Are these channels active during motor programming, or do they require action execution? Do these channels integrate non-numerical motor parameters, such as the associated effort? Are these channels motor effector-specific? These (and probably other) questions are waiting to be explored using this technique, which has the advantage of being sufficiently generalisable to be expanded to investigate most stimulus features.

All data generated or analysed during this study are available at: https://doi.org/10.5281/zenodo.8279068.

1. 1. Anobile G
   2. Petrizzo I
   3. Paiardini D
   4. Burr DC
   5. Cicchini GM

   (2024) Zenodo

   Sensorimotor mechanisms selective to numerosity: evidence from individual differences.

   https://doi.org/10.5281/zenodo.8279068

1. 1. Anobile G
   2. Cicchini GM
   3. Burr DC

   (2014) Separate mechanisms for perception of numerosity and density

   Psychological Science 25:265–270.

   https://doi.org/10.1177/0956797613501520

   • PubMed
   • Google Scholar
2. 1. Anobile G
   2. Arrighi R
   3. Togoli I
   4. Burr DC

   (2016) A shared numerical representation for action and perception

   eLife 5:e16161.

   https://doi.org/10.7554/eLife.16161

   • PubMed
   • Google Scholar
3. 1. Anobile G
   2. Domenici N
   3. Togoli I
   4. Burr D
   5. Arrighi R

   (2020) Distortions of visual time induced by motor adaptation

   Journal of Experimental Psychology. General 149:1333–1343.

   https://doi.org/10.1037/xge0000709

   • PubMed
   • Google Scholar
4. 1. Anobile G
   2. Arrighi R
   3. Castaldi E
   4. Burr DC

   (2021) A Sensorimotor Numerosity System

   Trends in Cognitive Sciences 25:24–36.

   https://doi.org/10.1016/j.tics.2020.10.009

   • PubMed
   • Google Scholar
5. 1. Arrighi R
   2. Togoli I
   3. Burr DC

   (2014) A generalized sense of number

   Proceedings. Biological Sciences 281:20141791.

   https://doi.org/10.1098/rspb.2014.1791

   • PubMed
   • Google Scholar
6. 1. Burr DC
   2. Ross J

   (2008) A visual sense of number

   Current Biology 18:425–428.

   https://doi.org/10.1016/j.cub.2008.02.052

   • Google Scholar
7. 1. Burr DC
   2. Anobile G
   3. Arrighi R

   (2017) Psychophysical evidence for the number sense

   Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 373:20170045.

   https://doi.org/10.1098/rstb.2017.0045

   • PubMed
   • Google Scholar
8. 1. Burr DC
   2. Anobile G
   3. Castaldi E
   4. Arrighi R

   (2021) Numbers in action

   The Behavioral and Brain Sciences 44:e185.

   https://doi.org/10.1017/S0140525X21000996

   • PubMed
   • Google Scholar
9. Book

   1. Butterworth B

   (2022)

   Can Fish Count?: What Animals Reveal About Our Uniquely Mathematical Minds (New edition)

   Basic Books.

   • Google Scholar
10. 1. Chen Q
    2. Li J

    (2014) Association between individual differences in non-symbolic number acuity and math performance: a meta-analysis

    Acta Psychologica 148:163–172.

    https://doi.org/10.1016/j.actpsy.2014.01.016

    • PubMed
    • Google Scholar
11. 1. Cordes S
    2. Gelman R
    3. Gallistel CR
    4. Whalen J

    (2001) Variability signatures distinguish verbal from nonverbal counting for both large and small numbers

    Psychonomic Bulletin & Review 8:698–707.

    https://doi.org/10.3758/bf03196206

    • PubMed
    • Google Scholar
12. 1. Decarli G
    2. Sella F
    3. Lanfranchi S
    4. Gerotto G
    5. Gerola S
    6. Cossu G
    7. Zorzi M

    (2023) Severe developmental dyscalculia is characterized by core deficits in both symbolic and nonsymbolic number sense

    Psychological Science 34:8–21.

    https://doi.org/10.1177/09567976221097947

    • PubMed
    • Google Scholar
13. Book

    1. Dehaene S

    (2011)

    The number sense: how the mind creates mathematics

    Oxford University Press.

    • Google Scholar
14. 1. Feigenson L
    2. Dehaene S
    3. Spelke E

    (2004) Core systems of number

    Trends in Cognitive Sciences 8:307–314.

    https://doi.org/10.1016/j.tics.2004.05.002

    • PubMed
    • Google Scholar
15. 1. Gomez A
    2. Piazza M
    3. Jobert A
    4. Dehaene-Lambertz G
    5. Dehaene S
    6. Huron C

    (2015) Mathematical difficulties in developmental coordination disorder: Symbolic and nonsymbolic number processing

    Research in Developmental Disabilities 43–44:167–178.

    https://doi.org/10.1016/j.ridd.2015.06.011

    • PubMed
    • Google Scholar
16. 1. Gomez A
    2. Piazza M
    3. Jobert A
    4. Dehaene-Lambertz G
    5. Huron C

    (2017) Numerical abilities of school-age children with Developmental Coordination Disorder (DCD): A behavioral and eye-tracking study

    Human Movement Science 55:315–326.

    https://doi.org/10.1016/j.humov.2016.08.008

    • PubMed
    • Google Scholar
17. 1. Halberda J
    2. Mazzocco MMM
    3. Feigenson L

    (2008) Individual differences in non-verbal number acuity correlate with maths achievement

    Nature 455:665–668.

    https://doi.org/10.1038/nature07246

    • PubMed
    • Google Scholar
18. 1. Kirschhock ME
    2. Nieder A

    (2022) Number selective sensorimotor neurons in the crow translate perceived numerosity into number of actions

    Nature Communications 13:6913.

    https://doi.org/10.1038/s41467-022-34457-5

    • PubMed
    • Google Scholar
19. 1. Maldonado Moscoso PA
    2. Cicchini GM
    3. Arrighi R
    4. Burr DC

    (2020) Adaptation to hand-tapping affects sensory processing of numerosity directly: evidence from reaction times and confidence

    Proceedings. Biological Sciences 287:20200801.

    https://doi.org/10.1098/rspb.2020.0801

    • PubMed
    • Google Scholar
20. 1. Mazzocco MMM
    2. Feigenson L
    3. Halberda J

    (2011) Impaired acuity of the approximate number system underlies mathematical learning disability (dyscalculia)

    Child Development 82:1224–1237.

    https://doi.org/10.1111/j.1467-8624.2011.01608.x

    • PubMed
    • Google Scholar
21. 1. Morrone MC
    2. Burr DC
    3. Di Pietro S
    4. Stefanelli MA

    (1999) Cardinal directions for visual optic flow

    Current Biology 9:763–766.

    https://doi.org/10.1016/s0960-9822(99)80338-8

    • PubMed
    • Google Scholar
22. 1. Peterzell DH
    2. Werner JS
    3. Kaplan PS

    (1995) Individual differences in contrast sensitivity functions: longitudinal study of 4-, 6- and 8-month-old human infants

    Vision Research 35:961–979.

    https://doi.org/10.1016/0042-6989(94)00117-5

    • PubMed
    • Google Scholar
23. 1. Peterzell DH
    2. Teller DY

    (1996) Individual differences in contrast sensitivity functions: the lowest spatial frequency channels

    Vision Research 36:3077–3085.

    https://doi.org/10.1016/0042-6989(96)00061-2

    • PubMed
    • Google Scholar
24. 1. Peterzell DH
    2. Chang SK
    3. Teller DY

    (2000) Spatial frequency tuned covariance channels for red-green and luminance-modulated gratings: psychophysical data from human infants

    Vision Research 40:431–444.

    https://doi.org/10.1016/s0042-6989(99)00188-1

    • PubMed
    • Google Scholar
25. 1. Peterzell DH
    2. Teller DY

    (2000) Spatial frequency tuned covariance channels for red-green and luminance-modulated gratings: psychophysical data from human adults

    Vision Research 40:417–430.

    https://doi.org/10.1016/s0042-6989(99)00187-x

    • PubMed
    • Google Scholar
26. 1. Peterzell DH
    2. Kennedy JF

    (2016) Discovering sensory processes using individual differences: a review and factor analytic manifesto

    Electronic Imaging 28:1–11.

    https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-112

    • Google Scholar
27. 1. Piazza M

    (2010) Neurocognitive start-up tools for symbolic number representations

    Trends in Cognitive Sciences 14:542–551.

    https://doi.org/10.1016/j.tics.2010.09.008

    • PubMed
    • Google Scholar
28. 1. Piazza M
    2. Facoetti A
    3. Trussardi AN
    4. Berteletti I
    5. Conte S
    6. Lucangeli D
    7. Dehaene S
    8. Zorzi M

    (2010) Developmental trajectory of number acuity reveals a severe impairment in developmental dyscalculia

    Cognition 116:33–41.

    https://doi.org/10.1016/j.cognition.2010.03.012

    • PubMed
    • Google Scholar
29. 1. Rammsayer TH
    2. Troche SJ

    (2014) In search of the internal structure of the processes underlying interval timing in the sub-second and the second range: a confirmatory factor analysis approach

    Acta Psychologica 147:68–74.

    https://doi.org/10.1016/j.actpsy.2013.05.004

    • PubMed
    • Google Scholar
30. 1. Ranzini M
    2. Semenza C
    3. Zorzi M
    4. Cutini S

    (2022) Influences of hand action on the processing of symbolic numbers: A special role of pointing?

    PLOS ONE 17:e0269557.

    https://doi.org/10.1371/journal.pone.0269557

    • PubMed
    • Google Scholar
31. 1. Reynaud A
    2. Hess RF

    (2017) Characterization of spatial frequency channels underlying disparity sensitivity by factor analysis of population data

    Frontiers in Computational Neuroscience 11:63.

    https://doi.org/10.3389/fncom.2017.00063

    • PubMed
    • Google Scholar
32. 1. Rose GJ

    (2017) The numerical abilities of anurans and their neural correlates: insights from neuroethological studies of acoustic communication

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 373:20160512.

    https://doi.org/10.1098/rstb.2016.0512

    • PubMed
    • Google Scholar
33. 1. Ross J

    (2003) Visual discrimination of number without counting

    Perception 32:867–870.

    https://doi.org/10.1068/p5029

    • PubMed
    • Google Scholar
34. 1. Sawamura H
    2. Shima K
    3. Tanji J

    (2002) Numerical representation for action in the parietal cortex of the monkey

    Nature 415:918–922.

    https://doi.org/10.1038/415918a

    • PubMed
    • Google Scholar
35. 1. Schneider M
    2. Beeres K
    3. Coban L
    4. Merz S
    5. Susan Schmidt S
    6. Stricker J
    7. De Smedt B

    (2017) Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: a meta-analysis

    Developmental Science 20:12372.

    https://doi.org/10.1111/desc.12372

    • PubMed
    • Google Scholar
36. 1. Simpson WA
    2. McFadden SM

    (2005) Spatial frequency channels derived from individual differences

    Vision Research 45:2723–2727.

    https://doi.org/10.1016/j.visres.2005.01.015

    • PubMed
    • Google Scholar
37. 1. Sixtus E
    2. Krause F
    3. Lindemann O
    4. Fischer MH

    (2023) A sensorimotor perspective on numerical cognition

    Trends in Cognitive Sciences 27:367–378.

    https://doi.org/10.1016/j.tics.2023.01.002

    • PubMed
    • Google Scholar
38. 1. Suzuki TN

    (2016) Semantic communication in birds: evidence from field research over the past two decades

    Ecological Research 31:307–319.

    https://doi.org/10.1007/s11284-016-1339-x

    • Google Scholar
39. 1. Templeton CN
    2. Greene E
    3. Davis K

    (2005) Allometry of alarm calls: black-capped chickadees encode information about predator size

    Science 308:1934–1937.

    https://doi.org/10.1126/science.1108841

    • PubMed
    • Google Scholar
40. 1. Togoli I
    2. Crollen V
    3. Arrighi R
    4. Collignon O

    (2020) The shared numerical representation for action and perception develops independently from vision

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 129:436–445.

    https://doi.org/10.1016/j.cortex.2020.05.004

    • PubMed
    • Google Scholar
41. 1. Togoli I
    2. Marlair C
    3. Collignon O
    4. Arrighi R
    5. Crollen V

    (2021) Tactile numerosity is coded in external space

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 134:43–51.

    https://doi.org/10.1016/j.cortex.2020.10.008

    • PubMed
    • Google Scholar
42. 1. Walsh V

    (2003) A theory of magnitude: common cortical metrics of time, space and quantity

    Trends in Cognitive Sciences 7:483–488.

    https://doi.org/10.1016/j.tics.2003.09.002

    • PubMed
    • Google Scholar
43. 1. Westendorp M
    2. Hartman E
    3. Houwen S
    4. Smith J
    5. Visscher C

    (2011) The relationship between gross motor skills and academic achievement in children with learning disabilities

    Research in Developmental Disabilities 32:2773–2779.

    https://doi.org/10.1016/j.ridd.2011.05.032

    • PubMed
    • Google Scholar
44. 1. Whalen J
    2. Gallistel CR
    3. Gelman R

    (1999) Nonverbal counting in humans: the psychophysics of number representation

    Psychological Science 10:130–137.

    https://doi.org/10.1111/1467-9280.00120

    • Google Scholar
45. 1. Wittlinger M
    2. Wehner R
    3. Wolf H

    (2006) The ant odometer: stepping on stilts and stumps

    Science 312:1965–1967.

    https://doi.org/10.1126/science.1126912

    • PubMed
    • Google Scholar

Author details

1. Giovanni Anobile

   Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   giovanni.anobile@unifi.it

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2796-0661

2. Irene Petrizzo

   Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8736-8278

3. Daisy Paiardini

   Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

4. David Burr

   1. Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
   2. School of Psychology, University of Sydney, Camperdown NSW, Sydney, Australia

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

5. Guido Marco Cicchini

   Institute of Neuroscience, CNR, Pisa, Italy

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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