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. 2018 Mar 7;1:4.
doi: 10.1038/s41746-017-0011-3. eCollection 2018.

The Details of Past Actions on a Smartphone Touchscreen Are Reflected by Intrinsic Sensorimotor Dynamics

Free PMC article

The Details of Past Actions on a Smartphone Touchscreen Are Reflected by Intrinsic Sensorimotor Dynamics

Myriam Balerna et al. NPJ Digit Med. .
Free PMC article


Unconstrained day-to-day activities are difficult to quantify and how the corresponding movements shape the brain remain unclear. Here, we recorded all touchscreen smartphone interactions at a sub-second precision and show that the unconstrained day-to-day behavior captured on the phone reflects in the simple sensorimotor computations measured in the laboratory. The behavioral diversity on the phone, the speed of interactions, the amount of social & non-social interactions, all uniquely influenced the trial-to-trial motor variability used to measure the amount of intrinsic neuronal noise. Surprisingly, both the motor performance and the early somatosensory cortical signals (assessed using EEG in passive conditions) became noisier with increased social interactions. Inter-individual differences in how people use the smartphone can help thus decompose the structure of low-level sensorimotor computations.

Keywords: Neurology; Sensorimotor processing.

Conflict of interest statement

Competing interestsAuthor A.G. is an inventor of a patent-pending technology used in this manuscript and co-founder of QuantActions GmbH, a for-profit company focused on smartphone touchscreen behavior. All other authors declare no competing financial interests.


Fig. 1
Fig. 1
The history of unconstrained touchscreen behavior reflects on the performance of a simple sensorimotor task. a Touchscreen activity was recorded for 21 days and followed by laboratory measurements of sensorimotor variability. b The task required responding to tactile stimuli by pressing and releasing a micro switch, as fast as possible, with the thumb. Reaction time is the time from the sensory stimulus to the press-down action and movement time from the pressed position to the release. c–f Adjusted response plots. Movement time variability (σ) was inversely proportional to the typical rate at which the touchscreen was used (c) the number of Apps used (d) and the amount of activity on Non-social Apps (e). The variability was directly proportional to the amount of activity on Social Apps (f). Linear regression statistics is imprinted on the figures. Insert in figure c shows how the strength of the relationship with the typical rate varies as a function of the data collection period [assessed by using a 72 h sliding window, sliding with 12 h steps] and that the data collected 10.5 days prior to the experiment yielded the strongest correlation
Fig. 2
Fig. 2
Early cortical somatosensory processing reflects the history of Social App usage. a We estimated the signal-to-noise ratio in the cortical responses upon a brief tactile stimulus presented to the right thumb tip, the hand was in a resting position during the recording. The head plot shows the electrode location with the best response (red). b Putative signal-to-noise ratio (SNR) at the electrode (SS, sum of squares). Individual volunteers (gray lines) and population mean (black). c Scalp map of SNR at 80 ms post stimulation. d Event-related coefficients with the SNR as dependent variable and touchscreen parameters based on the entire 21 days of recording as explanatory variables. Statistically significant coefficients (thickened lines, p < 0.05, corrected for multiple comparisons, ANOVA). d’ The strength of the relationship with the typical rate varies as a function of the data collection period [assessed by using a 72 h sliding window, sliding with 12 h steps]. The data collected 9 days prior to the experiment yielded the strongest correlation. e–h Head plots of the coefficients and the corresponding variables. The statistics included all electrodes and time points, but select time points are shown to illustrate the statistical maps of the significant relationships

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