Literature DB >> 32795062

Generalized priority-based model for smartphone screen touches.

Jean-Pascal Pfister1, Arko Ghosh2.   

Abstract

The distribution of intervals between human actions such as email posts or keyboard strokes demonstrates distinct properties at short versus long timescales. For instance, at long timescales, which are presumably controlled by complex process such as planning and decision making, it has been shown that those interevent intervals follow a scale-invariant (or power-law) distribution. In contrast, at shorter timescales-which are governed by different processes such as sensorimotor skill-they do not follow the same distribution and we know little about how they relate to the scale-invariant pattern. Here, we analyzed 9 million intervals between smartphone screen touches of 84 individuals which span several orders of magnitudes (from milliseconds to hours). To capture these intervals, we extend a priority-based generative model to smartphone touching events. At short timescale, the model is governed by refractory effects, while at longer timescales, the intertouch intervals are governed by the priority difference between smartphone tasks and other tasks. The flexibility of the model allows us to capture interindividual variations at short and long timescales, while its tractability enables efficient model fitting. According to our model, each individual has a specific power-law exponent which is tightly related to the effective refractory time constant suggesting that motor processes which influence the fast actions are related to the higher cognitive processes governing the longer interevent intervals.

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Year:  2020        PMID: 32795062     DOI: 10.1103/PhysRevE.102.012307

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  2 in total

1.  Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions.

Authors:  Enea Ceolini; Ruchella Kock; Guido P H Band; Gijsbert Stoet; Arko Ghosh
Journal:  iScience       Date:  2022-08-05

2.  Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy.

Authors:  Robert B Duckrow; Enea Ceolini; Hitten P Zaveri; Cornell Brooks; Arko Ghosh
Journal:  iScience       Date:  2021-05-13
  2 in total

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