Optimised information gathering in smartphone users

TL;DR

This study links the fractal-like patterns of human smartphone interactions to optimal information gathering strategies, revealing distinct temporal patterns for checking and sharing content that optimize efficiency.

Contribution

It demonstrates that human touchscreen interactions follow power-law distributions aligned with optimal information gathering and sharing strategies, supported by empirical data and simulations.

Findings

Inter-touch intervals follow power-law distributions over 5 orders of magnitude.

The checking pattern with an exponent of 1.5 is optimal for short-term information gathering.

Content sharing patterns with an exponent of 1.3 are aligned with maximal efficiency.

Abstract

Human activities from hunting to emailing are performed in a fractal-like scale invariant pattern. These patterns are considered efficient for hunting or foraging, but are they efficient for gathering information? Here we link the scale invariant pattern of inter-touch intervals on the smartphone to optimal strategies for information gathering. We recorded touchscreen touches in 65 individuals for a month and categorized the activity into checking for information vs. sharing content. For both categories, the inter-touch intervals were well described by power-law fits spanning 5 orders of magnitude, from 1 s to several hours. The power-law exponent typically found for checking was 1.5 and for generating it was 1.3. Next, by using computer simulations we addressed whether the checking pattern was efficient - in terms of minimizing futile attempts yielding no new information. We find that the best performing power law exponent depends on the duration of the assessment and the exponent of 1.5 was the most efficient in the short-term i.e. in the few minutes range. Finally, we addressed whether how people generated and shared content was in tune with the checking pattern. We assumed that the unchecked posts must be minimized for maximal efficiency and according to our analysis the most efficient temporal pattern to share content was the exponent of 1.3 - which was also the pattern displayed by the smartphone users. The behavioral organization for content generation is different from content consumption across time scales. We propose that this difference is a signature of optimal behavior and the short-term assessments used in modern human actions.