Effect of Presentation Format on Judgment of Long-Range Time Intervals

Front Psychol. 2019 Jun 28;10:1479. doi: 10.3389/fpsyg.2019.01479. eCollection 2019.


Investigations in the temporal estimation domain are quite vast in the range of milliseconds, seconds, and minutes. This study aimed to determine the psychophysical function that best describes long-range time interval estimation and evaluate the effect of numerals in duration presentation on the form of this function. Participants indicated on a line the magnitude of time intervals presented either as a number + time-unit (e.g., "9 months"; Group I), unitless numerals (e.g., "9"; Group II), or tagged future personal events (e.g., "Wedding"; Group III). The horizontal line was labeled rightward ("Very short" = >"Very long") or leftward ("Very long" = >"Very short") for Group I and II, but only rightward for Group III. None of the linear, power, logistic or logarithmic functions provided the best fit to the individual participant data in more than 50% of participants for any group. Individual power exponents were different only between the tagged personal events (Group III) and the other two groups. When the same analysis was repeated for the aggregated data, power functions provided a better fit than other tested functions in all groups with a difference in the power function parameters again between the tagged personal events and the other groups. A non-linear mixed effects analysis indicated a difference in the power function exponent between Group III and the other groups, but not between Group I and II. No effect of scale directionality was found in neither of the experiments in which scale direction was included as independent variable. These results suggest that the judgment of intervals in a number + time-unit presentation invoke, at least in part, processing mechanisms other than those used for time-domain. Consequently, we propose the use of event-tagged assessment for characterizing long-range interval representation. We also recommend that analyses in this field should not be restricted to aggregated data given the qualitative variation between participants.

Keywords: model comparison; numerical estimation; personal events; power functions; temporal estimation.