Research Article: Timelines of past events: Reconstructive retrieval of temporal patterns

Date Published: December 8, 2011

Publisher: University of Finance and Management in Warsaw

Author(s): Maria G. Carell.

http://doi.org/ 10.2478/v10053-008-0101-5

Abstract

Most naturalistic events are temporally and structurally complex in that they
comprise a number of elements and that each element may have different onset and
offset times within the event. This study examined temporal information
processing of complex patterns of partially overlapping stimulus events by using
2 tasks of temporal processing. Specifically, participants observed a pantomime
in which 5 actors appeared on the scene for different periods of time. At test,
they estimated the duration each actor was present or reconstructed the temporal
pattern of the pantomime by drawing a timeline for each actor. Participants made
large errors in the time estimation task, but they provided relatively accurate
responses by using the timeline as a retrieval support. These findings suggest
that temporal processing of complex asynchronous events is a challenging
cognitive task, but that reliance on visuo-spatial retrieval support, possibly
in combination with other temporal heuristics, may produce functional
approximations of complex temporal patterns.

Partial Text

Most people have a variety of tasks to complete during an ordinary day. Typically,
these tasks are not serial in that they follow a sequential timeline. Instead, many
everyday activities are partially overlapping with different onset and offset times,
and their completion also requires monitoring, rescheduling, and updating. For
example, one needs to remember to take medication before breakfast while preparing
coffee and boiling milk, and later having a lunch with a colleague who reminds one
that the meeting at 2 p.m. was postponed 2 hr. Most of these and other daily
activities require some form of temporal orientation as they should be completed
within a limited time window while completing other activities.

The timing data of both tasks were analyzed in terms of absolute and relative errors.
The former measure, referred to as the absolute timing error, was
obtained by calculating the absolute difference between the observed and expected
(actual) durations for each stimulus actor. For example, if the expected time was 95
s and the observed time was 80 s, then the absolute error would have been 15 s.
Relative timing error was based on a ratio between the expected
and observed duration (e.g., 95/80 = 1.19). This measure provides a standard score
across the different time intervals, with coefficients above 1.0 reflecting
overproductions and coefficients below 1.0 reflecting underproductions (see also
Brown, 1985; Carelli, Forman, & Mäntylä, 2008). The timeline
data was obtained by first transforming each response time to time units (where 10
mm = 6 s), and then calculating absolute and relative timing errors as indicated
above.

The starting point of this study was the observation that past timing research is not
easily applied to complex everyday tasks, which often involve multiple activities
with different onset and offset times. In this study, we examined the hypothesis
that temporal information processing of complex event information is based on
reconstructive retrieval operations, rather than on timing of absolute durations. We
hypothesized that, instead of relying on multiple mental clocks and costly
computations of time setting and monitoring, a more flexible strategy might be to
use a variety of temporal aids and heuristics in order to reconstruct and constrain
the temporal pattern of the observed event. The timeline task was used as a
procedure for examining these reconstructive processes in experimental settings.

 

Source:

http://doi.org/ 10.2478/v10053-008-0101-5

 

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