TY - CONF
T1 - Judging from experience: experienced sequences are predicted better than described sequences
AU - Kusev, Petko
AU - van Schaik, Paul
AU - Juliusson, Asgeir
AU - Kiley, Yvette
N1 - When attempting to predict future events, people commonly rely on historical data. Events in a time series can be experienced sequentially (dynamic mode), as in learning about decisions from experience (e.g., Kusev et al., in press, JEP:HPP), or, as with learning about decisions from descriptions, they can also be retrospectively viewed holistically (static mode) \ not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on three sorts of judgments: (i) predictions of the next event (forecast), (ii) estimation of the average value of all the events in the presented series (average) and (iii) judged satisfaction of workers given that the series represented their monthly income (satisfaction). Relative to the static mode participants\ responses in dynamic mode were anchored on more recent events for all three types of judgments but with different consequences \ hence dynamic presentation improved prediction accuracy, but not estimation.
PY - 2011
Y1 - 2011
N2 - When attempting to predict future events, people commonly rely on historical data. Events in a time series can be experienced sequentially (dynamic mode), as in learning about decisions from experience (e.g., Kusev et al., in press, JEP:HPP), or, as with learning about decisions from descriptions, they can also be retrospectively viewed holistically (static mode) – not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on three sorts of judgments: (i) predictions of the next event (forecast), (ii) estimation of the average value of all the events in the presented series (average) and (iii) judged satisfaction of workers given that the series represented their monthly income (satisfaction). Relative to the static mode participants‘ responses in dynamic mode were anchored on more recent events for all three types of judgments but with different consequences – hence dynamic presentation improved prediction accuracy, but not estimation.
AB - When attempting to predict future events, people commonly rely on historical data. Events in a time series can be experienced sequentially (dynamic mode), as in learning about decisions from experience (e.g., Kusev et al., in press, JEP:HPP), or, as with learning about decisions from descriptions, they can also be retrospectively viewed holistically (static mode) – not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on three sorts of judgments: (i) predictions of the next event (forecast), (ii) estimation of the average value of all the events in the presented series (average) and (iii) judged satisfaction of workers given that the series represented their monthly income (satisfaction). Relative to the static mode participants‘ responses in dynamic mode were anchored on more recent events for all three types of judgments but with different consequences – hence dynamic presentation improved prediction accuracy, but not estimation.
UR - http://www.psychonomic.org/annual-meeting.html
M3 - Paper
T2 - 52nd Annual Meeting of the Psychonomic Society
Y2 - 3 November 2011 through 6 November 2011
ER -