Fig 1.
The Continuous Temporal Expectancy Task results in large variation in reaction times.
(A) Illustration of the CTET paradigm (adapted from [44]) with stimuli presented for either 600 ms (900 ms experiment 2) if they were standard stimuli or for 1200 ms (1600 ms experiment 2) if they were targets. (B) Example sequence of reaction times exhibiting large variation to the 100 target images shown.
Fig 2.
A model of attention fluctuations to explain non-random fluctuations in reaction times.
(A) Our model is based on the hypothesis that attention fluctuates on a spectrum from highly external to highly internal with a non-random temporal structure, shown here for a DFA exponent of 0.8. The black dots indicate moments that target stimuli appear in the CTET experiment, which results in (B) a reaction-time series with a similar temporal structure under the assumption that reaction times are shorter when attention is strongly focused on external as opposed to internal sources of information. (C) 1/f signal produced with simulated sampling, showed a robust estimation of underlying temporal correlation with infrequent, semi-random sampling (p <.00001).
Fig 3.
Weak LRTC of reaction-time series are associated with fast reaction times.
The observed correlation (R2 = .52, p = .00002), shows that better performance is associated with less variability.
Table 1.
Results PANAS (positive affect).
Table 2.
Results PANAS (negative affect).
Fig 4.
Mood has an effect on average reaction time and reaction-time temporal structure.
Participants in the negative mood condition showed worse performnce than particiapnts in the neutral (t65 = 2.07, p = .042) and positve mood condition (t40 = 2.39, p = .022). Additionally, the temporal strucutre of reaction time series differed between positive—negative (t40 = 2.53, p = .016), Error bars represent 95% confidence intervals.
Fig 5.
More mind wandering episodes are associated with increased LRTC of response-time series.
The Correlation between DFA the of the subjective rating of attention, or mind wandering episodes shows that variability increases with more mind wandering (R2 = .43, p = .01).