Explicitly versus implicitly driven temporal expectations: No evidence for altered perceptual processing due to top-down modulations.

Learning the statistical regularities of environmental events is a powerful tool for enhancing performance. However, it remains unclear whether this often implicit type of behavioral facilitation can be proactively modulated by explicit knowledge about temporal regularities. Only recently, Menceloglu and colleagues (Attention, Perception & Psychophysics, 79(1), 169-179, 2017) tested for differences between implicit versus explicit statistical learning of temporal regularities by using a within-paradigm manipulation of metacognitive temporal knowledge. The authors reported that temporal expectations were enhanced if participants had explicit knowledge about temporal regularities. Here, we attempted to replicate and extend their results, and to provide a mechanistic framework for any effects by means of computational modelling. Participants performed a letter-discrimination task, with target letters embedded in congruent or incongruent flankers. Temporal predictability was manipulated block-wise, with targets occurring more often after either a short or a long delay period. During the delay a sound was presented in half of the trials. Explicit knowledge about temporal regularities was manipulated by changing instructions: Participants received no information (implicit), information about the most likely cue-target delay (explicit), or received 100% valid cues on each trial (highly explicit). We replicated previous effects of target-flanker congruence and sound presence. However, no evidence was found for an effect of explicit knowledge on temporal expectations using Bayesian statistics. Concordantly, computational modelling suggested that explicit knowledge may only influence non-perceptual processing such as response criteria. Together, our results indicate that explicit metacognitive knowledge does not necessarily alter sensory representations or temporal expectations but rather affects response strategies.

Individual differences in long-range time representation.

On the basis of experimental data, long-range time representation has been proposed to follow a highly compressed power function, which has been hypothesized to explain the time inconsistency found in financial discount rate preferences. The aim of this study was to evaluate how well linear and power function models explain empirical data from individual participants tested in different procedural settings. The line paradigm was used in five different procedural variations with 35 adult participants. Data aggregated over the participants showed that fitted linear functions explained more than 98% of the variance in all procedures. A linear regression fit also outperformed a power model fit for the aggregated data. An individual-participant-based analysis showed better fits of a linear model to the data of 14 participants; better fits of a power function with an exponent ß > 1 to the data of 12 participants; and better fits of a power function with ß < 1 to the data of the remaining nine participants. Of the 35 volunteers, the null hypothesis ß = 1 was rejected for 20. The dispersion of the individual ß values was approximated well by a normal distribution. These results suggest that, on average, humans perceive long-range time intervals not in a highly compressed, biased manner, but rather in a linear pattern. However, individuals differ considerably in their subjective time scales. This contribution sheds new light on the average and individual psychophysical functions of long-range time representation, and suggests that any attribution of deviation from exponential discount rates in intertemporal choice to the compressed nature of subjective time must entail the characterization of subjective time on an individual-participant basis.