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.

The natural environment is dynamic and moving objects become constantly occluded, engaging the brain in a challenging completion process to estimate where and when the object might reappear. Although motion extrapolation is critical in daily life—imagine crossing the street while an approaching car is occluded by a larger standing vehicle—its neural underpinnings are still not well understood. While the engagement of low‐level visual cortex during dynamic occlusion has been postulated, most of the previous group‐level fMRI‐studies failed to find evidence for an involvement of low‐level visual areas during occlusion. In this fMRI‐study, we therefore used individually defined retinotopic maps and multivariate pattern analysis to characterize the neural basis of visible and occluded changes in motion direction in humans. To this end, participants learned velocity‐direction change pairings (slow motion‐upwards; fast motion‐downwards or vice versa) during a training phase without occlusion and judged the change in stimulus direction, based on its velocity, during a following test phase with occlusion. We find that occluded motion direction can be predicted from the activity patterns during visible motion within low‐level visual areas, supporting the notion of a mental representation of motion trajectory in these regions during occlusion.

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 β > 1 to the data of 12 participants; and better fits of a power function with β < 1 to the data of the remaining nine participants. Of the 35 volunteers, the null hypothesis β = 1 was rejected for 20. The dispersion of the individual β 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.

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.