Two sources of uncertainty independently modulate temporal expectancy

Abstract

The environment is shaped by two sources of temporal uncertainty: the discrete probability of whether an event will occur and—if it does—the continuous probability of when it will happen. These two types of uncertainty are fundamental to every form of anticipatory behavior including learning, decision-making, and motor planning. It remains unknown how the brain models the two uncertainty parameters and how they interact in anticipation. It is commonly assumed that the discrete probability of whether an event will occur has a fixed effect on event expectancy over time. In contrast, we first demonstrate that this pattern is highly dynamic and monotonically increases across time. Intriguingly, this behavior is independent of the continuous probability of when an event will occur. The effect of this continuous probability on anticipation is commonly proposed to be driven by the hazard rate (HR) of events. We next show that the HR fails to account for behavior and propose a model of event expectancy based on the probability density function of events. Our results hold for both vision and audition, suggesting independence of the representation of the two uncertainties from sensory input modality. These findings enrich the understanding of fundamental anticipatory processes and have provocative implications for many aspects of behavior and its neural underpinnings.

FMTP: A unifying computational framework of temporal preparation across time scales.

“…Several studies on temporal preparation (Bausenhart et al, 2010;Grabenhorst et al, 2019Grabenhorst et al, , 2021Grosjean et al, 2001;Hohle, 1965;Jepma et al, 2012;Leth-Steensen, 2009;Seibold et al, 2011;Tomassini et al, 2019) have sought to assess which components of information processing are affected by temporal preparation within the theoretical framework sequential sampling models (SSMs). SSMs assume that a response on each trial results from a noisy process of evidence accumulation toward a decision boundary.…”

Underestimation in temporal numerosity judgments computationally explained by population coding model

“…Due to the uncertainty, the population activity for numerosity needs to be integrated across time. Temporal uncertainty can be modeled by using a Gaussian function along a temporal dimension 45 . We assumed that representation of the number of signals was integrated within a temporal window of integration 46 – 49 .…”

Temporal Preparation Drives Statistical Learning of Regular Intervals in Complex Temporal Environments

“…Our findings, demonstrating that statistical learning effects can emerge from temporal preparation, instantiate this perspective in the temporal domain. Similar to these memory models of statistical learning, Salet et al (2022) demonstrated that temporal preparation can naturally emerge from associative memory processes, without the need for a system that calculates hazards (Janssen & Shadlen, 2005; Trillenberg et al, 2000) or tracks probabilities (Grabenhorst et al, 2021; Grabenhorst et al, 2019). As a proof of concept, we showed that f MTP’s associative learning rules indirectly give rise to ‘regularity benefits’ in WAM (Figure 6).…”