Zapopan, Jalisco; November 22, 2022.—In 2021, the National Council for Science and Technology (CONACYT) issued a call for proposals Basic and/or Frontier Science: Paradigms and Controversies to enable researchers to evaluate paradigms, hypotheses, and theories that facilitate the understanding of phenomena across various fields of knowledge, providing financial support for projects that aim to analyze open-access (online) data to offer a novel scientific perspective.
That is how Rodrigo Sosa Sánchez, who holds a Ph.D. in Behavioral Science from the School of Education and Psychology at the Universidad Panamericana, Guadalajara campus, with the support of Dr. Emmanuel Alcalá of the Western Institute of Technology and Higher Education (ITESO) and Jonathan J. Buriticá, Associate Professor at the Center for Behavioral Studies and Research ( CEIC), decided to participate with a project on temporal estimation.
Time estimate
Temporal estimation (or interval estimation) is a psychological phenomenon that refers to the ability to structure our behavior in accordance with the temporal patterns of our environment.
For many researchers, this is a puzzling phenomenon, since biologically we have receptors to detect light, sound, textures, and even chemicals—allowing us to adapt to our environment—but we lack specialized receptors to detect the passage of time.
How do we estimate the duration of intervals? The short answer is that we have certain “pacemakers” in our nervous system that allow us to estimate ( with a certain degree of error) the “when” of events occurring around us.
In addition, our own behaviors sometimes serve as a guide to help us determine the right moment to act; this is because we also have receptors that detect the immediate consequences of our own actions.
How do we measure time?
Dr. Sosa Sánchez explains that our ability to estimate time is involved in virtually every aspect of our lives. For example, imagine that when we drive home, we usually stop at an intersection when the traffic light is red. If we assume that the duration of the red light is constant, then this is an opportunity to learn (we learn from the patterns in our environment).
In such a situation, in order to act effectively, we need to follow a sequence of two steps: (1) check to see if the traffic light has turned green, and then (2) step on the gas to continue on our way home. What do we do while we wait for the traffic light to turn from red to green? People do all sorts of things, but the fact is that, almost without exception, we’ll eventually glance at the traffic light to see if it’s safe to step on the gas or if we should wait a little longer.
“The interesting thing is that as we learn, our mistakes are likely distributed in a way similar to a Gaussian curve,” explains Dr. Sosa. In other words, sometimes when we look at a traffic light, it may have already turned green (we underestimate the passage of time) or it may be a fraction of a second away from turning green (we overestimate the passage of time).
The purpose of the project
The aim of this project, in the doctor’s words, is to describe the error-correction mechanisms involved in order to obtain a more accurate estimate of the intervals we regularly encounter. For example, if it takes us longer to look at the traffic light, the driver behind us might honk at us, and we might be startled by the sudden noise. Or, if we look too early, we interrupt the leisure activity or daydreaming that was occupying us while the light was red.
According to Dr. Sosa, the specific goal is to go beyond the organization of behavior once learning has been completed—which is what has been studied most extensively.
Specifically, the focus is on understanding the microstructure of error-correction learning and individual differences in the ability to learn these temporal patterns. In other words, the moment-to-moment adjustments individuals make to adapt to the temporal attributes of their environment, as well as the persistent individual traits that distinguish certain individuals in particular.
The support received
Dr. Sosa states that the commitment made to CONACYT is to develop a series of outputs, including: publishing a scientific article analyzing the data, participating in conferences and workshops, and creating a repository of digital tools for behavioral data analysis (see https://github.com/jealcalat/YEAB).
In addition, the support provided by the Universidad Panamericana of providing office space for Dr. Emmanuel Alcalá, an associate researcher who is now the project leader, as well as organizing one of the lectures included in the public outreach program.
Contribute to our understanding of behavior
Similarly, Sosa states:“Our conviction that we can contribute to the understanding of behavior was the driving force behind this research, since it is often necessaryto take a step back and take the time to think about things differently—usually in an abstract and formalized way.”
“This is a basic science research project—the kind of science that lays the groundwork for any field of knowledge. Thanks to it, we have the principles needed to build a coherent body of knowledge, thereby reducing our uncertainty about the universe around us,” he says.
He also points out that, in contrast,“cutting-edge science or applied science proposes strategies for addressing the phenomenon under study in relation to a real-world problem. Although the latter attracts a great deal of attention, it is important to emphasize that there can be no applied science without a solid foundation in basic science.”
“At this point, we cannot speculate on the results, since the project will involve decomposing the variance in time-series data from multiple individuals using hierarchical structures in terms of units of analysis—whether that be moment-to-moment observations within a single day or across days,” he states.
It also concludes that: “Broadly speaking, we expect to find certain patterns in how behavior is structured given the time constraints. The interesting part will be determining which of the aforementioned factors accounts for the largest proportion of the variance. We willalso identify what proportion of the variance falls outside the scope of the proposed factors—in other words, how much noise there is in the data.”
Research Team
Dr. Rodrigo Sosa Sánchez, Principal Investigator of the project.
Ph.D. in Behavioral Science, Tenured Research Professor (Level B) at the School of Education and Psychology, SNI Level I.
UP Campus Guadalajara
Dr. Emmanuel Alcalá, Research Associate.
Ph.D. in Behavioral Science, Professor in the Department of Mathematics and Physics, SNI Candidate.
Western Institute of Technology and Higher Education (ITESO)
Dr. Jonathan J. Buriticá Buriticá, external contributor.
Ph.D. in Behavioral Science, Associate Professor at the Center for Behavioral Studies and Research (CEIC), SNI Level I.
University of Guadalajara
