Who am I?
I am a Postdoctoral Research Fellow at the Centre for Brain, Mind and Markets at the University of Melbourne.
My general research interests lie at the intersection of human decision-making and neuroscience. I am particularly interested in understanding how computational resource constraints affect decision-making. I study how these constraints affect human capabilities and how humans, and their brains, cope with these biological limitations. To address these questions, I use insights from different disciplines, including computational complexity, experimental economics, and neuroscience. I use computational complexity theory to characterise the complexity of tasks and study its effect on decision-making and the underlying neural dynamics.
I am passionate about promoting digital literacy and open science. Indeed, for several years I supported the R/RStudio community at the University of Melbourne by teaching workshops, organising conferences, and coordinating coding meetups.
I have several academic degrees and professional experiences that have provided me with a diverse academic background. I hold a PhD in Decision, Risk and Financial Sciences from the University of Melbourne. Additionally, I have earned two bachelor's degrees, one in Mathematics and the other in Economics, both from the Universidad de los Andes. Further, I have a master's degree in Neuroeconomics from Maastricht University, during which I gained hands-on research experience through an internship at Caltech. Before pursuing my postgraduate studies, I worked as an analyst in the financial stability department at the central bank of Colombia.
Generic properties of a computational task predict human effort and performance
It has been shown that computational hardness of cognitive tasks affects people’s effort and ability to solve problems reliably. However, prior empirical studies lack generality. They quantify computational hardness of tasks based on particular algorithms or for specific problems. Here, we propose a set of measures of computational hardness of individual instances of a task in a way that is independent of any algorithm or computational model and can be generalized to other problems. Specifically, we introduce two measures, typical-case complexity (TCC), a measure of average hardness of a random ensemble of instances, and instance complexity (IC), an instance-specific metric. Both measures are related to structural properties of instances. We then test the effect of those measures on human behavior by asking participants to solve instances of two variants of the 0-1 knapsack problem, a canonical and ubiquitous NP-hard problem. We find that participants spent more time on instances with higher TCC and IC, but that decision quality was lower in those instances. We propose that the study of mathematical properties of tasks related to computational hardness can contribute to the development of computationally plausible accounts of human decision-making, just like stochastic properties have proven to be critical to our understanding of human decisions in probabilistic tasks.
The neural dynamics associated with computational complexity
Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in two task-independent measures of intrinsic computational hardness: complexity and proof hardness. We characterise a network of brain regions whose activation was correlated with both measures but in distinct ways, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity and proof hardness, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks.
Task-independent metrics of computational hardness predict human cognitive performance
The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness.