Biology subjects

Franco, J. P.

Publications and source records attributed to Franco, J. P..

3 recordsLinked to original sources

bioRxiv2022

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.

neuroscience↗

bioRxiv2021

Task-independent metrics of computational hardness predict performance of human problem-solving

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. TeaserThe ability of humans to solve cognitive tasks is affected by generic mathematical properties of problems related to their computational complexity.

animal behavior and cognition↗

bioRxiv2018

Where the really hard choices are: A general framework to quantify decision difficulty

Life presents us with decisions of varying degrees of difficulty. Many of them are NP-hard, that is, they are computationally intractable. Two important questions arise: which properties of decisions drive extreme computational hardness and what are the effects of these properties on human-decision making? Here, we postulate that we can study the effects of computational complexity on human decision-making by studying the mathematical properties of individual instances of NP-hard problems. We draw on prior work in computational complexity theory, which suggests that computational difficulty can be characterized based on the features of instances of a problem. This study is the first to apply this approach to human decision-making. We measured hardness, first, based on typical-case complexity (TCC), a measure of average complexity of a random ensemble of instances, and, second, based on instance complexity (IC), a measure that captures the hardness of a single instance of a problem, regardless of the ensemble it came from. We tested the relation between these measures and (i) decision quality as well as (ii) time expended in a decision, using two variants of the 0-1 knapsack problem, a canonical and ubiquitous computational problem. We show that participants expended more time on instances with higher complexity but that decision quality was lower in those instances. These results suggest that computational complexity is an inherent property of the instances of a problem, which affect human and other kinds of computers.

animal behavior and cognition↗