6 papers
Chronocooked: A Benchmark for Interval Timing in Reinforcement Learning Agents
Amrapali Pednekar, Alvaro Garrido-Perez, Yara Khaluf +1
Interval timing is extensively studied as an important aspect of human behaviour. As artificial agents are increasingly designed to function alongside humans, their interval timing…
Including the Cost of Irreducible Uncertainty in the Policy Compression Framework
Álvaro Garrido-Pérez, Pieter Simoens, Amrapali Pednekar +1
AI decision-support systems can benefit from anticipating biases in human decision-making. Many such biases may arise from human cognitive limitations. The policy compression frame…
Modulation of temporal decision-making in a deep reinforcement learning agent under the dual-task paradigm
Amrapali Pednekar, Álvaro Garrido-Pérez, Yara Khaluf +1
This study explores the interference in temporal processing within a dual-task paradigm from an artificial intelligence (AI) perspective. In this context, the dual-task setup is im…
Cognitive Effort in the Two-Step Task: An Active Inference Drift-Diffusion Model Approach
Alvaro Garrido Perez, Viktor Lemoine, Amrapali Pednekar +2
High-level theories rooted in the Bayesian Brain Hypothesis often frame cognitive effort as the cost of resolving the conflict between habits and optimal policies. In parallel, evi…
Emergent time-keeping mechanisms in a deep reinforcement learning agent performing an interval timing task
Amrapali Pednekar, Alvaro Garrido, Pieter Simoens +1
Drawing parallels between Deep Artificial Neural Networks (DNNs) and biological systems can aid in understanding complex biological mechanisms that are difficult to disentangle. Te…
Predicting change in time production -- A machine learning approach to time perception
Amrapali Pednekar, Alvaro Garrido, Yara Khaluf +1
Time perception research has advanced significantly over the years. However, some areas remain largely unexplored. This study addresses two such under-explored areas in timing rese…