This series of talks aims at encouraging networking among postdocs and promoting the exchange of ideas for potential collaborations. We have started this series of relatively informal seminars where the postdocs from the departments of Computer Science and Applied Mathematics, and occasionally from other local universities, can showcase their research and foster relationships and collaborations between the two departments and other universities.

Please note: These seminars are given by postdocs but are intended for all types of audience (students are welcome!).

Title:
"Interactions between hierarchical decision-making processes in dynamic environments"

Abstract:
In a constantly changing world, accurate decisions require flexible evidence accumulation where old information is discounted at a rate adapted to the frequency of environmental changes. However, sometimes humans and other animals must simultaneously infer the state of the environment and its volatility (hazard rate). To probe how these estimates impact one another when performed hierarchically, we develop and analyze a model of an ideal observer who makes noisy measurements of a two-state environment with an initially unknown hazard rate that is either high (changes happen often) or low (changes are rare).

Using log-likelihood ratios of the state and hazard rate to represent the observer’s beliefs about the environment, we track how the observer’s estimates evolve over time. We find that the accuracy of the hazard rate estimate builds up slowly, with information at change points (CPs) providing evidence for a high hazard rate and the time between CPs suggesting the hazard rate is low. In contrast, state estimation accuracy drops immediately after CPs when the observer has yet to track the change and recovers at a rate dependent on the observer’s estimated hazard rate. Quantifying this recovery rate, we find that there is a tradeoff between recovery speed and overall state accuracy and that the speed of post-CP recovery changes with trial duration as the observer becomes more confident about their hazard rate estimate.

We then compare our model that includes hazard rate inference to results from a normative model with a known hazard rate to assess how hierarchical inference processes impact state belief. We analyze the normative model using a set of nonlinear partial differential equations (PDEs), leading to faster and more accurate estimates than sampling methods. Comparing our model to this gold standard for state inference, we find that our model’s state inference improves over trial duration to match normative models as the hazard rate is learned. Thus, our setup can be used to identify situations that utilize hierarchical inference strategies and improve dynamic decision-making task design.

This series of talks aims at encouraging networking among postdocs and promoting the exchange of ideas for potential collaborations. We have started this series of relatively informal seminars where the postdocs from the departments of Computer Science and Applied Mathematics, and occasionally from other local universities, can showcase their research and foster relationships and collaborations between the two departments and other universities.

Please note: These seminars are given by postdocs but are intended for all types of audience (students are welcome!).

Title:
"Interactions between hierarchical decision-making processes in dynamic environments"

Abstract:
In a constantly changing world, accurate decisions require flexible evidence accumulation where old information is discounted at a rate adapted to the frequency of environmental changes. However, sometimes humans and other animals must simultaneously infer the state of the environment and its volatility (hazard rate). To probe how these estimates impact one another when performed hierarchically, we develop and analyze a model of an ideal observer who makes noisy measurements of a two-state environment with an initially unknown hazard rate that is either high (changes happen often) or low (changes are rare).

Using log-likelihood ratios of the state and hazard rate to represent the observer’s beliefs about the environment, we track how the observer’s estimates evolve over time. We find that the accuracy of the hazard rate estimate builds up slowly, with information at change points (CPs) providing evidence for a high hazard rate and the time between CPs suggesting the hazard rate is low. In contrast, state estimation accuracy drops immediately after CPs when the observer has yet to track the change and recovers at a rate dependent on the observer’s estimated hazard rate. Quantifying this recovery rate, we find that there is a tradeoff between recovery speed and overall state accuracy and that the speed of post-CP recovery changes with trial duration as the observer becomes more confident about their hazard rate estimate.

We then compare our model that includes hazard rate inference to results from a normative model with a known hazard rate to assess how hierarchical inference processes impact state belief. We analyze the normative model using a set of nonlinear partial differential equations (PDEs), leading to faster and more accurate estimates than sampling methods. Comparing our model to this gold standard for state inference, we find that our model’s state inference improves over trial duration to match normative models as the hazard rate is learned. Thus, our setup can be used to identify situations that utilize hierarchical inference strategies and improve dynamic decision-making task design.