Performance Hall

at Ohio Union

Jul 26 @ 09:00 UTC - Jul 26 @ 10:25 UTC

Public session

Jul 26 @ 10:40 UTC - Jul 26 @ 12:00 UTC

Public session

There are enormous incentives in all of science to exploit current knowledge and theory rather than explore new territory and develop new theory. Publications are the lifeblood of scientists and are easier to obtain by research exploiting what is already published, and by confirming current theoretical beliefs. Scientists’ training is extensive and narrowly focused in certain domains using specific techniques and testing a small range of theories, making it difficult to carry out research in any different way or with a different focus. Funding is easier to obtain and submissions to journals easier to succeed for research consonant with the beliefs of the reviewers, editors and granting agencies. Scientists never want their results to be challenged and their theories to be replaced. Scientists carry out their research with students and postdocs and need to see that the trainees publish, making exploration projects risky for them. Recent trends in Cognitive Science seem designed to further exacerbate these existing trends to exploit rather than explore: Scientists are already using Large Language Models (like Chat GPT) in every aspect of their profession, something sure to increase, and likely to homogenize research. The so-called reproducibility crisis and certain aspects of the open science movement demand replicability and foster ‘safe’ exploitation rather than ‘dangerous’ exploration. These trends are unfortunately likely to increase over the next twenty years. Countering these trends are scientists’ strong curiosity, something that seems to be found in young children (e.g. Alison Gopnik’s depiction of children are scientists). A bias to explore is likely produced by scientists’ motivation to obtain new measurement methods using new equipment, knowing that results not obtainable previously are a sure oath to success. That is, most of the important advances in science occur when new and unexpected results are found and existing theory is replaced, a factor motivating at least some scientists. There are some examples of foundations and private organizations funding basic research, probably more on the exploration than exploitation side of the ledger. Researchers seeking patents and researchers funded in business and industry, largely based on a profit motive, are probably mostly exploiting than exploring, but the distinction is rather fuzzy if one considers drug and biotech companies (and one cannot forget the considerable funding Bell Labs once put into basic research). This summary appears to suggest a strong exploitation bias, but if so, is this optimal? A proper balance of exploration and exploitation is needed to make maximum progress toward any of the main goals of science, and the optimal balance point likely differs for different goals. This symposium is aimed to discuss these issues and possible changes in practice that might enhance scientific progress toward any of its goals. The problem is one faced by all of science, including the fields represented by members of the Society for Mathematical Psychology.

Jul 26 @ 13:20 UTC - Jul 26 @ 14:20 UTC

Public session

Jul 26 @ 14:40 UTC - Jul 26 @ 16:00 UTC

Public session

There are enormous incentives in all of science to exploit current knowledge and theory rather than explore new territory and develop new theory. Publications are the lifeblood of scientists and are easier to obtain by research exploiting what is already published, and by confirming current theoretical beliefs. Scientists’ training is extensive and narrowly focused in certain domains using specific techniques and testing a small range of theories, making it difficult to carry out research in any different way or with a different focus. Funding is easier to obtain and submissions to journals easier to succeed for research consonant with the beliefs of the reviewers, editors and granting agencies. Scientists never want their results to be challenged and their theories to be replaced. Scientists carry out their research with students and postdocs and need to see that the trainees publish, making exploration projects risky for them. Recent trends in Cognitive Science seem designed to further exacerbate these existing trends to exploit rather than explore: Scientists are already using Large Language Models (like Chat GPT) in every aspect of their profession, something sure to increase, and likely to homogenize research. The so-called reproducibility crisis and certain aspects of the open science movement demand replicability and foster ‘safe’ exploitation rather than ‘dangerous’ exploration. These trends are unfortunately likely to increase over the next twenty years. Countering these trends are scientists’ strong curiosity, something that seems to be found in young children (e.g. Alison Gopnik’s depiction of children are scientists). A bias to explore is likely produced by scientists’ motivation to obtain new measurement methods using new equipment, knowing that results not obtainable previously are a sure oath to success. That is, most of the important advances in science occur when new and unexpected results are found and existing theory is replaced, a factor motivating at least some scientists. There are some examples of foundations and private organizations funding basic research, probably more on the exploration than exploitation side of the ledger. Researchers seeking patents and researchers funded in business and industry, largely based on a profit motive, are probably mostly exploiting than exploring, but the distinction is rather fuzzy if one considers drug and biotech companies (and one cannot forget the considerable funding Bell Labs once put into basic research). This summary appears to suggest a strong exploitation bias, but if so, is this optimal? A proper balance of exploration and exploitation is needed to make maximum progress toward any of the main goals of science, and the optimal balance point likely differs for different goals. This symposium is aimed to discuss these issues and possible changes in practice that might enhance scientific progress toward any of its goals. The problem is one faced by all of science, including the fields represented by members of the Society for Mathematical Psychology.

Jul 26 @ 16:00 UTC - Jul 26 @ 17:00 UTC

Public session

Jul 27 @ 09:00 UTC - Jul 27 @ 10:20 UTC

Public session

Jul 27 @ 10:40 UTC - Jul 27 @ 00:20 UTC

Public session

This symposium brings together recent advances from the vibrant intersection of deep learning, Bayesian inference, and computational cognitive models. The synthesis of these fields enables researchers to develop, fit, compare, and validate high-fidelity models of cognition which otherwise remain out of reach for standard statistical methods (e.g., maximum likelihood estimation of Markov Chain Monte Carlo). However, many aspects of this emerging new generation of computational tools remains underutilized in cognitive modeling. Thus, the symposium aims to highlight both methodological progress in the development of generative neural architectures for hyper efficient (aka amortized) statistical inference, as well as novel applications of these methods to substantive questions regarding the representation of cognitive processes. The methods part will focus on new approaches to robust amortized inference and new methods for comparing cognitive models on massive data sets. The symposium then highlights a few applications that benefit from simulation based inference approaches toward scientific progress. Topics will include an examination of how dynamic decision-making models can be compared using both posterior parameter estimation (using a free parameter to control the way evidence is represented) and model comparison using probabilistic classifiers. Another talk will investigate age differences in a standard diffusion decision model vs. the Ornstein-Uhlenbeck model. It will also include talks that highlight modular aspects of the simulation-based inference toolkit, including how frontier reinforcement learning models can be combined with arbitrary sequential sampling models to test novel mechanistic hypotheses concerning intertemporal (across trial) learning dynamics.

Jul 27 @ 13:40 UTC - Jul 27 @ 14:40 UTC

Public session

Jul 27 @ 15:00 UTC - Jul 27 @ 16:00 UTC

Public session

Jul 28 @ 09:00 UTC - Jul 28 @ 10:20 UTC

Public session

Jul 28 @ 10:40 UTC - Jul 28 @ 12:00 UTC

Public session

Jul 28 @ 13:40 UTC - Jul 28 @ 14:40 UTC

Public session

Jul 28 @ 15:00 UTC - Jul 28 @ 16:00 UTC

Public session