The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling

Kennedy, Lauren; Simpson, Daniel; Gelman, Andrew

doi:10.1007/s42113-019-00051-0

Cited by 32 publications

(29 citation statements)

References 14 publications

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“…In real life, an organism can be predicted in a constant manner for some conditions or simulated experiments. Statistical analysis can give a glance in a given event, but not count for universal purpose regarding time and space aspects of the same event [5,24]. The main limitation of statistical analysis is the constant relevant index of performance (parameters) a cognitive system might set for itself.…”

Section: Discussionmentioning

confidence: 99%

“…Since the equation defines the controller as a complex adaptive agent, then as an empirical consequence, time and entropy outputs assume a range of possible performances. However, the probabilistic distributions of I and T assume behavior in a sample space that does not have fixed intervals, since they come from complex adaptive systems (I i ) [2][3][4][5][6] and with a degree of freedom for any resultant that varies from individual to individual [6]. In this way, it is possible to assume that every learning process as well cognitive processing derives not from a predefined sample understanding it as information that is fully objective for the controller (biological system or artificial intelligence) in its potential of apprehension.…”

Section: Method: Productivity Equationmentioning

confidence: 99%

“…A large number of variables that constitute workflows are complex in nature [2] as a complex adaptive system [3]. Statistical analyses on the portion that occupies each category of information processing of a workflow on a scale of 0% to 100% is not recommended for analyzing how an organism process information due to the non-linear nature of the phenomena [4,5]. Since an individual might process information given in several different forms (ad hoc expressions), the complex adaptive system can reach a possible margin of work performances (index of performances).…”

Section: Introductionmentioning

confidence: 99%

“…Since an individual might process information given in several different forms (ad hoc expressions), the complex adaptive system can reach a possible margin of work performances (index of performances). The non-linearity of the complex adaptive systems prevents the possibility of comparing a group of individuals with specific and indeterminate cognitive patterns naturally [5,6], and their forms of work, to generate patterns of execution in the workflows of linear or non-linear dimensions [7][8][9][10]. For this empirical limitation, in order to arrange the best productivity, theoretical control methods can be mandatory to conceive the phenomena in its complex nature.…”

Section: Introductionmentioning

confidence: 99%

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Productivity Equation and the m Distributions of Information Processing in Workflows

Telles¹

2019

ASI

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This research proposes and investigates an equation for productivity in hybrid workflows regarding its robustness towards the definition of workflows as a hybrid probabilistic systems. The proposed equation and its derivations were formulated through a theoretical framework about information theory, probabilities and complex adaptive systems. By defining a productivity equation for organism-machine-environment interactions, discrete and continuous variables that constitute the systems can be controlled by a mathematical framework where prediction and monitoring aspects of optimization are possible without the limitation of strict empirical methods.

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Section: Discussionmentioning

confidence: 99%

Section: Method: Productivity Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Productivity Equation and the m Distributions of Information Processing in Workflows

Telles¹

2019

ASI

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“…A systematic quantitative characterization of model parameters provides knowledge of the likely values of the model parameters and has various benefits. First, it can promote more precise and realistic simulations that help to optimally calibrate and design experiments, avoiding unnecessary experimental costs (Gluth & Jarecki, 2019;Heck & Erdfelder, 2019;Kennedy, Simpson, & Gelman, 2019;Pitt & Myung, 2019;Schad, Betancourt, & Vasishth, 2020). Second, knowledge about the parameter space can be crucial in maximum-likelihood estimation where an informed guess of the starting point of optimization is often key to finding the globally best solution (Myung, 2003).…”

Section: Introductionmentioning

confidence: 99%

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Tran¹,

Maanen²,

Heathcote³

et al. 2020

Preprint

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Parametric cognitive models are increasingly popular tools for analysing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial---if not indispensable---information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters \citep{Lee2018}. In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model \citep[DDM;][]{Ratcliff2008}, the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

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Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

Wesner

Pomeranz

2021

Ecosphere

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Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, and sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models that have a link function and potentially dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non-informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator-prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.

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The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling

Cited by 32 publications

References 14 publications

Productivity Equation and the m Distributions of Information Processing in Workflows

Productivity Equation and the m Distributions of Information Processing in Workflows

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

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