scholarly journals A Tutorial on Cognitive Modeling for Cognitive Aging Researchers

2020 ◽  
Author(s):  
Nathaniel R. Greene ◽  
Stephen Rhodes
Keyword(s):  
Age Differences ◽  
Cognitive Processes ◽  
Cognitive Aging ◽  
Cognitive Modeling ◽  
Hierarchical Modeling ◽  
Model Fitting ◽  
Cognitive Models ◽  
Modeling Software ◽  
Fitting In ◽  
Observed Behavior

Cognitive aging researchers are interested in understanding how cognitive processes change in old age, but the standard analyses used on observed behavior (e.g., ANOVA) are inappropriate for measuring age differences in latent cognitive processes. Cognitive models formalize the relationship between underlying processes and observed behavior and are more suitable for identifying what processes are associated with aging. This article provides a tutorial on how to fit and interpret cognitive models to measure age differences in cognitive processes. We work with an example of a two choice discrimination task and describe how to fit models in the highly flexible modeling software Stan. We describe how to use hierarchical modeling to estimate both group and individual effects simultaneously, and we detail model fitting in a Bayesian statistical framework, which, among other benefits, enables aging researchers to quantify evidence for null effects. We contend that more widespread use of cognitive modeling among cognitive aging researchers may be useful for addressing potential issues of non-replicability in the field, as cognitive modeling is more suitable to addressing questions about what cognitive processes are (or are not) affected by aging.

scholarly journals Troubleshooting Bayesian cognitive models: A tutorial with matstanlib

2021 ◽  
Author(s):  
Beth Baribault ◽  
Anne Collins
Keyword(s):  
Cognitive Processes ◽  
Bayesian Model ◽  
Cognitive Modeling ◽  
Computational Models ◽  
Model Fitting ◽  
Psychological Research ◽  
Cognitive Models ◽  
Model Output ◽  
Mcmc Sampling ◽  
Bayesian Cognitive Modeling

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software such as Stan and PyMC3 that efficiently automates the Markov chain Monte Carlo (MCMC) sampling used for Bayesian model fitting. Unfortunately, Bayesian cognitive models can struggle to pass the computational checks required of all Bayesian models. If any failures are left undetected, inferences about cognition based on model output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and MCMC sampling, we outline the diagnostic metrics, procedures, and plots necessary to identify problems in model output with an emphasis on how these requirements have recently been improved. Throughout, we explain how the most commonly encountered problems may be remedied with specific, practical solutions. We also introduce matstanlib, our MATLAB modeling support library, and demonstrate how it facilitates troubleshooting of an example hierarchical Bayesian model of reinforcement learning implemented in Stan. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models.All code is freely available from github.com/baribault/matstanlib.

scholarly journals Cognitive Modeling in Intelligence Research: Advantages and Recommendations for their Application

2018 ◽  
Author(s):  
Gidon T. Frischkorn ◽  
Anna-Lena Schubert
Keyword(s):  
Working Memory ◽  
Cognitive Processes ◽  
Processing Speed ◽  
Cognitive Modeling ◽  
Cognitive Process ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Models ◽  
Model Fit ◽  
General Intelligence

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive process that may help overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

scholarly journals Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application

2018 ◽  
Vol 6 (3) ◽  
pp. 34 ◽  
Author(s):  
Gidon Frischkorn ◽  
Anna-Lena Schubert
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Processing Speed ◽  
Cognitive Modeling ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Models ◽  
Model Fit ◽  
General Intelligence

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

scholarly journals On the measurement and estimation of cognitive processes with electrophysiological recordings and reaction time modeling

2021 ◽  
Author(s):  
Gabriel Weindel
Keyword(s):  
Reaction Time ◽  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Model Fitting ◽  
Generative Model ◽  
Electrophysiological Recordings ◽  
Time Modeling ◽  
Experimental Effect ◽  
Decision Choices ◽  
Present Thesis

A primary goal in cognitive psychology is to describe the latent information processingunits that operate between the onset of a stimulus and a measured behavior. Mathe-matical models of cognition aim at decomposing behavior into such processing unitsby formalizing an assumed generative model. Unfortunately, a generative model mayexplain the behavioral data while not necessarily reflecting the underlying processes.Obtaining measurements between the stimulus and the responses could provideadditional information that fruitfully constrains the processing assumptions.The present thesis explores this issue by focusing on models of perceptual deci-sion making, a field with a long tradition of cognitive modeling. These models areconstructed to account for decision choices and their durations (reaction time inthe range of a second) on the basis of a decomposition into encoding, decision andresponse execution stages. We used electrophysiological measures (electromyographyand electroencephalography) to decompose each reaction time into different intervals,presumed to contain these stages. Simultaneously, we manipulated time-honoredexperimental factors to compare the cognitive locus of experimental effects inferredfrom both electrophysiological recordings and from model fitting procedures.Throughout four empirical chapters, we show that the inferences drawn from cogni-tive models conflict with the electrophysiological decomposition when: 1) the model’score assumption of independence between decision and non-decision processes isproven to be false; 2) standard modeling strategies are inadequate to capture thelocus of an experimental effect revealed by the electrophysiological decomposition;3) opposite experimental effects are revealed in decision vs. encoding and responseexecution processes.This thorough assessment of a generative model of decision making delineates itsvalidity, merits and limitations to account for the latent cognitive processes. Newinsights are thus provided on the information processes that allow humans to decidebetween alternatives.

ANALYSIS OF THE INFLUENCE OF ELEMENTS OF THE DIESEL COOLING SYSTEM ON ITS THERMAL STABILIZATION BY MEANS OF COMPUTER MODELING

2021 ◽  
Vol 75 (4) ◽  
pp. 3-12
Author(s):  
Zayats Yuriy Aleksandrovich ◽  
◽  
Zayats Tatiana Mikhailovna ◽  
Savelyev Maksim Anatolevich ◽  
◽  
...  
Keyword(s):  
Life Cycle ◽  
Computer Modeling ◽  
Cognitive Modeling ◽  
Cooling System ◽  
Target Function ◽  
Thermal Stabilization ◽  
Cognitive Models ◽  
Operating Conditions ◽  
Practical Significance ◽  
Mechatronic Systems

Logistics support of products at all stages of the life cycle is gaining increasing influence. This is facilitated by the increasing complexity of structures, a large number of elements, the intro-duction of mechatronic systems. Under these conditions, the relevance of developing methods for analyzing the design of samples increases. The developed model for analyzing the diesel cooling system is based on the principles of cognitive modeling. The practical significance of cognitive models is shown, which consists in the possibility of predicting changes in the influence of system elements on the target function in various operating conditions.

Are similes and metaphors interchangeable?

2017 ◽  
Vol 15 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Manuela Romano
Keyword(s):  
Cognitive Processes ◽  
Cognitive Models ◽  
Figures Of Speech ◽  
Conceptual Semantic ◽  
Discourse Type ◽  
Opposite Process ◽  
Usage Patterns ◽  
News Interviews

Abstract Since Aristotle, scholars have regarded similes and metaphors as equivalent figures of speech sharing very similar comprehension, interpretation and usage patterns. By analysing the use of similes in real discourse, the aim of this study is to show that these two analogical figures reflect different cognitive processes, as well as different discursive functions, using as a framework cognitive models. To this end, this work presents, first, the main differentiating features of the two figures existing in the literature. And, second, it analyses 100 natural-occurring similes in English opinion discourse (news, interviews and commentary sections) in order to explain the conceptual-semantic and formal-syntactic factors which explain why similes and metaphors are not interchangeable in the discourse type under study; that is, why metaphors can usually be transformed into similes by adding like, whereas the opposite process seems to depend on specific conditions of structure, use and interpretation.

scholarly journals Motivation as a Mediator of the Relation Between Cognitive Reserve and Cognitive Performance

2018 ◽  
Vol 75 (6) ◽  
pp. 1199-1205
Author(s):  
Fanny Vallet ◽  
Nathalie Mella ◽  
Andreas Ihle ◽  
Marine Beaudoin ◽  
Delphine Fagot ◽  
...  
Keyword(s):  
Intrinsic Motivation ◽  
Life Span ◽  
Cognitive Performance ◽  
Cognitive Processes ◽  
Cognitive Aging ◽  
Cognitive Reserve ◽  
Leisure Activities ◽  
Global Measure ◽  
Interindividual Differences

Abstract Objectives Interindividual differences in cognitive aging may be explained by differences in cognitive reserve (CR) that are built up across the life span. A plausible but underresearched mechanism for these differences is that CR helps compensating cognitive decline by enhancing motivation to cope with challenging cognitive situations. Theories of motivation on cognition suggest that perceived capacity and intrinsic motivation may be key mediators in this respect. Method In 506 older adults, we assessed CR proxies (education, occupation, leisure activities), motivation (perceived capacity, intrinsic motivation), and a global measure of cognitive functioning. Results Perceived capacity, but not intrinsic motivation, significantly mediated the relation between CR and cognitive performance. Discussion Complementary with neurobiological and cognitive processes, our results suggest a more comprehensive view of the role of motivational aspects built up across the life span in determining differences in cognitive performance in old age.

scholarly journals All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)

2019 ◽  
Author(s):  
Michael David Wilson ◽  
Russell Boag ◽  
Luke Joseph Gough Strickland
Keyword(s):  
Gold Standard ◽  
Cognitive Modeling ◽  
Best Practice ◽  
Cognitive Model ◽  
Cognitive Models ◽  
Reproducible Research ◽  
Minimum Information ◽  
Starting Point ◽  
Bare Minimum ◽  
Practical Recommendations

Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.

Arriving from Delphi at O'Hare: Predictive Cognitive Engineering in the O'Hare Modernization Project and Beyond

2005 ◽  
Vol 49 (1) ◽  
pp. 108-112
Author(s):  
Steven Estes
Keyword(s):  
Cognitive Modeling ◽  
Mental Workload ◽  
Cognitive Models ◽  
Cognitive Engineering ◽  
Work Time ◽  
Modeling Effort ◽  
International Airport

This paper describes a cognitive modeling effort for the O'Hare Modernization Project (OMP). Beginning with a statement of the problem, it describes how cognitive modeling was used to measure the mental workload and work time of controllers running various positions at O'Hare International Airport, both under the current airport configurations and a future set of configurations (proposed in the OMP). The O'Hare case is used as an exemplar of the type of data that can be acquired with relatively simple cognitive models

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