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.

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 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.

Reaction Time and Brain Waves in Omitted Stimulus Tasks

2010 ◽  
Vol 24 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Oscar H. Hernández ◽  
Muriel Vogel-Sprott
Keyword(s):  
Reaction Time ◽  
Cognitive Processes ◽  
Young Male ◽  
Event Related Potential ◽  
Auditory Stimuli ◽  
Motor Action ◽  
Cognitive Component ◽  
Sensory Modalities ◽  
Two Measures ◽  
Somatosensory Stimuli

A missing stimulus task requires an immediate response to the omission of a regular recurrent stimulus. The task evokes a subclass of event-related potential known as omitted stimulus potential (OSP), which reflects some cognitive processes such as expectancy. The behavioral response to a missing stimulus is referred to as omitted stimulus reaction time (RT). This total RT measure is known to include cognitive and motor components. The cognitive component (premotor RT) is measured by the time from the missing stimulus until the onset of motor action. The motor RT component is measured by the time from the onset of muscle action until the completion of the response. Previous research showed that RT is faster to auditory than to visual stimuli, and that the premotor of RT to a missing auditory stimulus is correlated with the duration of an OSP. Although this observation suggests that similar cognitive processes might underlie these two measures, no research has tested this possibility. If similar cognitive processes are involved in the premotor RT and OSP duration, these two measures should be correlated in visual and somatosensory modalities, and the premotor RT to missing auditory stimuli should be fastest. This hypothesis was tested in 17 young male volunteers who performed a missing stimulus task, who were presented with trains of auditory, visual, and somatosensory stimuli and the OSP and RT measures were recorded. The results showed that premotor RT and OSP duration were consistently related, and that both measures were shorter with respect to auditory stimuli than to visual or somatosensory stimuli. This provides the first evidence that the premotor RT is related to an attribute of the OSP in all three sensory modalities.

scholarly journals Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

2019 ◽  
Vol 7 (4) ◽  
pp. 856-872 ◽  
Author(s):  
Alexander Weigard ◽  
Andrew Heathcote ◽  
Dóra Matzke ◽  
Cynthia Huang-Pollock
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Reaction Time ◽  
Attention Deficit ◽  
Cognitive Modeling ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Unbiased Estimation ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Stop Signal Reaction Time

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on attention deficit/hyperactivity disorder (ADHD). However, this measurement model is limited by two factors that may bias SSRT estimation in this population: (a) excessive skew in “go” RT distributions and (b) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the stop signal. We used a Bayesian parametric approach that allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both go RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that performance on the stop-signal task among children with ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

Creative Stimulation in Conceptual Design

2002 ◽  
Author(s):  
Oren Benami ◽  
Yan Jin
Keyword(s):  
Conceptual Design ◽  
Design Process ◽  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Protocol Analysis ◽  
Cognitive Model ◽  
Human Cognition ◽  
Creative Design ◽  
New Ideas ◽  
The Relationship

Conceptual design is a process of creating functions, forms and behaviors. Although cognitive processes are utilized in the development of new ideas, conventional methodologies do not take human cognition into account. However, it is conceivable that if one could determine how cognitive processes are stimulated, then more effective conceptual design methods could be developed. In this paper, we develop a Cognitive Model of Creative Conceptual Design to capture the relationship between the properties that stimulate cognitive processes and the design operations that facilitate cognitive processes. Through cognitive modeling, protocol analysis, and cognitive experiments, this research showed that designers exhibit patterns of creative design behavior, and that these patterns can be captured and instilled into the design process, to promote creativity.

Modeling cognition in SI

Interpreting ◽  
1997 ◽  
Vol 2 (1-2) ◽  
pp. 91-117
Author(s):  
Deryle Lonsdale
Keyword(s):  
System Dynamics ◽  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Model System ◽  
Methodological Issues ◽  
Simultaneous Interpretation ◽  
The Future

In this paper we discuss methodological issues pertaining to cognitive modeling of simultaneous interpretation. We briefly introduce the notion of cognition and efforts to model aspects of language-related processing. Previous work identifying cognitive processes in SI is sampled, and empirical SI studies are also mentioned. A rationale for modeling SI cognition follows, and relevant issues are sketched: whom to model, system dynamics, applicable technologies, and various possible processing scenarios. A discussion of evaluation considerations and requisite data resources follows. Throughout, we raise questions that must be addressed by the SI community, among both researchers and practitioners, if modeling SI is to be successfully realized in the future.

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 Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment

2021 ◽  
Author(s):  
David Izydorczyk ◽  
Arndt Bröder
Keyword(s):  
Cognitive Processes ◽  
Cognitive Modeling ◽  
Predictive Performance ◽  
Model Fit ◽  
Parameter Estimates ◽  
Data Sets ◽  
Parameter Recovery ◽  
Exemplar Models ◽  
Modeling Procedure ◽  
Direct Recall

AbstractExemplar models are often used in research on multiple-cue judgments to describe the underlying process of participants’ responses. In these experiments, participants are repeatedly presented with the same exemplars (e.g., poisonous bugs) and instructed to memorize these exemplars and their corresponding criterion values (e.g., the toxicity of a bug). We propose that there are two possible outcomes when participants judge one of the already learned exemplars in some later block of the experiment. They either have memorized the exemplar and their respective criterion value and are thus able to recall the exact value, or they have not learned the exemplar and thus have to judge its criterion value, as if it was a new stimulus. We argue that psychologically, the judgments of participants in a multiple-cue judgment experiment are a mixture of these two qualitatively distinct cognitive processes: judgment and recall. However, the cognitive modeling procedure usually applied does not make any distinction between these processes and the data generated by them. We investigated potential effects of disregarding the distinction between these two processes on the parameter recovery and the model fit of one exemplar model. We present results of a simulation as well as the reanalysis of five experimental data sets showing that the current combination of experimental design and modeling procedure can bias parameter estimates, impair their validity, and negatively affect the fit and predictive performance of the model. We also present a latent-mixture extension of the original model as a possible solution to these issues.

scholarly journals Metaphors’ effect on off-line cognition: A preliminary study based on a reaction time task

2021 ◽  
Vol 4 (11) ◽  
pp. 139-146
Author(s):  
Fatemeh Shafiei ◽  
Habibollah Ghassemzadeh
Keyword(s):  
Reaction Time ◽  
Cognitive Processes ◽  
Conceptual Metaphor ◽  
Reaction Time Task ◽  
Semantic Networks ◽  
Time Task ◽  
A Value ◽  
Preliminary Study ◽  
The One ◽  
The Mind

The modality of apprehension and processing of metaphorical expressions in comparison with non-metaphorical ones has hitherto captivated numerous researchers in manifold fields of study, such as linguistics, psychology, and cognitive sciences. More specially, metaphors used in a one-sentence paragraph have been the subjects of many studies. However, cognitive functions of structural metaphors haven’t been entirely noteworthy in contrast with non-metaphorical expressions employed in textual context. In this study, the interrelationship between memory and conceptual metaphor in significant cognitive processes has been examined in a textual context. In this respect, the hypothesis, that conceptual metaphor as a value can assist with the recognition and recollection process and incorporate the quintessence of our cerebrations, has been put to test. To evaluate this assumption, the reaction time task is used. Each testable case has been subjected to analysis within two analogous contexts, in a metaphorical and non-metaphorical manner. Afterwards, terms were displayed, and the subjects needed to determine as swiftly as possible whether these vocabularies were exemplified or not. The results indicated that the terms pertaining to the schema and other terms included in metaphorical context would be processed faster than the one with non-metaphorical context. With regard to the obtained data, it seems that the conceptual metaphor generates semantic networks in the mind which will be more accessible to memory upon information retrieval.

Cognition and the Design of Products Large and Small

2003 ◽  
Vol 3 (2) ◽  
pp. 164-177
Author(s):  
K. L. Kumar
Keyword(s):  
Cognitive Processes ◽  
Cognitive Modeling ◽  
New Products ◽  
Design And Technology ◽  
Innovative Design ◽  
World Class ◽  
Graphical Communication ◽  
Senior Secondary Schools ◽  
The Mind ◽  
Senior Secondary

Innovative design of new products proceeds by way of cognitive processes of analysis, critical thinking, creativity, conceptualization, cognitive modeling, synthesis, prototyping, and evaluation. Design phases invariably consist of divergence, transformation, and convergence operations. Designing is a creative faculty of the mind, akin to the conceptual faculty of learning arts, sciences, and languages. The author dwells briefly on cognitive, graphical communication, morphological, philosophical, and psychological aspects of design, together with educational imperatives, and proposes that designing new products requires the same cognitive processes regardless of their size, shape, and complexity.The author has drawn upon his own experience of designing a variety of things and has quoted references to design of household artifacts, office equipment, and industrial products. Reference is made to the ‘Design and Technology’ subject being taught at junior and senior secondary schools in Botswana and elsewhere. Examples are also drawn from some recent world-class designs. These establish the belief that human design cognition is the same for all products, small or large.

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