scholarly journals The Predictive Power of Spatial Relational Reasoning Models: A New Evaluation Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Ragni ◽  
Daniel Brand ◽  
Nicolas Riesterer
Keyword(s):  
Cognitive Modeling ◽  
Computational Models ◽  
Predictive Power ◽  
Model Comparison ◽  
Cognitive Models ◽  
Relational Reasoning ◽  
Response Patterns ◽  
Research Focus ◽  
Evaluation Approach ◽  
The Individual

In the last few decades, cognitive theories for explaining human spatial relational reasoning have increased. Few of these theories have been implemented as computational models, however, even fewer have been compared computationally to each other. A computational model comparison requires, among other things, a still missing quantitative benchmark of core spatial relational reasoning problems. By presenting a new evaluation approach, this paper addresses: (1) developing a benchmark including raw data of participants, (2) reimplementation, adaptation, and extension of existing cognitive models to predict individual responses, and (3) a thorough evaluation of the cognitive models on the benchmark data. The paper shifts the research focus of cognitive modeling from reproducing aggregated response patterns toward assessing the predictive power of models for the individual reasoner. It demonstrate that not all psychological effects can discern theories. We discuss implications for modeling spatial relational reasoning.

Bringing Computational Models of Word Naming Down to the Item Level

1997 ◽  
Vol 8 (6) ◽  
pp. 411-416 ◽  
Author(s):  
Daniel H. Spieler ◽  
David A. Balota
Keyword(s):  
Word Recognition ◽  
Computational Models ◽  
Predictive Power ◽  
Neighborhood Density ◽  
Word Naming ◽  
Connectionist Models ◽  
Item Level ◽  
The Individual ◽  
Level Performance ◽  
Naming Latencies

Early noncomputational models of word recognition have typically attempted to account for effects of categorical factors such as word frequency (high vs low) and spelling-to-sound regularity (regular vs irregular) More recent computational models that adhere to general connectionist principles hold the promise of being sensitive to underlying item differences that are only approximated by these categorical factors In contrast to earlier models, these connectionist models provide predictions of performance for individual items In the present study, we used the item-level estimates from two connectionist models (Plaut, McClelland, Seidenberg, & Patterson, 1996, Seidenberg & McClelland, 1989) to predict naming latencies on the individual items on which the models were trained The results indicate that the models capture, at best, slightly more variance than simple log frequency and substantially less than the combined predictive power of log frequency, neighborhood density, and orthographic length. The discussion focuses on the importance of examining the item-level performance of word-naming models and possible approaches that may improve the models' sensitivity to such item differences

scholarly journals Parameter and model recovery of reinforcement learning models for restless bandit problems

2021 ◽  
Author(s):  
Ludwig Danwitz ◽  
David Mathar ◽  
Elke Smith ◽  
Deniz Tuzsus ◽  
Jan Peters
Keyword(s):  
Kalman Filter ◽  
Cognitive Modeling ◽  
Computational Models ◽  
Model Comparison ◽  
Performance Metrics ◽  
Model Parameters ◽  
Parameter Recovery ◽  
Restless Bandit ◽  
Model Free ◽  
Lower Accuracy

Multi-armed restless bandit tasks are regularly applied in psychology and cognitive neuroscience to assess exploration and exploitation behavior in structured environments. These models are also readily applied to examine effects of (virtual) brain lesions on performance, and to infer neurocomputational mechanisms using neuroimaging or pharmacological approaches. However, to infer individual, psychologically meaningful parameters from such data, computational cognitive modeling is typically applied. Recent studies indicate that softmax (SM) decision rule models that include a representation of environmental dynamics (e.g. the Kalman Filter) and additional parameters for modeling exploration and perseveration (Kalman SMEP) fit human bandit task data better than competing models. Parameter and model recovery are two central requirements for computational models: parameter recovery refers to the ability to recover true data-generating parameters; model recovery refers to the ability to correctly identify the true data generating model using model comparison techniques. Here we comprehensively examined parameter and model recovery of the Kalman SMEP model as well as nested model versions, i.e. models without the additional parameters, using simulation and Bayesian inference. Parameter recovery improved with increasing trial numbers, from around .8 for 100 trials to around .93 for 300 trials. Model recovery analyses likewise confirmed acceptable recovery of the Kalman SMEP model. Model recovery was lower for nested Kalman filter models as well as delta rule models with fixed learning rates. Exploratory analyses examined associations of model parameters with model-free performance metrics. Random exploration, captured by the inverse softmax temperature, was associated with lower accuracy and more switches. For the exploration bonus parameter modeling directed exploration, we confirmed an inverse- U-shaped association with accuracy, such that both an excess and a lack of directed exploration reduced accuracy. Taken together, these analyses underline that the Kalman SMEP model fulfills basic requirements of a cognitive model.

scholarly journals Opening Burton's Clock: Psychiatric insights from computational cognitive models

2018 ◽  
Author(s):  
Daniel Bennett ◽  
Yael Niv
Keyword(s):  
Psychiatric Illness ◽  
Computational Models ◽  
Model Comparison ◽  
Model Fitting ◽  
Computational Modelling ◽  
Quantitative Model ◽  
Cognitive Models ◽  
Qualitative Comparison ◽  
Computational Psychiatry ◽  
Reinforcement Learning Models

Computational psychiatry is a nascent field that seeks to use computational tools from neuroscience and cognitive science to understand psychiatric illness. In this chapter, we make the case for computational cognitive models as a bridge between the cognitive and affective deficits experienced by those with a psychiatric illness and the neurocomputational dysfunctions that underlie these deficits. We first review the history of computational modelling in psychiatry and conclude that a key moment of maturation in this field occurred with the transition from qualitative comparison between computational models and human behaviour to formal quantitative model fitting and model comparison. We then summarise current research at one of the most exciting frontiers of computational psychiatry: reinforcement learning models of mood disorders. We review state-of-the-art applications of such models to major depression and bipolar disorder, and outline important open questions to be addressed by the coming wave of research in computational psychiatry.

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.

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.

A Critical Review of Theories and Models of Technology Adoption and Acceptance in Information System Research

2015 ◽  
Vol 6 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Ali Tarhini ◽  
Nalin Asanka Gamagedara Arachchilage ◽  
Ra'ed Masa'deh ◽  
Muhammad Sharif Abbasi
Keyword(s):  
Information System ◽  
Technology Adoption ◽  
Environmental Factors ◽  
Critical Review ◽  
Predictive Power ◽  
Theoretical Models ◽  
System Research ◽  
Information System Research ◽  
Individual Level ◽  
The Individual

Previous research shows that selecting an appropriate theory or model has always remained a critical task for IS researchers. To the best of the authors' knowledge, there are few papers that review and compare the acceptance theories and models at the individual level. Hence, this article aims to overcome this problem by providing a critical review of eight of the most influential theories that have been used to predict and explain human behaviour towards adoption of various technologies at the individual level. This article also summarizes their evolution; highlight the key constructs, extensions, strengths, and criticisms from a selective list of published articles appeared in the literature related to IS. This review provides a holistic picture for future researchers in selecting appropriate single/multiple theoretical models/constructs based on their strengths and weaknesses and in terms of predictive power and path significance. It is concluded that a well-established theory should consider the personal, social, cultural, technological, organizational and environmental factors

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.

scholarly journals The Exponential-Weight Mean-Variance Model: A novel computational model for the Balloon Analogue Risk Task

2019 ◽  
Author(s):  
Harhim Park ◽  
Jaeyeong Yang ◽  
Jasmin Vassileva ◽  
Woo-Young Ahn
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Model Comparison ◽  
Exponential Weight ◽  
Multiple Model ◽  
Variance Model ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Post Hoc ◽  
Mean Variance

The Balloon Analogue Risk Task (BART) is a popular task used to measure risk-taking behavior. To identify cognitive processes associated with choice behavior on the BART, a few computational models have been proposed. However, the extant models are either too simplistic or fail to show good parameter recovery performance. Here, we propose a novel computational model, the exponential-weight mean-variance (EWMV) model, which addresses the limitations of existing models. By using multiple model comparison methods, including post hoc model fits criterion and parameter recovery, we showed that the EWMV model outperforms the existing models. In addition, we applied the EWMV model to BART data from healthy controls and substance-using populations (patients with past opiate and stimulant dependence). The results suggest that (1) the EWMV model addresses the limitations of existing models and (2) heroin-dependent individuals show reduced risk preference than other groups in the BART.

scholarly journals An MEG Study of Picture Naming

1998 ◽  
Vol 10 (5) ◽  
pp. 553-567 ◽  
Author(s):  
Willem J. M. Levelt ◽  
Peter Praamstra ◽  
Antje S. Meyer ◽  
Päivi Helenius ◽  
Riitta Salmelin
Keyword(s):  
Picture Naming ◽  
Time Windows ◽  
Naming Latency ◽  
Cortical Activation ◽  
Response Patterns ◽  
Phonological Encoding ◽  
A Chain ◽  
Wernicke’S Area ◽  
The Individual ◽  
The Right

The purpose of this study was to relate a psycholinguistic processing model of picture naming to the dynamics of cortical activation during picture naming. The activation was recorded from eight Dutch subjects with a whole-head neuromagnetometer. The processing model, based on extensive naming latency studies, is a stage model. In preparing a picture's name, the speaker performs a chain of specific operations. They are, in this order, computing the visual percept, activating an appropriate lexical concept, selecting the target word from the mental lexicon, phonological encoding, phonetic encoding, and initiation of articulation. The time windows for each of these operations are reasonably well known and could be related to the peak activity of dipole sources in the individual magnetic response patterns. The analyses showed a clear progression over these time windows from early occipital activation, via parietal and temporal to frontal activation. The major specific findings were that (1) a region in the left posterior temporal lobe, agreeing with the location of Wernicke's area, showed prominent activation starting about 200 msec after picture onset and peaking at about 350 msec, (i.e., within the stage of phonological encoding), and (2) a consistent activation was found in the right parietal cortex, peaking at about 230 msec after picture onset, thus preceding and partly overlapping with the left temporal response. An interpretation in terms of the management of visual attention is proposed.

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