scholarly journals Cause for pause before leaping to conclusions about stepping

2016 ◽  
Author(s):  
Ariel Zylberberg ◽  
Michael N. Shadlen
Keyword(s):  
Action Potentials ◽  
Model Comparison ◽  
High Rate ◽  
Association Cortex ◽  
Lateral Intraparietal Cortex ◽  
Bayesian Model Comparison ◽  
Cortex Area ◽  
Biased Diffusion ◽  
Sensory Evidence ◽  
Intermediate Rate

AbstractMany neurons in parietal and prefrontal association cortex undergo gradual changes in firing rate during the formation of some perceptual decisions. These dynamics are often ramp-like increases or decreases depending on the sign and strength of the sensory evidence and are thus hypothesized to represent the accumulation of noisy samples of evidence, analogous to biased diffusion. This idea was challenged recently. An analysis of sequences of action potentials recorded from neurons in the lateral intraparietal cortex (area LIP) suggests that the spikes on single trials are explained by rates that undergo a discrete step from an intermediate rate to either a low or high rate at a random time during deliberation. The average of such steps, like the average of biased diffusion, is consistent with the ramp-like firing rates observed in LIP, but a Bayesian model comparison deemed stepping superior. Here we show that a shortcoming in the mathematical depiction of drift-diffusion led to a severe bias in the model comparison. We conclude that at present there is no compelling evidence that favors the stepping account.

Bayesian model comparison

2014 ◽  
pp. 101-117
Author(s):  
Michael D. Lee ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison

scholarly journals A complete search for redshift z ≳ 6.5 quasars in the VIKING survey

2020 ◽  
Vol 501 (2) ◽  
pp. 1663-1676
Author(s):  
R Barnett ◽  
S J Warren ◽  
N J G Cross ◽  
D J Mortlock ◽  
X Fan ◽  
...  
Keyword(s):  
Model Comparison ◽  
High Redshift ◽  
Detailed Comparison ◽  
Selection Function ◽  
Data Set ◽  
Bayesian Model Comparison ◽  
Fitting Method ◽  
Complete Search ◽  
Potential Contaminant ◽  
Ranked List

ABSTRACT We present the results of a new, deeper, and complete search for high-redshift 6.5 < z < 9.3 quasars over 977 deg2 of the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey. This exploits a new list-driven data set providing photometry in all bands Z, Y, J, H, Ks, for all sources detected by VIKING in J. We use the Bayesian model comparison (BMC) selection method of Mortlock et al., producing a ranked list of just 21 candidates. The sources ranked 1, 2, 3, and 5 are the four known z > 6.5 quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-χ2 SED fitting. We find that: (i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag deeper, (ii) the minimum-χ2 SED-fitting method is extremely efficient but reaches 0.7 mag less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high χ2 values, include bright examples of galaxies with very strong [O iii] λλ4959,5007 emission in the Y band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint z > 7 quasars, not previously accounted for, and that requires better characterization.

scholarly journals A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Visual Integration ◽  
Bayesian Model Comparison ◽  
Comparison Approach

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

2021 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Bayes Factors ◽  
Prior Model ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Model Comparison ◽  
Posterior Model Probability ◽  
Model Probability

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

Combined-chain nested sampling for efficient Bayesian model comparison

2017 ◽  
Vol 70 ◽  
pp. 84-93 ◽  
Author(s):  
R. Wesley Henderson ◽  
Paul M. Goggans ◽  
Lei Cao
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Nested Sampling ◽  
Bayesian Model Comparison

scholarly journals Negative Affect Induces Rapid Learning of Counterfactual Representations: A Model-based Facial Expression Analysis Approach

2019 ◽  
Author(s):  
Nathaniel Haines ◽  
Olga Rass ◽  
Yong-Wook Shin ◽  
Joshua W. Brown ◽  
Woo-Young Ahn
Keyword(s):  
Facial Expression ◽  
Negative Affect ◽  
Expression Analysis ◽  
Model Comparison ◽  
Empirical Support ◽  
Functional Changes ◽  
Model Based ◽  
Facial Expression Analysis ◽  
Bayesian Model Comparison ◽  
Counterfactual Emotions

AbstractCounterfactual emotions including regret and disappointment play a crucial role in how people make decisions. For example, people often behave such that their decisions minimize potential regret or disappointment and therefore maximize subjective pleasure. Importantly, functional accounts of emotion suggest that the experience and future expectation of counterfactual emotions should promote goal-oriented behavioral change. Although many studies find empirical support for such functional theories, the cognitive-emotional mechanisms through which counterfactual thinking facilitates changes in behavior remain unclear. Here, we leverage computational models of risky decision-making that extend regret and disappointment theory to experience-based tasks, which we use to determine how people learn counterfactual representations of their decisions across time. Further, we use computer-vision to detect positive and negative affect (valence) intensity from participants’ faces in response to feedback, which we use to determine how experienced emotion may influence cognitive mechanisms of learning, reward sensitivity, or exploration/exploitation—any of which could result in functional changes in behavior. Using hierarchical Bayesian modeling and Bayesian model comparison methods, we found that: (1) people learn to explicitly represent and subjectively weight counterfactual outcomes with increasing experience, and (2) people update their counterfactual expectations more rapidly as they experience increasingly intense negative affect. Our findings support functional accounts of regret and disappointment and demonstrate the potential for computational modeling and model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.

scholarly journals Relaxometry Models Compared with Bayesian Technique: Ganglioside Micelle Example

2020 ◽  
Author(s):  
Pär Håkansson
Keyword(s):  
Model Comparison ◽  
Spin Dynamics ◽  
Model Development ◽  
Relaxation Dispersion ◽  
Site Model ◽  
Research Areas ◽  
Bayesian Model Comparison ◽  
Physico Chemical ◽  
Complex Liquids ◽  
Bayesian Evidence

<div> <div> <div> <p>In this work a methodology to perform Bayesian model-comparison is developed and exemplified in the analysis of magnetic relaxation dispersion (NMRD) experiments of water in Ganglioside micelle system. The NMRD powerful probe of slow dynamics in complex liquids is obtained. There are many interesting systems to study with NMRD, such as ionic and Lyotropic liquids or electrolytes. However, to progress in the understanding of the physical chemistry of studied systems relatively detailed theoretical NMRD-models are required. To improve the models they need to be carefully compared, in addition to physico-chemical considerations of molecular and spin dynamics. The comparison is performed by computing the Bayesian evidence in terms of a thermodynamic integral solved with Markov chain Monte Carlo. The result leads to a conclusion of two micelle water sites, and rules out lower and higher complexity level, i.e., one and three sites. In contrast, and provided only with the quality of best fit, suggest a three site model. The two approximate selection tools, Akaike and Baysian information criterions, may lead to wrong conclusions compared to the the full integration. The methodology is expected to be one of several important tools in NMRD model development, however, is completely general and should find awakened use in many research areas. </p> </div> </div> </div>

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