scholarly journals Adaptive stimulus selection for multi-alternative psychometric functions with lapses

2018 ◽  
Author(s):  
Ji Hyun Bak ◽  
Jonathan W. Pillow
Keyword(s):  
Multinomial Logistic Regression ◽  
Stimulus Selection ◽  
Selection Methods ◽  
Computationally Efficient ◽  
Information Theoretic ◽  
Building Models ◽  
Speed Up ◽  
Selection For ◽  
Regression Mixture ◽  
External Stimuli

Psychometric functions (PFs) quantify how external stimuli affect behavior and play an important role in building models of sensory and cognitive processes. Adaptive stimulus selection methods seek to select stimuli that are maximally informative about the PF given data observed so far in an experiment and thereby reduce the number of trials required to estimate the PF. Here we develop new adaptive stimulus selection methods for flexible PF models in tasks with two or more alternatives. We model the PF with a multinomial logistic regression mixture model that incorporates realistic aspects of psychophysical behavior, including lapses and multiple alternatives for the response. We propose an information-theoretic criterion for stimulus selection and develop computationally efficient methods for inference and stimulus selection based on semi-adaptive Markov Chain Monte Carlo (MCMC) sampling. We apply these methods to data from macaque monkeys performing a multi-alternative motion discrimination task, and show in simulated experiments that our method can achieve a substantial speed-up over random designs. These advances will reduce the data needed to build accurate models of multi-alternative PFs and can be extended to high-dimensional PFs that would be infeasible to characterize with standard methods.

scholarly journals Meet me in the middle: brain-behavior mediation analysis for fMRI experiments

2020 ◽  
Author(s):  
Jules Brochard ◽  
Jean Daunizeau
Keyword(s):  
Mediation Analysis ◽  
Statistical Tests ◽  
Brain Regions ◽  
Computationally Efficient ◽  
Information Theoretic ◽  
Decision Under Risk ◽  
Specificity And Sensitivity ◽  
Fmri Study ◽  
Brain Behavior ◽  
External Stimuli

AbstractFunctional outcomes (e.g., subjective percepts, emotions, memory retrievals, decisions, etc…) are partly determined by external stimuli and/or cues. But they may also be strongly influenced by (trial-by-trial) uncontrolled variations in brain responses to incoming information. In turn, this variability provides information regarding how stimuli and/or cues are processed by the brain to shape behavioral responses. This can be exploited by brain-behavior mediation analysis to make specific claims regarding the contribution of brain regions to functionally-relevant input-output transformations. In this work, we address four challenges of this type of approach, when applied in the context of mass-univariate fMRI data analysis: (i) we quantify the specificity and sensitivity profiles of different variants of mediation statistical tests, (ii) we evaluate their robustness to hemo-dynamic and other confounds, (iii) we identify the sorts of brain mediators that one can expect to detect, and (iv) we disclose possible interpretational issues and address them using complementary information-theoretic approaches. En passant, we propose a computationally efficient algorithmic implementation of the approach that is amenable to whole-brain exploratory analysis. We also demonstrate the strengths and weaknesses of brain-behavior mediation analysis in the context of an fMRI study of decision under risk. Finally, we discuss the limitations and possible extensions of the approach.

Improvement of effectiveness in maize breeding

2006 ◽  
Vol 54 (3) ◽  
pp. 351-358 ◽  
Author(s):  
P. Pepó
Keyword(s):  
Recurrent Selection ◽  
Genetic Manipulation ◽  
Field Testing ◽  
Tissue Cultures ◽  
Maize Breeding ◽  
Breeding Programmes ◽  
Speed Up ◽  
Selection For

Plant regeneration via tissue culture is becoming increasingly more common in monocots such as maize (Zea mays L.). Pollen (gametophytic) selection for resistance to aflatoxin in maize can greatly facilitate recurrent selection and the screening of germplasm for resistance at much less cost and in a shorter time than field testing. In vivo and in vitro techniques have been integrated in maize breeding programmes to obtain desirable agronomic attributes, enhance the genes responsible for them and speed up the breeding process. The efficiency of anther and tissue cultures in maize and wheat has reached the stage where they can be used in breeding programmes to some extent and many new cultivars produced by genetic manipulation have now reached the market.

scholarly journals Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1501
Author(s):  
Camil Băncioiu ◽  
Remus Brad
Keyword(s):  
Feature Selection ◽  
Causal Inference ◽  
Selection Methods ◽  
Conditional Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Inference Method ◽  
Information Theoretic ◽  
Theoretic Concept ◽  
Test Computation

This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as part of an experiment involving IPC–MB, an efficient Markov blanket discovery algorithm, applicable both as a feature selection algorithm and as a causal inference method. The results show outstanding efficiency gains for IPC–MB when the G-test is computed with the proposed method, compared to the unoptimized G-test, but also when compared to IPC–MB++, a variant of IPC–MB which is enhanced with an AD–tree, both static and dynamic. Even if this proposed method of computing the G-test is presented here in the context of IPC–MB, it is in fact bound neither to IPC–MB in particular, nor to feature selection or causal inference applications in general, because this method targets the information-theoretic concept that underlies the G-test, namely conditional mutual information. This aspect grants it wide applicability in data sciences.

scholarly journals Using systematic sampling selection for Monte Carlo solutions of Feynman-Kac equations

2008 ◽  
Vol 40 (2) ◽  
pp. 454-472 ◽  
Author(s):  
Ivan Gentil ◽  
Bruno Rémillard
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
Selection Method ◽  
Systematic Sampling ◽  
Selection Methods ◽  
Convergence Properties ◽  
Main Motivation ◽  
Empirical Basis ◽  
Convergence Results ◽  
Selection For

While the convergence properties of many sampling selection methods can be proven, there is one particular sampling selection method introduced in Baker (1987), closely related to ‘systematic sampling’ in statistics, that has been exclusively treated on an empirical basis. The main motivation of the paper is to start to study formally its convergence properties, since in practice it is by far the fastest selection method available. We will show that convergence results for the systematic sampling selection method are related to properties of peculiar Markov chains.

scholarly journals APPLYING CONSUMER BEHAVIOR MODEL TO THE STUFF LABOR MOTIVATION

2021 ◽  
pp. 44-49
Author(s):  
S. I. Dobrydnev ◽  
T. S. Dobrydneva
Keyword(s):  
Consumer Behavior ◽  
Human Behavior ◽  
Stimulus Selection ◽  
Behavior Model ◽  
Methodological Feature ◽  
Building Models ◽  
Type Definition ◽  
Emotional Aspects ◽  
Definition Of ◽  
Motivation Model

The article appeals to the problem of designing motivation model for the labor behavior of company stuff. Human behavior is one of the key areas of research in many fields of knowledge. The main forms of human behavior in economics are consumer and labor behavior. For each of them, extensive theoretical and practical material has been developed, a significant variety of behaviors has been proposed. Moreover, in the absence of general models of human behavior that would be applicable in any field of his activity, each science develops its own methodological apparatus and builds models based on its own approaches. Models of consumer behavior describe a clearly defined object (purchasing act), are specific and practically oriented. Patterns of labour behaviour are more general and relate to conduct in general, but not to a specific act of activity. The article attempts to apply the principles of building models of consumer behavior to modeling labor behavior. The model of type “Definition of target actions — Stimulus selection — Information and desire — Choice and location — Check and preference — Confirmation and relation” is proposed. The content of these stages for the task of changing labor behavior is shown. A methodological feature of the model is the isolation of rational and emotional aspects in some elements of labor behavior.

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