How are Bayesian models really used? Reply to Frank (2013).

نویسنده

  • Ansgar D Endress
چکیده

In response to the proposal that cognitive phenomena might be best understood in terms of cognitive theories (Endress, 2013), Frank (2013) outlined an important research program, suggesting that Bayesian models should be used as rigorous, mathematically attractive implementations of psychological theories. This research program is important and promising. However, I show that it is not followed in practice. I then turn to Frank's defense of the assumption that learners prefer more specific rules (the "size principle"), and show that the results allegedly supporting this assumption do not provide any support for it. Further, I demonstrate that, in contrast to Frank's criticisms, there is no circularity in an account of rule-learning based on "common-sense psychology", and that Frank's other criticisms of this account are unsupported. I conclude that the research program outlined by Frank is important and promising, but needs to be followed in practice. Be that as it might, the rule-learning experiments discussed by Frank are still better explained by simple psychological mechanisms.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling Factors Affecting Tax Evasion in Iran's Economy Based on the Bayesian averaging approach

This study seeks to model tax evasion and identify how effective factors affect tax evasion in the Iranian economy. Recent models show the failure of traditional models; Models do not have enough ability to model hidden variables such as tax evasion. The present study considers this failure in identifying explanatory variables and experimental model design. To achieve this, the Bayesian averagi...

متن کامل

Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering

Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...

متن کامل

Throwing out the Bayesian baby with the optimal bathwater: response to Endress (2013).

A recent probabilistic model unified findings on sequential generalization ("rule learning") via independently-motivated principles of generalization (Frank & Tenenbaum, 2011). Endress critiques this work, arguing that learners do not prefer more specific hypotheses (a central assumption of the model), that "common-sense psychology" provides an adequate explanation of rule learning, and that Ba...

متن کامل

Introducing of Dirichlet process prior in the Nonparametric Bayesian models frame work

Statistical models are utilized to learn about the mechanism that the data are generating from it. Often it is assumed that the random variables y_i,i=1,…,n ,are samples from the probability distribution F which is belong to a parametric distributions class. However, in practice, a parametric model may be inappropriate to describe the data. In this settings, the parametric assumption could be r...

متن کامل

Spatial count models on the number of unhealthy days in Tehran

Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on poisson (poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • Cognition

دوره 130 1  شماره 

صفحات  -

تاریخ انتشار 2014