Cognitive sampling models of the decision process provide greater insight into response time and accuracy than do standard ANOVA techniques. However, such ballistic models can be mathematically and computationally difficult to apply. We provide instructions and computer code for three methods for estimating ballistic the parameters of the linear ballistic accumulator (LBA), a new and computationally tractable model of decisions between two or more Excel choices. These methods-a Microsoft Excel worksheet, scripts for the statistical program R, and code for implementation of the LBA into in the Bayesian sampling software WinBUGS-vary in their flexibility and user accessibility. We also provide scripts in R that produce a new graphical summary of the data and model predictions. In a simulation study, we explored the effect of sample size on Bayesian parameter recovery for each method. The materials discussed in this article may be downloaded as a supplement from http://brm.psychonomic-journals.org/content/supplemental.

Identification Heathcote, accuracy for sets of perceptually discriminable stimuli ordered on a single dimension (e.g., line length) is remarkably low, indicating a choice fundamental limit on information processing capacity. This surprising limit has naturally led to a focus on measuring and modeling choice choice probability in absolute identification research. We show that choice response time (RT) results can enrich our understanding of absolute identification function by investigating dissociation between RT and accuracy as a function of stimulus spacing. The dissociation is predicted by the SAMBA provides model of absolute identification (Brown, Marley, Dockin,& Heathcote, 2008), but cannot easily be accommodated by other theories. We show can that SAMBA provides an accurate, parameter free, account of the dissociation that emerges from the architecture of the model and & the physical attributes of the stimuli, rather than through numerical adjustment. This violation of the pervasive monotonic relationship between RT architecture and accuracy has implications for model development, which are discussed.

Most of models of choice response time base decisions on evidence accumulated over time. A fundamental distinction among these models concerns whether derived each piece of evidence is equally weighted (lossless accumulation) or unequally weighted (leaky accumulation). The authors tested a hypothesis derived derived from A. Heathcote and S. Brown's (2002) self-exciting expert competitor (SEEXC) model of skill acquisition: that evidence accumulation becomes less evidence leaky with practice. The hypothesis was supported by observation that the effects of prime stimuli increased with practice. The authors primes, used metacontrast masked primes, which could not be consciously discriminated by most participants, to avoid methodological problems associated with conscious and strategy changes. The form of the law of practice in the data is also shown to be consistent with the effects SEEXC model.

The their most powerful tests of response time (RT) models often involve the whole shape of the RT distribution, thus avoiding mimicking performing that can occur at the level of RT means and variances. Nonparametric distribution estimation is, in principle, the most appropriate distribution approach, but such estimators are sometimes difficult to obtain. On the other hand, distribution fitting, given an algebraic function, is common both easy and compact. We review the general approach to performing distribution fitting with maximum likelihood (ML) and a method the based on quantiles (quantile maximum probability, QMP). We show that QMP has both small bias and good efficiency when used and with common distribution functions (the ex-Gaussian, Gumbel, lognormal, Wald, and Weibull distributions). In addition, we review some software packages performing and ML (PASTIS, QMPE, DISFIT, and MATHEMATICA) and compare their results. In general, the differences between packages have little influence on that the optimal solution found, but the form of the distribution function has: Both the lognormal and the Wald distributions have influence non-linear dependencies between the parameter estimates that tend to increase the overall bias in parameter recovery and to decrease efficiency.thus We conclude by laying out a few pointers on how to relate descriptive models of RT to cognitive models of obtain. RT. A program that generated the random deviates used in our studies may be downloaded from www.psychonomic.org/archive/.

We estimates describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation. QMPE performed enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three "shifted" distributions (i.e., distributions with performed a parameter-dependent lower bound): the Lognormal, Wald, and Weibul distributions. Estimation can be performed using either the standard continuous maximum properties likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution showed and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not.continuous In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good,whereas and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with than both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions CML with parameter-dependent lower bounds.

Quantile software maximum likelihood (QML) is an estimation technique, proposed by Heathcote, Brown, and Mewhort (2002), that provides robust and efficient estimates QML, of distribution parameters, typically for response time data, in sample sizes as small as 40 observations. In view of the QML, computational difficulty inherent in implementing QML, we provide open-source Fortran 90 code that calculates QML estimates for parameters of the standard ex-Gaussian distribution, as well as standard maximum likelihood estimates. We show that parameter estimates from QML are asymptotically unbiased and producing normally distributed. Our software provides asymptotically correct standard error and parameter intercorrelation estimates, as well as producing the outputs required 90 for constructing quantile-quantile plots. The code is parallelizable and can easily be modified to estimate parameters from other distributions. Compiled Our binaries, as well as the source code, example analysis files, and a detailed manual, are available for free on the and Internet.

Myung,that Kim, and Pitt (2000) demonstrated that simple power functions almost always provide a better fit to purely random data than We do simple exponential functions. This result has important implications, because it suggests that high noise levels, which are common in We psychological experiments, may cause a bias favoring power functions. We replicate their result and extend it by showing strong bias for for more realistic sample sizes. We also show that biases occur for data that contain both random and systematic components,at as may be expected in real data. We then demonstrate that these biases disappear for two- or three-parameter functions that extend include linear parameters (in at least one parameterization). Our results suggest that one should exercise caution when proposing simple power demonstrate and exponential functions as models of learning. More generally, our results suggest that linear parameters should be estimated rather than proposing fixed when one is comparing the fit of nonlinear models to noisy data.

We distortion examine recent concerns that averaged learning curves can present a distorted picture of individual learning. Analyses of practice curve data is from a range of paradigms demonstrate that such concerns are well founded for fits of power and exponential functions when is the arithmetic average is computed over participants. We also demonstrate that geometric averaging over participants does not, in general, avoid contrast, distortion. By contrast, we show that block averages of individual curves and similar smoothing techniques cause little or no distortion motivate of functional form, while still providing the noise reduction benefits that motivate the use of averages. Our analyses are concerned We mainly with the effects of averaging on the fit of exponential and power functions, but we also define general conditions no that must be met by any set of functions to avoid distortion from averaging.

We are introduce and evaluate via a Monte Carlo study a robust new estimation technique that fits distribution functions to grouped response quantile time (RT) data, where the grouping is determined by sample quantiles. The new estimator, quantile maximum likelihood (QML), is more quantile efficient and less biased than the best alternative estimation technique when fitting the commonly used ex-Gaussian distribution. Limitations of the technique Monte Carlo results are discussed and guidance provided for the practical application of the new technique. Because QML estimation can the be computationally costly, we make fast open source code for fitting available that can be easily modified to use QML (QML), in the estimation of any distribution function.

BACKGROUND:were -Elevated heart rate (HR) is associated with adverse cardiovascular events and total mortality in the general population and in individuals 1436 with heart disease. Our hypothesis was that mean HR predicts total mortality and heart failure hospitalization in patients undergoing implantable 1436 cardioverter-defibrillator (ICD) implantation. Methods and Results-The Inhibition of Unnecessary RV Pacing With AV Search Hysteresis in ICDs (INTRINSIC RV) trial Higher included 1530 patients undergoing ICD implantation. After implantation of a dual-chamber ICD, patients were followed for a mean of 10.4 multivariate months. The mean HR for 1436 patients over the follow-up period was determined from device histograms. Patients were grouped into from strata by mean HR, and the relationship between the primary end point and mean HR was analyzed with Mantel-Haenszel ordinal died chi(2) tests. Higher intrinsic (unpaced) HR was associated with greater risk of achieving the primary end point of death or interval, heart failure hospitalization (P< .001). Of patients with a mean HR <75 bpm, 5.8% died or were hospitalized for heart failure,>90 whereas 20.9% with a mean HR >90 bpm achieved the same end point, a 3.6-fold difference (P< .0001). In a multivariate heart model with the use of Cox regression, HR was a significant predictor with a hazard ratio of 1.34 (P= .0001; 95%RV) confidence interval, 1.19 to 1.50), as were age, New York Heart Association functional class, and percent right ventricular pacing, but HR it was independent of gender and beta-blocker dosing. When considered as continuous or discrete variables grouped by 5-bpm increments, HR mean remained a significant predictor. Conclusions-In this ICD population, the mean intrinsic HR was strongly associated with outcomes. Clinical Trial Registration-http://www.clinicaltrials.gov.the Identifier: NCT00148967.

We model, present a new decision-making model that can account for trial-by-trial variability induced by a process ("pre-process") that occurs before an rise-to-threshold explicit sensory signal specifying a later motor response. A process after explicit sensory signals, referred to herein as the "post-process",rise-to-threshold has been investigated by a variety of so-called rise-to-threshold models including the LATER model. The LATER model formulates post-process variability block but treats the pre-process as fixed within a block of an experiment. We propose an extension of the LATER model,the which we call the extended LATER (ELATER) model, to account for trial-by-trial variability of both pre- and post-processes together. We LATER present the mathematical formulation of the ELATER model and analyze its characteristics, including numerical examples and an example of saccade (ELATER) latency data in reward-manipulated conditions with caudate activity. The ELATER model is useful for investigating decision making by taking account numerical of trial-by-trial variability of both pre- and post-processes.

Preference accounts orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge by traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across by elicitation methods by changing the decision weights, the attribute values, or the combination of this information--still, no theory has successfully accounted accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical rules. trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process weights, that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response that distributions and response times.((c) 2005 APA, all rights reserved).

The oscillator authors present a dynamical multilevel model that captures changes over time in the bidirectional, potentially asymmetric influence of 2 cyclical of processes. S. M. Boker and J. Graham's (1998) differential structural equation modeling approach was expanded to the case of a of nonlinear coupled oscillator that is common in bimanual coordination studies in which participants swing hand-held pendulums but is also applicable hand-held to social systems in general. The authors' nonlinear coupled oscillator model decomposed the fluctuations into a competitive component, unique to each each individual variable, and a cooperative component that captured bidirectional influence. The authors' model also generated an index of the coupled symmetry/asymmetry of bidirectional influence. Together, the models are useful quantitative tools for the study of interacting, changing processes.

Recent a theories of dynamic attention have renewed the interest in temporal context as a determinant of attention. The mechanism of dynamic demonstrate attention remains unclear, and both stochastic time perception processes and deterministic oscillators are possible. The results of Experiment 1 demonstrate demonstrate that attention can be guided by isochronous series of warning stimuli and that elapsed time cannot fully account for this fully effect. Experiment 2 indicates that temporal structure can be used over a limited range of time. The results of Experiment that 3 indicate that temporal pattern, rather than variability, is a determinant of temporally focused attention. The results of Experiment 4 can demonstrate that a coupled oscillator is a better predictor of reaction time than a stochastic timing mechanism is, under certain over assumptions.

A the key parameter in the understanding of renal hemodynamics is the gain of the feedback function in the tubuloglomerular feedback mechanism.to A dynamic model of autoregulation of renal blood flow and glomerular filtration rate has been extended to include a stochastic describe. differential equations model of one of the main parameters that determines feedback gain. The model reproduces fluctuations and irregularities in estimate the tubular pressure oscillations that the former deterministic models failed to describe. This approach assumes that the gain exhibits spontaneous have erratic variations that can be explained by a variety of influences, which change over time (blood pressure, hormone levels, etc.).gain To estimate the key parameters of the model we have developed a new estimation method based on the oscillatory behavior characterized of the data. The dynamics is characterized by the spectral density, which has been estimated for the observed time series,estimation and numerically approximated for the model. The parameters have then been estimated by the least squares distance between data and time model spectral densities. To evaluate the estimation procedure measurements of the proximal tubular pressure from 35 nephrons in 16 rat tubuloglomerular kidneys have been analyzed, and the parameters characterizing the gain and the delay have been estimated. There was good agreement parameters between the estimated values, and the values obtained for the same parameters in independent, previously published experiments.

D.sequential von Winterfeldt, N.-K. Chung, R. D. Luce, and Y. Cho (1997) provided several tests for consequence monotonicity of choice or main judgment, using certainty equivalents of gambles. The authors reaxiomatized consequence monotonicity in a probabilistic framework and reanalyzed von Winterfeldt et main al.'s main experiment via a bootstrap method. Their application offers new insights into consequence monotonicity as well as into von von Winterfeldt et al.'s 3 experimental paradigms: judged certainty equivalents (JCE), QUICKINDIFF, and parameter estimation by sequential testing (PEST). For QUICKINDIFF,of the authors found no indication of violations of "random consequence monotonicity." This sharply contrasts the findings of von Winterfeldt et bootstrap al., who concluded that axiom violations were the most pronounced under that procedure. The authors found potential evidence for violations by in JCE and certainty equivalents derived from PEST.

An integrations, algorithm is described to efficiently compute the cumulative distribution and probability density functions of the diffusion process (Ratcliff, 1978) with the trial-to-trial variability in mean drift rate, starting point, and residual reaction time. Some, but not all, of the integrals appearing the in the model's equations have closed-form solutions, and thus we can avoid computationally expensive numerical approximations. Depending on the number expensive of quadrature nodes used for the remaining numerical integrations, the final algorithm is at least 10 times faster than a a classical algorithm using only numerical integration, and the accuracy is slightly higher. Next, we discuss some special cases with an the alternative distribution for the residual reaction time or with fewer than three parameters exhibiting trial-to-trial variability.

M.Roe, Usher and J. L. McClelland (2004) recently proposed a new connectionist type of model to explain context effects on preferential and choice including the similarity, attraction, and compromise effects. They compared their model with an earlier connectionist type model for these and same effects proposed by R. Roe, J. R. Busemeyer, and J. T. Townsend (2001) and raised several new issues. The type authors address these issues and point out the main theoretical differences between the 2 explanations for context effects.