Biomedical subjects

Andrew Heathcote

Publications and source records attributed to Andrew Heathcote.

13 recordsLinked to original sources

PubMed2006

Word frequency and word likeness mirror effects in episodic recognition memory.

Estes and Maddox (2002) suggested that the word frequency mirror effect in episodic recognition memory might be due to word likeness rather than to the frequency of experience with a word per se. We examined their suggestion using a factorial manipulation of frequency and neighborhood density, a measure used in lexical memory research to measure orthographic word likeness. For study with no specified task, main effects of density and frequency were in the mirror order, confirming the hypothesized mirror effect of word likeness but not its role in producing the frequency mirror effect. Lexical decision study increased the size of both mirror effects, even though the density manipulation had a negligible effect on lexical decision performance for words. Post hoc analyses showed that neither mirror effect could be explained by differences in lower order measures of word likeness (letter and bigram frequency). The joint orders of frequency and density results were mirrored across new and old conditions in accordance with attention likelihood theory (ALT), but density effects on z-ROC slope suggest that ALT may require extension to accommodate the effect of word likeness on response confidence.

Humans↗

PubMed2005

A ballistic model of choice response time.

Almost all models of response time (RT) use a stochastic accumulation process. To account for the benchmark RT phenomena, researchers have found it necessary to include between-trial variability in the starting point and/or the rate of accumulation, both in linear (R. Ratcliff & J. N. Rouder, 1998) and nonlinear (M. Usher & J. L. McClelland, 2001) models. The authors show that a ballistic (deterministic within-trial) model using a simplified version of M. Usher and J. L. McClelland's (2001) nonlinear accumulation process with between-trial variability in accumulation rate and starting point is capable of accounting for the benchmark behavioral phenomena. The authors successfully fit their model to R. Ratcliff and J. N. Rouder's (1998) data, which exhibit many of the benchmark phenomena.

Choice Behavior↗

PubMed2005

Practice increases the efficiency of evidence accumulation in perceptual choice.

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

Analysis of Variance↗

PubMed2005

Electrophysiological correlates of anticipatory task-switching processes.

Recent studies show a differential switch-related positivity emerging before a switch trial and reflecting anticipatory task-set reconfiguration processes. In this study, the switch-related positivity was examined in a cued task-switching paradigm. Cue-stimulus and response-stimulus intervals were independently manipulated to dissociate between the effects of anticipatory preparation and passive dissipation of task-set interference. Reaction time switch cost declined with increasing cue-stimulus and response-stimulus intervals, suggesting a contribution from both active preparation and passive interference processes. In cue-related difference waveforms, a switch positivity peaked around 350-400 ms and is interpreted as reflecting differential activation of task-set reconfiguration. In stimulus-related difference waveforms, a switch-related negativity is believed to indicate the role of S-R priming and response interference in task-switching.

Adolescent↗

PubMed2005

Nonlinear dynamical analysis of noisy time series.

Empirical time series in the life sciences are often nonstationary and have small signal-to-noise ratios, making it difficult to accurately detect and characterize dynamical structure. The usual response to high noise is averaging, but time domain averaging is inappropriate, especially when the dynamics are nonlinear. We review alternative delay-space averaging methods based on the topology and short-term predictability of nonlinear dynamics and illustrate their application using the TISEAN software (Hegger, Kantz & Schreiber, 1999). The methods were applied to a Lorenz series, which resembles the dynamics found by Kelly, Heathcote, Heath and Longstaff (2001) in series of decision times. The Lorenz series was corrupted with up to 80% additive Gaussian noise, a lower signal-to-noise ratio than has been used in any previous test of these methods, but consistent with Kelly et al.'s data. Prediction methods performed the best for detecting nonstationarity and nonlinear dynamics, and optimal predictability provided an objective criterion for setting the parameters required by the analyses. Local linear filtering methods performed best for characterization, producing informative plots that revealed the nature of the underlying dynamics. These results suggest that a methodology based on delay-space averaging and prediction could be useful with noisy empirical data series.

Animals↗

PubMed2004

QMPE: estimating Lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound.

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

Algorithms↗

PubMed2004

Fitting wald and ex-Wald distributions to response time data: an example using functions for the S-PLUS package.

Schwarz (2001, 2002) proposed the ex-Wald distribution, obtained from the convolution of Wald and exponential random variables, as a model of simple and go/no-go response time. This article provides functions for the S-PLUS package that produce maximum likelihood estimates of the parameters for the ex-Wald, as well as for the shifted Wald and ex-Gaussian, distributions. In a Monte Carlo study, the efficiency and bias of parameter estimates were examined. Results indicated that samples of at least 400 are necessary to obtain adequate estimates of the ex-Wald and that, for some parameter ranges, much larger samples may be required. For shifted Wald estimation, smaller samples of around 100 were adequate, at least when fits identified by the software as having ill-conditioned maximums were excluded. The use of all functions is illustrated using data from Schwarz (2001). The S-PLUS functions and Schwarz's data may be downloaded from the Psychonomic Society's Web archive, www. psychonomic.org/archive/.

Humans↗

PubMed2004

Fitting distributions using maximum likelihood: methods and packages.

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

Humans↗

PubMed2003

Item recognition memory and the receiver operating characteristic.

Four experiments were conducted to investigate the effects of study time, study repetition, semantic and orthographic similarity, and category length on item recognition memory receiver operating characteristics (ROCs). Analyses of ROC shape rejected A. P. Yonelinas's (1994) dual-process model. The normal unequal variance signal-detection model provided a better account of the data, except for a small but consistent excess of high-confidence errors. It was found that z-transformed ROC slope was increased by similarity, category length, and study item repetition, rejecting R. Ratcliff, G. McKoon, and M. Tindall's (1994) "constancy-of-slopes" generalization for these variables, but slope was relatively unaffected by massed study time.

Humans↗

PubMed2003

Averaging learning curves across and within participants.

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

Algorithms↗

PubMed2003

QMLE: fast, robust, and efficient estimation of distribution functions based on quantiles.

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

Data Interpretation, Statistical↗

PubMed2003

Bias in exponential and power function fits due to noise: comment on Myung, Kim, and Pitt.

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

Data Interpretation, Statistical↗

PubMed2002

Quantile maximum likelihood estimation of response time distributions.

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

Data Interpretation, Statistical↗