Chris Donkin

Short-term memory scanning viewed as exemplar-based categorization

Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to explain relations between classification and recognition. However, a major gap in research is that the models have not been tested on their ability to provide a theoretical account of RTs and other aspects of performance in the classic Sternberg (1966) short-term memory-scanning paradigm, perhaps the most venerable of all recognition-RT tasks. The present research fills that gap by demonstrating that the EBRW model accounts in natural fashion for a wide variety of phenomena involving diverse forms of short-term memory scanning. The upshot is that similar cognitive operating principles may underlie the domains of multidimensional classification and short-term old-new recognition.

Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes?

Quantitative models for response time and accuracy are increasingly used as tools to draw conclusions about psychological processes. Here we investigate the extent to which these substantive conclusions depend on whether researchers use the Ratcliff diffusion model or the Linear Ballistic Accumulator model. Simulations show that the models agree on the effects of changes in the rate of information accumulation and changes in non-decision time, but that they disagree on the effects of changes in response caution. In fits to empirical data, however, the models tend to agree closely on the effects of an experimental manipulation of response caution. We discuss the implications of these conflicting results, concluding that real manipulations of caution map closely, but not perfectly to response caution in either model. Importantly, we conclude that inferences about psychological processes made from real data are unlikely to depend on the model that is used.

An integrated model of choices and response times in absolute identification.

Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The models predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time.

The Overconstraint of Response Time Models: Rethinking the Scaling Problem

Theories of choice response time (RT) provide insight into the psychological underpinnings of simple decisions. Evidence accumulation (or sequential sampling) models are the most successful theories of choice RT. These models all have the same “scaling” property—that a subset of their parameters can be multiplied by the same amount without changing their predictions. This property means that a single parameter must be fixed to allow the estimation of the remaining parameters. In the present article, we show that the traditional solution to this problem has overconstrained these models, unnecessarily restricting their ability to account for data and making implicit—and therefore unexamined—psychological assumptions. We show that versions of these models that address the scaling problem in a minimal way can provide a better description of data than can their overconstrained counterparts, even when increased model complexity is taken into account.

Getting more from accuracy and response time data: Methods for fitting the linear ballistic accumulator

Cognitive models of the decision process provide greater insight into response time and accuracy than do standard ANOVA techniques. However, such models can be mathematically and computationally difficult to apply. We provide instructions and computer code for three methods for estimating the parameters of the linear ballistic accumulator (LBA), a new and computationally tractable model of decisions between two or more choices. These methods—a Microsoft Excel worksheet, scripts for the statistical program R, and code for implementation of the LBA into the Bayesian sampling software WinBUGS—vary in their flexibility and user accessibility. We also provide scripts in R that produce a graphical summary of the data and model predictions. In a simulation study, we explored the effect of sample size on 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.

A Power-Law Model of Psychological Memory Strength in Short- and Long-Term Recognition

A classic law of cognition is that forgetting curves are closely approximated by power functions. This law describes relations between different empirical dependent variables and the retention interval, and the precise form of the functional relation depends on the scale used to measure each variable. In the research reported here, we conducted a recognition task involving both short- and long-term probes. We discovered that formal memory-strength parameters from an exemplar-recognition model closely followed a power function of the lag between studied items and a test probe. The model accounted for rich sets of response time (RT) data at both individual-subject and individual-lag levels. Because memory strengths were derived from model fits to choices and RTs from individual trials, the psychological power law was independent of the scale used to summarize the forgetting functions. Alternative models that assumed different functional relations or posited a separate fixed-strength working memory store fared considerably worse than the power-law model did in predicting the data.

Converging measures of workload capacity.

Does processing more than one stimulus concurrently impede or facilitate performance relative to processing just one stimulus? This fundamental question about workload capacity was surprisingly difficult to address empirically until Townsend and Nozawa (1995) developed a set of nonparametric analyses called systems factorial technology. We develop an alternative parametric approach based on the linear ballistic accumulator decision model (Brown & Heathcote, 2008), which uses the model’s parameter estimates to measure processing capacity. We show that these two methods have complementary strengths, and that, in a data set where participants varied greatly in capacity, the two approaches provide converging evidence.

Cognitive control and counterproductive oculomotor capture by reward-related stimuli

Two experiments investigated the extent to which value-modulated oculomotor capture is subject to top-down control. In these experiments, participants were never required to look at the reward-related stimuli; indeed, doing so was directly counterproductive because it caused omission of the reward that would otherwise have been obtained. In Experiment 1, participants were explicitly informed of this omission contingency. Nevertheless, they still showed counterproductive oculomotor capture by reward-related stimuli, suggesting that this effect is relatively immune to cognitive control. Experiment 2 more directly tested whether this capture is controllable by comparing the performance of participants who either had or had not been explicitly informed of the omission contingency. There was no evidence that value-modulated oculomotor capture differed between the two conditions, providing further evidence that this effect proceeds independently of cognitive control. Taken together, the results of the present research provide strong evidence for the automaticity and cognitive impenetrability of value-modulated attentional capture.

The structure of short-term memory scanning: an investigation using response time distribution models

A classic question in cognitive psychology concerns the nature of memory search in short-term recognition. Despite its long history of investigation, however, there is still no consensus on whether memory search takes place serially or in parallel or is based on global access. In the present investigation, we formalize a variety of models designed to account for detailed response time distribution data in the classic Sternberg (Science 153: 652–654, 1966) memory-scanning task. The models vary in their mental architectures (serial exhaustive, parallel self-terminating, and global access). Furthermore, the component processes within the architectures that make match/mismatch decisions are formalized as linear ballistic accumulators (LBAs). In fast presentation rate conditions, the parallel and global access models provide far better accounts of the data than does the serial model. LBA drift rates are found to depend almost solely on the lag between study items and test probes, whereas response thresholds change with memory set size. Under slow presentation rate conditions, even simple versions of the serial-exhaustive model provide accounts of the data that are as good as those of the parallel and global access models. We provide alternative interpretations of the results in our General Discussion.

Logical Rules and the Classification of Integral-Dimension Stimuli.

A classic distinction in perceptual information processing is whether stimuli are composed of separable dimensions, which are highly analyzable, or integral dimensions, which are processed holistically. Previous tests of a set of logical-rule models of classification have shown that separable-dimension stimuli are processed serially if the dimensions are spatially separated and as a mixture of serial and parallel processes if the dimensions are spatially overlapping (Fifić, Little, & Nosofsky, 2010; Little, Nosofsky, & Denton, 2011). In the current research, the logical-rule models are applied to predict response-time (RT) data from participants trained to classify integral-dimension color stimuli into rule-based categories. In dramatic contrast to the previous results for separable-dimension stimuli, analysis of the current data indicated that processing was best captured by a single-channel coactive model. The results converge with previous operations that suggest holistic processing of integral-dimension stimuli and demonstrate considerable generality for the application of the logical-rule models to predicting RT data from rule-based classification experiments.