Signal Detection Models with Random Participant and Item Effects

The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis with aggregated data is not consistent. In fact, mnemonic ability is underestimated, even in the large-sample limit. We present two hierarchical Bayesian models that simultaneously account for participant and item variability. We show how these models provide for accurate estimation of participants’ mnemonic ability as well as the memorability of items. The model is benchmarked with a simulation study and applied to a novel data set. This research is supported by NSF grants SES-0095919 and SES-0351523, NIH grant R01-MH071418, a University of Missouri Research Leave grant and fellowships from the Spanish Ministry of Education and the University of Leuven, Belgium.     

Bayesian inference for psychology. Part II: Example applications with JASP

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.     

Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).     

Flexible attention allocation to visual and auditory working memory tasks: manipulating reward induces a trade-off

Prominent roles for general attention resources are posited in many models of working memory, but the manner in which these can be allocated differs between models or is not sufficiently specified. We varied the payoffs for correct responses in two temporally-overlapping recognition tasks, a visual array comparison task and a tone sequence comparison task. In the critical conditions, an increase in reward for one task corresponded to a decrease in reward for the concurrent task, but memory load remained constant. Our results show patterns of interference consistent with a trade-off between the tasks, suggesting that a shared resource can be flexibly divided, rather than only fully allotted to either of the tasks. Our findings support a role for a domain-general resource in models of working memory, and furthermore suggest that this resource is flexibly divisible.     

Visual short-term memory always requires general attention

The role of attention in visual memory remains controversial; while some evidence has suggested that memory for binding between features demands no more attention than does memory for the same features, other evidence has indicated cognitive costs or mnemonic benefits for explicitly attending to bindings. We attempted to reconcile these findings by examining how memory for binding, for features, and for features during binding is affected by a concurrent attention-demanding task. We demonstrated that performing a concurrent task impairs memory for as few as two visual objects, regardless of whether each object includes one or more features. We argue that this pattern of results reflects an essential role for domain-general attention in visual memory, regardless of the simplicity of the to-be-remembered stimuli. We then discuss the implications of these findings for theories of visual working memory.     

A Bayesian hierarchical model for the measurement of working memory capacity

Working memory is the memory system that allows for conscious storage and manipulation of information. The capacity of working memory is extremely limited. Measurements of this limit, and what affects it, are critical to understanding working memory. Cowan (2001) and Pashler (1988) suggested applying multinomial tree models to data from change detection paradigms in order to estimate working memory capacity. Both Pashler and Cowan suggested simple formulas for estimating capacity with these models. However, in many cases, these simple formulas are inadequate, and may lead to inefficient or biased estimation of working memory capacity. I propose a Bayesian hierarchical alternative to the Pashler and Cowan formulas, and show that the hierarchical model outperforms the traditional formulas. The models are easy to use and appropriate for a wide range of experimental designs. An easy-to-use graphical user interface for fitting the hierarchical model to data is available.