A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm.

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html .

Default "Gunel and Dickey" Bayes factors for contingency tables.

The analysis of R×C contingency tables usually features a test for independence between row and column counts. Throughout the social sciences, the adequacy of the independence hypothesis is generally evaluated by the outcome of a classical p-value null-hypothesis significance test. Unfortunately, however, the classical p-value comes with a number of well-documented drawbacks. Here we outline an alternative, Bayes factor method to quantify the evidence for and against the hypothesis of independence in R×C contingency tables. First we describe different sampling models for contingency tables and provide the corresponding default Bayes factors as originally developed by Gunel and Dickey (Biometrika, 61(3):545-557 (1974)). We then illustrate the properties and advantages of a Bayes factor analysis of contingency tables through simulations and practical examples. Computer code is available online and has been incorporated in the "BayesFactor" R package and the JASP program ( jasp-stats.org ).

Human Performance in Competitive and Collaborative Human-Machine Teams.

In the modern world, many important tasks have become too complex for a single unaided individual to manage. Teams conduct some safety-critical tasks to improve task performance and minimize the risk of error. These teams have traditionally consisted of human operators, yet, nowadays, artificial intelligence and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task modeled after a classic arcade game to investigate the performance of human-machine and human-human teams. We manipulated the group conditions between team members; sometimes, they were instructed to collaborate, compete, or work separately. We evaluated players' performance in the main task (gameplay) and, in post hoc analyses, participant behavioral patterns to inform group strategies. We compared game performance between team types (human-human vs. human-machine) and group conditions (competitive, collaborative, independent). Adapting workload capacity analysis to human-machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human-human pairings, but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human-machine team performance measurement are discussed.

An R package for state-trace analysis.

State-trace analysis (Bamber, Journal of Mathematical Psychology, 19, 137-181, 1979) is a graphical analysis that can determine whether one or more than one latent variable mediates an apparent dissociation between the effects of two experimental manipulations. State-trace analysis makes only ordinal assumptions and so, is not confounded by range effects that plague alternative methods, especially when performance is measured on a bounded scale (such as accuracy). We describe and illustrate the application of a freely available GUI driven package, StateTrace, for the R language. StateTrace automates many aspects of a state-trace analysis of accuracy and other binary response data, including customizable graphics and the efficient management of computationally intensive Bayesian methods for quantifying evidence about the outcomes of a state-trace experiment, developed by Prince, Brown, and Heathcote (Psychological Methods, 17, 78-99, 2012).

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).

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.

Decision processes and the slowing of simple choices in schizophrenia.

Individuals diagnosed with schizophrenia have slowed response times (RT). We examined the role of decision processes in the slowing of simple choice responses. We updated Schatz's (1998) meta-analysis of deficits in speed and extend it to systematically examine the effects of schizophrenia on choice accuracy. We then report an experiment requiring decisions about motion direction, which we analyzed using an evidence accumulation model of choice, the linear ballistic accumulator (LBA; Brown & Heathcote, 2008). By simultaneously taking into account all aspects of behavior, the LBA was more sensitive to deficits than mean RT or accuracy alone. It also identified the 2 underlying causes of slowing: more cautious decisions (i.e., requiring more evidence before making a decision) and perceptual deficits. The schizophrenia group displayed strong sequential effects that were captured by the response on the previous trial affecting the relative amount of evidence required for choice in the LBA. These results illustrate that evidence accumulation models provide a sensitive tool that can be used to identify the cognitive mechanisms causing slowing in schizophrenia.

Turning the hands of time again: a purely confirmatory replication study and a Bayesian analysis.

In a series of four experiments, Topolinski and Sparenberg (2012) found support for the conjecture that clockwise movements induce psychological states of temporal progression and an orientation toward the future and novelty. Here we report the results of a preregistered replication attempt of Experiment 2 from Topolinski and Sparenberg (2012). Participants turned kitchen rolls either clockwise or counterclockwise while answering items from a questionnaire assessing openness to experience. Data from 102 participants showed that the effect went slightly in the direction opposite to that predicted by Topolinski and Sparenberg (2012), and a preregistered Bayes factor hypothesis test revealed that the data were 10.76 times more likely under the null hypothesis than under the alternative hypothesis. Our findings illustrate the theoretical importance and practical advantages of preregistered Bayes factor replication studies, both for psychological science and for empirical work in general.

Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions.

The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time (RT) task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke et al. (2013) have developed a Bayesian parametric approach (BPA) that allows for the estimation of the entire distribution of SSRTs. The BPA assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to estimate the parameters of the SSRT distribution. Here we present an efficient and user-friendly software implementation of the BPA-BEESTS-that can be applied to individual as well as hierarchical stop-signal data. BEESTS comes with an easy-to-use graphical user interface and provides users with summary statistics of the posterior distribution of the parameters as well various diagnostic tools to assess the quality of the parameter estimates. The software is open source and runs on Windows and OS X operating systems. In sum, BEESTS allows experimental and clinical psychologists to estimate entire distributions of SSRTs and hence facilitates the more rigorous analysis of stop-signal data.

Linear deterministic accumulator models of simple choice.

We examine theories of simple choice as a race among evidence accumulation processes. We focus on the class of deterministic race models, which assume that the effects of fluctuations in the parameters of the accumulation processes between-choice trials (between-choice noise) dominate the effects of fluctuations occurring while making a choice (within-choice noise) in behavioral data (i.e., response times and choices). The latter deterministic approximation, when combined with the assumption that accumulation is linear, leads to a class of models that can be readily applied to simple-choice behavior because they are computationally tractable. We develop a new and mathematically simple exemplar within the class of linear deterministic models, the Lognormal race (LNR). We then examine how the LNR, and another widely applied linear deterministic model, Brown and Heathcote's (2008) LBA, account for a range of benchmark simple-choice effects in lexical-decision task data reported by Wagenmakers et al. (2008). Our results indicate that the LNR provides an accurate description of this data. Although the LBA model provides a slightly better account, both models support similar psychological conclusions.

5F sphincterotomes and 4F sphincterotomes are equivalent for the selective cannulation of the common bile duct.

BACKGROUND: Cannulation of the common bile duct (CBD) is the first step in endoscopic retrograde cholangiopancreatography (ERCP). Cannulation difficulty is a known risk factor for post-ERCP complications and may be minimized by the use of a smaller caliber sphincterotome. OBJECTIVE: To compare the efficacy of CBD cannulation with a 4 F versus a 5 F sphincterotome. DESIGN: A randomized controlled trial, with concealed allocation and double-blinding. PATIENTS: Adult patients undergoing their first ERCP at a tertiary referral center. INTERVENTION: Patients were randomized to undergo CBD cannulation with either a 4 F or 5 F sphincterotome. MAIN OUTCOME MEASUREMENTS: Successful deep cannulation in <15 attempts was the primary outcome. Secondary outcomes included number of attempts/time to cannulation, incidence of complications within 24 hours, and overall cannulation success (including patients before and after crossover). Analysis was intention to treat and included standard descriptive and inferential methods. RESULTS: A total of 107 patients were randomized: 51 (4 F) versus 56 (5 F). The majority were female (71%) and white (92%). Baseline demographics, presenting symptoms, and laboratory values were similar between groups. Similar success in initial cannulation was observed: 84.3% (4 F) and 83.9% (5 F). No differences were noted in time to cannulation (5.12 min [SD, 4.8] for 4 F vs 4.46 min [SD, 4.13] for 5 F; p = NS), number of attempts to cannulation (6.2 [SD, 5.2] for 4 F vs 5.7 [SD, 4.9] for 5 F; p = NS), or complications. The overall cannulation success was 92.2% (4 F) and 92.9% (5 F). LIMITATIONS: Premature termination of the trial resulted in decreased power. CONCLUSIONS: There exists no significant difference in efficacy between 4 F and 5 F sphincterotomes. The choice of initial sphincterotome should be dictated by physician preference.