Variable rather than extreme slow reaction times distinguish brain states during sustained attention

A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.

Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

It is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

Decision Making by Ensembles of Accumulators

Evidence accumulation is a computational framework that accounts for behavior as well as the dynamics of individual neurons involved in decision making. Linking these two levels of description reveals a scaling paradox: How do choices and response times (RT) explained by models assuming single accumulators arise from a large ensemble of idiosyncratic accumulator neurons? We created a simulation model that makes decisions by aggregating across ensembles of accumulators, thereby instantiating the essential structure of neural ensembles that make decisions. Across different levels of simulated choice difficulty and speed-accuracy emphasis, choice proportions and RT distributions simulated by the ensembles are invariant to ensemble size and the accumulated evidence at RT is invariant across RT when the accumulators are at least moderately correlated in either baseline evidence or rates of accumulation and when RT is not governed by the most extreme accumulators. To explore the relationship between the low-level ensemble accumulators and high-level cognitive models, we fit simulated ensemble behavior with a standard LBA model. The standard LBA model generally recovered the core accumulator parameters (particularly drift rates and residual time) of individual ensemble accumulators with high accuracy, with variability parameters of the standard LBA modulating as a function of various ensemble parameters. Ensembles of accumulators also provide an alternative conception of speed-accuracy tradeoff without relying on varying thresholds of individual accumulators, instead by adjusting how ensembles of accumulators are aggregated or by how accumulators are correlated within ensembles. These results clarify relationships between neural and computational accounts of decision making.

Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

It is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5,000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

How Much Time Do People With Aphasia Need to Respond During Picture Naming? Estimating Optimal Response Time Cutoffs Using a Multinomial Ex-Gaussian Approach

Purpose Aphasia is a language disorder caused by acquired brain injury, which generally involves difficulty naming objects. Naming ability is assessed by measuring picture naming, and models of naming performance have mostly focused on accuracy and excluded valuable response time (RT) information. Previous approaches have therefore ignored the issue of processing efficiency, defined here in terms of optimal RT cutoff, that is, the shortest deadline at which individual people with aphasia produce their best possible naming accuracy performance. The goals of this study were therefore to (a) develop a novel model of aphasia picture naming that could accurately account for RT distributions across response types; (b) use this model to estimate the optimal RT cutoff for individual people with aphasia; and (c) explore the relationships between optimal RT cutoff, accuracy, naming ability, and aphasia severity. Method A total of 4,021 naming trials across 10 people with aphasia were scored for accuracy and RT onset. Data were fit using a novel ex-Gaussian multinomial RT model, which was then used to characterize individual optimal RT cutoffs. Results Overall, the model fitted the empirical data well and provided reliable individual estimates of optimal RT cutoff in picture naming. Optimal cutoffs ranged between approximately 5 and 10 s, which has important implications for assessment and treatment. There was no direct relationship between aphasia severity, naming RT, and optimal RT cutoff. Conclusion The multinomial ex-Gaussian modeling approach appears to be a promising and straightforward way to estimate optimal RT cutoffs in picture naming in aphasia. Limitations and future directions are discussed.

Another Warning about Median Reaction Time --- Version of 11 Feb 2020

Contrary to the warning of Miller (1988), Rousselet and Wilcox (2020) argued that it is better to summarize each participant’s single-trial reaction times (RTs) in a given condition with the median than with the mean when comparing the central tendencies of RT distributions across experimental conditions. They acknowledged that median RTs can produce inflated Type I error rates when conditions differ in the number of trials tested, consistent with Miller’s warning, but they showed that the bias responsible for this error rate inflation could be eliminated with a bootstrap bias correction technique. The present simulations extend their analysis by examining the power of bias-corrected medians to detect true experimental effects and by comparing this power with the power of analyses using means and regular medians. Unfortunately, although bias-corrected medians solve the problem of inflated Type I error rates, their power is lower than that of means or regular medians in many realistic situations. In addition, even when conditions do not differ in the number of trials tested, the power of tests (e.g., t-tests) is generally lower using medians rather than means as the summary measures. Thus, the present simulations demonstrate that summary means will often provide the most powerful test for differences between conditions, and they show what aspects of the RT distributions determine the size of the power advantage for means.

Another Warning about Median Reaction Time --- Version of 11 Feb 2020

The present simulations examine the power of bias-corrected medians to detect true experimental effects on reaction time and compare this power with the power of analyses using means and regular medians. Unfortunately, although bias-corrected medians solve the problem of inflated Type I error rates, their power is lower than that of means or regular medians in many realistic situations. The simulations demonstrate that means will often provide the most powerful test for condition differences, and they show what aspects of the RT distributions should be checked to determine whether means or medians will provide greater power.

How do recall requirements affect decision-making in free recall initiation? A linear ballistic accumulator approach

Models of free recall describe free recall initiation as a decision-making process in which items compete to be retrieved. Recently, Osth and Farrell (2019) applied evidence accumulation models to complete RT distributions and serial positions of participants' first recalls in free recall which resulted in some novel conclusions about primacy and recency effects. Specifically, the results of the modeling favored an account in which primacy was due to reinstatement of the start-of-the-list and recency was found to be exponential in shape. In this work, we examine what happens when participants are given alternative recall instructions. Prior work has demonstrated weaker primacy and greater recency when fewer items are required to report \cite{WardTan19}, and a key question is whether this change in instructions qualitatively changes the nature of the recall process, or merely changes the parameters of the recall competition. We conducted an experiment where participants studied 6 or 12 item lists and were post-cued as to whether to retrieve a single item, or as many items as possible. Subsequently, we applied LBA models with various assumptions about primacy and recency, implemented using hierarchical Bayesian techniques. While greater recency was observed when only one item was required for output, the model selection did not suggest there were qualitative differences between the two conditions. Specifically, start-of-list reinstatement and exponential recency functions were favored in both conditions.

Caffeine Content and Perceived Sensory Characteristics of Pod Coffee: Effects on Mood and Cognitive Performance

Encapsulated (Pod) coffee is increasingly popular and available in a range of flavor and intensity profiles. This study examined consumption of different coffee Pods on mood and cognitive performance. Thirty-eight participants (n=6 males, 32 females; age: 23.9±5.4 years; weight: 64.3±11.9 kg; BMI: 22.4±2.7 kg•m-2; mean±SD) completed 3 trials, consuming either Cosi, Dharkan, or Kazaar Pods following overnight caffeine abstention. Mood and cognitive performance (choice reaction-time (CRT), visual scanning (VS), Stroop) were measured before and 30 min post coffee consumption. Sensory characteristics were measured during coffee consumption. Accuracy, Reaction Time (RT) central tendency and whole RT distributions were analyzed. Bitterness, flavour-intensity, aroma and perceived caffeine content ratings increased for Cosi, Dharkan and Kazaar Pods respectively. Reduced ratings of sleepiness and headache; and increased ratings of concentration, alertness, excitement and happiness were observed with all Pods. Coffee improved CRT latency (before: 469±55 vs. after: 459±50 ms; p=0.031), but not visual scanning performance. Stroop RTs were faster after coffee (before: 854±193 vs. after: 766±156 ms; p < 0.001); with control, congruent and incongruent trials facilitated by different aspects of the RT distribution. Consumption of Nespresso® Pod coffee improves mood and cognitive performance irrespective of caffeine content, habitual caffeine use and Pod sensory characteristics. However, the effects on cognitive function appear to be task dependent.

Using response time distributions and race models to characterize primacy and recency effects in free recall initiation

Memory models have typically characterized retrieval in free recall as multi-alternative decision making. However, the majority of these models have only been applied to mean response times (RTs), and have not accounted for the complete RT distributions. We show that RT distributions carry diagnostic information about how items enter into competition for recall, and how that competition impacts on the dynamics of recall. We jointly fit RT distributions and serial position functions of free recall initiation with both a racing diffusion model and the linear ballistic accumulator (LBA: Brown &amp; Heathcote, 2008) model in a hierarchical Bayesian framework while factorially varying different assumptions of how primacy and recency are generated. Recency was either a power law or an exponential function. Primacy was treated either as a strength boost to the early list items so that both primacy and recency items jointly compete to be retrieved, a rehearsal process whereby the first item is sometimes rehearsed to the end of the list to make it functionally recent, or due to reinstatement of the start of the list. While serial position curves do not distinguish between these accounts, they make distinct predictions about how RT distributions vary across serial positions. Results from a number of datasets strongly favor the reinstatement account of primacy with an exponential recency function. These results suggest that models of free recall can be more constrained by considering complete RT distributions.