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 (Psychological Review, 126, 578-609, 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 (Ward & Tan, Memory & Cognition, 2019), 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 six- 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 that there were qualitative differences between the two conditions. Specifically, start-of-list reinstatement and exponential recency functions were favored in both conditions.

Double responding: A new constraint for models of speeded decision making.

Evidence accumulation models (EAMs) have become the dominant models of speeded decision making, which are able to decompose choices and response times into cognitive parameters that drive the decision process. Several models within the EAM framework contain fundamentally different ideas of how the decision making process operates, though previous assessments have found that these models display a high level of mimicry, which has hindered the ability of researchers to contrast these different theoretical viewpoints. Our study introduces a neglected phenomenon that we term "double responding", which can help to further constrain these models. We show that double responding produces several interesting benchmarks, and that the predictions of different EAMs can be distinguished in standard experiment paradigms when they are constrained to account for the choice response time distributions and double responding behaviour in unison. Our findings suggest that lateral inhibition (e.g., the leaky-competing accumulator) provides models with a universal ability to make accurate predictions for these data. Furthermore, only models containing feed-forward inhibition (e.g., the diffusion model) performed poorly under both of our proposed extensions of the standard EAM framework to double responding, suggesting a general inability of feed-forward inhibition to accurately predict these data. We believe that our study provides an important step forward in further constraining models of speeded decision making, though additional research on double responding is required before broad conclusions are made about which models provide the best explanation of the underlying decision-making process.

A method, framework, and tutorial for efficiently simulating models of decision-making.

Evidence accumulation models (EAMs) have become the dominant models of rapid decision-making. Several variants of these models have been proposed, ranging from the simple linear ballistic accumulator (LBA) to the more complex leaky-competing accumulator (LCA), and further extensions that include time-varying rates of evidence accumulation or decision thresholds. Although applications of the simpler variants have been widespread, applications of the more complex models have been fewer, largely due to their intractable likelihood function and the computational cost of mass simulation. Here, I present a framework for efficiently fitting complex EAMs, which uses a new, efficient method of simulating these models. I find that the majority of simulation time is taken up by random number generation (RNG) from the normal distribution, needed for the stochastic noise of the differential equation. To reduce this inefficiency, I propose using the well-known concept within computer science of "look-up tables" (LUTs) as an approximation to the inverse cumulative density function (iCDF) method of RNG, which I call "LUT-iCDF". I show that when using an appropriately sized LUT, simulations using LUT-iCDF closely match those from the standard RNG method in R. My framework, which I provide a detailed tutorial on how to implement, includes C code for 12 different variants of EAMs using the LUT-iCDF method, and should make the implementation of complex EAMs easier and faster.

Bayesian analysis of the piecewise diffusion decision model.

Most past research on sequential sampling models of decision-making have assumed a time homogeneous process (i.e., parameters such as drift rates and boundaries are constant and do not change during the deliberation process). This has largely been due to the theoretical difficulty in testing and fitting more complex models. In recent years, the development of simulation-based modeling approaches matched with Bayesian fitting methodologies has opened the possibility of developing more complex models such as those with time-varying properties. In the present work, we discuss a piecewise variant of the well-studied diffusion decision model (termed pDDM) that allows evidence accumulation rates to change during the deliberation process. Given the complex, time-varying nature of this model, standard Bayesian parameter estimation methodologies cannot be used to fit the model. To overcome this, we apply a recently developed simulation-based, hierarchal Bayesian methodology called the probability density approximation (PDA) method. We provide an analysis of this methodology and present results of parameter recovery experiments to demonstrate the strengths and limitations of this approach. With those established, we fit pDDM to data from a perceptual experiment where information changes during the course of trials. This extensible modeling platform opens the possibility of applying sequential sampling models to a range of complex non-stationary decision tasks.

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model.

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.

One Standard for All: Uniform Scale for Comparing Individuals and Groups in Hierarchical Bayesian Evidence Accumulation Modeling.

In recent years, a growing body of research uses Evidence Accumulation Models (EAMs) to study individual differences and group effects. This endeavor is challenging because fitting EAMs requires constraining one of the EAM parameters to be equal for all participants, which makes a strong and possibly unlikely assumption. Moreover, if this assumption is violated, differences or lack thereof may be wrongly found. To overcome this limitation, in this study, we introduce a new method that was originally suggested by van Maanen & Miletic (2021), which employs Bayesian hierarchical estimation. In this new method, we set the scale at the population level, thereby allowing for individual and group differences, which is realized by de facto fixing a population-level hyper-parameter through its priors. As proof of concept, we ran two successful parameter recovery studies using the Linear Ballistic Accumulation model. The results suggest that the new method can be reliably used to study individual and group differences using EAMs. We further show a case in which the new method reveals the true group differences whereas the classic method wrongly detects differences that are truly absent.

Does allowing for changes of mind influence initial responses?

Evidence accumulation models (EAMs) have become the dominant theoretical framework for rapid decision-making, and while many theoretically distinct variants exist, comparisons have proved challenging due to strong mimicry in their predictions about choice response time data. One solution to reduce mimicry is constraining these models with double responses, which are a second response that is made after the initial response. However, instructing participants that they are allowed to change their mind could influence their strategy for initial responding, meaning that explicit double responding paradigms may not generalise to standard paradigms. Here, we provide a validation of explicit double responding paradigms, by assessing whether participants' initial decisions - as measured by diffusion model parameters - differ based on whether or not they were instructed that they could change their response after their initial response. Across three experiments, our results consistently indicate that allowing for changes of mind does not influence initial responses, with Bayesian analyses providing at least moderate evidence in favour of the null in all cases. Our findings suggest that explicit double responding paradigms should generalise to standard paradigms, validating the use of explicit double responding in future rapid decision-making studies.

Think fast! The implications of emphasizing urgency in decision-making.

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding - which intuitively should selectively influence decision threshold - may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analysis of 6 data sets from previous studies using two different EAMs - the diffusion model and the linear ballistic accumulator (LBA) - with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

On the limits of familiarity accounts in lexical decision: The case of repetition effects.

Recent modelling accounts of the lexical decision task have suggested that the reading system performs evidence accumulation to carry out some functions. Evidence accumulation models have been very successful in accounting for effects in the lexical decision task, including the dissociation of repetition effects for words and nonwords (facilitative for words but inhibitory for nonwords). The familiarity of a repeated item triggers its recognition, which facilitates 'word' responses but hampers nonword rejection. However, reports of facilitative repetition effects for nonwords with several repetitions in short blocks challenge this hypothesis and favour models based on episodic retrieval. To shed light on the nature of the repetition effects for nonwords in lexical decision, we conducted four experiments to examine the impact of extra-lexical source of information-we induced the use of episodic retrieval traces via instructions and list composition. When the initial block was long, the repetition effect for nonwords was inhibitory, regardless of the instructions and list composition. However, the inhibitory effect was dramatically reduced when the initial block included two presentations of the stimuli and it was even facilitatory when the initial block was short. This composite pattern suggests that evidence accumulation models of lexical decision should take into account all sources of evidence-including episodic retrieval-during the process of lexical decision.

Some Evidence for an Association Between Early Life Adversity and Decision Urgency.

The relationship between early life adversity and adult outcomes is traditionally investigated relative to risk and protective factors (e.g., resilience, cognitive appraisal), and poor self-control or decision-making. However, life history theory suggests this relationship may be adaptive-underpinned by mechanisms that use early environmental cues to alter the developmental trajectory toward more short-term strategies. These short-term strategies have some theoretical overlap with the most common process models of decision-making-evidence accumulation models-which model decision urgency as a decision threshold. The current study examined the relationship between decision urgency (through the linear ballistic accumulator) and early life adversity. A mixture of analysis methods, including a joint model analysis designed to explicitly account for uncertainty in estimated decision urgency values, revealed weak-to-strong evidence in favor of a relationship between decision urgency and early life adversity, suggesting a possible effect of life history strategy on even the most basic decisions.

The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision.

Many decisions are thought to arise via the accumulation of noisy evidence to a threshold or bound. In perception, the mechanism explains the effect of stimulus strength, characterized by signal-to-noise ratio, on decision speed, accuracy and confidence. It also makes intriguing predictions about the noise itself. An increase in noise should lead to faster decisions, reduced accuracy and, paradoxically, higher confidence. To test these predictions, we introduce a novel sensory manipulation that mimics the addition of unbiased noise to motion-selective regions of visual cortex, which we verified with neuronal recordings from macaque areas MT/MST. For both humans and monkeys, increasing the noise induced faster decisions and greater confidence over a range of stimuli for which accuracy was minimally impaired. The magnitude of the effects was in agreement with predictions of a bounded evidence accumulation model.

Reactive control processes contributing to residual switch cost and mixing cost across the adult lifespan.

In task-switching paradigms, performance is better when repeating the same task than when alternating between tasks (switch cost) and when repeating a task alone rather than intermixed with another task (mixing cost). These costs remain even after extensive practice and when task cues enable advanced preparation (residual costs). Moreover, residual reaction time mixing cost has been consistently shown to increase with age. Residual switch and mixing costs modulate the amplitude of the stimulus-locked P3b. This mixing effect is disproportionately larger in older adults who also prepare more for and respond more cautiously on these "mixed" repeat trials (Karayanidis et al., 2011). In this paper, we analyze stimulus-locked and response-locked P3 and lateralized readiness potentials to identify whether residual switch and mixing cost arise from the need to control interference at the level of stimulus processing or response processing. Residual mixing cost was associated with control of stimulus-level interference, whereas residual switch cost was also associated with a delay in response selection. In older adults, the disproportionate increase in mixing cost was associated with greater interference at the level of decision-response mapping and response programming for repeat trials in mixed-task blocks. These findings suggest that older adults strategically recruit greater proactive and reactive control to overcome increased susceptibility to post-stimulus interference. This interpretation is consistent with recruitment of compensatory strategies to compensate for reduced repetition benefit rather than an overall decline on cognitive flexibility.

Model-based analysis of context-specific cognitive control.

Interference resolution is improved for stimuli presented in contexts (e.g., locations) associated with frequent conflict. This phenomenon, the context-specific proportion congruent (CSPC) effect, has challenged the traditional juxtaposition of "automatic" and "controlled" processing because it suggests that contextual cues can prime top-down control settings in a bottom-up manner. We recently obtained support for this "priming of control" hypothesis with functional magnetic resonance imaging by showing that CSPC effects are mediated by contextually cued adjustments in processing selectivity. However, an equally plausible explanation is that CSPC effects reflect adjustments in response caution triggered by expectancy violations (i.e., prediction errors) when encountering rare events as compared to common ones (e.g., incongruent trials in a task context associated with infrequent conflict). Here, we applied a quantitative model of choice, the linear ballistic accumulator (LBA), to distil the reaction time and accuracy data from four independent samples that performed a modified flanker task into latent variables representing the psychological processes underlying task-related decision making. We contrasted models which differentially accounted for CSPC effects as arising either from contextually cued shifts in the rate of sensory evidence accumulation ("drift" models) or in the amount of evidence required to reach a decision ("threshold" models). For the majority of the participants, the LBA ascribed CSPC effects to increases in response threshold for contextually infrequent trial types (e.g., congruent trials in the frequent conflict context), suggesting that the phenomenon may reflect more a prediction error-triggered shift in decision criterion rather than enhanced sensory evidence accumulation under conditions of frequent conflict.

Variability in proactive and reactive cognitive control processes across the adult lifespan.

Task-switching paradigms produce a highly consistent age-related increase in mixing cost [longer response time (RT) on repeat trials in mixed-task than single-task blocks] but a less consistent age effect on switch cost (longer RT on switch than repeat trials in mixed-task blocks). We use two approaches to examine the adult lifespan trajectory of control processes contributing to mixing cost and switch cost: latent variables derived from an evidence accumulation model of choice, and event-related potentials (ERP) that temporally differentiate proactive (cue-driven) and reactive (target-driven) control processes. Under highly practiced and prepared task conditions, aging was associated with increasing RT mixing cost but reducing RT switch cost. Both effects were largely due to the same cause: an age effect for mixed-repeat trials. In terms of latent variables, increasing age was associated with slower non-decision processes, slower rate of evidence accumulation about the target, and higher response criterion. Age effects on mixing costs were evident only on response criterion, the amount of evidence required to trigger a decision, whereas age effects on switch cost were present for all three latent variables. ERPs showed age-related increases in preparation for mixed-repeat trials, anticipatory attention, and post-target interference. Cue-locked ERPs that are linked to proactive control were associated with early emergence of age differences in response criterion. These results are consistent with age effects on strategic processes controlling decision caution. Consistent with an age-related decline in cognitive flexibility, younger adults flexibly adjusted response criterion from trial-to-trial on mixed-task blocks, whereas older adults maintained a high criterion for all trials.

Advance preparation in task-switching: converging evidence from behavioral, brain activation, and model-based approaches.

Recent research has taken advantage of the temporal and spatial resolution of event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI) to identify the time course and neural circuitry of preparatory processes required to switch between different tasks. Here we overview some key findings contributing to understanding strategic processes in advance preparation. Findings from these methodologies are compatible with advance preparation conceptualized as a set of processes activated for both switch and repeat trials, but with substantial variability as a function of individual differences and task requirements. We then highlight new approaches that attempt to capitalize on this variability to link behavior and brain activation patterns. One approach examines correlations among behavioral, ERP and fMRI measures. A second "model-based" approach accounts for differences in preparatory processes by estimating quantitative model parameters that reflect latent psychological processes. We argue that integration of behavioral and neuroscientific methodologies is key to understanding the complex nature of advance preparation in task-switching.