When presented with a large array of possible alternatives consumers must quickly screen out undesirable options before more carefully deliberating over a smaller set. We consider the situation where these screening decisions are made sequentially and independently for each alternative. Understanding how attribute information is processed during these multi-attribute screening decisions provides useful insights into consumer decision making. Previous approaches to this problem have used equipment such as eye trackers, or intervened in the decision making process by obscuring information and requiring the participant reveal it. Here, we classify a broad set of decision strategies into higher-order classes based on whether those strategies assume processing of each attribute is complete or selective, and whether “good” attributes can compensate “bad” attributes. We then introduce a hierarchical latent mixture modelling approach that uses response times and choices to infer the higher-order decision class that best explains each individual’s screening decisions. We test the model against empirical data where the strategy decision makers ought to use was directly manipulated, demonstrating the model identifies the expected attribute processing strategy for all participants. In simulation, we demonstrate good recovery of the exhaustive set of decision classes we investigated, and extended this to show the model appropriately identifies different decision classes when different classes are present across a sample of participants. Our modelling approach thus provides a two-stage, principled solution to the challenge of identifying individual differences in preferential decision making: grouping a large set of candidate decision strategies into a smaller set of higher-order classes, and then discriminating between those higher-order classes in data with quantitative cognitive models of choices and response times. This allows the researcher to relax assumptions that a sample of participants adheres to the same decision strategy, and also capitalising on the benefits of hierarchical modelling to jointly estimate population parameters and random effects.

Two popular methods of preference elicitation are rankings and best–worst scaling (BWS). Rankings, while simple and widely adopted, can be burdensome with larger item sets and fail to capture indifference between options that are neither loved nor hated. Best–worst scaling is a survey method that sidesteps the set size problem by capitalizing on people’s natural capacity to identify preferences at the extremes. Across three experiments, our primary finding is that elicited preferences for ranking and BWS methods align, and that BWS methods provide additional resolution to resolve the indifference between middling options where rankings can struggle as well as the relative importance of each option. Moreover, we show that BWS methods exhibit greater test–retest reliability compared to rankings, even over time frames as short as minutes. Taken together, our results privilege BWS as a reliable and readily accessible alternative to ranking methods for preference elicitation.

There are many well-studied models for preference, such as Luce's choice model and other random utility accounts. More recently, there has been some interest in extending preference models to also account for decision times. We identify a limit to this extension, by noting a correspondence between preference models and theories of simple, rapid decision-making (response time models). Some preference models are known to predict "separable" choices and response times. This mathematical property has been extensively studied - empirically and theoretically - for simple decision-making. In that context, there are well-known and robust empirical phenomena describing predictable differences between the speeds of different response choices. This link provides insight into the interpretability of some random utility models as response time models, and also constraints on the development of theories of preference.

Algorithmic recommendations have drastically expanded in recent years to aid human decision-making. In this paper, we seek to understand the users of these tools and when, where, and why they obtain algorithmic advice. We do so examining data from two behavioural decision-making experiments (N = 216) and applying the Timed Racing Diffusion Model (TRDM) across choices and response times. Our experiments find that people are sensitive to when algorithmic advice is worthwhile obtaining. Notably, our results privilege experience and show that opportunities to test the recommendation accuracy can be as useful as descriptive information stating the same. Our main finding, however, centers on the time-course of when individuals choose to obtain a recommendation. We find that over time, algorithmic advice is sought as a means to terminate difficult decisions that one cannot derive on one’s own. The TRDM proposes a unifying cognitive mechanism for this pattern of recommendation seeking based on decision urgency though our individual differences analyses identify a diversity of strategies adapted to the same decision environment. Overall, our findings characterise decision-makers as adept users of decision aid tools, and that despite the possibility of recommendation errors, individuals are capable of appreciating the utility of helpful, albeit imperfect, recommendations.

Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become important both in basic scientific applications and solution-focussed applied work. Hierarchical Bayesian estimation frameworks for the linear ballistic accumulator model (LBA) and the diffusion decision model (DDM) have been widely used, but still suffer from some key limitations, particularly for large sample sizes, for models with many parameters, and when linking decision-relevant covariates to model parameters. We extend upon previous work with methods for estimating the LBA and DDM in hierarchical Bayesian frameworks that include random effects which are correlated between people, and include regression-model links between decision-relevant covariates and model parameters. Our methods work equally well in cases where the covariates are measured once per person (e.g., personality traits or psychological tests) or once per decision (e.g., neural or physiological data). We provide methods for exact Bayesian inference, using particle-based MCMC, and also approximate methods based on variational Bayesian (VB) inference. The VB methods are sufficiently fast and efficient that they can address large-scale estimation problems, such as with very large data sets. We evaluate the performance of these methods in applications to data from three existing experiments. Detailed algorithmic implementations and code are freely available for all methods.

Self-reports are used ubiquitously to probe people’s thoughts, feelings, and behaviors and inform medical decisions, enterprise operations, and government policy and legislation. Despite their pervasive use, self-report measures such as Likert scales have a profound problem: Standard analytic approaches do not control for the confounding effects of idiosyncratic response biases. Here, we present a model-based solution to this problem. Our model disentangles response bias from latent constructs of interest to obtain less biased scores of the latent states of respondents. Inspired by Thurstonian approaches in the psychophysics literature, the model requires nothing further than standard Likert scale design assumptions. The model uses a data-driven approach to control for response biases, without the need to prespecify bias types or response strategies. We demonstrate the model’s ability to uncover more precise estimates of latent state associations, outperforming bias-affected standard scoring techniques, and garner insights into previously undetected codependencies between certain latent states and particular forms of response bias. The model is thus a tool which outperforms standard scoring methods and generates insights into, and controls for, the potentially confounding effects of response bias on self-report Likert scale data.

Estimating quantitative cognitive models from data is a staple of modern psychological science, but can be difficult and inefficient. Particle Metropolis within Gibbs (PMwG) is a robust and efficient sampling algorithm that supports model estimation in a hierarchical Bayesian framework. This tutorial shows how cognitive modeling can proceed efficiently using pmwg, a new open-source package for the R language. We step through implementing the pmwg package with simple signal detection theory models, to more complex cognitive models in which two tasks are jointly modeled together. Through this process, we also address questions of model adequacy and model selection, which must be solved in order to answer meaningful psychological questions. PMwG, and the pmwg package, has the potential to move the field of psychology ahead in new and interesting directions, and to resolve questions that were once too hard to answer with previously available sampling methods.

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.

Task-switching paradigms have been used to investigate cognitive control processes, emulating everyday tasks which require regulating cognitive resources. Cognitive modelling approaches, in the context of task-switching have sought to identify constituent processes contributing to task-switching performance. However, these approaches have typically used diffusion decision models (DDM) that are not designed to capture task-switching behaviour. Here, we extend a recent model of task-switching (Steyvers et al., 2019) to capture the complex dynamics of task activation in a cued-trials task-switching paradigm in a sample of community-dwelling older adults (n=177, 60-70 years). The model is based on an evidence-accumulation architecture extended to incorporate cognitive processes associated with task-set maintenance and updating that occur in the cue-target interval, namely task-set activation and deactivation. Results show that, with the additional processes associated with task activation, the model captures qualitative and quantitative trends in behavioural data that are consistent with task-switching literature (e.g., switch cost, mixing cost, and congruency costs) and provides a strong, quantitative basis for some of the theoretically proposed processes underlying task-switching performance (e.g., task activation). On examination of the parameter values of the weakest and strongest performers on five different task-switching metrics, the model provided unique insights into individual differences on the cognitive processes driving performance in cued trials task-switching behaviour. By disentangling key cognitive processes thought to underlie task switching performance (e.g., task set reconfiguration and task set inertia), our model provides greater precision with which to describe the underlying processes that have been observed beyond the standard mean RT measures.

Predictive inference is an important cognitive function and there are many tasks which measure it, and the error driven learning that underpins it. Context is a key contribution to this learning, with different contexts requiring different learning strategies. A factor not often considered however, is the conditions and time-frame over which a model of that context is developed. This study required participants to learn under two changing, unsignalled contexts with opposing optimal responses to large errors - change-points and oddballs. The changes in context occurred under two task structures: 1) a fixed task structure, with consecutive, short blocks of each context, and 2) a random task structure, with the context randomly selected for each new block. Through this design we examined the conditions under which learning contexts can be differentiated from each other, and the time-frame over which that learning occurs. We found that participants responded in accordance with the optimal strategy for each contexts, and did so within a short period of time, over very few meaningful errors. We further found that the responses became more optimal throughout the experiment, but only for periods of context consistency (the fixed task structure), and if the first experienced context involved meaningful errors. These results show that people will continue to refine their model of the environment across multiple trials and blocks, leading to more context-appropriate responding - but only in certain conditions. This highlights the importance of considering the task structure, and the time-frames of model development those patterns may encourage. This has implications for interpreting differences in learning across different contexts

Discrete choice (DCE) and rating scale experiments (RSE) are commonly applied procedures for eliciting preference judgments in a plethora of applied settings such as consumer choices, health care, and transport economics. An almost universal assumption is that actual "ground truth" preferences do not depend on which elicitation procedure is used. It is usually not possible to test this assumption, because typical studies feature response options for which there is no objectively correct response. To make progress on testing this assumption, we conducted a perceptual discrimination experiment where response options varied on a single attribute -- stimulus saturation level -- with a known objectively correct response. We had the same participants complete both a choice task (CT) and rating scale (RS) version of the experiment, allowing a direct examination of the assumption of a common representation. Our CT featured many characteristics that define a DCE, however, in order to have a known objectively correct response, it also differed in a few important ways. To test the assumption of a common representation, we developed a cognitive model with a response mechanism for both CT and RS. This enabled us to compare a model version that featured one shared latent stimulus representation across CT and RS versus a version which featured separate representations. Our results support the assumption that a single internal state supports both CT and RS responses, and also suggest that the CT method might provide more sensitive measurement of internal states than the RS method.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

Many decision making theories assume a principle of sequentially sampling decision-relevant evidence from the stimulus environment, where sampled evidence is dynamically accumulated toward a threshold to trigger a decision in favour of the threshold-crossing option. A core prediction of sequential sampling models is that options more likely to be chosen are chosen more quickly. This result has been empirically supported hundreds of times for low-level speeded perceptual decisions - the traditional domain of sequential sampling models. More recently, sequential sampling models have been generalised and applied to higher-level preferential, or value-based, decisions - decisions for which there is no objectively correct option. Preferential options are typically composed of multiple attributes, like a phone defined by its price, camera quality, memory capacity, and so on. Here, we show that decisions for such multi-attribute preferential options with defined features violate the core prediction of sequential sampling models: options more likely to be chosen are not chosen more quickly. We find this invariance across 4 data sets spanning multi-attribute choices made in unconstrained conditions, under time pressure, and for multi-attribute options with artificial or marketplace compositions. The result remains whether the relationship between choice frequency and choice time is inspected at the lower level of component attributes or the higher level of whole options. Our finding places critical constraints on the capacity to generalise sequential sampling models from low-level perceptual decisions to high-level multi-attribute preferential choice.

A fundamental aspect of decision making is the speed-accuracy tradeoff (SAT): slower decisions tend to be more accurate, but since time is a scarce resource people prefer to conclude decisions more quickly. The current research adds to the SAT literature by documenting two previously unrecognized influences on the SAT: perception shifts and goal activation. Decision makers' perceptions of what constitutes a fast or a slow decision, and what constitutes an accurate or inaccurate decision, are based on prior experience, and these perceptions influence decision speed. Similarly, previous experience in a decision context associates the context with a particular decision goal. Thus, in later decisions the decision context will activate this goal, and thereby influence decision speed. Both of these mechanisms contribute to a specific decision bias: decision speeds are biased toward original decision speeds in a decision context. Four experiments provide evidence for the bias and the two contributing mechanisms.

Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mind wandering is ubiquitous in everyday life and has a pervasive and profound impact on task-related performance. A range of psychological processes have been proposed to underlie these performance-related decrements, including failures of executive control, volatile information processing, and shortcomings in selective attention to critical task-relevant stimuli. Despite progress in the development of such theories, existing descriptive analyses have limited capacity to discriminate between the theories. We propose a cognitive-model based analysis that simultaneously explains self-reported mind wandering and task performance. We quantitatively compare six explanations of poor performance in the presence of mind wandering. The competing theories are distinguished by whether there is an impact on executive control and, if so, how executive control acts on information processing, and whether there is an impact on volatility of information processing. Across two experiments using the sustained attention to response task, we find quantitative evidence that mind wandering is associated with two latent factors. Our strongest conclusion is that executive control is impaired: increased mind wandering is associated with reduced ability to inhibit habitual response tendencies. Our nuanced conclusion is that executive control deficits manifest in reduced ability to selectively attend to the information value of rare but task-critical events.