The timing database: An open-access, live repository for interval timing studies.

Interval timing refers to the ability to perceive and remember intervals in the seconds to minutes range. Our contemporary understanding of interval timing is derived from relatively small-scale, isolated studies that investigate a limited range of intervals with a small sample size, usually based on a single task. Consequently, the conclusions drawn from individual studies are not readily generalizable to other tasks, conditions, and task parameters. The current paper presents a live database that presents raw data from interval timing studies (currently composed of 68 datasets from eight different tasks incorporating various interval and temporal order judgments) with an online graphical user interface to easily select, compile, and download the data organized in a standard format. The Timing Database aims to promote and cultivate key and novel analyses of our timing ability by making published and future datasets accessible as open-source resources for the entire research community. In the current paper, we showcase the use of the database by testing various core ideas based on data compiled across studies (i.e., temporal accuracy, scalar property, location of the point of subjective equality, malleability of timing precision). The Timing Database will serve as the repository for interval timing studies through the submission of new datasets.

Specificity and sensitivity of the fixed-point test for binary mixture distributions.

When two cognitive processes contribute to a behavioral output-each process producing a specific distribution of the behavioral variable of interest-and when the mixture proportion of these two processes varies as a function of an experimental condition, a common density point should be present in the observed distributions of the data across said conditions. In principle, one can statistically test for the presence (or absence) of a fixed point in experimental data to provide evidence in favor of (or against) the presence of a mixture of processes, whose proportions are affected by an experimental manipulation. In this paper, we provide an empirical diagnostic of this test to detect a mixture of processes. We do so using resampling of real experimental data under different scenarios, which mimic variations in the experimental design suspected to affect the sensitivity and specificity of the fixed-point test (i.e., mixture proportion, time on task, and sample size). Resampling such scenarios with real data allows us to preserve important features of data which are typically observed in real experiments while maintaining tight control over the properties of the resampled scenarios. This is of particular relevance considering such stringent assumptions underlying the fixed-point test. With this paper, we ultimately aim at validating the fixed-point property of binary mixture data and at providing some performance metrics to researchers aiming at testing the fixed-point property on their experimental data.

Non-parametric mixture modeling of cognitive psychological data: A new method to disentangle hidden strategies.

In a wide variety of cognitive domains, participants have access to several alternative strategies to perform a particular task and, on each trial, one specific strategy is selected and executed. Determining how many strategies are used by a participant as well as their identification at a trial level is a challenging problem for researchers. In the current paper, we propose a new method - the non-parametric mixture model - to efficiently disentangle hidden strategies in cognitive psychological data, based on observed response times. The developed method derived from standard hidden Markov modeling. Importantly, we used a model-free approach where a particular shape of a response time distribution does not need to be assumed. This has the considerable advantage of avoiding potentially unreliable results when an inappropriate response time distribution is assumed. Through three simulation studies and two applications to real data, we repeatedly demonstrated that the non-parametric mixture model is able to reliably recover hidden strategies present in the data as well as to accurately estimate the number of concurrent strategies. The results also showed that this new method is more efficient than a standard parametric approach. The non-parametric mixture model is therefore a useful statistical tool for strategy identification that can be applied in many areas of cognitive psychology. To this end, practical guidelines are provided for researchers wishing to apply the non-parametric mixture models on their own data set.

EEG-based Identification of Evidence Accumulation Stages in Decision-Making.

Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stages while performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA.

Towards a mechanistic understanding of the human subcortex.

The human subcortex is a densely populated part of the brain, of which only 7% of the individual structures are depicted in standard MRI atlases. In vivo MRI of the subcortex is challenging owing to its anatomical complexity and its deep location in the brain. The technical advances that are needed to reliably uncover this 'terra incognita' call for an interdisciplinary human neuroanatomical approach. We discuss the emerging methods that could be used in such an approach and the incorporation of the data that are generated from these methods into model-based cognitive neuroscience frameworks.

Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results.

For multi-factor analyses of response times, descriptive models (e.g., linear regression) arguably constitute the dominant approach in psycholinguistics. In contrast empirical cognitive models (e.g., sequential sampling models, SSMs) may fit fewer factors simultaneously, but decompose the data into several dependent variables (a multivariate result), offering more information to analyze. While SSMs are notably popular in the behavioural sciences, they are not significantly developed in language production research. To contribute to the development of this modelling in language, we (i) examine SSMs as a measurement modelling approach for spoken word activation dynamics, and (ii) formally compare SSMs to the default method, regression. SSMs model response activation or selection mechanisms in time, and calculate how they are affected by conditions, persons, and items. While regression procedures also model condition effects, it is only in respect to the mean RT, and little work has been previously done to compare these approaches. Through analyses of two language production experiments, we show that SSMs reproduce regression predictors, and further extend these effects through a multivariate decomposition (cognitive parameters). We also examine a combined regression-SSM approach that is hierarchical Bayesian, which can jointly model more conditions than classic SSMs, and importantly, achieve by-item modelling with other conditions. In this analysis, we found that spoken words principally differed from one another by their activation rates and production times, but not their thresholds to be activated.

Caution in decision-making under time pressure is mediated by timing ability.

The time available to inform decisions is often limited, for example because of a response deadline. In such circumstances, accurate knowledge of the amount of time available for a decision is crucial for optimal choice behavior. However, the relation between temporal cognition and decision-making under time pressure is poorly understood. Here, we test how the precision of the internal representation of time affects choice behavior when decision time is limited by a deadline. We show that participants with a precise internal representation of time respond more cautiously in decision-making. Furthermore, we provide an empirical test of theoretical accounts of decision-making that argue that it is optimal to commit to a decision based on increasingly less evidence as the deadline approaches (so-called 'collapsing decision bounds'). These theories entail that the speed of collapse of the decision bound should depend on the precision of the internal representation of the deadline. However, although we find evidence that participants collapse decision bounds, we found no relation between the amount of collapse and the internal representation of time.

Correction to: Fast and slow errors: Logistic regression to identify patterns in accuracy-response time relationships.

This paper originally published with incorrect figures, it has been corrected.

Fast and slow errors: Logistic regression to identify patterns in accuracy-response time relationships.

Understanding error and response time patterns is essential for making inferences in several domains of cognitive psychology. Crucial insights on cognitive performance and typical behavioral patterns are disclosed by using distributional analyses such as conditional accuracy functions (CAFs) instead of mean statistics. Several common behavioral error patterns revealed by CAFs are frequently described in the literature: response capture (associated with relatively fast errors), time pressure or urgency paradigms (slow errors), or cue-induced speed-accuracy trade-off (evenly distributed errors). Unfortunately, the standard way of computing CAFs is problematic, because accuracy is averaged in RT bins. Here we present a novel way of analyzing accuracy-RT relationships on the basis of nonlinear logistic regression, to handle these problematic aspects of RT binning. First we evaluate the parametric robustness of the logistic regression CAF through parameter recovery. Second, we apply the function to three existing data sets showing that specific parametric changes in the logistic regression CAF can consistently describe common behavioral patterns (such as response capture, time pressure, and speed-accuracy trade-off). Finally, we discuss potential modifications for future research.

Free-energy minimization in joint agent-environment systems: A niche construction perspective.

The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the 'fit' between an embodied agent and its niche, where the quantity of free-energy is a measure for the 'misfit' or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the 'desire paths' emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy.

Lesions to the left lateral prefrontal cortex impair decision threshold adjustment for lexical selection.

Patients with lesions in the left prefrontal cortex (PFC) have been shown to be impaired in lexical selection, especially when interference between semantically related alternatives is increased. To more deeply investigate which computational mechanisms may be impaired following left PFC damage due to stroke, a psychometric modelling approach is employed in which we assess the cognitive parameters of the patients from an evidence accumulation (sequential information sampling) modelling of their response data. We also compare the results to healthy speakers. Analysis of the cognitive parameters indicates an impairment of the PFC patients to appropriately adjust their decision threshold, in order to handle the increased item difficulty that is introduced by semantic interference. Also, the modelling contributes to other topics in psycholinguistic theory, in which specific effects are observed on the cognitive parameters according to item familiarization, and the opposing effects of priming (lower threshold) and semantic interference (lower drift) which are found to depend on repetition. These results are developed for the blocked-cyclic picture naming paradigm, in which pictures are presented within semantically homogeneous (HOM) or heterogeneous (HET) blocks, and are repeated several times per block. Overall, the results are in agreement with a role of the left PFC in adjusting the decision threshold for lexical selection in language production.

Striatal activation reflects urgency in perceptual decision making.

Deciding between multiple courses of action often entails an increasing need to do something as time passes - a sense of urgency. This notion of urgency is not incorporated in standard theories of speeded decision making that assume information is accumulated until a critical fixed threshold is reached. Yet, it is hypothesized in novel theoretical models of decision making. In two experiments, we investigated the behavioral and neural evidence for an "urgency signal" in human perceptual decision making. Experiment 1 found that as the duration of the decision making process increased, participants made a choice based on less evidence for the selected option. Experiment 2 replicated this finding, and additionally found that variability in this effect across participants covaried with activation in the striatum. We conclude that individual differences in susceptibility to urgency are reflected by striatal activation. By dynamically updating a response threshold, the striatum is involved in signaling urgency in humans.

Visual input that matches the content of visual working memory requires less (not faster) evidence sampling to reach conscious access.

The content of visual working memory (VWM) affects the processing of concurrent visual input. Recently, it has been demonstrated that stimuli are released from interocular suppression faster when they match rather than mismatch a color that is memorized for subsequent recall. In order to investigate the nature of the interaction between visual representations elicited by VWM and visual representations elicited by retinal input, we modeled the perceptual processes leading up to this difference in suppression durations. We replicated the VWM modulation of suppression durations, and fitted sequential sampling models (linear ballistic accumulators) to the response time data. Model comparisons revealed that the data was best explained by a decrease in threshold for visual input that matches the content of VWM. Converging evidence was obtained by fitting similar sequential sampling models (shifted Wald model) to published datasets. Finally, to confirm that the previously observed threshold difference reflected processes occurring before rather than after the stimuli were released from suppression, we applied the same procedure to the data of an experiment in which stimuli were not interocularly suppressed. Here, we found no decrease in threshold for stimuli that match the content of VWM. We discuss our findings in light of a preactivation hypothesis, proposing that matching visual input taps into the same neural substrate that is already activated by a representation concurrently maintained in VWM, thereby reducing its threshold for reaching visual awareness.

The impact of MRI scanner environment on perceptual decision-making.

Despite the widespread use of functional magnetic resonance imaging (fMRI), few studies have addressed scanner effects on performance. The studies that have examined this question show a wide variety of results. In this article we report analyses of three experiments in which participants performed a perceptual decision-making task both in a traditional setting as well as inside an MRI scanner. The results consistently show that response times increase inside the scanner. Error rates also increase, but to a lesser extent. To reveal the underlying mechanisms that drive the behavioral changes when performing a task inside the MRI scanner, the data were analyzed using the linear ballistic accumulator model of decision-making. These analyses show that, in the scanner, participants exhibit a slow down of the motor component of the response and have less attentional focus on the task. However, the balance between focus and motor slowing depends on the specific task requirements.

The subthalamic nucleus during decision-making with multiple alternatives.

Several prominent neurocomputational models predict that an increase of choice alternatives is modulated by increased activity in the subthalamic nucleus (STN). In turn, increased STN activity allows prolonged accumulation of information. At the same time, areas in the medial frontal cortex such as the anterior cingulate cortex (ACC) and the pre-SMA are hypothesized to influence the information processing in the STN. This study set out to test concrete predictions of STN activity in multiple-alternative decision-making using a multimodal combination of 7 Tesla structural and functional Magnetic Resonance Imaging, and ancestral graph (AG) modeling. The results are in line with the predictions in that increased STN activity was found with an increasing amount of choice alternatives. In addition, our study shows that activity in the ACC is correlated with activity in the STN without directly modulating it. This result sheds new light on the information processing streams between medial frontal cortex and the basal ganglia.

Trial-by-trial fluctuations in CNV amplitude reflect anticipatory adjustment of response caution.

The contingent negative variation, a slow cortical potential, occurs when humans are warned by a stimulus about an upcoming task. The cognitive processes that give rise to this EEG potential are not yet well understood. To explain these processes, we adopt a recently developed theoretical framework from the area of perceptual decision-making. This framework assumes that the basal ganglia control the tradeoff between fast and accurate decision-making in the cortex. It suggests that an increase in cortical excitability serves to lower response caution, which results in faster but more error prone responding. We propose that the CNV reflects this increased cortical excitability. To test this hypothesis, we conducted an EEG experiment in which participants performed the random dot motion task either under speed or under accuracy stress. Our results show that trial-by-trial fluctuations in participants' response speed as well as model-based estimates of response caution correlated with single-trial CNV amplitude under conditions of speed but not accuracy stress. We conclude that the CNV might reflect adjustments of response caution, which serves to enhance quick decision-making.

Implicit association tests: Stimuli validation from participant responses.

The Implicit Association Test (IAT, Greenwald et al., J. Pers. Soc. Psychol., 74, 1998, 1464) is a popular instrument for measuring attitudes and (stereotypical) biases. Greenwald et al. (Behav. Res. Methods, 54, 2021, 1161) proposed a concrete method for validating IAT stimuli: appropriate stimuli should be familiar and easy to classify - translating to rapid (response times <800 ms) and accurate (error < 10%) participant responses. We conducted three analyses to explore the theoretical and practical utility of these proposed validation criteria. We first applied the proposed validation criteria to the data of 15 IATs that were available via Project Implicit. A bootstrap approach with 10,000 'experiments' of 100 participants showed that 5.85% of stimuli were reliably valid (i.e., we are more than 95% confident that a stimulus will also be valid in a new sample of 18- to 25-year-old US participants). Most stimuli (78.44%) could not be reliably validated, indicating a less than 5% certainty in the outcome of stimulus (in)validity for a new sample of participants. We then explored how stimulus validity differs across IATs. Results show that only some stimuli are consistently (in)valid. Most stimuli show between-IAT variances, which indicate that stimulus validity differs across IAT contexts. In the final analysis, we explored the effect of stimulus type (images, nouns, names, adjectives) on stimulus validity. Stimulus type was a significant predictor of stimulus validity. Although images attain the highest stimulus validity, raw data show large differences within stimulus types. Together, the results indicate a need for revised validation criteria. We finish with practical recommendations for stimulus selection and (post-hoc) stimulus validation.

Most quantifiers have many meanings.

In this paper, we investigate, by means of a computational model, how individuals map quantifiers onto numbers and how they order quantifiers on a mental line. We selected five English quantifiers (few, fewer than half, many, more than half, and most) which differ in truth conditions and vagueness. We collected binary truth value judgment data in an online quantifier verification experiment. Using a Bayesian three-parameter logistic regression model, we separated three sources of individual differences: truth condition, vagueness, and response error. Clustering on one of the model's parameter that corresponds to truth conditions revealed four subgroups of participants with different quantifier-to-number mappings and different ranges of the mental line of quantifiers. Our findings suggest multiple sources of individual differences in semantic representations of quantifiers and support a conceptual distinction between different types of imprecision in quantifier meanings. We discuss the consequence of our findings for the main theoretical approaches to quantifiers: the bivalent truth-conditional approach and the fuzzy logic approach. We argue that the former approach neither can explain inter-individual differences nor intra-individual differences in truth conditions of vague quantifiers. The latter approach requires further specification to fully account for individual differences demonstrated in this study.

The Curve of Learning With and Without Instructions.

In skill acquisition, instructing individuals the stimulus-response mappings indicating how to perform and act, yields better performance. Additionally, performance is helped by repeated practice. Whether providing instructions and repeated practice interact to achieve optimal performance remains debated. This paper addresses that question by analyzing the learning curves of individuals learning stimulus-response mappings of varying complexity. We particularly focus on the question whether instructions lead to improved performance in the longer run. Via evidence accumulation modeling, we find no evidence for this assertion. Instructions seem to provide individuals with a head start, leading to better initial performance in the early stages of learning, without long-lasting effects on behavior. We discuss the results in light of related studies that do report long-lasting effects of instructions, and propose that the complexity of a skill determines whether long-lasting benefits of initial instructions exist.

Probabilistic causal reasoning under time pressure.

While causal reasoning is a core facet of our cognitive abilities, its time-course has not received proper attention. As the duration of reasoning might prove crucial in understanding the underlying cognitive processes, we asked participants in two experiments to make probabilistic causal inferences while manipulating time pressure. We found that participants are less accurate under time pressure, a speed-accuracy-tradeoff, and that they respond more conservatively. Surprisingly, two other persistent reasoning errors-Markov violations and failures to explain away-appeared insensitive to time pressure. These observations seem related to confidence: Conservative inferences were associated with low confidence, whereas Markov violations and failures to explain were not. These findings challenge existing theories that predict an association between time pressure and all causal reasoning errors including conservatism. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize that causal judgements are the result of multiple processes.