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.

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.

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.

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.

The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.

One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in 'mutation sampling' when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.

Uncovering the Structure of Semantic Representations Using a Computational Model of Decision-Making.

According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between-subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., "Most of the gleerbs are feezda.") based on the numerical information provided in the second sentence, (e.g., "60% of the gleerbs are feezda"). To investigate between-subject and within-subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between-subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion-dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth-conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics.

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.

Quantifiers satisfying semantic universals have shorter minimal description length.

Despite wide variation among natural languages, there are linguistic properties thought to be universal to all or nearly all languages. Here, we consider universals at the semantic level, in the domain of quantifiers, which are given by the properties of monotonicity, quantity, and conservativity, and we investigate whether these universals might be explained by differences in complexity. First, we use a minimal pair methodology and compare the complexities of individual quantifiers using approximate Kolmogorov complexity. Second, we use a simple yet expressive grammar to generate a large collection of quantifiers and we investigate their complexities at an aggregate level in terms of both their minimal description lengths and their approximate Kolmogorov complexities. For minimal description length we find that quantifiers satisfying semantic universals are simpler: they have a shorter minimal description length. For approximate Kolmogorov complexity we find that monotone quantifiers have a lower Kolmogorov complexity than non-monotone quantifiers and for quantity and conservativity we find that approximate Kolmogorov complexity does not scale robustly. These results suggest that the simplicity of quantifier meanings, in terms of their minimal description length, partially explains the presence of semantic universals in the domain of quantifiers.

Capturing Dynamic Performance in a Cognitive Model: Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator.

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science.

Three levels at which the user's cognition can be represented in artificial intelligence.

Artificial intelligence (AI) plays an important role in modern society. AI applications are omnipresent and assist many decisions we make in daily life. A common and important feature of such AI applications are user models. These models allow an AI application to adapt to a specific user. Here, we argue that user models in AI can be optimized by modeling these user models more closely to models of human cognition. We identify three levels at which insights from human cognition can be-and have been-integrated in user models. Such integration can be very loose with user models only being inspired by general knowledge of human cognition or very tight with user models implementing specific cognitive processes. Using AI-based applications in the context of education as a case study, we demonstrate that user models that are more deeply rooted in models of cognition offer more valid and more fine-grained adaptations to an individual user. We propose that such user models can also advance the development of explainable AI.

Conceptually plausible Bayesian inference in interval timing.

In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.

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.

The interpretation of behavior-model correlations in unidentified cognitive models.

The rise of computational modeling in the past decade has led to a substantial increase in the number of papers that report parameter estimates of computational cognitive models. A common application of computational cognitive models is to quantify individual differences in behavior by estimating how these are expressed in differences in parameters. For these inferences to hold, models need to be identified, meaning that one set of parameters is most likely, given the behavior under consideration. For many models, model identification can be achieved up to a scaling constraint, which means that under the assumption that one parameter has a specific value, all remaining parameters are identified. In the current note, we argue that this scaling constraint implies a strong assumption about the cognitive process that the model is intended to explain, and warn against an overinterpretation of the associative relations found in this way. We will illustrate these points using signal detection theory, reinforcement learning models, and the linear ballistic accumulator model, and provide suggestions for a clearer interpretation of modeling results.

Investigating the origin and consequences of endogenous default options in repeated economic choices.

Classical value-based decision theories state that economic choices are solely based on the value of available options. Experimental evidence suggests, however, that individuals' choices are biased towards default options, prompted by the framing of decisions. Although the effects of default options created by exogenous framing-such as how choice options are displayed-are well-documented, little is known about the potential effects and properties of endogenous framing, that is, originating from an individual's internal state. In this study, we investigated the existence and properties of endogenous default options in a task involving choices between risky lotteries. By manipulating and examining the effects of three experimental features-time pressure, time spent on task and relative choice proportion towards a specific option-, we reveal and dissociate two features of endogenous default options which bias individuals' choices: a natural tendency to prefer certain types of options (natural default), and the tendency to implicitly learn a default option from past choices (learned default). Additional analyses suggest that while the natural default may bias the standard choice process towards an option category, the learned default effects may be attributable to a second independent choice process. Overall, these investigations provide a first experimental evidence of how individuals build and apply diverse endogenous default options in economic decision-making and how this biases their choices.

A theoretical analysis of the reward rate optimality of collapsing decision criteria.

A standard assumption of most sequential sampling models is that decision-makers rely on a decision criterion that remains constant throughout the decision process. However, several authors have recently suggested that, in order to maximize reward rates in dynamic environments, decision-makers need to rely on a decision criterion that changes over the course of the decision process. We used dynamic programming and simulation methods to quantify the reward rates obtained by constant and dynamic decision criteria in different environments. We further investigated what influence a decision-maker's uncertainty about the stochastic structure of the environment has on reward rates. Our results show that in most dynamic environments, both types of decision criteria yield similar reward rates, across different levels of uncertainty. This suggests that a static decision criterion might provide a robust default setting.

Time perception and timed decision task performance during passive heat stress.

This study investigates the hypotheses that during passive heat stress, the change in perception of time and change in accuracy of a timed decision task relate to changes in thermophysiological variables gastrointestinal temperature and heart rate (HR), as well as subjective measures of cognitive load and thermal perception. Young adult males (N = 29) participated in two 60-min head-out water immersion conditions (36.5°C-neutral and 38.0°C-warm). Cognitive task measurements included accuracy (judgment task), response time (judgment ask), and time estimation (interval timing task). Physiological measurements included gastrointestinal temperature and heart rate. Subjective measurements included cognitive task load (NASA-TLX), rate of perceived exertion, thermal sensation, and thermal comfort. Gastrointestinal temperature and HR were significantly higher in warm versus neutral condition (gastrointestinal temperature: 38.4 ± 0.2°C vs. 37.2 ± 0.2°C, p < 0.01; HR: 105 ± 8 BPM vs. 83 ± 9 BPM, p < 0.01). The change in accuracy was significantly associated with the change in gastrointestinal temperature, and attenuated by change in thermal sensation and change in HR (r2=0.40, p< 0.01). Change in response time was significantly associated with the change in gastrointestinal temperature (r2=0.26, p< 0.002), and change in time estimation was best explained by a change in thermal discomfort (r2=0.18, p< 0.01). Changes in cognitive performance during passive thermal stress are significantly associated with changes in thermophysiological variables and thermal perception. Although explained variance is low (<50%), decreased accuracy is attributed to increased gastrointestinal temperature, yet is attenuated by increased arousal (expressed as increased HR and warmth thermal sensation).

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model.

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

Proactive interference in aging: A model-based study.

Proactive interference occurs when previously learned information interrupts the storage or retrieval of new information. Congruent with previous reports, traditional analyses dealing with response times and error rates separately have indicated an increase in sensitivity to proactive interference in older adults. We reanalyzed the same data using diffusion decision model (DDM). Such models enable a more fine-grained interpretation concerning the latent processing mechanisms underlying performance. Now a different picture emerged. The DDM results showed that older adults needed more evidence than young adults before responding. The results also clearly indicated that peripheral processes (encoding time and motor execution), as well as recognition memory, decline with age. However, the drift rates, reflecting proactive interference, were similar, suggesting-contrary to earlier reports-that the inhibitory processes observed with this paradigm remain intact in older adults.

Core body temperature speeds up temporal processing and choice behavior under deadlines.

Evidence suggests that human timing ability is compromised by heat. In particular, some studies suggest that increasing body temperature speeds up an internal clock, resulting in faster time perception. However, the consequences of this speed-up for other cognitive processes remain unknown. In the current study, we rigorously tested the speed-up hypothesis by inducing passive hyperthermia through immersion of participants in warm water. In addition, we tested how a change in time perception affects performance in decision making under deadline stress. We found that participants underestimate a prelearned temporal interval when body temperature increases, and that their performance in a two-alternative forced-choice task displays signatures of increased time pressure. These results show not only that timing plays an important role in decision-making, but also that this relationship is mediated by temperature. The consequences for decision-making in job environments that are demanding due to changes in body temperature may be considerable.

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.