Is There Evidence for a Mixture of Processes in Speed-Accuracy Trade-Off Behavior?

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.

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.

Age-related differences in food-specific inhibitory control: Electrophysiological and behavioral evidence in healthy aging

The number of older adults in the United States is estimated to nearly double from 52 million to 95 million by 2060. Approximately 80-85% of older adults are diagnosed with a chronic health condition. Many of these chronic health conditions are influenced by diet and physical activity, suggesting improved diet and eating behaviors could improve health-related outcomes. One factor that might improve dietary habits in older adults is food-related inhibitory control. We tested whether food-related inhibitory control, as measured via behavioral data (response time, accuracy) and scalp-recorded event-related potentials (ERP; N2 and P3 components), differed between younger and older adults over age 55. Fifty-nine older adults (31 females [52.5%], M_age = 64, SD_age = 7.5) and 114 younger adults (82 females [71.9%], M_age = 20.8) completed two go/no-go tasks, one inhibiting to high-calorie stimuli and one inhibiting to low-calorie stimuli, while electroencephalogram (EEG) data were recorded. Older adults had slower overall response times than younger adults, but this was not specific to either food task. There was not a significant difference in accuracy between younger and older adults, but both groups' accuracy and response times were significantly better during the high-calorie task than the low-calorie task. For both the N2 and P3 ERP components, younger adults had larger no-go ERP amplitudes than older adults, but this effect was not food-specific, reflecting overall generalized lower inhibitory control processing in older adults. P3 amplitude for the younger adults demonstrated a specific food-related effect (greater P3 amplitude for high-calorie no-go than low-calorie no-go) that was not present for older adults. Findings support previous research demonstrating age-related differences in inhibitory control though those differences may not be specific to inhibiting towards food.

Stressfulness of the design influences consistency of cognitive measures and their correlation with animal personality traits in wild mice (Mus musculus)

Individual variation in cognition is being increasingly recognized as an important evolutionary force but contradictory results so far hamper a general understanding of consistency and association with other behaviors. Partly, this might be caused by external factors imposed by the design. Stress, for example, is known to influence cognition, with mild stress improving learning abilities, while strong or chronic stress impairs them. Also, there might be intraspecific variation in how stressful a given situation is perceived. We investigated two personality traits (stress coping and voluntary exploration), spatial learning with two mazes, and problem-solving in low- and high-stress tests with a group of 30 female wild mice (Mus musculus domesticus). For each test, perceived stress was assessed by measuring body temperature change with infrared thermography, a new non-invasive method that measures skin temperature as a proxy of changes in the sympathetic system activity. While spatial learning and problem-solving were found to be repeatable traits in mice in earlier studies, none of the learning measures were significantly repeatable between the two stress conditions in our study, indicating that the stress level impacts learning. We found correlations between learning and personality traits; however, they differed between the two stress conditions and between the cognitive tasks, suggesting that different mechanisms underlie these processes. These findings could explain some of the contradictory findings in the literature and argue for very careful design of cognitive test setups to draw evolutionary implications.

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.

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.

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.

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.

Three Boundary Conditions for Computing the Fixed-Point Property in Binary Mixture Data

The notion of "mixtures" has become pervasive in behavioral and cognitive sciences, due to the success of dual-process theories of cognition. However, providing support for such dual-process theories is not trivial, as it crucially requires properties in the data that are specific to mixture of cognitive processes. In theory, one such property could be the fixed-point property of binary mixture data, applied-for instance- to response times. In that case, the fixed-point property entails that response time distributions obtained in an experiment in which the mixture proportion is manipulated would have a common density point. In the current article, we discuss the application of the fixed-point property and identify three boundary conditions under which the fixed-point property will not be interpretable. In Boundary condition 1, a finding in support of the fixed-point will be mute because of a lack of difference between conditions. Boundary condition 2 refers to the case in which the extreme conditions are so different that a mixture may display bimodality. In this case, a mixture hypothesis is clearly supported, yet the fixed-point may not be found. In Boundary condition 3 the fixed-point may also not be present, yet a mixture might still exist but is occluded due to additional changes in behavior. Finding the fixed-property provides strong support for a dual-process account, yet the boundary conditions that we identify should be considered before making inferences about underlying psychological processes.

Cognitive Modeling at ICCM: State of the Art and Future Directions

The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.