Temporal Distinctiveness in Task Switching: Assessing the Mixture-Distribution Assumption

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.

Component processes underlying voluntary task selection: Separable contributions of task-set inertia and reconfiguration

Most theories describing the cognitive processes underlying task switching allow for contributions of active task-set reconfiguration and task set inertia. Manipulations of the Cue-to-Stimulus-Interval (CSI) are generally thought to influence task set reconfiguration, while Response-to-Cue (RCI) manipulations are thought to influence task set inertia. Together, these intervals compose the Response-to-Stimulus (RSI) interval. However, these theories do not adequately account for voluntary task switching, because a participant can theoretically prepare for an upcoming trial at any point. We used drift diffusion models to examine the contributions of reconfiguration and task set inertia to performance in single- and double-registrant-registrant voluntary task switching. In both paradigms, RSI length moderated nondecision time, suggesting both switch-specific and general preparation prior to cue presentation. In only the double-registrant registrant paradigm, RSI length additionally moderated task set inertia and CSI length affected general (but not switch-specific) preparation. The effects of cue timing (CSI length) depended upon required response to the cue. Future work should attempt to corroborate our findings regarding switch-specific and general preparation effects of interval lengths using EEG.

The persisting influence of unattended auditory information: Negative priming in intentional auditory attention switching

We studied negative priming (NP) in auditory attention switching. In a cued variant of dichotic listening, two spoken number words were presented, one to each ear, one spoken by a female, and one spoken by a male voice. A visual cue indicated whether the male or female voice was the target. A numerical magnitude judgement of the target number was required. The selection criterion could either switch or repeat across trials, so there were attention switch and repetition trials. Two experiments examined NP (distractor becomes target) and also included a "competitor priming" (CP) condition (target becomes distractor), relative to a "no priming" condition (target and distractor not related to previous trial). In Experiment 1, we investigated the basic priming effects. In Experiment 2, we additionally varied the response-cue interval (RCI; 100 ms vs. 1,900 ms) to examine time-related changes in priming. We found longer response times (RT) for switch trials compared with repetition trials (attention switch costs)-that is, when the internal processing context changed. In addition, we found longer RT for NP trials as well as reduced switch costs in long RCI, suggesting that previously relevant attentional settings dissipate over longer time. However, NP was not influenced by attention switches, and it was also not affected by RCI. Hence, NP in auditory attention switching does not seem strongly context or time sensitive.

Exploring individual differences in task switching

Previous research has shown that there are significant task-switching costs even when participants have time to prepare for task switching after cueing. We investigated individual differences in task switching by monitoring errors and response times of individual participants. In Experiment 1A, 58 participants were encouraged to finish the session early by completing 200 consecutive trials without making an error. In case of a mistake, they had to repeat their effort until the experimental session expired. Using this demanding procedure, 16 participants managed to complete early. Among these 16 we identified 9 best performers who showed no significant switch costs. We conducted follow-up Experiment 1B on these best performers by systematically varying cue-stimulus intervals and inter-trial intervals. The results confirmed that these participants had no significant RT and ER switch costs when they had time to prepare the task between cue and target onset. However, significant switch costs emerged when cue and target stimulus were presented simultaneously. In Experiment 1C, using three classical task-switching paradigms, we compared the best performers with 9 controls who had made frequent errors in Experiment 1A. Although the best performers responded faster and made fewer errors, they only showed reduced switch costs in a pre-cued paradigm that had been extensively practiced. In two other paradigms with simultaneous presentation of cue and target stimulus, best performers had switch costs and showed considerable individual differences similar to the controls. We conclude that there are considerable individual differences in task switching and that smaller individual switch costs are mainly related to efficient task preparation. We speculate that efficient task preparation may be linked to better executive control and general intelligence.

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.

Neurocomputational Nosology: Malfunctions of Models and Mechanisms

Executive dysfunctions, psychopathologies arising from problems in the control and regulation of behavior, can occur as a result of the faulty execution of formal information processing models or as a result of malfunctioning neural mechanisms. The models correspond to the formal descriptions of how signals in the environment must be transformed in order to behave adaptively, and the mechanisms correspond to the signal transformations that nervous systems implement in order to execute those cognitive functions. Mechanisms in the form of repeated patterns of neural dynamics execute information processing models. Two distinct modes of malfunction can occur when neural dynamics execute models of information processing. The processing models describing behavior may fail to be executed correctly by neural mechanisms. Or, the neural mechanisms may malfunction, failing to implement the right computation. As an example of malfunctioning models in executive cognition, purported failures of rule following can be understood as failures to appropriately execute a suite of processing models. As an example of malfunctioning mechanisms of executive cognition, maladaptive behavior resulting from dysfunction in the medial prefrontal cortex (mPFC) can be understood as failures in the signal transformations carried out therein. The purpose of these examples is to illustrate the potential benefits of considering models and mechanisms in the diagnosis and etiology of neuropsychological illness and dysfunction, especially disorders of executive cognition.