Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract?

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent; not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition.     

Methodological and empirical issues when dissociating cue-related from task-related processes in the explicit task-cuing procedure

In the explicit cuing version of the task-switching paradigm, each individual task is indicated by a unique task cue. Consequently, a task switch is accompanied by a cue switch. Recently, it has been proposed that priming of cue encoding contributes to the empirically observed switch costs. This proposal was experimentally supported by using a 2:1 mapping of cues to tasks, so that a cue switch does not necessarily imply a task switch. The results indeed suggested a substantial contribution of “cue-switch costs” to task-switch costs. Here we argue that the 2:1 mapping potentially leads to an underestimation of “pure” task-switch costs. To support this argument, we report the results of a new study in which we used “transition cues” that indicate the identity of the current task based on the identity of the preceding task. This new type of cue allows a full factorial manipulation of cue switches and task switches because it includes the condition in which a cue repetition can also indicate a task switch (i.e., when the “switch” cue is repeated). We discuss the methodological implications and argue that the present approach has merits relative to the previously used 2:1 mapping of cues to tasks.     

Proactive interference in aging: A model-based study

Psychonomic Bulletin & Review - Proactive interference occurs when previously learned information interrupts the storage or retrieval of new information. Congruent with previous reports,...     

Testing theories of post-error slowing

People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and-to a lesser extent-a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing.     

Early evidence affects later decisions: Why evidence accumulation is required to explain response time data

Models of decision making differ in how they treat early evidence as it recedes in time. Standard models, such as the drift diffusion model, assume that evidence is gradually accumulated until it reaches a boundary and a decision is initiated. One recent model, the urgency gating model, has proposed that decision making does not require the accumulation of evidence at all. Instead, accumulation could be replaced by a simple urgency factor that scales with time. To distinguish between these fundamentally different accounts of decision making, we performed an experiment in which we manipulated the presence, duration, and valence of early evidence. We simulated the associated response time and error rate predictions from the drift diffusion model and the urgency gating model, fitting the models to the empirical data. The drift diffusion model predicted that variations in the evidence presented early in the trial would affect decisions later in that same trial. The urgency gating model predicted that none of these variations would have any effect. The behavioral data showed clear effects of early evidence on the subsequent decisions, in a manner consistent with the drift diffusion model. Our results cannot be explained by the urgency gating model, and they provide support for an evidence accumulation account of perceptual decision making.     

The speed and accuracy of perceptual decisions in a random-tone pitch task

Research in perceptual decision making is dominated by paradigms that tap the visual system, such as the random-dot motion (RDM) paradigm. In this study, we investigated whether the behavioral signature of perceptual decisions in the auditory domain is similar to those observed in the visual domain. We developed an auditory version of the RDM task, in which tones correspond to dots and pitch corresponds to motion (the random-tone pitch task, RTP). In this task, participants have to decide quickly whether the pitch of a “sound cloud” of tones is moving up or down. Stimulus strength and speed–accuracy trade-off were manipulated. To describe the relationship between stimulus strength and performance, we fitted the proportional-rate diffusion model to the data. The results showed a close coupling between stimulus strength and the speed and accuracy of perceptual decisions in both tasks. Additionally, we fitted the full drift diffusion model (DDM) to the data and showed that three of the four participants had similar speed–accuracy trade-offs in both tasks. However, for the RTP task, drift rates were larger and nondecision times slower, suggesting that some DDM parameters might be dependent on stimulus modality (drift rate and nondecision time), whereas others might not be (decision bound). The results illustrate that the RTP task is suitable for investigating the dynamics of auditory perceptual choices. Future studies using the task might help to investigate modality-specific effects on decision making at both the behavioral and neuronal levels.     

At your own peril: An ERP study of voluntary task set selection processes in the medial frontal cortex

A tool that is commonly used to investigate selection among different alternatives in a changing environment is the task-switching paradigm. Functional neuroimaging has pointed out a role for the posterior medial frontal cortex and the posterior parietal cortex in the voluntary selection of task sets. In the present study, we set out to investigate the temporal dynamics of these agency-related processes (in task choice vs. no-choice conditions) using event-related brain potentials (ERPs). The results revealed agency-related modulations of a series of ERP components, including (1) an early parieto-occipital activation, taken to reflect the evaluation of choice versus no choice; (2) a subsequent medial frontal expression of the voluntary selection between task sets; (3) a CNV-like sustained negativity in preparation for the target; (4) a target-induced N210—P210 complex, taken to reflect early sensory-perceptual processing; and (5) a target-induced P3, associated with the evaluation of the stimulus and its designated response vis-à-vis the chosen versus competing task sets. Together, these results indicate that the opportunity to choose between tasks invokes activity originating from the medial frontal cortex, associated with voluntary task set selection, but also activation at different time points in a number of other brain areas, not necessarily captured by functional neuroimaging.     

How to measure post-error slowing: A confound and a simple solution

In many response time tasks, people slow down after they make an error. This phenomenon of post-error slowing (PES) is thought to reflect an increase in response caution, that is, a heightening of response thresholds in order to increase the probability of a correct response at the expense of response speed. In many empirical studies, PES is quantified as the difference in response time (RT) between post-error trials and post-correct trials. Here we demonstrate that this standard measurement method is prone to contamination by global fluctuations in performance over the course of an experiment. Diffusion model simulations show how global fluctuations in performance can cause either spurious detection of PES or masking of PES. Both confounds are highly undesirable and can be eliminated by a simple solution: quantify PES as the difference in RT between post-error trials and the associated pre-error trials. Experimental data are used as an empirical illustration.