Leendert van Maanen

Stroop and picture-word interference are two sides of the same coin

This article presents a cognitive model that reconciles a surprising observation in the picture—word interference (PWI) paradigm with the general notion that PWI is a form of Stroop interference. Dell’Acqua, Job, Peressotti, and Pascali (2007) assessed PWI using a psychological refractory period (PRP) paradigm, and concluded that the locus of interference in PWI is during the perceptual encoding stage. Stroop interference, on the other hand, is generally attributed to response selection. Based on these findings it was argued that PWI is not a Stroop effect. The present article discusses an alternative interpretation of these results. We assume that both effects are caused by the same interference mechanism, but that the processing speed associated with the different stimuli (colors vs. words) accounts for the previously reported differences. We support this argument by presenting a single computational model that accounts for both PWI and Stroop phenomena in single task and PRP settings.

The optimality of sensory processing during the speed-accuracy tradeoff.

When people make decisions quickly, accuracy suffers. Traditionally, speed–accuracy tradeoffs (SATs) have been almost exclusively ascribed to changes in the amount of sensory evidence required to support a response (“response caution”) and the neural correlates associated with the later stages of decision making (e.g., motor response generation and execution). Here, we investigated whether performance decrements under speed pressure also reflect suboptimal information processing in early sensory areas such as primary visual cortex (V1). Human subjects performed an orientation discrimination task while emphasizing either response speed or accuracy. A model of choice behavior revealed that the rate of sensory evidence accumulation was selectively modulated when subjects emphasized accuracy, but not speed, suggesting that changes in sensory processing also influence the SAT. We then used fMRI and a forward encoding model to derive orientation-selective tuning functions based on activation patterns in V1. When accuracy was emphasized, the extent to which orientation-selective tuning profiles exhibited a theoretically optimal gain pattern predicted both response accuracy and the rate of sensory evidence accumulation. However, these relationships were not observed when subjects emphasized speed. Collectively, our findings suggest that, in addition to lowered response thresholds, the performance decrements observed during speeded decision making may result from a failure to optimally process sensory signals.

Informing Cognitive Abstractions Through Neuroimaging: The Neural Drift Diffusion Model

Trial-to-trial fluctuations in an observers state of mind have a direct influence on their behavior. However, characterizing an observers state of mind is difficult to do with behavioral data alone, particularly on a single-trial basis. In this article, we extend a recently developed hierarchical Bayesian framework for integrating neurophysiological information into cognitive models. In so doing, we develop a novel extension of the well-studied drift diffusion model (DDM) that uses single-trial brain activity patterns to inform the behavioral model parameters. We first show through simulation how the model outperforms the traditional DDM in a prediction task with sparse data. We then fit the model to experimental data consisting of a speed-accuracy manipulation on a random dot motion task. We use our cognitive modeling approach to show how prestimulus brain activity can be used to simultaneously predict response accuracy and response time. We use our model to provide an explanation for how activity in a brain region affects the dynamics of the underlying decision process through mechanisms assumed by the model. Finally, we show that our model performs better than the traditional DDM through a cross-validation test. By combining accuracy, response time, and the blood oxygen level-dependent response into a unified model, the link between cognitive abstraction and neuroimaging can be better understood.

RACE/A: An architectural account of the interactions between learning, task control, and retrieval dynamics.

This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture-word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term declarative learning, and between sequential sampling and task control. In a traditional sequential sampling model, the onset of the process within the task is unclear, as is the number of sampling processes. RACE/A provides a theoretical basis for estimating the onset of sequential sampling processes during task execution and allows for easy modeling of multiple sequential sampling processes within a task.

An accumulator model of semantic interference

To explain latency effects in picture-word interference tasks, cognitive models need to account for both interference and stimulus onset asynchrony (SOA) effects. As opposed to most models of picture-word interference, which model the time course during the task in a ballistic manner, the RACE model (retrieval by accumulating evidence) presented in this paper accounts for semantic interference during the interval between the retrieval onset and the actual retrieval. RACE is implemented as an extension to the ACT-R architecture of cognition. By modeling the retrieval process, RACE offers a more precise account of semantic memory retrieval latencies in different interference and SOA conditions than other ACT-R models. In this paper, we discuss the architectural assumptions underlying RACE and simulations of a picture-word interference experiment [Glaser, W. R., & Dungelhoff, F. J. (1984). The time course of picture-word interference. Journal of Experimental Psychology: Human Perception and Performance, 10(5), pp. 640-654.].

Bromocriptine Does Not Alter Speed–Accuracy Tradeoff

Being quick often comes at the expense of being accurate. This speed–accuracy tradeoff is a central feature of many types of decision making. It has been proposed that dopamine plays an important role in adjusting responses between fast and accurate behavior. In the current study we investigated the role of dopamine in perceptual decision making in humans, focusing on speed–accuracy tradeoff. Using a cued version of the random dot motion task, we instructed subjects to either make a fast or an accurate decision. We investigated decision making behavior in subjects who were given bromocriptine (a dopamine receptor agonist) or placebo. We analyzed the behavioral data using two accumulator models, the drift diffusion model, and the linear ballistic accumulator model. On a behavioral level, there were clear differences in decision threshold between speed and accuracy focus, but decision threshold did not differ between the drug and placebo sessions. Bayesian analyses support the null hypothesis that there is no effect of bromocriptine on decision threshold. On the neural level, we replicate previous findings that the striatum and pre-supplementary motor area are active when preparing for speed, compared with accurate decisions. We do not find an effect of bromocriptine on this activation. Therefore, we conclude that bromocriptine does not alter speed–accuracy tradeoff.

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.

Piéron's law and optimal behavior in perceptual decision-making

Piéron’s Law is a psychophysical regularity in signal detection tasks that states that mean response times decrease as a power function of stimulus intensity. In this article, we extend Piéron’s Law to perceptual two-choice decision-making tasks, and demonstrate that the law holds as the discriminability between two competing choices is manipulated, even though the stimulus intensity remains constant. This result is consistent with predictions from a Bayesian ideal observer model. The model assumes that in order to respond optimally in a two-choice decision-making task, participants continually update the posterior probability of each response alternative, until the probability of one alternative crosses a criterion value. In addition to predictions for two-choice decision-making tasks, we extend the ideal observer model to predict Piéron’s Law in signal detection tasks. We conclude that Piéron’s Law is a general phenomenon that may be caused by optimality constraints.

The Locus of the Gratton Effect in Picture–Word Interference

Between-trial effects in Stroop-like interference tasks are linked to differences in the amount of cognitive control. Trials following an incongruent trial show less interference, an effect suggested to result from the increased control caused by the incongruent previous trial (known as the Gratton effect). In this study, we show that cognitive control not only results in a different amount of interference but also in a different locus of the interference. That is, the stage of the task that shows the most interference changes as a function of the preceding trial. Using computational cognitive modeling, we explain these effects by a difference in the amount of processing of the irrelevant dimension of the stimulus.

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.