Corey N. White

Diffusion models of the flanker task: Discrete versus gradual attentional selection

The present study tested diffusion models of processing in the flanker task, in which participants identify a target that is flanked by items that indicate the same (congruent) or opposite response (incongruent). Single- and dual-process flanker models were implemented in a diffusion-model framework and tested against data from experiments that manipulated response bias, speed/accuracy tradeoffs, attentional focus, and stimulus configuration. There was strong mimcry among the models, and each captured the main trends in the data for the standard conditions. However, when more complex conditions were used, a single-process spotlight model captured qualitative and quantitative patterns that the dual-process models could not. Since the single-process model provided the best balance of fit quality and parsimony, the results indicate that processing in the simple versions of the flanker task is better described by gradual rather than discrete narrowing of attention.

Anxiety Enhances Threat Processing Without Competition Among Multiple Inputs: A Diffusion Model Analysis

Enhanced processing of threatening information is a well established phenomenon among high-anxious individuals. This effect is most reliably shown in situations where 2 or more items compete for processing resources, suggesting that input competition is a critical component of the effect. However, it could be that there are small effects in situations without input competition, but the dependent measures typically used are not sensitive enough to detect them. The present study analyzed data from a noncompetition task, single-string lexical decision, with the diffusion model, a decision process model that provides a more direct measure of performance differences than either response times or accuracy alone. The diffusion model analysis showed a consistent processing advantage for threatening words in high-anxious individuals, whereas traditional comparisons showed no significant differences. These results challenge the view that input competition is necessary for enhanced threat processing. Implications for theories of anxiety are discussed.

Dysphoria and memory for emotional material: A diffusion-model analysis

Depression-related differences in memory for emotional material are well established, but recognition memory and lexical decision tasks often fail to produce consistent results. The null results from these tasks could be due to inadequacies in traditional analyses rather than the absence of effects. In particular, analyses of accuracy or mean reaction times rely on only a fraction of the behavioural data and are sensitive to individual differences in response biases. The diffusion model addresses these limitations by incorporating all of the behavioural data and separating out response biases. We applied the diffusion model to data from lexical decision and recognition memory tasks and showed consistent effects, specifically a positive emotional bias in non-dysphoric subjects and even-handedness in dysphoric subjects. This pattern was not apparent with comparisons of reaction times or accuracy, consistent with previous null findings. These results suggest a relationship between dysphoria and the internal representation of emotional information.

Perceptual Criteria in the Human Brain

A critical component of decision making is the ability to adjust criteria for classifying stimuli. fMRI and drift diffusion models were used to explore the neural representations of perceptual criteria in decision making. The specific focus was on the relative engagement of perceptual- and decision-related neural systems in response to adjustments in perceptual criteria. Human participants classified visual stimuli as big or small based on criteria of different sizes, which effectively biased their choices toward one response over the other. A drift diffusion model was fit to the behavioral data to extract estimates of stimulus size, criterion size, and difficulty for each participant and condition. These parameter values were used as modulated regressors to create a highly constrained model for the fMRI analysis that accounted for several components of the decision process. The results show that perceptual criteria values were reflected by activity in left inferior temporal cortex, a region known to represent objects and their physical properties, whereas stimulus size was reflected by activation in occipital cortex. A frontoparietal network of regions, including dorsolateral prefrontal cortex and superior parietal lobule, corresponded to the decision variables resulting from the downstream stimulus–criterion comparison, independent of stimulus type. The results provide novel evidence that perceptual criteria are represented in stimulus space and serve as inputs to be compared with the presented stimulus, recruiting a common network of decision regions shown to be active in other simple decisions. This work advances our understanding of the neural correlates of decision flexibility and adjustments of behavioral bias.

Decomposing decision components in the stop-signal task: A model-based approach to individual differences in inhibitory control

The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.

Diffusion Model Drift Rates Can Be Influenced by Decision Processes: An Analysis of the Strength-Based Mirror Effect

Improving memory for studied items (targets) often helps participants reject nonstudied items (lures), a pattern referred to as the strength-based mirror effect (SBME). Criss (2010) demonstrated the SBME in diffusion model drift rates; that is, the target drift rate was higher and the lure drift rate was lower for lists of words studied 5 times versus lists of words studied once. She interpreted the drift rate effect for lures as evidence for the differentiation process, whereby strong memory traces produce a poorer match to lure items than do weak memory traces. However, she noted that strength may have also affected a model parameter called the drift criterion-a participant-controlled decision parameter that defines the zero point in drift rate. We directly contrasted the differentiation and drift-criterion accounts by manipulating list strength either at both encoding and retrieval (which produces a differentiation difference in the studied traces) or at retrieval only (which equates differentiation from the study list but provides the opportunity to change decision processes based on strength). Across 3 experiments, results showed that drift rates for lures were lower on strong tests than on weak tests, and this effect was observed even when strength was varied at retrieval alone. Therefore, results provided evidence that the SBME is produced by changes in decision processes, not by differentiation of memory traces.

Decomposing bias in different types of simple decisions.

The ability to adjust bias, or preference for an option, allows for great behavioral flexibility. Decision bias is also important for understanding cognition as it can provide useful information about underlying cognitive processes. Previous work suggests that bias can be adjusted in 2 primary ways: by adjusting how the stimulus under consideration is processed, or by adjusting how the response is prepared. The present study explored the experimental, behavioral, and theoretical distinctions between these biases. Different bias manipulations were employed in parallel across perceptual and memory-based decisions to assess the generality of the 2 biases. This is the 1st study to directly test whether conceptually similar bias instructions can induce dissociable bias effects across different decision tasks. The results show that stimulus and response biases can be separately induced in both tasks, suggesting that the biases generalize across different types of decisions. When analyzing behavioral data, the 2 biases can be differentiated by focusing on the time course of bias effects and/or by fitting choice reaction time models to the data. These findings have strong theoretical implications about how observed bias relates to underlying cognitive processes and how it should be used when testing cognitive theories. Guidelines are presented to help researchers identify how to induce the biases experimentally, how to dissociate them in the behavioral data, and how to quantify them using drift diffusion models. Because decision bias is pervasive across many domains of cognitive science, these guidelines can be useful for future work exploring decision bias and choice preferences.

Using Covert Response Activation to Test Latent Assumptions of Formal Decision-Making Models in Humans

Most decisions that we make build upon multiple streams of sensory evidence and control mechanisms are needed to filter out irrelevant information. Sequential sampling models of perceptual decision making have recently been enriched by attentional mechanisms that weight sensory evidence in a dynamic and goal-directed way. However, the framework retains the longstanding hypothesis that motor activity is engaged only once a decision threshold is reached. To probe latent assumptions of these models, neurophysiological indices are needed. Therefore, we collected behavioral and EMG data in the flanker task, a standard paradigm to investigate decisions about relevance. Although the models captured response time distributions and accuracy data, EMG analyses of response agonist muscles challenged the assumption of independence between decision and motor processes. Those analyses revealed covert incorrect EMG activity (“partial error”) in a fraction of trials in which the correct response was finally given, providing intermediate states of evidence accumulation and response activation at the single-trial level. We extended the models by allowing motor activity to occur before a commitment to a choice and demonstrated that the proposed framework captured the rate, latency, and EMG surface of partial errors, along with the speed of the correction process. In return, EMG data provided strong constraints to discriminate between competing models that made similar behavioral predictions. Our study opens new theoretical and methodological avenues for understanding the links among decision making, cognitive control, and motor execution in humans. SIGNIFICANCE STATEMENT Sequential sampling models of perceptual decision making assume that sensory information is accumulated until a criterion quantity of evidence is obtained, from where the decision terminates in a choice and motor activity is engaged. The very existence of covert incorrect EMG activity (“partial error”) during the evidence accumulation process challenges this longstanding assumption. In the present work, we use partial errors to better constrain sequential sampling models at the single-trial level.

Using fMRI to Constrain Theories of Cognition

Research on cognition often leads to debates that are centered on how many processes exist and how they interact to guide behavior. These debates occur across a range of domains and are often difficult to resolve with behavioral data because similar behavioral predictions can be made by models with different core assumptions. Such model mimicry limits researchers’ ability to find differential support for one type of model over the other using behavioral data alone. We argue that functional neuroimaging can help overcome this problem by providing additional dependent measures to constrain model testing. Recent advances in analysis, like multivariate approaches, expand the amount and type of data available for model testing. We illustrate the benefits of this approach by highlighting imaging results that directly speak to the debate over the nature of recollection processes in memory. These results show how functional neuroimaging can advance studies of cognition by providing richer data sets for contrasting cognitive models.

Gender and cognitive-emotional factors as predictors of pre-sleep arousal and trait hyperarousal in insomnia

OBJECTIVE Elevated pre-sleep arousal has been consistently associated with insomnia, yet the cognitive-emotional mechanisms involved in sleep-related arousal remain unclear. The purpose of this study was to identify predictors of pre-sleep arousal and trait hyperarousal from a set of variables that included self-reported affect, sleep-related cognitions, locus of control, and gender. METHODS Cross-sectional data were analyzed for 128 participants (89 females) who met criteria for psychophysiological insomnia and completed a set of questionnaires that included the beliefs and attitudes about sleep (BAS), positive and negative affect schedule (negative subscale (nPANAS) and positive subscale (pPANAS)), sleep locus of control (SLOC), Pre-Sleep Arousal Scale (PSAS), hyperarousal scale (HAS) and demographic information. Step-wise regression was conducted with a set of independent variables, with PSAS and HAS serving as separate dependent variables. RESULTS Trait hyperarousal was associated with higher levels of both negative and positive emotionality, as well as negative beliefs about sleep, in both genders. Pre-sleep arousal was associated with greater negative emotionality and internal sleep locus of control, varying by gender. Among women, high pre-sleep arousal was associated with negative emotionality, while in men greater pre-sleep arousal was associated with an internal sleep locus of control. CONCLUSION These findings have clinical implications, suggesting that men and women may require different cognitive targets when addressing pre-sleep arousal.