A spurious correlation between difference scores in evidence-accumulation model parameters.

Perceptual decision-making performance depends on several cognitive and neural processes. Here, we fit Ratcliff's diffusion model to accuracy data and reaction-time distributions from one numerical and one verbal two-choice perceptual-decision task to deconstruct these performance measures into the rate of evidence accumulation (i.e., drift rate), response criterion setting (i.e., boundary separation), and peripheral aspects of performance (i.e., nondecision time). These theoretical processes are then related to individual differences in brain activation by means of multiple regression. The sample consisted of 24 younger and 15 older adults performing the task in fMRI before and after 100 daily 1-hr behavioral training sessions in a multitude of cognitive tasks. Results showed that individual differences in boundary separation were related to striatal activity, whereas differences in drift rate were related to activity in the inferior parietal lobe. These associations were not significantly modified by adult age or perceptual expertise. We conclude that the striatum is involved in regulating response thresholds, whereas the inferior parietal lobe might represent decision-making evidence related to letters and numbers.

The diffusion model (Ratcliff, 1978) takes into account the reaction time distributions of both correct and erroneous responses from binary decision tasks. This high degree of information usage allows the estimation of different parameters mapping cognitive components such as speed of information accumulation or decision bias. For three of the four main parameters (drift rate, starting point, and non-decision time) trial-to-trial variability is allowed. We investigated the influence of these variability parameters both drawing on simulation studies and on data from an empirical test-retest study using different optimization criteria and different trial numbers. Our results suggest that less complex models (fixing intertrial variabilities of the drift rate and the starting point at zero) can improve the estimation of the psychologically most interesting parameters (drift rate, threshold separation, starting point, and non-decision time).

Decades of research show that people from adverse environments tend to score lower on cognitive tasks. These findings have been associated with deficits in various cognitive abilities, which has led to the proliferation of deficit models. These models emphasize the negative effects of chronic stress on social and cognitive development. More recently, research has started to focus on the ways in which adversity exposure may lead to adaptations specific cognitive abilities and strategies. Such adaptations could potentially lead to enhancements in abilities that are useful for solving the unique challenges posed by adverse environments, such as threat detection. A better integration of deficit and adaptation perspectives is important both for basic science and the development of interventions. Despite their importance, deficit and adaptation perspectives are not well-integrated. This dissertation argues that one reason for this might be that research from both perspectives tends to focus on raw performance measures, such as response times and accuracy. However, these measures have two important limitations. First, performance on EF tasks is not just influenced by specific EF abilities, but also by other cognitive processes, such as response caution. Second, performance on EF tasks is additionally influenced by general processes that shape performance across multiple cognitive tasks. These limitations hinder our understanding of how adversity shapes cognitive abilities. To address the first limitation, this dissertation uses Drift Diffusion Modeling (DDM). The DDM explains response times and accuracy on tasks that require binary decision-making as a combination of (1) the speed with which someone accumulates information (Drift rate), (2) the amount of information someone needs before making a decision (Boundary separation), and (3) the speed of non-decisional processes, such as stimulus encoding and respons execution (Non-decision time). By applying DDM to empirical task data, we can obtain participant-level estimates of these three cognitive processes. To address the second limitation, this dissertation uses structural equation modeling. This technique allows us to investigate the extent to which performance on EF tasks is shaped by task-specific processes versus task-general processes. This dissertation shows that adversity researchers may overestimate the association between adversity and specific EF abilities. We find that negative associations between adversity and drift rates are mostly driven by general processing speed. After correcting for general factors, we find little to no evidence for associations between adversity and specific EF abilities. Finally, we show that people with more adversity exposure sometimes use different strategies compared to people with less adversity exposure. These findings show that researchers should account for the fact that performance on EF tasks cannot be explained by specific EF abilities alone. Instead, they should tease apart the distinct cognitive processes that affect performance to better understand how adversity shapes cognitive development. Ideally, studies would include two or more tasks per EF ability in order to estimate abilities on a latent level. Cognitive modeling can be a valuable technique for the next generation of adversity researchers.

The influence of reward anticipation on conflict control in children and adolescents: Evidences from hierarchical drift-diffusion model and event-related potentials

to clarify the specific factors that determine how, why, and under what conditions incentives enhance cognitive processing and associated control functions.

Background: Visual hallucinations (VH) are a common symptom in dementia with Lewy bodies (DLB); however, their cognitive underpinnings remain unclear. Hallucinations have been related to cognitive slowing in DLB and may arise due to impaired sensory input, dysregulation in top-down influences over perception, or an imbalance between the two, resulting in false visual inferences. Methods: Here we employed a drift diffusion model yielding estimates of perceptual encoding time, decision threshold, and drift rate of evidence accumulation to (i) investigate the nature of DLB-related slowing of responses and (ii) their relationship to visuospatial performance and visual hallucinations. The EZ drift diffusion model was fitted to mean reaction time (RT), accuracy and RT variance from two-choice reaction time (CRT) tasks and data were compared between groups of mild cognitive impairment (MCI-LB) LB patients (n = 49) and healthy older adults (n = 25). Results: No difference was detected in drift rate between patients and controls, but MCI-LB patients showed slower non-decision times and boundary separation values than control participants. Furthermore, non-decision time was negatively correlated with visuospatial performance in MCI-LB, and score on visual hallucinations inventory. However, only boundary separation was related to clinical incidence of visual hallucinations. Conclusions: These results suggest that a primary impairment in perceptual encoding may contribute to the visuospatial performance, however a more cautious response strategy may be related to visual hallucinations in Lewy body disease. Interestingly, MCI-LB patients showed no impairment in information processing ability, suggesting that, when perceptual encoding was successful, patients were able to normally process information, potentially explaining the variability of hallucination incidence.

Processing speed is impaired in patients with psychosis, and deteriorates as a function of normal aging. These observations, in combination with other lines of research, suggest that psychosis may be a syndrome of accelerated aging. But do patients with psychosis perform poorly on tasks of processing speed for the same reasons as older adults? Fifty-one patients with psychotic illnesses and 90 controls with similar mean IQ (aged 19-69 years, all African American) completed a computerized processing-speed task, reminiscent of the classic digit-symbol coding task. The data were analyzed using the drift-diffusion model (DDM), and Bayesian inference was used to determine whether psychosis and aging had similar or divergent effects on the DDM parameters. Psychosis and aging were both associated with poor performance, but had divergent effects on the DDM parameters. Patients had lower information-processing efficiency ("drift rate") and longer nondecision time than controls, and psychosis per se did not influence response caution. By contrast, the primary effect of aging was to increase response caution, and had inconsistent effects on drift rate and nondecision time across patients and controls. The results reveal that psychosis and aging influenced performance in different ways, suggesting that the processing-speed impairment in psychosis is more than just accelerated aging. This study also demonstrates the potential utility of computational models and Bayesian inference for finely mapping the contributions of cognitive functions on simple neurocognitive tests.

The relationship between baseline EEG spectra power and memory performance in older African Americans endorsing cognitive concerns in a community setting

Stage-specific computational mechanisms of working memory deficits in first-episode and chronic schizophrenia.

Components of task switching: A closer look at task switching and cue switching

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.Electronic supplementary materialThe online version of this article (doi:10.3758/s13414-013-0447-8) contains supplementary material, which is available to authorized users.

Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stages while performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA.

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants’ threshold of when enough evidence is present to act.

Modeling reaction time data using diffusion models comes with the advantage of separating different processes involved in decision making. Each parameter of the diffusion model is assumed to translate into one (or more) process(es). If this assumption holds true, selective manipulations of specific processes involved in decision making should selectively influence the related parameter, but not any other parameter (high convergent and discriminant validities). We present two experiments (total N = 104) and one simulation study that-using a manipulation of the difficulty of encoding in a lexical decision task-tested the convergent validity of the nondecision time t₀. As hypothesized, we found large effects on t₀ in both experiments but also medium to large effects on the drift rate v as well as on the starting point z. Our simulation study suggests that the effects on the drift rate, but not on the starting point, might be explained by incorrect assumptions about the intertrial variability of the nondecision time. Our results speak in favor of a high convergent validity of t₀ but question the discriminant validities of v and z, at least under the assumption that our manipulation affected encoding selectively. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Objectives: Socioemotional Selectivity Theory suggests that the emotional significance of social partners should have a greater influence on older adults' choices, potentially leading them to weigh social relationships more heavily when deciding whom to trust. We tested whether this preferential trust in closer relationships stems from a preexisting bias (predisposition) or more efficient information processing (evaluation) and how these processes change across the adult life span.Methods: In a sample of 163 healthy adults (ages 18–80), we applied a diffusion decision model (DDM) to behavioral data from an economic trust game. Participants made investment decisions with a friend, a stranger, and a computer partner.Results: Participants invested more in friends than in strangers. The DDM revealed this was driven by a higher rate of evidence accumulation (drift rate, v) for friends, not a shift in response bias (starting point, z). Age was positively correlated with non-decision time (t), reflecting general slowing, but was not associated with other parameters. The evaluative enhancement for friends was preserved across the life span.Discussion: Preferential trust in close others arises from more efficient evaluative processing, not a simple predisposition. This core social-cognitive mechanism remains preserved in older adulthood, underscoring the value of cognitive modeling for uncovering information-processing mechanisms that support socioemotional priorities as people age.