Modeling aging effects on two-choice tasks: response signal and response time data

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

Quantitative susceptibility mapping of brain iron in healthy aging and cognition

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can assess the magnetic properties of cerebral iron in vivo. Although brain iron is necessary for basic neurobiological functions, excess iron content disrupts homeostasis, leads to oxidative stress, and ultimately contributes to neurodegenerative disease. However, some degree of elevated brain iron is present even among healthy older adults. To better understand the topographical pattern of iron accumulation and its relation to cognitive aging, we conducted an integrative review of 47 QSM studies of healthy aging, with a focus on five distinct themes. The first two themes focused on age-related increases in iron accumulation in deep gray matter nuclei versus the cortex. The overall level of iron is higher in deep gray matter nuclei than in cortical regions. Deep gray matter nuclei vary with regard to age-related effects, which are most prominent in the putamen, and age-related deposition of iron is also observed in frontal, temporal, and parietal cortical regions during healthy aging. The third theme focused on the behavioral relevance of iron content and indicated that higher iron in both deep gray matter and cortical regions was related to decline in fluid (speed-dependent) cognition. A handful of multimodal studies, reviewed in the fourth theme, suggest that iron interacts with imaging measures of brain function, white matter degradation, and the accumulation of neuropathologies. The final theme concerning modifiers of brain iron pointed to potential roles of cardiovascular, dietary, and genetic factors. Although QSM is a relatively recent tool for assessing cerebral iron accumulation, it has significant promise for contributing new insights into healthy neurocognitive aging.

A spatially continuous diffusion model of visual working memory

We present results from five visual working memory (VWM) experiments in which participants were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices. Current VWM models for this task include discrete models that assume an item is either within working memory or not and resource models that assume that memory strength varies as a function of the number of items. Because these models do not include processes that allow them to account for RT data, we implemented them within the spatially continuous diffusion model (SCDM, Ratcliff, 2018) and use the experimental data to evaluate these combined models. In the SCDM, evidence retrieved from memory is represented as a spatially continuous normal distribution and this drives the decision process until a criterion (represented as a 1-D line) is reached, which produces a decision. Noise in the accumulation process is represented by continuous Gaussian process noise over spatial position. The models that fit best from the discrete and resource-based classes converged on a common model that had a guessing component and that allowed the height of the normal memory-strength distribution to vary with number of items. The guessing component was implemented as a regular decision process driven by a flat evidence distribution, a zero-drift process. The combination of choice and RT data allows models that were not identifiable based on choice data alone to be discriminated.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

The effect of speed-stress on driving behavior: A diffusion model analysis

In everyday driving on the road, people are often required to make fast decisions that could compromise the accuracy of choices. We present a diffusion model analysis of the adjustments drivers make to the decision process under speed-stress. Participants operated a PC-based driving simulator while performing one of two decision-making tasks that required a driving action as a response to the stimulus. In a one-choice driving task, participants were asked to drive around a lead car when its brake lights were turned on. A two-choice driving task used a brightness-discrimination task in which participants were asked to drive to the left and back behind a lead car if there were more black than white pixels in a display and to the right and back if there were more white than black pixels. Speed-stress was operationalized by instructing drivers to respond as quickly as possible and by manipulating the distance drivers were required to maintain behind the lead car. Results showed the expected speed-accuracy tradeoff; however, the cost on accuracy in the two-choice task was relatively small. The model-based analysis showed that this was achieved by lowering the decision criteria and speeding up nondecision processes without disrupting components that produce evidence for the decision process. In fact, in the one-choice task, evidence accumulation rate in the speed-stress condition was found to be higher than in the accuracy-stress condition. We concluded that drivers were able to comply with speed-stress demands with relatively safe adjustments that imposed minimal costs on the accuracy of choices.

Age-related differences in frontoparietal activation for target and distractor singletons during visual search

Age-related decline in visual search performance has been associated with different patterns of activation in frontoparietal regions using functional magnetic resonance imaging (fMRI), but whether these age-related effects represent specific influences of target and distractor processing is unclear. Therefore, we acquired event-related fMRI data from 68 healthy, community-dwelling adults ages 18-78 years, during both conjunction (T/F target among rotated Ts and Fs) and feature (T/F target among Os) search. Some displays contained a color singleton that could correspond to either the target or a distractor. A diffusion decision analysis indicated age-related increases in sensorimotor response time across all task conditions, but an age-related decrease in the rate of evidence accumulation (drift rate) was specific to conjunction search. Moreover, the color singleton facilitated search performance when occurring as a target and disrupted performance when occurring as a distractor, but only during conjunction search, and these effects were independent of age. The fMRI data indicated that decreased search efficiency for conjunction relative to feature search was evident as widespread frontoparietal activation. Activation within the left insula mediated the age-related decrease in drift rate for conjunction search, whereas this relation in the FEF and parietal cortex was significant only for individuals younger than 30 or 44 years, respectively. Finally, distractor singletons were associated with significant parietal activation, whereas target singletons were associated with significant frontoparietal deactivation, and this latter effect increased with adult age. Age-related differences in frontoparietal activation therefore reflect both the overall efficiency of search and the enhancement from salient targets.

Performance-contingent reward increases the use of congruent distracting information

In conflict tasks like the Simon task, participants are instructed to respond to a task-relevant target dimension while ignoring additional distracting information. In the Simon task the distracting spatial information can be congruent or incongruent with the task-relevant target information, causing a congruency effect. As seen in the proportion congruency effect and the congruency sequence effect, this congruency effect is larger in mostly congruent blocks and following congruent trials, respectively. Common theories suggest that when the proportion of incongruent trials is high or after an incongruent trial, focus on the task-relevant target information is increased and distracting information is inhibited. In two experiments, we investigated how reward modulates these phenomena. Specifically, performance-contingent reward - but not non-contingent reward - increased the usage of the distracting information in mostly congruent blocks or following congruent trials, while the adaptation to incongruency (i.e., mostly incongruent blocks or preceding incongruent trials) was the same in all conditions. Additional diffusion model analyses found that this effect of performance-contingent reward was captured by the drift rate parameter. These results suggest an increased focus on the target information by incongruent trials independent from reward, while the adaptation to (mostly) congruent trials characterized by increased usage of distracting information can be motivationally boosted. That is, performance-contingent reward increases the use of congruent distracting information beyond a mere relaxation of the increased target-focus following (mostly) congruent trials.

Chromatic discrimination measures in mature observers depend on the response window

Our past anecdotal evidence prompted that a longer response window (RW) in the Trivector test (Cambridge Colour Test) improved mature observers’ estimates of chromatic discrimination. Here, we systematically explored whether RW variation affects chromatic discrimination thresholds measured by the length of Protan, Deutan and Tritan vectors. We employed the Trivector test with three RWs: 3 s, 5 s, and 8 s. Data of 30 healthy normal trichromats were stratified as age groups: ‘young’ (20–29 years), ‘middle-aged’ (31–48 years), and ‘mature’ (57–64 years). We found that for the ‘young’ and ‘middle-aged’, the thresholds were comparable at all tested RWs. However, the RW effect was apparent for the ‘mature’ observers: their Protan and Tritan thresholds decreased at 8-s RW compared to 3-s RW; moreover, their Tritan threshold decreased at 5-s RW compared to 3-s RW. Elevated discrimination thresholds at shorter RWs imply that for accurate performance, older observers require longer stimulus exposure and are indicative of ageing effects manifested by an increase in critical processing duration. Acknowledging low numbers in our ‘middle-aged’ and ‘mature’ samples, we consider our study as pilot. Nonetheless, our findings encourage us to advocate a RW extension in the Trivector protocol for testing mature observers, to ensure veridical measures of their chromatic discrimination by disentangling these from other ageing effects—slowing down of both motor responses and visual processing.

Modeling evidence accumulation decision processes using integral equations: Urgency-gating and collapsing boundaries

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mental speed is high until age 60 as revealed by analysis of over a million participants

Response speeds in simple decision-making tasks begin to decline from early and middle adulthood. However, response times are not pure measures of mental speed but instead represent the sum of multiple processes. Here we apply a Bayesian diffusion model to extract interpretable cognitive components from raw response time data. We apply our model to cross-sectional data from 1.2 million participants to examine age differences in cognitive parameters. To efficiently parse this large dataset, we apply a Bayesian inference method for efficient parameter estimation using specialized neural networks. Our results indicate that response time slowing begins as early as age 20, but this slowing was attributable to increases in decision caution and to slower non-decisional processes, rather than to differences in mental speed. Slowing of mental speed was observed only after approximately age 60. Our research thus challenges widespread beliefs about the relationship between age and mental speed.

Age differences in diffusion model parameters: a meta-analysis

Older adults typically show slower response times in basic cognitive tasks than younger adults. A diffusion model analysis allows the clarification of why older adults react more slowly by estimating parameters that map distinct cognitive components of decision making. The main components of the diffusion model are the speed of information uptake (drift rate), the degree of conservatism regarding the decision criterion (boundary separation), and the time taken up by non-decisional processes (i.e., encoding and motoric response execution; non-decision time). While the literature shows consistent results regarding higher boundary separation and longer non-decision time for older adults, results are more complex when it comes to age differences in drift rates. We conducted a multi-level meta-analysis to identify possible sources of this variance. As possible moderators, we included task difficulty and task type. We found that age differences in drift rate are moderated both by task type and task difficulty. Older adults were inferior in drift rate in perceptual and memory tasks, but information accumulation was even increased in lexical decision tasks for the older participants. Additionally, in perceptual and lexical decision tasks, older individuals benefitted from high task difficulty. In the memory tasks, task difficulty did not moderate the negative impact of age on drift. The finding of higher boundary separation and longer non-decision time in older than younger adults generalized over task type and task difficulty. The results of our meta-analysis are consistent with recent findings of a more pronounced age-related decline in memory than in vocabulary performance.

Stability and Change in Diffusion Model Parameters over Two Years

In recent years, mathematical models of decision making, such as the diffusion model, have been endorsed in individual differences research. These models can disentangle different components of the decision process, like processing speed, speed–accuracy trade-offs, and duration of non-decisional processes. The diffusion model estimates individual parameters of cognitive process components, thus allowing the study of individual differences. These parameters are often assumed to show trait-like properties, that is, within-person stability across tasks and time. However, the assumption of temporal stability has so far been insufficiently investigated. With this work, we explore stability and change in diffusion model parameters by following over 270 participants across a time period of two years. We analysed four different aspects of stability and change: rank-order stability, mean-level change, individual differences in change, and profile stability. Diffusion model parameters showed strong rank-order stability and mean-level changes in processing speed and speed–accuracy trade-offs that could be attributed to practice effects. At the same time, people differed little in these patterns across time. In addition, profiles of individual diffusion model parameters proved to be stable over time. We discuss implications of these findings for the use of the diffusion model in individual differences research.

Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?

Online data collection is being used more and more, especially in the face of the COVID crisis. To examine the quality of such data, we chose to replicate lexical decision and item recognition paradigms from Ratcliff et al. (Cognitive Psychology, 60, 127-157, 2010) and numerosity discrimination paradigms from Ratcliff and McKoon (Psychological Review, 125, 183-217, 2018) with subjects recruited from Amazon Mechanical Turk (AMT). Along with these tasks, we collected data from either an IQ test or a math computation test. Subjects in the lexical decision and item recognition tasks were relatively well-behaved, with only a few giving a significant number of responses with response times (RTs) under 300 ms at chance accuracy, i.e., fast guesses, and a few with unstable RTs across a session. But in the numerosity discrimination tasks, almost half of the subjects gave a significant number of fast guesses and/or unstable RTs across the session. Diffusion model parameters were largely consistent with the earlier studies as were correlations across tasks and correlations with IQ and age. One surprising result was that eliminating fast outliers from subjects with highly variable RTs (those eliminated from the main analyses) produced diffusion model analyses that showed patterns of correlations similar to the subjects with stable performance. Methods for displaying data to examine stability, eliminating subjects, and implementing RT data collection on AMT including checks on timing are also discussed.

Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains?

In comparison to young adults, middle-aged and old people show lower scores in intelligence tests and slower response times in elementary cognitive tasks. Whether these well-documented findings can both be attributed to a general cognitive slow-down across the life-span has become subject to debate in the last years. The drift diffusion model can disentangle three main process components of binary decisions, namely the speed of information processing, the conservatism of the decision criterion and the non-decision time (i.e., time needed for processes such as encoding and motor response execution). All three components provide possible explanations for the association between response times and age. We present data from a broad study using 18 different response time tasks from three different content domains (figural, numeric, verbal). Our sample included people between 18 to 62 years of age, thus allowing us to study age differences across young-adulthood and mid-adulthood. Older adults generally showed longer non-decision times and more conservative decision criteria. For speed of information processing, we found a more complex pattern that differed between tasks. We estimated mediation models to investigate whether age differences in diffusion model parameters account for the negative relation between age and intelligence, across different intelligence process domains (processing capacity, memory, psychometric speed) and different intelligence content domains (figural, numeric, verbal). In most cases, age differences in intelligence were accounted for by age differences in non-decision time. Content domain-general, but not content domain-specific aspects of non-decision time were related to age. We discuss the implications of these findings on how cognitive decline and age differences in mental speed might be related.

Effects of aging in a task-switch paradigm with the diffusion decision model

We investigated aging effects in a task-switch paradigm with degraded stimuli administered to college students, 61-74 year olds, and 75-89 year olds. We studied switch costs (the performance difference between task-repeat and task-switch trials) in terms of accuracy and mean reaction times (RTs). Previous aging research focused on switch costs in terms of mean RTs (with accuracy at ceiling). Our results emphasize the importance of distinguishing between switch costs indexed by accuracy and by RTs because these measures lead to different interpretations. We used the Diffusion Decision Model (DDM; Ratcliff, 1978) to study the cognitive components contributing to switch costs. The DDM decomposed the cognitive process of task switching into multiple components. Two parameters of the model, the quality of evidence on which decisions were based (drift rate) and the duration of processes outside the decision process (nondecision time component), indexed different sources of switch costs. We found that older participants had larger switch costs indexed by nondecision time component than younger participants. This result suggests age-related deficits in preparatory cognitive processes. We also found group differences in switch costs indexed by drift rate for switch trials with high stimulus interference (stimuli with features relevant for both tasks). This result suggests that older participants have less effective cognitive processes involved in resolving interference. Our findings show that age-related effects in separate components of switch costs can be studied with the DDM. Our results demonstrate the utility of using discrimination tasks with degraded stimuli in conjunction with model-based analyses. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Response-level processing during visual feature search: Effects of frontoparietal activation and adult age

Previous research suggests that feature search performance is relatively resistant to age-related decline. However, little is known regarding the neural mechanisms underlying the age-related constancy of feature search. In this experiment, we used a diffusion decision model of reaction time (RT), and event-related functional magnetic resonance imaging (fMRI) to investigate age-related differences in response-level processing during visual feature search. Participants were 80 healthy, right-handed, community-dwelling individuals, 19–79 years of age. Analyses of search performance indicated that targets accompanied by response-incompatible distractors were associated with a significant increase in the nondecision-time (t0) model parameter, possibly reflecting the additional time required for response execution. Nondecision time increased significantly with increasing age, but no age-related effects were evident in drift rate, cautiousness (boundary separation, a), or in the specific effects of response compatibility. Nondecision time was also associated with a pattern of activation and deactivation in frontoparietal regions. The relation of age to nondecision time was indirect, mediated by this pattern of frontoparietal activation and deactivation. Response-compatible and -incompatible trials were associated with specific patterns of activation in the medial and superior parietal cortex, and frontal eye field, but these activation effects did not mediate the relation between age and search performance. These findings suggest that, in the context of a highly efficient feature search task, the age-related influence of frontoparietal activation is operative at a relatively general level, which is common to the task conditions, rather than at the response level specifically.

Computational Modeling Applied to the Dot-Probe Task Yields Improved Reliability and Mechanistic Insights

BACKGROUND

Biased patterns of attention are implicated as key mechanisms across many forms of psychopathology and have given rise to automated mechanistic interventions designed to modify such attentional preferences. However, progress is substantially hindered by limitations in widely used methods to quantify attention, bias leading to imprecision of measurement.

METHODS

In a sample of patients who were clinically anxious (n = 70), we applied a well-validated form of computational modeling (drift-diffusion model) to trial-level reaction time data from a two-choice "dot-probe task"-the dominant paradigm used in hundreds of attention bias studies to date-in order to model distinct components of task performance.

RESULTS

While drift-diffusion model-derived attention bias indices exhibited convergent validity with previous approaches (e.g., conventional bias scores, eye tracking), our novel analytic approach yielded substantially improved split-half reliability, modestly improved test-retest reliability, and revealed novel mechanistic insights regarding neural substrates of attention bias and the impact of an automated attention retraining procedure.

CONCLUSIONS

Computational modeling of attention bias task data may represent a new way forward to improve precision.

Adults with Poor Reading Skills, Older Adults, and College Students: the Meanings They Understand During Reading Using a Diffusion Model Analysis

When a word is read in a text, the aspects of its meanings that are encoded should be those relevant to the text and not those that are irrelevant. We tested whether older adults, college students, and adults with poor literacy skills accomplish contextually relevant encoding. Participants read short stories, which were followed by true/false test sentences. Among these were sentences that matched the relevant meaning of a word in a story and sentences that matched a different meaning. We measured the speed and accuracy of responses to the test sentences and used a decision model to separate the information that a reader encodes from the reader's speed/accuracy tradeoff settings. We found that all three groups encoded meanings as contextually relevant. The findings illustrate how a decision-making model combined with tests of particular comprehension processes can lead to further understanding of reading skills.

The effects of sleep deprivation on item and associative recognition memory

Sleep deprivation adversely affects the ability to perform cognitive tasks, but theories range from predicting an overall decline in cognitive functioning because of reduced stability in attentional networks to specific deficits in various cognitive domains or processes. We measured the effects of sleep deprivation on two memory tasks, item recognition ("was this word in the list studied") and associative recognition ("were these two words studied in the same pair"). These tasks test memory for information encoded a few minutes earlier and so do not address effects of sleep deprivation on working memory or consolidation after sleep. A diffusion model was used to decompose accuracy and response time distributions to produce parameter estimates of components of cognitive processing. The model assumes that over time, noisy evidence from the task stimulus is accumulated to one of two decision criteria, and parameters governing this process are extracted and interpreted in terms of distinct cognitive processes. Results showed that sleep deprivation reduces drift rate (evidence used in the decision process), with little effect on the other components of the decision process. These results contrast with the effects of aging, which show little decline in item recognition but large declines in associative recognition. The results suggest that sleep deprivation degrades the quality of information stored in memory and that this may occur through degraded attentional processes. (PsycINFO Database Record

Experimental validation of the diffusion model based on a slow response time paradigm

The diffusion model (Ratcliff, Psychol Rev 85(2):59-108, 1978) is a stochastic model that is applied to response time (RT) data from binary decision tasks. The model is often used to disentangle different cognitive processes. The validity of the diffusion model parameters has, however, rarely been examined. Only few experimental paradigms have been analyzed with those being restricted to fast response time paradigms. This is attributable to a recommendation stated repeatedly in the diffusion model literature to restrict applications to fast RT paradigms (more specifically, to tasks with mean RTs below 1.5 s per trial). We conducted experimental validation studies in which we challenged the necessity of this restriction. We used a binary task that features RTs of several seconds per trial and experimentally examined the convergent and discriminant validity of the four main diffusion model parameters. More precisely, in three experiments, we selectively manipulated these parameters, using a difficulty manipulation (drift rate), speed-accuracy instructions (threshold separation), a more complex motoric task (non-decision time), and an asymmetric payoff matrix (starting point). The results were similar to the findings from experimental validation studies based on fast RT paradigms. Thus, our experiments support the validity of the parameters of the diffusion model and speak in favor of an extension of the model to paradigms based on slower RTs.

Functional modular architecture underlying attentional control in aging

Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19-78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age.

The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging

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.

Aging and confidence judgments in item recognition

We examined the effects of aging on performance in an item-recognition experiment with confidence judgments. A model for confidence judgments and response time (RTs; Ratcliff & Starns, 2013) was used to fit a large amount of data from a new sample of older adults and a previously reported sample of younger adults. This model of confidence judgments allows us to distinguish between changes evidence from memory and changes in decision-related components and it accounts for both RT distributions and response proportions. Older adults took longer to respond than younger adults, older adults exhibited a small decrease in the strength of evidence from memory compared with younger adults and a slight bias toward judging items as "old." The difference in RTs between the 2 age groups was primarily explained by the difference in the nondecision component. Although our small sample size makes the general conclusions about aging tentative, the results are consistent with other research examining the effects of aging in two-choice RT tasks and response-signal tasks, and the study demonstrates that confidence judgment choice proportion and RT distribution data from older adults can be fit with the response time and confidence 2 (RTCON2) model. (PsycINFO Database Record

Visual Hallucinations Are Characterized by Impaired Sensory Evidence Accumulation: Insights From Hierarchical Drift Diffusion Modeling in Parkinson's Disease

BACKGROUND

Models of hallucinations emphasize imbalance between sensory input and top-down influences over perception, as false perceptual inference can arise when top-down predictions are afforded too much precision (certainty) relative to sensory evidence. Visual hallucinations in Parkinson's disease (PD) are associated with lower-level visual and attentional impairments, accompanied by overactivity in higher-order association brain networks. PD therefore provides an attractive framework to explore contributions of bottom-up versus top-down disturbances in hallucinations.

METHODS

We characterized sensory processing during perceptual decision making in patients with PD with (n = 20) and without (n = 25) visual hallucinations and control subjects (n = 12), by fitting a hierarchical drift diffusion model to an attentional task. The hierarchical drift diffusion model uses Bayesian estimates to decompose task performance into parameters reflecting drift rates of evidence accumulation, decision thresholds, and nondecision time.

RESULTS

We observed slower drift rates in patients with hallucinations, which were less sensitive to changes in task demand. In contrast, wider decision boundaries and shorter nondecision times relative to control subjects were found in patients with PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerges as a unique feature of PD hallucinators.

CONCLUSIONS

We integrate these results with evidence accumulation and predictive coding models of hallucinations, suggesting that in PD sensory evidence is less informative and may therefore be down-weighted, resulting in overreliance on top-down influences. Considering impaired drift rates as an approximation of reduced sensory precision, our findings provide a novel computational framework to specify impairments in sensory processing that contribute to development of visual hallucinations.

Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition

Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age-related disadvantages for older readers to be caused by inefficiencies in temporal sampling and activation and/or inhibition processes.

Model Complexity in Diffusion Modeling: Benefits of Making the Model More Parsimonious

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).

Comparing fixed and collapsing boundary versions of the diffusion model

Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsing boundaries provides a better account of human data than a model with fixed boundaries. We compared the models using data from four new numerosity discrimination experiments and two previously published motion discrimination experiments. When model selection was based on BIC values, the fixed boundary model was preferred over the collapsing boundary model for all of the experiments. When model selection was carried out using a parametric bootstrap cross-fitting method (PBCM), which takes into account the flexibility of the alternative models and the ability of one model to account for data from another model, data from 5 of 6 experiments favored either fixed boundaries or boundaries with only negligible collapse. We found that the collapsing boundary model produces response times distributions with the same shape as those produced by the fixed boundary model and that its parameters were not well-identified and were difficult to recover from data. Furthermore, the estimated boundaries of the best-fitting collapsing boundary model were relatively flat and very similar to those of the fixed-boundary model. Overall, a diffusion model with decision boundaries that converge over time does not provide an improvement over the standard diffusion model for our tasks with human data.

Empirical recommendations for improving the stability of the dot-probe task in clinical research

The dot-probe task has been widely used in research to produce an index of biased attention based on reaction times (RTs). Despite its popularity, very few published studies have examined psychometric properties of the task, including test-retest reliability, and no previous study has examined reliability in clinically anxious samples or systematically explored the effects of task design and analysis decisions on reliability. In the current analysis, we used dot-probe data from 3 studies in which attention bias toward threat-related faces was assessed at multiple (≥5) time-points. Two of the studies were similar (adults with social anxiety disorder, similar design features) whereas 1 was more disparate (pediatric healthy volunteers, distinct task design). We explored the effects of analysis choices (e.g., bias score formula, outlier handling method) on reliability and searched for convergent findings across the 3 studies. We found that, when concurrently considering the 3 studies, the most reliable RT index of bias used data from dot-bottom trials, comparing congruent to incongruent trials, with rescaled outliers, particularly after averaging across more than 1 assessment point. Although reliability of RT bias indices was moderate to low, within-session variability in bias (attention bias variability; ABV), a recently proposed RT index, was more reliable across sessions. Several eyetracking-based indices of attention bias (available in the pediatric healthy sample only) showed reliability that matched the optimal RT index (ABV). On the basis of these findings, we make specific recommendations to researchers using the dot-probe, particularly those wishing to investigate individual differences and/or single-patient applications.

Age related differences in reaction time components and diffusion properties of normal-appearing white matter in healthy adults

Deterioration of the white matter (WM) is viewed as the neural substrate of age differences in speed of information processing (reaction time, RT). However, the relationship between WM and RT components is rarely examined in healthy aging. We assessed the relationship between RT components derived from the Ratcliff diffusion model and micro-structural properties of normal-appearing WM (NAWM) in 90 healthy adults (age 18-82 years). We replicated all major extant findings pertaining to age differences in RT components and WM: lower drift rate, greater response conservativeness, longer non-decision time, lower fractional anisotropy (FA), greater mean (MD), axial (AD) and radial (RD) diffusivity were associated with advanced age. Age differences in anterior regions of the cerebral WM exceeded those in posterior regions. However, the only relationship between RT components and WM was the positive association between DR in the body of the corpus callosum and non-decision time. Thus, in healthy adults, age differences in NAWM diffusion properties are not a major contributor to age differences in RT components. Longitudinal studies with more precise and specific estimates of regional myelin content and evaluation of the contribution of age-related vascular risk factors are necessary to understand cerebral substrates of age-related cognitive slowing.

Diffusion models in experimental psychology: a practical introduction

Stochastic diffusion models (Ratcliff, 1978) can be used to analyze response time data from binary decision tasks. They provide detailed information about cognitive processes underlying the performance in such tasks. Most importantly, different parameters are estimated from the response time distributions of correct responses and errors that map (1) the speed of information uptake, (2) the amount of information used to make a decision, (3) possible decision biases, and (4) the duration of nondecisional processes. Although this kind of model can be applied to many experimental paradigms and provides much more insight than the analysis of mean response times can, it is still rarely used in cognitive psychology. In the present paper, we provide comprehensive information on the theory of the diffusion model, as well as on practical issues that have to be considered for implementing the model.

Measuring psychometric functions with the diffusion model

The diffusion decision model (Ratcliff, 1978) was used to examine discrimination for a range of perceptual tasks: numerosity discrimination, number discrimination, brightness discrimination, motion discrimination, speed discrimination, and length discrimination. The model produces a measure of the quality of the information that drives decision processes, a measure termed drift rate in the model. As drift rate varies across experimental conditions that differ in difficulty, a psychometric function that plots drift rate against difficulty can be constructed. Psychometric functions for the tasks in this article usually plot accuracy against difficulty, but for some levels of difficulty, accuracy can be at ceiling. The diffusion model extends the range of difficulty that can be evaluated because drift rates depend on response times (RTs) as well as accuracy, and when RTs decrease across conditions that are all at ceiling in accuracy, then drift rates will distinguish among the conditions. Signal detection theory assumes that the variable driving performance is the z-transform of the accuracy value, and, somewhat surprisingly, this closely matches drift rate extracted from the diffusion model when accuracy is not at ceiling, but not sometimes when accuracy is high. Even though the functions are similar in the middle of the range, the interpretations of the variability in the models (e.g., perceptual variability, decision process variability) are incompatible.

Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

Animals choose actions based on imperfect, ambiguous data. “Noise” inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system’s final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.

Impact of aging on the dynamics of memory retrieval: A time-course analysis

The response-signal speed-accuracy trade-off (SAT) procedure was used to provide an in-depth investigation of the impact of aging on the dynamics of short-term memory retrieval. Young and older adults studied sequentially presented 3-item lists, immediately followed by a recognition probe. Analyses of composite list and serial position SAT functions found no differences in overall accuracy, but indicated slower retrieval speed for older adults. Analysis of false alarms to recent negatives (lures from the previous study list) revealed no differences in the timing or magnitude of early false alarms that are thought to reflect familiarity-based judgments. However, onset and accrual of recollective processing required for resolving interference was slower for older adults. These findings suggest that older adults have a selective impairment on controlled and recollective retrieval operations, and further specify this impairment to arise primarily from delayed onset of cognitive control potentially coupled with reduced availability of recollective information.

Modeling fan effects on the time course of associative recognition

We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items in memory) on speed-accuracy tradeoff functions obtained in a previous response signal experiment involving briefly studied materials and in a new experiment involving well-learned materials. High fan lowered asymptotic accuracy or the rate of rise in accuracy across lags, or both. We developed an Adaptive Control of Thought-Rational (ACT-R) model for the response signal procedure to explain these effects. The model assumes that high fan results in weak associative activation that slows memory retrieval, thereby decreasing the probability that retrieval finishes in time and producing a speed-accuracy tradeoff function. The ACT-R model provided an excellent account of the data, yielding quantitative fits that were as good as those of the best descriptive model for response signal data.

What cognitive processes drive response biases? A diffusion model analysis

We used a diffusion model to examine the effects of response-bias manipulations on response time (RT) and accuracy data collected in two experiments involving a two-choice decision making task. We asked 18 subjects to respond “low” or “high” to the number of asterisks in a 10×10 grid, based on an experimenter-determined decision cutoff. In the model, evidence is accumulated until either a “low” or “high” decision criterion is reached, and this, in turn, initiates a response. We performed two experiments with four experimental conditions. In conditions 1 and 2, the decision cutoff between low and high judgments was fixed at 50. In condition 1, we manipulated the frequency with which low- and high-stimuli were presented. In condition 2, we used payoff structures that mimicked the frequency manipulation. We found that manipulating stimulus frequency resulted in a larger effect on RT and accuracy than did manipulating payoff structure. In the model, we found that manipulating stimulus frequency produced greater changes in the starting point of the evidence accumulation process than did manipulating payoff structure. In conditions 3 and 4, we set the decision cutoff at 40, 50, or 60 (Experiment 1) and at 45 or 55 (Experiment 2). In condition 3, there was an equal number of low- and high-stimuli, whereas in condition 4 there were unequal proportions of low- and high-stimuli. The model analyses showed that starting-point changes accounted for biases produced by changes in stimulus proportions, whereas evidence biases accounted for changes in the decision cutoff.

Effects of aging and IQ on item and associative memory

The effects of aging and IQ on performance were examined in 4 memory tasks: item recognition, associative recognition, cued recall, and free recall. For item and associative recognition, accuracy and the response time (RT) distributions for correct and error responses were explained by Ratcliff's (1978) diffusion model at the level of individual participants. The values of the components of processing identified by the model for the recognition tasks, as well as accuracy for cued and free recall, were compared across levels of IQ (ranging from 85 to 140) and age (college age, 60-74 years old, and 75-90 years old). IQ had large effects on drift rate in recognition and recall performance, except for the oldest participants with some measures near floor. Drift rates in the recognition tasks, accuracy in recall, and IQ all correlated strongly. However, there was a small decline in drift rates for item recognition and a large decline for associative recognition and cued recall accuracy (70%). In contrast, there were large effects of age on boundary separation and nondecision time (which correlated across tasks) but small effects of IQ. The implications of these results for single- and dual-process models of item recognition are discussed, and it is concluded that models that deal with both RTs and accuracy are subject to many more constraints than are models that deal with only one of these measures. Overall, the results of the study show a complicated but interpretable pattern of interactions that present important targets for modeling.

Functional Dedifferentiation and Altered Connectivity in Older Adults: Neural Accounts of Cognitive Aging

Aging is associated with myriad changes in behavioral performance and brain structure and function. Given this complex interplay of brain and behavior, two streams of findings are reviewed here that show that aging is generally associated with dedifferentiated neural processes, and also changes in functional connectivity. This article considers an integrated view of how such age-related dedifferentiation of neural function and changes in functional connectivity are related, highlighting some recent findings on differences in small-world architecture in the functional connectivity of young and older adults. These findings suggest that both neural connectivity and the organization of these connections are important determinants of processing efficiency with aging that may be the underlying mechanisms for dedifferentiation. Thus, the evaluation of the neurocognitive effects of aging on functional connectivity provides an alternative framework that captures the behavioral and brain changes that are observed in cognitive aging.

The effects of aging on the speed-accuracy compromise: Boundary optimality in the diffusion model

We evaluated age-related differences in the optimality of decision boundary settings in a diffusion model analysis. In the model, the width of the decision boundary represents the amount of evidence that must accumulate in favor of a response alternative before a decision is made. Wide boundaries lead to slow but accurate responding, and narrow boundaries lead to fast but inaccurate responding. There is a single value of boundary separation that produces the most correct answers in a given period of time, and we refer to this value as the reward rate optimal boundary (RROB). We consistently found across a variety of decision tasks that older adults used boundaries that were much wider than the RROB value. Young adults used boundaries that were closer to the RROB value, although age differences in optimality were smaller with instructions emphasizing speed than with instructions emphasizing accuracy. Young adults adjusted their boundary settings to more closely approach the RROB value when they were provided with accuracy feedback and extensive practice. Older participants showed no evidence of making boundary adjustments in response to feedback or task practice, and they consistently used boundary separation values that produced accuracy levels that were near asymptote. Our results suggest that young adults attempt to balance speed and accuracy to achieve the most correct answers per unit time, whereas older adultts attempt to minimize errors even if they must respond quite slowly to do so.

Individual differences, aging, and IQ in two-choice tasks

The effects of aging and IQ on performance were examined in three two-choice tasks: numerosity discrimination, recognition memory, and lexical decision. The experimental data, accuracy, correct and error response times, and response time distributions, were well explained by Ratcliff's (1978) diffusion model. The components of processing identified by the model were compared across levels of IQ (ranging from 83 to 146) and age (college students, 60-74, and 75-90 year olds). Declines in performance with age were not significantly different for low compared to high IQ subjects. IQ but not age had large effects on the quality of the evidence that was obtained from a stimulus or memory, that is, the evidence upon which decisions were based. Applying the model to individual subjects, the components of processing identified by the model for individuals correlated across tasks. In addition, the model's predictions and the data were examined for the "worst performance rule", the finding that age and IQ have larger effects on slower responses than faster responses.

Assessment of motor function of the hand in aged rhesus monkeys

In the elderly, intact motor functions of the upper extremity are critical for the completion of activities of daily living. Many studies have provided insight into age-related changes in motor function. However, the precise nature and extent of motor impairments of the upper extremity remains unclear. In the current study we have modified two tasks to assess hand/digit function in both young and aged rhesus monkeys. We tested monkeys from 9 to 26 years of age on these tasks to determine the level of fine motor performance across the adult age range. Compared to young monkeys (9-12 years of age), aged monkeys (15-26 years of age) were mildly impaired on fine motor control of the digits. These findings are consistent with previous studies that have found age-related impairment in fine motor function. However, the magnitude and extent of impairment in the current study does differ from previous findings and is likely due to methodological differences in the degree of task complexity.