Effects of aging and IQ on item and associative memory

Manipulating and measuring variation in deep neural network (DNN) representations of objects

We explore how DNNs can be used to develop a computational understanding of individual differences in high-level visual cognition given their ability to generate rich meaningful object representations informed by their architecture, experience, and training protocols. As a first step to quantifying individual differences in DNN representations, we systematically explored the robustness of a variety of representational similarity measures: Representational Similarity Analysis (RSA), Centered Kernel Alignment (CKA), and Projection-Weighted Canonical Correlation Analysis (PWCCA), with an eye to how these measures are used in cognitive science, cognitive neuroscience, and vision science. To manipulate object representations, we next created a large set of models varying in random initial weights and random training image order, training image frequencies, training category frequencies, and model size and architecture and measured the representational variation caused by each manipulation. We examined both small (All-CNN-C) and commonly-used large (VGG and ResNet) DNN architectures. To provide a comparison for the magnitude of representational differences, we established a baseline based on the representational variation caused by image-augmentation techniques used to train those DNNs. We found that variation in model randomization and model size never exceeded baseline. By contrast, differences in training image frequency and training category frequencies caused representational variation that exceeded baseline, with training category frequency manipulations exceeding baseline earlier in the networks. These findings provide insights into the magnitude of representational variations that can be expected with a range of manipulations and provide a springboard for further exploration of systematic model variations aimed at modeling individual differences in high-level visual cognition.

Information accumulation on the item versus source test of source monitoring: Insights from diffusion modeling

Source monitoring involves attributing previous experiences (e.g., studied words as items) to their origins (e.g., screen positions as sources). The present study aimed toward a better understanding of temporal aspects of item and source processing. Participants made source decisions for recognized items either in succession (i.e., the standard format) or in separate test blocks providing independent measures of item and source decision speed. Comparable speeds of item and source decision across the test formats would suggest a full separation between item and source processing, whereas different speeds would imply their (partial) temporal overlap. To test these alternatives, we used the drift rate parameter of the diffusion model (Ratcliff, Psychological Review, 85, 59-108, 1978). We examined whether the drift rates, together with the other parameters, assessed separately for the item and source decision varied as a function of the test format. Threshold separation and nondecision time differed between the test formats, but item and source decision speeds represented by drift rates did not change significantly. Thus, despite facilitation on the source decision when the item decision was immediately followed by a test for source memory than when item and source were tested in separate blocks, findings did not suggest that source information already begins accumulating in the item test in the standard format. We discuss the temporal sequence of item and source processing in light of different assumptions about the contribution of familiarity and recollection.

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Word or pseudoword? The lexicality effect in naming and lexical decision tasks during advanced aging

Although there is evidence that recognizing pseudowords is more difficult than recognizing words during childhood, adulthood, and early old age (60-75 years), it is not yet clear what happens during advanced aging or the fourth age, a stage when the decline of fluid intelligence strongly affects processing speed, but a good performance of crystallized intelligence is described through an increase in vocabulary and knowledge. The objective of this study was to determine the lexicality effect in advanced aging, specifically exploring how the ability to recognize words and pseudowords (ortho-phonologically plausible for Spanish) is affected during the third and fourth-ages. The lexicality effect was measured using naming and lexical decision tasks. Response time and accuracy were compared between a fourth-age group (80+ years) and two third-age groups (60-69 and 70-79 years) through linear regression models. The results showed that, in general, the fourth-age group had longer response times and reduced accuracy when recognizing words and pseudowords. Moreover, they showed a significant lexicality effect (which increases from the third- age onwards), reflected in higher costs during pseudoword recognition, especially when the task required more cognitive effort (lexical decision task). These results were consistent with the impact of the deterioration of fluid intelligence on the speed of lexical recognition and with the better performance that crystallized intelligence can generate on accuracy, especially in the early stages of old age. Additionally, this study supports the fact that pseudoword recognition resists cognitive decline, as accentuated deterioration is visualized only after 80 years.

The role of metacognitive uncertainty in the delayed retrieval shift of older adults

Strategic choice behavior of older adults in many skill acquisition tasks can be characterized as a delayed and/or incomplete shift to a more efficient retrieval-based strategy, even when older adults possess the requisite knowledge to use it successfully. The noun-pair lookup task (NPLT) requires verification of whether a centrally presented word pair matches one of a set of pairs displayed at the top of the screen. Because the pairings do not change, verification can be made from memory as the associations are learned. This study examines the role of metacognitive uncertainty in explaining older adults' delayed retrieval shift in the NPLT. Older and younger adults participated in a NPLT with previously learned items and new items. For each trial, the look-up table was shown only if the scanning strategy was selected. Some participants were given a precue informing whether the item had been previously learned. Retrieval strategy choice was low for older adults but precueing increased its frequency. Older adults' retrieval choices had minimal costs on NPLT accuracy, suggesting that the delayed retrieval shift can be exacerbated by metacognitive uncertainty that was reduced by precueing. The role of metacognitive uncertainty in older adults' retrieval avoidance was supported by a robust item-level regression effect of retrospective confidence judgments during prelearning tests and later NPLT retrieval strategy choices for older adults.

Variability across subjects in free recall versus cued recall

Memory scientists usually compare mean performance on some measure(s) (accuracy, confidence, latency) as a function of experimental condition. Some researchers have made within-subject variability in task performance a focal outcome measure (e.g., Yao et al., Journal of Clinical and Experimental Neuropsychology, 38, 227-237, 2016). Here, we explored between-subject variability in accuracy as a function of experimental conditions. This work was inspired by an incidental finding in a previous study, in which we observed greater variability in accuracy of memory performance on cued recall (CR) versus free recall (FR) of English animal/object nouns (Mah et al., Frontiers in Psychology, 14, 1146200, 2023). Here we report experiments designed to assess the reliability of that pattern and to explore its causes (e.g., differential interpretation of instructions, [un]relatedness of CR word pairs, encoding time). In Experiment 1 (N = 120 undergraduates), we replicated the CR:FR variability difference with a more representative set of English nouns. In Experiments 2A (N = 117 Prolific participants) and 2B (N = 127 undergraduates), we found that the CR:FR variability difference persisted in a forced-recall procedure. In Experiment 3 (N = 260 Prolific participants), we used meaningfully related word pairs and still found greater variability in CR than in FR performance. In Experiment 4 (N = 360 Prolific participants), we equated CR and FR study phases by having all participants study pairs and, again, observed greater variability in CR than FR. The same was true in Experiment 5 (N = 120 undergraduates), in which study time was self-paced. Comparisons of variability across subjects can yield insights into the mechanisms underlying task performance.

Cognitive strategies in matrix-reasoning tasks: State of the art

Cognitive strategies in matrix-reasoning tasks have been investigated for the last decade and a half. Several steps were made since the first paper in the field, but the advances have been sparse and with little connection. Here we present a review of the state of the art in this subject. We introduce how this topic was born and how to measure these strategies, covering the diverse methods and measures that are presented in the literature to progress on this subject and the applications that were developed, as well as the knowledge that resulted from these applications. Furthermore, the future directions are discussed with the intention to engage new researchers in this topic, as well as to bring awareness to limitations that were found given the available scientific literature.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

The Mediatory Role of Executive Functioning on the Association Between Sleep and Both Everyday Memory and ADHD Symptoms in Children and Youth With Down Syndrome

People with Down syndrome (DS) commonly experience challenges with sleep, executive functioning, everyday memory, and symptoms of attention deficit hyperactivity disorder (ADHD). A path analysis was conducted to determine if executive function mediated the relationship between sleep problems and both everyday memory and ADHD symptoms. Parents of 96 children and youth with DS completed questionnaires related to sleep, executive functioning, everyday memory, and ADHD symptoms. Results showed that executive functioning fully mediated the relation between sleep and both everyday memory and ADHD symptoms. Implications for education and intervention for children and youth with DS are discussed.

Integrated diffusion models for distance effects in number memory

I evaluated three models for the representation of numbers in memory. These were integrated with the diffusion decision model to explain accuracy and response time (RT) data from a recognition memory experiment in which the stimuli were two-digit numbers. The integrated models accounted for distance/confusability effects: when a test number was numerically close to a studied number, accuracy was lower and RTs were longer than when a test number was numerically far from a studied number. For two of the models, the representations of numbers are distributed over number (with Gaussian or exponential distributions) and the overlap between the distributions of a studied number and a test number provides the evidence (drift rate) on which a decision is made. For the third, the exponential gradient model, drift rate is an exponential function of the numerical distance between studied and test numbers. The exponential gradient model fit the data slightly better than the two overlap models. Monte Carlo simulations showed that the variability in the important parameter estimates from fitting data collected over 30-40 min is smaller than the variability among individuals, allowing differences among individuals to be studied. A second experiment compared number memory and number discrimination tasks and results showed different distance effects. Number memory had an exponential-like distance-effect and number discrimination had a linear function which shows radically different representations drive the two tasks.

Constraining functional coactivation with a cluster-based structural connectivity network

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.

Visual word recognition among oldest old people: The effect of age and cognitive load

During the fourth age, a marked physiological deterioration and critical points of dysfunction are observed, during which cognitive performance exhibits a marked decline in certain skills (fluid intelligence) but good performance of others (crystallized intelligence). Experimental evidence describes important constraints on word production during old age, accompanied by a relative stabilization of speech comprehension. However, cognitive changes associated with advanced aging could also affect comprehension, particularly word recognition. The present study examines how the visual recognition of words is affected during the fourth age when tasks involving different cognitive loads are applied. Through linear regression models, performance was compared between two third-age groups and a fourth-age group on reaction time (RT) and accuracy in naming, priming and lexical decision experiments. The fourth-age group showed a significant RT increase in all experiments. In contrast, accuracy was good when the task involved a low cognitive demand (Experiments 1 and 2); however, when a decisional cognitive factor was included (Experiment 3), the fourth-age group performed significantly worse than the younger third-age group. We argue that the behavior observed among fourth-age individuals is consistent with an unbalanced cognitive configuration, in which the fluid intelligence deficit significantly reduces the speed necessary to recognize words, independent of the cognitive load associated with the test. In contrast, the maintenance in crystallized intelligence improves the accuracy of the process, strengthening linguistic functionality in the advanced stages of old age.

Practice-related changes in perceptual evidence accumulation correlate with changes in working memory

It has been proposed that evidence accumulation determines not only the speed and accuracy of simple perceptual decisions but also influences performance on tasks assessing higher-order cognitive abilities, such as working memory (WM). Accordingly, estimates of evidence accumulation based on diffusion decision modeling of perceptual decision-making tasks have been found to correlate with WM performance. Here we use diffusion decision modeling in combination with latent factor modeling to test the stronger prediction that practice-induced changes in evidence accumulation correlate with changes in WM performance. Analyses are based on data from the COGITO Study, in which 101 young adults practiced a battery of cognitive tasks, including three simple two-choice reaction time tasks and three WM tasks, in 100 day-to-day training sessions distributed over 6 months. In initial analyses, drift rates were found to correlate across the three choice tasks, such that latent factors of evidence accumulation could be established. These latent factors of evidence accumulation were positively correlated with latent factors of practiced and unpracticed WM tasks, both before and after practice. As predicted, individual differences in changes of evidence accumulation correlated positively with changes in WM performance. Our findings support the proposition that decision making and WM both rely on the active maintenance of task-relevant internal representations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The effect of aging on decision-making while driving: A diffusion model analysis

We present a diffusion model analysis of the effect of aging on decision processes during driving. Our goal was to examine the changes in the underlying components as a function of age and both task and environment difficulty. Younger and older adults performed each of three decision-making tasks while operating a computer-based driving simulator in which the task required a driving action. The first task was a one-choice task in which the response to brake lights turning on was to drive around a lead car. The second and third tasks were two-choice brightness-discrimination tasks in which participants were asked to drive the car to the left/right if there were more black/white pixels in an array of black and white pixels. Results showed that older adults were slower in the one-choice task and made more errors in the two-choice tasks than younger adults. The behavioral data were fitted well by one- and two-choice diffusion models, showing lower evidence accumulation rates (drift rates) in older than younger adults. Moreover, in the two-choice tasks under higher environmental demands, older adults showed a lower decision criterion (boundary separation) to compensate for a slower decision process. Together, the differences we found in the decision components between age groups provided an example of a subtle interaction between speed and accuracy in older versus younger adults, and this demonstrates the utility of this modeling approach in studying age effects in driving. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Discriminating memory disordered patients from controls using diffusion model parameters from recognition memory

One hundred and five memory disordered (MD) patients and 57 controls were tested on item recognition memory and lexical decision tasks, and diffusion model analyses were conducted on accuracy and response time distributions for correct and error responses. The diffusion model fit the data well for the MD patients and control subjects, the results replicated earlier studies with young and older adults, and individual differences were consistent between the item recognition and lexical decision tasks. In the diffusion model analysis, MD patients had lower drift rates (with mild Alzheimer's [AD] patients lower than mild cognitive impairment [MCI] patients) as well as wider boundaries and longer nondecision times. These data and results were used in a series of studies to examine how well MD patients could be discriminated from controls using machine-learning techniques, linear discriminant analysis, logistic regression, and support vector machines (all of which produced similar results). There was about 83% accuracy in separating MD from controls, and within the MD group, AD patients had about 90% accuracy and MCI patients had about 68% accuracy (controls had about 90% accuracy). These methods might offer an adjunct to traditional clinical diagnosis. Limitations are noted including difficulties in obtaining a matched group of control subjects as well as the possibility of misdiagnosis of MD patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., "inhibition", "updating") through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

The critical role of interference control in metaphor comprehension evidenced by the drift-diffusion model

We address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant's detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in metaphor comprehension using the drift-diffusion model, in an attempt to provide more systematic accounts of the processes underlying metaphor comprehension. Results showed that there were significant negative correlations between response times in metaphor comprehension and scores of the Controlled Oral Word Association Test (COWAT)-Semantic, suggesting that better performances in comprehending metaphors were strongly associated with better interference control. Using the drift-diffusion model, we found that the familiarity, compared to context, had greater leverage in the decision process for metaphor comprehension. Moreover, individuals with better performance in the COWAT-Semantic test demonstrated higher drift rates. In conclusion, with more fine-grained analysis of the decisions involved in metaphor comprehension using the drift-diffusion model, we argue that interference control plays an important role in processing metaphors.

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.

Binding Costs in Processing Efficiency as Determinants of Cognitive Ability

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person's ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall-1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the relation between reaction time tasks and working memory capacity hinges on the complexity of the reaction time tasks. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability.

A diffusion model analysis of belief bias: Different cognitive mechanisms explain how cognitive abilities and thinking styles contribute to conflict resolution in reasoning

Recent results have challenged the widespread assumption of dual process models of belief bias that sound reasoning relies on slow, careful reflection, whereas biased reasoning is based on fast intuition. Instead, parallel process models of reasoning suggest that rule- and belief-based problem features are processed in parallel and that reasoning problems that elicit a conflict between rule- and belief-based problem features may also elicit more than one Type 1 response. This has important implications for individual-differences research on reasoning, because rule-based responses by certain individuals may reflect that these individuals were either more likely to give a rule-based default response or that they successfully inhibited and overrode a belief-based default response. In two studies, we used the diffusion model to describe decision making in a transitive reasoning task. In Study 1, 41 participants were asked to evaluate conclusions based on their validity. In Study 2, 133 participants evaluated conclusions based on their validity or believability. We tested which diffusion model parameters reflected conflict resolution and related those model parameters to individual differences in cognitive abilities and thinking styles. Individual differences in need for cognition predicted successful conflict resolution under logic instruction, which suggests that a disposition to engage in reflective thinking facilitates the inhibition and override of Type 1 responses. Intelligence, however, was negatively related to successful conflict resolution under belief instruction, which suggests that individuals with high cognitive abilities quickly generated a higher-level logical response that interfered with their ability to evaluate lower-level intrinsic problem features. Taken together, this double dissociation indicates that cognitive abilities and thinking styles affect the processing of conflict information through different mechanisms and at different stages: Greater cognitive abilities facilitate the efficient creation of decoupled problem representations, whereas a greater disposition to engage in critical thinking facilitates the detection and override of Type 1 responses.

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n = 272), which includes healthy participants (n = 130) as well-participants with schizophrenia (n = 50), bipolar disorder (n = 49), and attention-deficit/hyperactivity disorder (n = 43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

Examining aging and numerosity using an integrated diffusion model

Two experiments are presented that use tasks common in research in numerical cognition with young adults and older adults as subjects. In these tasks, one or two arrays of dots are displayed, and subjects decide whether there are more or fewer dots of one kind than another. Results show that older adults, relative to young adults, tend to rely more on the perceptual feature, area, in making numerosity judgments when area is correlated with numerosity. Also, convex hull unexpectedly shows different effects depending on the task (being either correlated with numerosity or anticorrelated). Accuracy and response time (RT) data are interpreted with the integration of the diffusion decision model with models for the representation of numerosity. One model assumes that the representation of the difference depends on the difference between the numerosities and that standard deviations (SDs) increase linearly with numerosity, and the other model assumes a log representation with constant SDs. The representational models have coefficients that are applied to differences between two numerosities to produce drift rates and SDs in drift rates in the decision process. The two tasks produce qualitatively different patterns of RTs: One model fits results from one task, but the results are mixed for the other task. The effects of age on model parameters show a modest decrease in evidence driving the decision process, an increase in the duration of processes outside the decision process (nondecision time), and an increase in the amount of evidence needed to make a decision (boundary separation). (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Reversal learning in young and middle-age neurotypicals: Individual difference reaction time considerations

Reversal learning is frequently used to assess components of executive function that contribute to understanding age-related cognitive differences. Reaction time (RT) is less characterized in the reversal learning literature, perhaps due to the daunting task of analyzing the entire RT distribution, but has been deemed a generally sensitive measure of cognitive aging. The current study extends our prior work to further characterize distributional properties of the reversal RT distribution and to distinguish groups of individuals with fractionated profiles of performance, which may be of clinical importance within the context of cognitive aging. Participant sample included young (n = 43) and community-dwelling, healthy, middle-aged (n = 139) adults. To explore individual differences, recursive partitioning analysis achieved a high classification rate by specifying decision tree rules that split participants into young and middle-aged groups. Mu (μ, efficient RT) was the most successful parameter in distinguishing age groups while sigma ( σ ) and tau ( τ , ex-Gaussian indices of intra-individual variability) revealed more subtle individual differences. Accuracy measures did not contribute to separating the groups, suggesting that fractionated components of RT, as opposed to accuracy, can distinguish differences between young and middle-aged participants.

Hippocampal formation volume, its subregions, and its specific contributions to visuospatial memory tasks

Visuospatial memory (VSM) is the ability to represent and manipulate visual and spatial information. This cognitive function depends on the functioning of the hippocampal formation (HF), located in the medial portion of the temporal cortex. The present study aimed to investigate whether there is an association between the volume of the HF and performance in VSM tests. High-resolution structural images (T1) and neuropsychological tests evaluating VSM were performed on 31 healthy individuals. A VSM index was created by grouping 5 variables from 5 tasks (4 from the CANTAB battery and 1 from the Rey-Osterrieth Complex Figure test). Multiple linear regression models using the volumes of HF subregions as independent variables and the VSM index as the dependent variable were conducted to test the hypothesis that memory performance could be predicted by HF volumes. We also conducted analyses to explore the role of covariates that may mediate this relationship, specifically age and intelligence quotient (IQ). We found significant associations between the hippocampal subregions of the left hemisphere and the VSM index (F(7,22)=2.758, P=0.032, R2a=0.298). When IQ was accounted for as a covariate, we also found significant results for the right hemisphere (F(8,21)=2.804, P=0.028, R2a=0.517). We concluded that the bilateral hippocampal formations contributed to performance on VSM tasks. Also, VSM processing is essential for a diverse set of daily activities and may be influenced by demographic variables in healthy subjects.

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).

Centroparietal activity mirrors the decision variable when tracking biased and time-varying sensory evidence

Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

A diffusion model analysis of sustained attention in children with attention deficit hyperactivity disorder

OBJECTIVE

Whether children with attention deficit hyperactivity disorder (ADHD) have deficits in sustained attention remains unresolved due to the ongoing use of cognitive paradigms that are not optimized for studying vigilance and the fact that relatively few studies report performance over time.

METHOD

In three independent samples of school-age children with (total N = 128) and without ADHD (total N = 59), we manipulated event rate, difficulty of discrimination, and use signal detection (SDT) and diffusion models (DM) to evaluate the cause of the vigilance decrement during a continuous performance task.

RESULTS

For both groups of children, a bias toward "no-go" over time (as indexed by the SDT parameter B″ and the DM parameter z/a) was responsible for generating the vigilance decrement. However, among children with ADHD, the rate at which information accumulated to make a no-go decision (vNoGo) also increased with time on task, representing a possible secondary mechanism that biases children against engagement. At all time points, children with ADHD demonstrated reduced sensitivity to discriminate targets from nontargets.

CONCLUSION

Children with ADHD are particularly sensitive to the cost of task engagement, but nonspecific slower drift rate may ultimately provide a better conceptualization of the cognitive atypicalities commonly observed in that group. Results are interpreted in the context of updated conceptualizations of sustained attention and vigilance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Risk- and protective factors for memory plasticity in aging

Risk and protective factors for cognitive function in aging may affect how much individuals benefit from their environment or life experiences by preserving or improving cognitive abilities. We investigated the relations between such factors and outcome from episodic-memory training in 136 healthy young and older adults. Tested risk factors included carrying the ɛ4 variant of the apolipoprotein E allele (APOE), age, body mass index, blood pressure, and cholesterol. Protective factors included higher levels of education, intelligence quotient (IQ), physical activity, fatty acids, and vitamin D. Average increases in memory performance were seen after training, with ample variation between individuals. Being young, female, and having higher IQ were positive predictors of memory improvement. No other relationships were observed. Similar benefit was observed across APOE allelic variation. This indicates that beyond IQ, age, and sex, known risk -and protective factors of cognitive function in aging were not significantly related to memory plasticity.

Decision making in numeracy tasks with spatially continuous scales

A diffusion model of decision making on continuous response scales is applied to three numeracy tasks. The goal is to explain the distributions of responses on the continuous response scale and the time taken to make decisions. In the model, information from a stimulus is spatially continuously distributed, the response is made by accumulating information to a criterion, which is a 1D line, and the noise in the accumulation process is continuous Gaussian process noise over spatial position. The model is fit to the data from three experiments. In one experiment, a one or two digit number is displayed and the task is to point to its location on a number line ranging from 1 to 100. This task is used extensively in research in education but there has been no model for it that accounts for both decision times and decision choices. In the second task, an array of dots is displayed and the task is to point to the position of the number of dots on an arc ranging from 11 to 90. In a third task, an array of dots is displayed and the task is to speak aloud the number of dots. The model we propose accounts for both accuracy and response time variables, including the full distributions of response times. It also provides estimates of the acuity of decisions (standard deviations in the evidence distributions) and it shows how representations of numeracy information are task-dependent. We discuss how our model relates to research on numeracy and the neuroscience of numeracy, and how it can produce more comprehensive measures of individual differences in numeracy skills in tasks with continuous response scales than have hitherto been available.

Unraveling the Relation between EEG Correlates of Attentional Orienting and Sound Localization Performance: A Diffusion Model Approach

Understanding the contribution of cognitive processes and their underlying neurophysiological signals to behavioral phenomena has been a key objective in recent neuroscience research. Using a diffusion model framework, we investigated to what extent well-established correlates of spatial attention in the electroencephalogram contribute to behavioral performance in an auditory free-field sound localization task. Younger and older participants were instructed to indicate the horizontal position of a predefined target among three simultaneously presented distractors. The central question of interest was whether posterior alpha lateralization and amplitudes of the anterior contralateral N2 subcomponent (N2ac) predict sound localization performance (accuracy, mean RT) and/or diffusion model parameters (drift rate, boundary separation, non-decision time). Two age groups were compared to explore whether, in older adults (who struggle with multispeaker environments), the brain-behavior relationship would differ from younger adults. Regression analyses revealed that N2ac amplitudes predicted drift rate and accuracy, whereas alpha lateralization was not related to behavioral or diffusion modeling parameters. This was true irrespective of age. The results indicate that a more efficient attentional filtering and selection of information within an auditory scene, reflected by increased N2ac amplitudes, was associated with a higher speed of information uptake (drift rate) and better localization performance (accuracy), while the underlying response criteria (threshold separation), mean RTs, and non-decisional processes remained unaffected. The lack of a behavioral correlate of poststimulus alpha power lateralization constrasts with the well-established notion that prestimulus alpha power reflects a functionally relevant attentional mechanism. This highlights the importance of distinguishing anticipatory from poststimulus alpha power modulations.

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined.

Does response modality influence conflict? Modelling vocal and manual response Stroop interference

In the Stroop task, color words are presented in colored fonts and the task of the subject is to either name the word or name the color. If the word and font color are in agreement, then the stimulus is said to be congruent (e.g., RED in red font color); however, if the word and font color are not in agreement, the stimulus is said to be incongruent (e.g., RED in blue font color). Conflict in the Stroop task is measured by both RT and accuracy. In prior research, the amount of conflict differs depending on the response modality, vocal versus manual. We applied a model for multichoice decision-making (and confidence), the RTCON2 model (Ratcliff & Starns, 2013) to the data from 4 experiments, 2 with 2-choice manual responses, 1 with 4-choice manual touch screen responses, 1 with both 4-choice vocal responses, and 4-choice manual keyboard responses. Changes in the rate of information accumulation captured conflict effects for the manual-response versions, but not for the vocal-response version. Adding an extra nondecision time parameter allowed RTCON2 to account for the data patterns in the vocal-response version. However, to fully understand conflict in the vocal-response Stroop task, a model of conflict processing in the vocal word production system must be developed that would explain the additional processing time in the nondecision time parameter. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Modeling memory dynamics in visual expertise

The development of visual expertise is accompanied by enhanced visual object recognition memory within an expert domain. We aimed to understand the relationship between expertise and memory by modeling cognitive mechanisms. Participants with a measured range of birding expertise were recruited and tested on memory for birds (expert domain) and cars (novice domain). Participants performed an old-new continuous recognition memory task whereby on each trial an image of a bird or car was presented that was either new or had been presented earlier with lag j. The Linear Ballistic Accumulator model (LBA; Brown & Heathcote, 2008) was first used to decompose accuracy and response time (RT) into drift rate, response threshold, and nondecision time, with the measured level of visual expertise as a potential covariate on each model parameter. An Expertise × Category interaction was observed on drift rates such that expertise was positively correlated with memory performance recognizing bird images but not car images as old versus new. To then model the underlying processes responsible for variation in drift rate with expertise, we used a model of drift rates building on the Exemplar-Based Random Walk model (Nosofsky, Cox, Cao, & Shiffrin, 2014; Nosofsky & Palmeri, 1997), which revealed that expertise was associated with increases in memory strength and increases in the distinctiveness of stored exemplars. Taken together, we provide insight using formal cognitive modeling into how improvements in recognition memory with expertise are driven by enhancements in the representations of objects in an expert domain. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A parameter recovery assessment of time-variant models of decision-making

Evidence accumulation models have been one of the most dominant modeling frameworks used to study rapid decision-making over the past several decades. These models propose that evidence accumulates from the environment until the evidence for one alternative reaches some threshold, typically associated with caution, triggering a response. However, researchers have recently begun to reconsider the fundamental assumptions of how caution varies with time. In the past, it was typically assumed that levels of caution are independent of time. Recent investigations have however suggested the possibility that levels of caution decrease over time and that this strategy provides more efficient performance under certain conditions. Our study provides the first comprehensive assessment of this newer class of models accounting for time-varying caution to determine how robustly their parameters can be estimated. We assess five overall variants of collapsing threshold/urgency signal models based on the diffusion decision model, linear ballistic accumulator model, and urgency gating model frameworks. We find that estimation of parameters, particularly those associated with caution/urgency modulation are most robust for the linearly collapsing threshold diffusion model followed by an urgency-gating model with a leakage process. All other models considered, particularly those with ballistic accumulation or nonlinear thresholds, are unable to recover their own parameters adequately, making their usage in parameter estimation contexts questionable.

Bayes factors for the linear ballistic accumulator model of decision-making

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA's inferential properties, in simulation studies.

Two types of backward crosstalk: Sequential modulations and evidence from the diffusion model

In multitasking, the backward crosstalk effect (BCE) means that Task 1 performance is influenced by characteristics of Task 2. For example, (1) RT1 is shorter when the two responses are given on the same (compatible trial) compared with opposite sides (incompatible conflict-trial; compatibility-based BCE), and (2) RT1 is longer when Task 2 is a no-go relative to a go task (no-go BCE). We investigated the impact of recently experienced trial and conflict history on the size of such BCEs. Similar to the Gratton effect in standard conflict tasks, clear sequential modulations were observed for the two kinds of BCEs, which were present following (1) compatible trials and (2) go-trials and inverted following (1) incompatible and (2) no-go trials. Furthermore, recent evidence from mental chronometry studies suggests that the compatibility-based BCE is located inside the response selection stage, while the no-go-based BCE arises in motor execution. Against this background, a diffusion model analysis was carried out to reveal the reason(s) for the sequential modulations. As expected, for the compatibility-based BCE, changes in drift rate explain the sequential modulations, but for the no-go BCE changes in non-decision time are important. The present results indicate that both BCEs not only differ fundamentally in their underlying processes, but also in the way cognitive control is adjusted.

Aging and recognition memory: A meta-analysis

Recognizing a stimulus as previously encountered is a crucial everyday life skill and a critical task motivating theoretical development in models of human memory. Although there are clear age-related memory deficits in tasks requiring recall or memory for context, the existence and nature of age differences in recognition memory remain unclear. The nature of any such deficits is critical to understanding the effects of age on memory because recognition tasks allow fewer strategic backdoors to supporting memory than do tasks of recall. Consequently, recognition may provide the purest measure of age-related memory deficit of all standard memory tasks. We conducted a meta-analysis of 232 prior experiments on age differences in recognition memory. As an organizing framework, we used signal-detection theory (Green & Swets, 1966; Macmillan & Creelman, 2005) to characterize recognition memory in terms of both discrimination between studied items and unstudied lures (d') and response bias or criterion (c). Relative to young adults, older adults showed reduced discrimination accuracy and a more liberal response criterion (i.e., greater tendency to term items new). Both of these effects were influenced by multiple, differing variables, with larger age deficits when studied material must be discriminated from familiar or related material, but smaller when studying semantically rich materials. These results support a view in which neither the self-initiation of mnemonic processes nor the deployment of strategic processes is the only source of age-related memory deficits, and they add to our understanding of the mechanisms underlying those changes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

Ageing impacts on decision-making behaviour across a range of cognitive tasks and scenarios. Computational modelling has proved valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations remains unclear. Here, we report that age-related effects on neural signatures of decision formation are inconsistent with behavioural fits derived from a prominent accumulation-to-bound model. Most notably, model-predicted bound differences were absent neurophysiologically. However, constraining the model to match the decision-predictive elements of the brain signals provided more parsimonious fits to behaviour and generated predictions regarding the neural data that were empirically validated. These included a task-dependent slowing of evidence accumulation among older adults and reduced between-trial accumulation rate variability, which was linked to enhanced attentional engagement. Our findings highlight how combining neurophysiological measurements with computational modelling can yield unique insights into group differences in neural decision mechanisms.

Impact of Context Information on Metaphor Elaboration

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

Decision making on spatially continuous scales

A new diffusion model of decision making in continuous space is presented and tested. The model is a sequential sampling model in which both spatially continuously distributed evidence and noise are accumulated up to a decision criterion (a 1 dimensional [1D] line or a 2 dimensional [2D] plane). There are two major advances represented in this research. The first is to use spatially continuously distributed Gaussian noise in the decision process (Gaussian process or Gaussian random field noise) which allows the model to represent truly spatially continuous processes. The second is a series of experiments that collect data from a variety of tasks and response modes to provide the basis for testing the model. The model accounts for the distributions of responses over position and response time distributions for the choices. The model applies to tasks in which the stimulus and the response coincide (moving eyes or fingers to brightened areas in a field of pixels) and ones in which they do not (color, motion, and direction identification). The model also applies to tasks in which the response is made with eye movements, finger movements, or mouse movements. This modeling offers a wide potential scope of applications including application to any device or scale in which responses are made on a 1D continuous scale or in a 2D spatial field. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The mapping between transformed reaction time costs and models of processing in aging and cognition

Older adults tend to have slower response times (RTs) than younger adults on cognitive tasks. This makes the examination of domain-specific deficits in aging difficult, as differences between conditions in raw RTs (RT costs) typically increase with slower average RTs. Here, we examine the mapping between 2 established approaches to dealing with this confound in the literature. The first is to use transformed RT costs, with the z-score and proportional transforms both being commonly used. The second is to use mathematical models of choice RT behavior, such as the drift-diffusion model (Ratcliff, 1978). We simulated data for younger and older adults from the drift-diffusion model under 4 scenarios: (a) a domain specific deficit, (b) general slowing, (c) strategic slowing, and (d) a slowing of nondecision processes. In each scenario we varied the size of the difference between younger and older adults in the model parameters, and examined corresponding effect sizes and Type I error rates in the raw and transformed RT costs. The z-score transformation provided better control of Type I error rates than the raw or proportional costs, though did not fully control for differences in the general slowing and strategic slowing scenarios. We recommend that RT analyses are ideally supplemented by analyses of error rates where possible, as these may help to identify the presence of confounds. To facilitate this, it would be beneficial to include conditions that elicit below ceiling accuracy in tasks.

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 influence of episodic memory decline on value-based choice

Recent studies suggest the involvement of episodic memory in value-based decisions as a source of information about subjective values of choice options. We therefore tested the link between age-related memory decline and inconsistencies in value-based decisions in 30 cognitively healthy older adults. Within the pre-registered experiment, the inconsistencies were measured in two ways: i) the consistency between stated preferences and revealed choices; ii) the amount of intransitivities in choice triplets, revealed in a forced paired choice task including all possible pairings of 20 food products. Although no significant association of memory functions to number of intransitive triplets was observed, participants with lower memory scores were more likely to choose the item for which they stated a lower preference. The results suggest a higher noise in the underlying preference signal in participants with lower memory. We discuss the results in the context of the unique needs of elderly consumers.