When Load is Low, Working Memory is Shielded From Long-Term Memory's Influence.

Previous studies found that episodic long-term memory (eLTM) enhances working memory (WM) performance when both novel and previously learnt word pairs must be retained on a short-term basis. However, there is uncertainty regarding how and when WM draws on eLTM. Three possibilities are (a) that people draw on eLTM only if WM capacity is exceeded; (b) that there is always a contribution of eLTM to WM performance, irrespective of whether prior knowledge is helpful or not; or (c) benefits of prior knowledge are specific to comparisons between conditions which are similarly ambiguous concerning whether LTM may be useful. We built on the assumption that under conditions of a contribution from LTM, these LTM traces of memoranda could benefit or hamper performance in WM tasks depending on the match between the traces stored in LTM and the ones to-be stored in WM in the current trial, yielding proactive facilitation (PF) and proactive interference (PI), respectively. Across four experiments, we familiarized participants with some items before they completed a separate WM task. In accordance with possibility (a) we show that there are indeed conditions in which only WM contributes to performance. Performance deteriorated with the addition of stimuli from eLTM when WM load was low, but not when it was high; and an exchange of information between LTM and WM occurred only when WM capacity was exceeded, with PI and PF effects affecting immediate memory performance in verbal and visual tasks only at higher set sizes.

The common factor of executive functions measures nothing but speed of information uptake.

There is an ongoing debate about the unity and diversity of executive functions and their relationship with other cognitive abilities such as processing speed, working memory capacity, and intelligence. Specifically, the initially proposed unity and diversity of executive functions is challenged by discussions about (1) the factorial structure of executive functions and (2) unfavorable psychometric properties of measures of executive functions. The present study addressed two methodological limitations of previous work that may explain conflicting results: The inconsistent use of (a) accuracy-based vs. reaction time-based indicators and (b) average performance vs. difference scores. In a sample of 148 participants who completed a battery of executive function tasks, we tried to replicate the three-factor model of the three commonly distinguished executive functions shifting, updating, and inhibition by adopting data-analytical choices of previous work. After addressing the identified methodological limitations using drift-diffusion modeling, we only found one common factor of executive functions that was fully accounted for by individual differences in the speed of information uptake. No variance specific to executive functions remained. Our results suggest that individual differences common to all executive function tasks measure nothing more than individual differences in the speed of information uptake. We therefore suggest refraining from using typical executive function tasks to study substantial research questions, as these tasks are not valid for measuring individual differences in executive functions.

A diffusion model analysis of age and individual differences in the retro-cue benefit.

The limited capacity of working memory (WM) constrains how well we can think and act. WM capacity is reduced in old age, with one explanation for this decline being a deficit in using attention to control WM contents. The retro-cue paradigm has been used to examine the ability to focus attention in WM. So far, there are conflicting findings regarding an aging deficit in the retro-cue effect. The present study evaluated age-related changes and individual differences in the retro-cue effect through a well-established computational model that combines speed and accuracy to extract underlying psychological parameters. We applied the drift-diffusion model to the data from a large sample of younger and older adults (total N = 346) that completed four retro-cue tasks. Retro-cues increased the quality of the evidence entering the decision process, reduced the time taken for memory retrieval, and changed response conservativeness for younger and older adults. An age-related decline was observed only in the retro-cue boost for evidence quality, and this was the only parameter capturing individual differences in focusing efficiency. Our results suggest that people differ in how well they can strengthen and protect a focused representation to boost evidence-quality accumulation, and this ability declines with aging.

Individual differences in updating are not related to reasoning ability and working memory capacity.

Previous research assumes that executive functions such as inhibition, shifting, and updating explain individual differences in cognitive abilities. Of these three executive functions, updating was previously found to relate most strongly to fluid intelligence. However, this relationship could be a methodological artifact: Measures of inhibition and shifting usually isolate the contribution of this executive function to performance by contrasting conditions with high and low demands on these processes, whereas updating is measured by overall accuracy in working memory tasks involving updating. This updating measure conflates updating-specific individual differences (e.g., removal of outdated information) with variance in working memory maintenance. Reanalyzing data (N = 111) from von Bastian et al. (2016), we separated updating-specific variance from working memory maintenance variance. Updating contributed only 15% to individual differences in performance in the updating tasks, and it correlated neither with fluid intelligence nor with independent working memory measures reflecting storage and processing or relational integration. In contrast, the working memory maintenance component of the updating task correlated with both abilities. These findings challenge the view that updating contributes to variance in higher cognitive abilities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In Search of the Executive Cognitive Processes Proposed by Process-Overlap Theory.

Process-Overlap Theory (POT) suggests that measures of cognitive abilities sample from sets of independent cognitive processes. These cognitive processes can be separated into domain-general executive processes, sampled by the majority of cognitive ability measures, and domain-specific processes, sampled only by measures within a certain domain. According to POT, fluid intelligence measures are related because different tests sample similar domain-general executive cognitive processes to some extent. Re-analyzing data from a study by De Simoni and von Bastian (2018), we assessed domain-general variance from executive processing tasks measuring inhibition, shifting, and efficiency of removal from working memory, as well as examined their relation to a domain-general factor extracted from fluid intelligence measures. The results showed that domain-general factors reflecting general processing speed were moderately and negatively correlated with the domain-general fluid intelligence factor (r = -.17--.36). However, domain-general factors isolating variance specific to inhibition, shifting, and removal showed only small and inconsistent correlations with the domain-general fluid intelligence factor (r = .02--.22). These findings suggest that (1) executive processing tasks sample only few domain-general executive processes also sampled by fluid intelligence measures, as well as (2) that domain-general speed of processing contributes more strongly to individual differences in fluid intelligence than do domain-general executive processes.

Intelligence test items varying in capacity demands cannot be used to test the causality of working memory capacity for fluid intelligence.

There is a strong relationship between fluid intelligence and working memory capacity (WMC). Yet, the cognitive mechanisms underlying this relationship remain elusive. The capacity hypothesis states that this relationship is due to limitations in the amount of information that can be stored and held active in working memory. Previous research aimed at testing the capacity hypothesis assumed that it implies stronger relationships of intelligence test performance with WMC for test items with higher capacity demands. The present article addresses this assumption through simulations of three theoretical models implementing the capacity hypothesis while systematically varying different psychometric variables. The results show that almost any relation between the capacity demands of items and their correlation with WMC can be obtained. Therefore, the assumption made by previous studies does not hold: The capacity hypothesis does not imply stronger correlations of WMC and intelligence test items with higher capacity demands. Items varying in capacity demands cannot be used to test the causality of WMC (or any other latent variable) for fluid intelligence.

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.

Diffusion modeling and intelligence: Drift rates show both domain-general and domain-specific relations with intelligence.

Several previous studies reported relationships between speed of information processing as measured with the drift parameter of the diffusion model (Ratcliff, 1978) and general intelligence. Most of these studies utilized only few tasks and none of them used more complex tasks. In contrast, our study (N = 125) was based on a large battery of 18 different response time tasks that varied both in content (numeric, figural, and verbal) and complexity (fast tasks with mean RTs of ca. 600 ms vs. more complex tasks with mean RTs of ca. 3,000 ms). Structural equation models indicated a strong relationship between a domain-general drift factor and general intelligence. Beyond that, domain-specific speed of information processing factors were closely related to the respective domain scores of the intelligence test. Furthermore, speed of information processing in the more complex tasks explained additional variance in general intelligence. In addition to these theoretically relevant findings, our study also makes methodological contributions showing that there are meaningful interindividual differences in content specific drift rates and that not only fast tasks, but also more complex tasks can be modeled with the diffusion model. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The validity of the online thought-probing procedure of mind wandering is not threatened by variations of probe rate and probe framing.

Recently, there has been a surge of interest in the measurement of mind wandering during ongoing tasks. The frequently used online thought-probing procedure (OTPP), in which individuals are probed on whether their thoughts are on-task or not while performing an ongoing task, has repeatedly been criticized, because variations in the frequency of thought probes and the order in which on-task and off-task thoughts are referred to have been shown to affect mind-wandering rates. Hitherto, it is unclear whether this susceptibility to measurement variation only affects mean response rates in probe-caught mind wandering or poses an actual threat to the validity of the OTPP, endangering the replicability and generalizability of study results. Here, we show in a sample of 177 students that variations of the frequency or framing of thought probes do not affect the validity of the OTPP. While we found that more frequent thought probing reduced the rate of probe-caught mind wandering, we did not replicate the effect that mind wandering is more likely to be reported when off-task thoughts are referred to first rather than second. Crucially, associations between probe-caught mind wandering and task performance, as well as associations between probe-caught mind wandering and covariates (trait mind wandering, reaction-time variability in the metronome-response task, and working-memory capacity) did not change with variations of the probing procedure. Therefore, it seems unlikely that the great heterogeneity in the way the OTPP is implemented across different studies endangers the replicability and generalizability of study results. Data and analysis code are available at https://osf.io/7w8bm/ .

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.

Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application.

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

Is general intelligence little more than the speed of higher-order processing?

Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage. (PsycINFO Database Record