Attentional control as an ability to regulate information processing during goal-directed behavior is critical to many theories of human cognition and thought to predict a large range of everyday behaviors. However, in recent years, failures to reliably assess individual differences in attentional control have sparked a debate concerning whether attentional control, as currently conceptualized and assessed, can be regarded as a valid psychometric construct. In this consensus paper, we summarize the current debate from theoretical, methodological, and analytical perspectives. First, we propose a consensus-based definition of attentional control and the cognitive mechanisms that potentially contribute to individual differences in attentional control. Next, guided by the findings of an in-depth literature survey, we discuss the psychometric considerations that are critical when assessing attentional control. We then provide suggestions for recent methodological and analytical approaches that can alleviate the most common concerns. We conclude that, to truly advance our understanding of the construct of attentional control, we must develop a theory-driven and empirically supported consensus on how we define, operationalize, and assess attentional control. This consensus paper presents a first step to achieve this goal; a shift toward transparent reporting, sharing of materials and data, and cross-laboratory efforts will further accelerate progress.

The decreased self-reported MW in older adults and the further decline in AD are consistent with the cognitive resource account of MW. Behavioral indices suggest that AD is on a continuum with healthy aging, with the exception of posterror slowing that may suggest performance monitoring deficits in early AD individuals (e.g., lack of error awareness). (PsycINFO Database Record

Research into the nature of 1/f-like, nonoscillatory electrophysiological activity has grown exponentially in recent years in cognitive neuroscience. The shape of this activity has been linked to the balance between excitatory and inhibitory neural circuits, which is thought to be important for information processing. However, to date, it is not known whether the presentation of a stimulus induces changes in the parameters of 1/f activity in scalp recordings, separable from event-related potentials (ERPs). Here, we analyzed event-related broadband changes in human EEG both before and after removing ERPs to demonstrate their confounding effect, and to establish whether there are genuine stimulus-induced changes in 1/f. Using data from a passive and an active auditory task (n = 23, 61% female), we found that the shape of the post-event spectra between 2 and 25 Hz differed significantly from the pre-event spectra even after removing the frequency-content of ERPs. Further, a significant portion of this difference could be accounted for by a rotational shift in 1/f activity, manifesting as an increase in low and a decrease in high frequencies. Importantly, the magnitude of this rotational shift was related to the attentional demands of the task. This change in 1/f is consistent with increased inhibition following stimulus onset, and likely reflects a disruption of ongoing excitatory activity proportional to processing demands. Finally, these findings contradict the central assumption of baseline normalization strategies in time-frequency analyses, namely, that background EEG activity is stationary across time. As such, they have far-reaching consequences relevant for several subfields of neuroscience.

The resting-state human electroencephalogram (EEG) power spectrum is dominated by alpha (8–12 Hz) and theta (4–8 Hz) oscillations, and also includes non-oscillatory broadband activity inversely related to frequency (1/f activity). Gratton proposed that alpha and theta oscillations are both related to cognitive control function, though in a complementary manner. Alpha activity is hypothesized to facilitate the maintenance of representations, such as task sets in preparation for expected task conditions. In contrast, theta activity would facilitate changes in representations, such as the updating of task sets in response to unpredicted task demands. Therefore, theta should be related to reactive control (which may prompt changes in task representations), while alpha may be more relevant to proactive control (which implies the maintenance of current task representations). Less is known about the possible relationship between 1/f activity and cognitive control, which was analyzed here in an exploratory fashion. To investigate these hypothesized relationships, we recorded eyes-open and eyes-closed resting-state EEG from younger and older adults and subsequently tested their performance on a cued flanker task, expected to elicit both proactive and reactive control processes. Results showed that alpha power and 1/f offset were smaller in older than younger adults, whereas theta power did not show age-related reductions. Resting alpha power and 1/f offset were associated with proactive control processes, whereas theta power was related to reactive control as measured by the cued flanker task. All associations were present over and above the effect of age, suggesting that these resting-state EEG correlates could be indicative of trait-like individual differences in cognitive control performance, which may be already evident in younger adults, and are still similarly present in healthy older adults.

Typically, time-frequency analysis (TFA) of electrophysiological data is aimed at isolating narrowband signals (oscillatory activity) from broadband non-oscillatory (1/ f ) activity, so that changes in oscillatory activity resulting from experimental manipulations can be assessed. A widely used method to do this is to convert the data to the decibel (dB) scale through baseline division and log transformation. This procedure assumes that, for each frequency, sources of power (i.e., oscillations and 1/ f activity) scale by the same factor relative to the baseline (multiplicative model). This assumption may be incorrect when signal and noise are independent contributors to the power spectrum (additive model). Using resting-state EEG data from 80 participants, we found that the level of 1/ f activity and alpha power are not positively correlated within participants, in line with the additive but not the multiplicative model. Then, to assess the effects of dB conversion on data that violate the multiplicativity assumption, we simulated a mixed design study with one between-subject (noise level, i.e., level of 1/ f activity) and one within-subject (signal amplitude, i.e., amplitude of oscillatory activity added onto the background 1/ f activity) factor. The effect size of the noise level × signal amplitude interaction was examined as a function of noise difference between groups, following dB conversion. Findings revealed that dB conversion led to the over- or under-estimation of the true interaction effect when groups differing in 1/ f levels were compared, and it also led to the emergence of illusory interactions when none were present. This is because signal amplitude was systematically underestimated in the noisier compared to the less noisy group. Hence, we recommend testing whether the level of 1/ f activity differs across groups or conditions and using multiple baseline correction strategies to validate results if it does. Such a situation may be particularly common in aging, developmental, or clinical studies.