Quantifying error in effect size estimates in attention, executive function, and implicit learning.

Accurate quantification of effect sizes has the power to motivate theory and reduce misinvestment of scientific resources by informing power calculations during study planning. However, a combination of publication bias and small sample sizes (∼N = 25) hampers certainty in current effect size estimates. We sought to determine the extent to which sample sizes may produce errors in effect size estimates for four commonly used paradigms assessing attention, executive function, and implicit learning (attentional blink, multitasking, contextual cueing, and serial response task). We combined a large data set with a bootstrapping approach to simulate 1,000 experiments across a range of N (13-313). Beyond quantifying the effect size and statistical power that can be anticipated for each study design, we demonstrate that experiments with lower N may double or triple information loss. We also show that basing power calculations on effect sizes from similar studies yields a problematically imprecise estimate between 40% and 67% of the time, given commonly used sample sizes. Last, we show that skewness of intersubject behavioral effects may serve as a predictor of an erroneous estimate. We conclude with practical recommendations for researchers and demonstrate how our simulation approach can yield theoretical insights that are not readily achieved by other methods such as identifying the information gained from rejecting the null hypothesis and quantifying the contribution of individual variation to error in effect size estimates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

State-dependent effects of neural stimulation on brain function and cognition.

Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such 'state-dependent' changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.

Effects of tDCS on visual statistical learning.

Visual statistical learning describes the encoding of structure in sensory input, and it has important consequences for cognition and behaviour. Higher-order brain regions in the prefrontal and posterior parietal cortices have been associated with statistical learning behaviours. Yet causal evidence of a cortical contribution remains limited. In a recent study, the modulation of cortical activity by transcranial direct current stimulation (tDCS) disrupted statistical learning in a spatial contextual cueing phenomenon; supporting a cortical role. Here, we examined whether the same tDCS protocol would influence statistical learning assessed by the Visual Statistical Learning phenomenon (i.e., Fiser and Aslin, 2001), which uses identity-based regularities while controlling for spatial location. In Experiment 1, we employed the popular exposure-test design to tap the learning of structure after passive viewing. Using a large sample (N = 150), we found no effect of the tDCS protocol when compared to a sham control nor to an active control region. In Experiment 2 (N = 80), we developed an online task that was sensitive to the timecourse of learning. Under these task conditions, we did observe a stimulation effect on learning, consistent with the previous work. The way tDCS affected learning appeared to be task-specific; expediting statistical learning in this case. Together with the existing evidence, these findings support the hypothesis that cortical areas are involved in the visual statistical learning process, and suggest the mechanisms of cortical involvement may be task-dependent and dynamic across time.

Cathodal electrical stimulation of frontoparietal cortex disrupts statistical learning of visual configural information.

Attentional performance is facilitated by exploiting regularities and redundancies in the environment by way of incidental statistical learning. For example, during visual search, response times to a target are reduced by repeating distractor configurations-a phenomenon known as contextual cueing (Chun & Jiang, 1998). A range of neuroscientific methods have provided evidence that incidental statistical learning relies on subcortical neural structures associated with long-term memory, such as the hippocampus. Functional neuroimaging studies have also implicated the prefrontal cortex (PFC) and posterior parietal cortex (PPC) in contextual cueing. However, the extent to which these cortical regions are causally involved in statistical learning remains unclear. Here, we delivered anodal, cathodal, or sham transcranial direct current stimulation (tDCS) to the left PFC and left PPC online while participants performed a contextual cueing task. Cathodal stimulation of both PFC and PPC disrupted the early cuing effect, relative to sham and anodal stimulation. These findings causally implicate frontoparietal regions in incidental statistical learning that acts on visual configural information. We speculate that contextual cueing may rely on the availability of cognitive control resources in frontal and parietal regions.

Stimulus uncertainty enhances long-term potentiation-like plasticity in human motor cortex.

Plasticity can be induced in human cortex using paired associative stimulation (PAS), which repeatedly and predictably pairs a peripheral electrical stimulus with transcranial magnetic stimulation (TMS) to the contralateral motor region. Many studies have reported small or inconsistent effects of PAS. Given that uncertain stimuli can promote learning, the predictable nature of the stimulation in conventional PAS paradigms might serve to attenuate plasticity induction. Here, we introduced stimulus uncertainty into the PAS paradigm to investigate if it can boost plasticity induction. Across two experimental sessions, participants (n = 28) received a modified PAS paradigm consisting of a random combination of 90 paired stimuli and 90 unpaired (TMS-only) stimuli. Prior to each of these stimuli, participants also received an auditory cue which either reliably predicted whether the upcoming stimulus was paired or unpaired (no uncertainty condition) or did not predict the upcoming stimulus (maximum uncertainty condition). Motor evoked potentials (MEPs) evoked from abductor pollicis brevis (APB) muscle quantified cortical excitability before and after PAS. MEP amplitude increased significantly 15 min following PAS in the maximum uncertainty condition. There was no reliable change in MEP amplitude in the no uncertainty condition, nor between post-PAS MEP amplitudes across the two conditions. These results suggest that stimulus uncertainty may provide a novel means to enhance plasticity induction with the PAS paradigm in human motor cortex. To provide further support to the notion that stimulus uncertainty and prediction error promote plasticity, future studies should further explore the time course of these changes, and investigate what aspects of stimulus uncertainty are critical in boosting plasticity.

Associative plasticity in the human motor cortex is enhanced by concurrently targeting separate muscle representations with excitatory and inhibitory protocols.

Paired associative stimulation (PAS) induces changes in the excitability of human sensorimotor cortex that outlast the procedure. PAS typically involves repeatedly pairing stimulation of a peripheral nerve that innervates an intrinsic hand muscle with transcranial magnetic stimulation over the representation of that muscle in the primary motor cortex. Depending on the timing of the stimuli (interstimulus interval of 25 or 10 ms), PAS leads to either an increase (PAS25) or a decrease (PAS10) in excitability. Both protocols, however, have been associated with an increase in excitability of nearby muscle representations not specifically targeted by PAS. Based on these spillover effects, we hypothesized that an additive, excitability-enhancing effect of PAS25 applied to one muscle representation may be produced by simultaneously applying PAS25 or PAS10 to a nearby representation. In different experiments prototypical PAS25 targeting the left thumb representation [abductor pollicis brevis (APB)] was combined with either PAS25 or PAS10 applied to the left little finger representation [abductor digiti minimi (ADM)] or, in a control experiment, with PAS10 also targeting the APB. In an additional control experiment PAS10 targeted both representations. The plasticity effects were quantified by measuring the amplitude of motor evoked potentials (MEPs) recorded before and after PAS. As expected, prototypical PAS25 was associated with an increase in MEP amplitude in the APB muscle. This effect was enhanced when PAS also targeted the ADM representation but only when a different interstimulus timing (PAS10) was used. These results suggest that PAS-induced plasticity is modified by concurrently targeting separate motor cortical representations with excitatory and inhibitory protocols.