No abstract

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.

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.

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 muscle quantified cortical excitability before and after PAS. MEP amplitude increased significantly 15 minutes 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 boost 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. Highlights• Plasticity can be induced in human cortex by repeated pairing of stimuli.• This form of induced plasticity mimics behavioral associative learning. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2• We manipulated stimulus uncertainty, which influences learning and plasticity.• When stimulus uncertainty was high, plasticity was enhanced in human motor cortex.

An accurate quantification of effect sizes for an experimental manipulation has the power to motivate theory, and to reduce misinvestment in scientific resources by informing power calculations during study planning. Such a quantification could theoretically be achieved by a meta-analysis. However a combination of publication bias and small sample sizes (~N = 25) hampers certainty that such an analysis would yield a non-erroneous estimate. We sought to determine the extent to which each of these caveats may produce error in effect size estimates for 4 commonly used paradigms assessing attention, executive function and implicit learning (attentional blink (AB), multitasking (MT), contextual cueing (CC), serial response task (SRT)). We combined a large dataset with a bootstrapping approach to simulate 1000 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 values of N can lead to problematic information loss, potentially biasing power calculations. Furthermore, we show that for the CC and SRT, a meta-analysis of experiments with lower N is unlikely to ever converge on the true effect size, owing to underspecification of the mapping between theory and statistical model. 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 when qualitative individual differences exist in response to an experimental manipulation.