Processing of a given target is facilitated when it is defined within the same (e.g., visual–visual), compared to a different (e.g., tactile–visual), perceptual modality as on the previous trial [Spence, C., Nicholls, M., & Driver, J. The cost of expecting events in the wrong sensory modality. Perception & Psychophysics, 63, 330–336, 2001]. The present study was designed to identify electrocortical (EEG) correlates underlying this “modality shift effect.” Participants had to discriminate (via foot pedal responses) the modality of the target stimulus, visual versus tactile (Experiment 1), or respond based on the target-defining features (Experiment 2). Thus, modality changes were associated with response changes in Experiment 1, but dissociated in Experiment 2. Both experiments confirmed previous behavioral findings with slower discrimination times for modality change, relative to repetition, trials. Independently of the target-defining modality, spatial stimulus characteristics, and the motor response, this effect was mirrored by enhanced amplitudes of the anterior N1 component. These findings are explained in terms of a generalized “modality-weighting” account, which extends the “dimension-weighting” account proposed by Found and Müller [Searching for unknown feature targets on more than one dimension: Investigating a “dimension-weighting” account. Perception & Psychophysics, 58, 88–101, 1996] for the visual modality. On this account, the anterior N1 enhancement is assumed to reflect the detection of a modality change and initiation of the readjustment of attentional weight-setting from the old to the new target-defining modality in order to optimize target detection.

Previous research has shown that people solve insight or creative problems better when in a positive mood (assessed or induced), although the precise mechanisms and neural substrates of this facilitation remain unclear. We assessed mood and personality variables in 79 participants before they attempted to solve problems that can be solved by either an insight or an analytic strategy. Participants higher in positive mood solved more problems, and specifically more with insight, compared with participants lower in positive mood. fMRI was performed on 27 of the participants while they solved problems. Positive mood (and to a lesser extent and in the opposite direction, anxiety) was associated with changes in brain activity during a preparatory interval preceding each solved problem; modulation of preparatory activity in several areas biased people to solve either with insight or analytically. Analyses examined whether (a) positive mood modulated activity in brain areas showing responsivity during preparation; (b) positive mood modulated activity in areas showing stronger activity for insight than noninsight trials either during preparation or solution; and (c) insight effects occurred in areas that showed mood-related effects during preparation. Across three analyses, the ACC showed sensitivity to both mood and insight, demonstrating that positive mood alters preparatory activity in ACC, biasing participants to engage in processing conducive to insight solving. This result suggests that positive mood enhances insight, at least in part, by modulating attention and cognitive control mechanisms via ACC, perhaps enhancing sensitivity to detect non-prepotent solution candidates.

Transcranial magnetic stimulation (TMS) is a tool for inducing transient disruptions of neural activity noninvasively in conscious human volunteers. In recent years, the investigative domain of TMS has expanded and now encompasses causal structure–function relationships across the whole gamut of cognitive functions and associated cortical brain regions. Consequently, the importance of how to determine the target stimulation site has increased and a number of alternative methods have emerged. Comparison across studies is precluded because different studies necessarily use different tasks, sites, TMS conditions, and have different goals. Here, therefore, we systematically compare four commonly used TMS coil positioning approaches by using them to induce behavioral change in a single cognitive study. Specifically, we investigated the behavioral impact of right parietal TMS during a number comparison task, while basing TMS localization either on (i) individual fMRI-guided TMS neuronavigation, (ii) individual MRI-guided TMS neuronavigation, (iii) group functional Talairach coordinates, or (iv) 10–20 EEG position P4. We quantified the exact behavioral effects induced by TMS using each approach, calculated the standardized experimental effect sizes, and conducted a statistical power analysis in order to calculate the optimal sample size required to reveal statistical significance. Our findings revealed a systematic difference between the four approaches, with the individual fMRI-guided TMS neuronavigation yielding the strongest and the P4 stimulation approach yielding the smallest behavioral effect size. Accordingly, power analyses revealed that although in the fMRI-guided neuronavigation approach five participants were sufficient to reveal a significant behavioral effect, the number of necessary participants increased to n = 9 when employing MRI-guided neuronavigation, to n = 13 in case of TMS based on group Talairach coordinates, and to n = 47 when applying TMS over P4. We discuss these graded effect size differences in light of the revealed interindividual variances in the actual target stimulation site within and between approaches.