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The purpose of this study was to evaluate the viability of using neurological imaging to classify transformational leaders, versus non-transformational leaders, as identified through existing psychometric methods. Specifically, power spectral analysis measures based on electroencephalograms (EEG) were used to develop and validate a discriminant function that can classify individuals according to their transformational leadership behavior. Resting, eyes closed EEG was recorded from 19 scalp locations for 200 civilian and military leaders. We also assessed follower or peer perceptions of transformational leadership through the use of the Multifactor Leadership Questionnaire (MLQ). Our discriminant analysis, which involved a two-step, neural variable reduction and selection process, was 92.5% accurate in its classification of leaders. Patterns in the spectral measures of the brain of leaders, including activity and network dynamic metrics, are discussed as potential correlates of transformational leadership behavior. The current work provides a better understanding of the latent and dynamic neurological mechanisms that may underpin the transformational leadership qualities of individuals.
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Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.
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The authors argue that transformational leaders possess special qualities, including the ability to communicate enthusiasm and vision, a positive outlook, intuitive insight, and emotional competency. The authors have tapped into research indicating that there are linkages between the emotional dimensions of leadership and behavioral aspects of transformational leaders. It is proposed that transformational leader may need to be high on emotional intelligence, defined in terms of being in touch with their own emotions, and being able to understand and regulate other's emotions, suggested that this provides additional insight into the leadership–performance link.
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It remains controversial whether Broca's aphasia is an articulatory deficit, a lexical-access problem, or agrammatism. In spite of recent neuroimaging studies, the causal link between cortical activity and linguistic subcomponents has not been elucidated. Here we report an experiment with event-related transcranial magnetic stimulation (TMS) to clarify the role of Broca's area, more specifically, the left inferior frontal gyrus (F3op/F3t), in syntactic processing. An experimental paradigm contrasted sentences requiring syntactic decisions with those requiring semantic decisions. We found selective priming effects on syntactic decisions when TMS was administered to the left F3op/F3t at a specific timing, but not to the left middle frontal gyrus (F2). Our results provide direct evidence of the involvement of the left F3op/F3t in syntactic processing.
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There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.
fMRI reveals reciprocal inhibition between social and physical cognitive domains
  • Dawson Jack
  • A J Begany
  • K L Leckie
  • R L Barry
  • K P Ciccia
  • A H Snyder
Jack, Dawson, A. J., Begany, K. L., Leckie, R. L., Barry, K. P., Ciccia, A. H., & Snyder, A. Z. (2012). fMRI reveals reciprocal inhibition between social and physical cognitive domains. NeuroImage, 66C, 385-401. doi:S1053-8119(12)01064-6 [pii]10.1016/j.neuroimage.2012.10.061
Understanding the Neuroscience of Two Approaches to Coaching and Attentional Regulatory Focus
  • A Passarelli
  • R E Boyatzis
  • A J Dawson
  • A Jack
Passarelli, A., Boyatzis, R.E., Dawson, A.J. & Jack, A. (in review). Understanding the Neuroscience of Two Approaches to Coaching and Attentional Regulatory Focus
Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding
  • R A Poldrack
Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron, 72(5), 692-697. doi:10.1016/j.neuron.2011.11.001S0896-6273(11)00989-5 [pii]