Rubin, T., Koyejo, O. O., Jones, M. N., & Yarkoni, T.
(2016). Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain. Advances In Neural Information Processing Systems.
Abstract: This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization
of the Correspondence-LDA model that allows for variable spatial representations
to be associated with topics, and increased flexibility in terms of the strength
of the correspondence between data types induced by the model. We present
three variants of GC-LDA, each of which associates topics with a different spatial
representation, and apply them to a corpus of neuroimaging data. In the context of
this dataset, each topic corresponds to a functional brain region, where the region’s
spatial extent is captured by a probability distribution over neural activity, and the
region’s cognitive function is captured by a probability distribution over linguistic
terms. We illustrate the qualitative improvements offered by GC-LDA in terms
of the types of topics extracted with alternative spatial representations, as well
as the model’s ability to incorporate a-priori knowledge from the neuroimaging
literature. We furthermore demonstrate that the novel features of GC-LDA improve
predictions for missing data.
McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., ... & Yarkoni, T.
(2016). How open science helps researchers succeed. eLife, 5, e16800.
Abstract: Open access, open data, open source, and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities, and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.
De la Vega, A. I., Chang, L. J., Banich, M. T., Wager, T. D., & Yarkoni, T.
(2016). Large-scale meta-analysis of human medial frontal cortex reveals tripartite functional organization. Journal of Neuroscience, 36, 6553-6562.
Abstract: The functional organization of human medial frontal cortex (MFC) is a subject of intense study. Using fMRI, the MFC has been associated with diverse psychological processes, including motor function, cognitive control, affect, and social cognition. However, there have been few large-scale efforts to comprehensively map specific psychological functions to subregions of medial frontal anatomy. Here we applied a meta-analytic data-driven approach to nearly 10,000 fMRI studies to identify putatively separable regions of MFC and determine which psychological states preferentially recruit their activation. We identified regions at several spatial scales on the basis of meta-analytic coactivation, revealing three broad functional zones along a rostrocaudal axis composed of 2–4 smaller subregions each. Multivariate classification analyses aimed at identifying the psychological functions most strongly predictive of activity in each region revealed a tripartite division within MFC, with each zone displaying a relatively distinct functional signature. The posterior zone was associated preferentially with motor function, the middle zone with cognitive control, pain, and affect, and the anterior with reward, social processing, and episodic memory. Within each zone, the more fine-grained subregions showed distinct, but subtler, variations in psychological function. These results provide hypotheses about the functional organization of medial prefrontal cortex that can be tested explicitly in future studies.
Westfall, J., & Yarkoni, T.
(2016). Statistically controlling for confounding constructs is harder than you think. PLOS ONE, 11(3): e0152719.
Abstract: Social scientists often seek to demonstrate that a construct has incremental validity over and
above other related constructs. However, these claims are typically supported by measurementlevel
models that fail to consider the effects of measurement (un)reliability. We use intuitive
examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that
common strategies for establishing incremental construct validity using multiple regression
analysis exhibit extremely high Type I error rates under parameter regimes common in many
psychological domains. Counterintuitively, we find that error rates are highest—in some cases
approaching 100%—when sample sizes are large and reliability is moderate. Our findings
suggest that a potentially large proportion of incremental validity claims made in the literature
are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to
explore the statistical properties of these and other incremental validity arguments. We conclude
by reviewing SEM-based statistical approaches that appropriately control the Type I error rate
when attempting to establish incremental validity.
Pauli, W. M., O'Reilly, R. C., Yarkoni, T.
, & Wager, T. D. (2016). Regional specialization within the human striatum for diverse psychological functions. Proceedings of the National Academy of Sciences, 113, 1907-1912.
Abstract: Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. But despite continuous efforts to subdivide the human striatum based on anatomical and restingstate functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5809 human imaging studies. We (a) identified five distinct striatal zones, which exhibited discrete patterns of co-activation with cortical brain regions across distinct psychological processes, and (b) identified the different psychological processes associated with each zone. We found that the reported pattern of cortical activation reliably predicted which striatal zone was most strongly activated. Critically, activation in each functional zone could be associated with distinct psychological processes directly, rather than inferred indirectly from psychological functions attributed to associated cortices. Consistent with well-established findings, we found an association of the ventral striatum with reward processing. Confirming less well-established findings, the ventral striatum and adjacent anterior caudate were associated with evaluating the value of rewards and actions, respectively. Furthermore, our results confirmed a sometimes overlooked specialization of the posterior caudate nucleus for executive functions, often considered the exclusive domain of fronto-parietal cortical circuits. Our findings provide a precise functional map of regional specialization within the human striatum, both in terms of the differential cortical regions and psychological functions associated with each striatal zone.
Ashar, Y. K., Andrews-Hanna, J. R., Yarkoni, T.
, Sills, J., Halifax, J., Dimidjian, S., & Wager, T. D. (2016). Effects of Compassion Meditation on a Psychological Model of Charitable Donation. Emotion, 16(5):691-705.
Abstract: Compassion is critical for societal wellbeing. Yet, it remains unclear how specific thoughts and feelings motivate compassionate behavior, and we lack a scientific understanding of how to effectively cultivate compassion. Here, we conducted 2 studies designed to a) develop a psychological model predicting compassionate behavior, and b) test this model as a mediator of a Compassion Meditation (CM) intervention and identify the “active ingredients” of CM. In Study 1, we developed a model predicting compassionate behavior, operationalized as real-money charitable donation, from a linear combination of self-reported tenderness, personal distress, perceived blamelessness, and perceived instrumental value of helping with high cross-validated accuracy, r = .67, p < .0001. Perceived similarity to suffering others did not predict charitable donation when controlling for other feelings and attributions. In Study 2, a randomized controlled trial, we tested the Study 1 model as a mediator of CM and investigated active ingredients. We compared a smartphone-based CM program to 2 conditions—placebo oxytocin and a Familiarity intervention—to control for expectancy effects, demand characteristics, and familiarity effects. Relative to control conditions, CM increased charitable donations, and changes in the Study 1 model of feelings and attributions mediated this effect (pab = .002). The Familiarity intervention led to decreases in primary outcomes, while placebo oxytocin had no significant effects on primary outcomes. Overall, this work contributes a quantitative model of compassionate behavior, and informs our understanding of the change processes and intervention components of CM.