Tal Yarkoni
University of Texas at Austin
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Tal Yarkoni.
Science | 2015
Brian A. Nosek; George Alter; George C. Banks; Denny Borsboom; Sara Bowman; S. J. Breckler; Stuart Buck; Christopher D. Chambers; G. Chin; Garret Christensen; M. Contestabile; A. Dafoe; E. Eich; J. Freese; Rachel Glennerster; D. Goroff; Donald P. Green; B. Hesse; Macartan Humphreys; John Ishiyama; Dean Karlan; A. Kraut; Arthur Lupia; P. Mabry; T. Madon; Neil Malhotra; E. Mayo-Wilson; M. McNutt; Edward Miguel; E. Levy Paluck
Author guidelines for journals could help to promote transparency, openness, and reproducibility Transparency, openness, and reproducibility are readily recognized as vital features of science (1, 2). When asked, most scientists embrace these features as disciplinary norms and values (3). Therefore, one might expect that these valued features would be routine in daily practice. Yet, a growing body of evidence suggests that this is not the case (4–6).
The Journal of Neuroscience | 2012
Michael W. Cole; Tal Yarkoni; Grega Repovs; Alan Anticevic; Todd S. Braver
Control of thought and behavior is fundamental to human intelligence. Evidence suggests a frontoparietal brain network implements such cognitive control across diverse contexts. We identify a mechanism—global connectivity—by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) regions activity was found to predict performance in a high control demand working memory task and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the frontoparietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brainwide influence that facilitates the ability to implement control processes central to human intelligence.
Psychonomic Bulletin & Review | 2008
Tal Yarkoni; David A. Balota; Melvin J. Yap
Visual word recognition studies commonly measure the orthographic similarity of words using Coltheart’s orthographic neighborhood size metric (ON). Although ON reliably predicts behavioral variability in many lexical tasks, its utility is inherently limited by its relatively restrictive definition. In the present article, we introduce a new measure of orthographic similarity generated using a standard computer science metric of string similarity (Levenshtein distance). Unlike ON, the new measure—named orthographic Levenshtein distance 20 (OLD20)—incorporates comparisons between all pairs of words in the lexicon, including words of different lengths. We demonstrate that OLD20 provides significant advantages over ON in predicting both lexical decision and pronunciation performance in three large data sets. Moreover, OLD20 interacts more strongly with word frequency and shows stronger effects of neighborhood frequency than does ON. The discussion section focuses on the implications of these results for models of visual word recognition.
Perspectives on Psychological Science | 2009
Tal Yarkoni
Vul, Harris, Winkielman, and Pashler (2009), (this issue) argue that correlations in many cognitive neuroscience studies are grossly inflated due to a widespread tendency to use nonindependent analyses. In this article, I argue that Vul et al.s primary conclusion is correct, but for different reasons than they suggest. I demonstrate that the primary cause of grossly inflated correlations in whole-brain fMRI analyses is not nonindependence, but the pernicious combination of small sample sizes and stringent alpha-correction levels. Far from defusing Vul et al.s conclusions, the simulations presented suggest that the level of inflation may be even worse than Vul et al.s empirical analysis would suggest.
PLOS ONE | 2009
Tal Yarkoni; M Deanna; Jeremy R. Gray; Thomas E. Conturo; Todd S. Braver
Background Reaction time (RT) is one of the most widely used measures of performance in experimental psychology, yet relatively few fMRI studies have included trial-by-trial differences in RT as a predictor variable in their analyses. Using a multi-study approach, we investigated whether there are brain regions that show a general relationship between trial-by-trial RT variability and activation across a range of cognitive tasks. Methodology/Principal Findings The relation between trial-by-trial differences in RT and brain activation was modeled in five different fMRI datasets spanning a range of experimental tasks and stimulus modalities. Three main findings were identified. First, in a widely distributed set of gray and white matter regions, activation was delayed on trials with long RTs relative to short RTs, suggesting delayed initiation of underlying physiological processes. Second, in lateral and medial frontal regions, activation showed a “time-on-task” effect, increasing linearly as a function of RT. Finally, RT variability reliably modulated the BOLD signal not only in gray matter but also in diffuse regions of white matter. Conclusions/Significance The results highlight the importance of modeling trial-by-trial RT in fMRI analyses and raise the possibility that RT variability may provide a powerful probe for investigating the previously elusive white matter BOLD signal.
Nature Reviews Neuroscience | 2017
Russell A. Poldrack; Chris I. Baker; Joke Durnez; Krzysztof J. Gorgolewski; Paul M. Matthews; Marcus R. Munafò; Thomas E. Nichols; Jean Baptiste Poline; Edward Vul; Tal Yarkoni
Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.
Cognitive, Affective, & Behavioral Neuroscience | 2005
Jeremy R. Gray; Gregory C. Burgess; Alexandre Schaefer; Tal Yarkoni; Randy J. Larsen; Todd S. Braver
To test for a relation between individual differences in personality and neural-processing efficiency, we used functional magnetic resonance imaging (fMRI) to assess brain activity within regions associated with cognitive control during a demanding working memory task. Fifty-three participants completed both the self-report behavioral inhibition sensitivity (BIS) and behavioral approach sensitivity (BAS) personality scales and a standard measure of fluid intelligence (Raven’s Advanced Progressive Matrices). They were then scanned as they performed a three-back working memory task. A mixed blocked/ event-related fMRI design enabled us to identify both sustained and transient neural activity. Higher BAS was negatively related to event-related activity in the dorsal anterior cingulate, the lateral prefrontal cortex, and parietal areas in regions of interest identified in previous work. These relationships were not explained by differences in either behavioral performance or fluid intelligence, consistent with greater neural efficiency. The results reveal the high specificity of the relationships among personality, cognition, and brain activity. The data confirm that affective dimensions of personality are independent of intelligence, yet also suggest that they might be interrelated in subtle ways, because they modulate activity in overlapping brain regions that appear to be critical for task performance.
Frontiers in Neuroinformatics | 2015
Krzysztof J. Gorgolewski; Gaël Varoquaux; Gabriel Rivera; Yannick Schwarz; Satrajit S. Ghosh; Camille Maumet; Vanessa Sochat; Thomas E. Nichols; Russell A. Poldrack; Jean Baptiste Poline; Tal Yarkoni; Daniel S. Margulies
Here we present NeuroVault—a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.
Trends in Cognitive Sciences | 2010
Tal Yarkoni; Russell A. Poldrack; David C. Van Essen; Tor D. Wager
Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.
NeuroImage | 2008
Tal Yarkoni; Nicole K. Speer; Jeffrey M. Zacks
When reading a narrative, comprehension and retention of information benefit considerably from the use of situation models--coherent representations of the characters, locations, and activities described in the text. Here we used functional magnetic resonance imaging (fMRI) to explore the neural mechanisms supporting situation model processing. Participants read blocks of sentences that were either unrelated to one another or formed coherent narratives. A timecourse-based approach was used to identify regions that differentiated narrative-level comprehension from sentence-level comprehension. Most brain regions that showed modulation of activation during narrative-level comprehension were also modulated to a lesser extent during sentence-level comprehension, suggesting a shared reliance on general coherence-building mechanisms. However, tentative evidence was found for narrative-specific activation in dorsomedial prefrontal cortex. Additional analyses identified spatiotemporally distinct neural contributions to situation model processing, with posterior parietal regions supporting situation model construction and frontotemporal regions supporting situation model maintenance. Finally, a set of subsequent memory analyses demonstrated that the boost in comprehension and memory performance observed for coherent materials was attributable to the use of integrative situation models rather than lower-level differences in sentence-level or word-level encoding. These results clarify the functional contributions of distinct brain systems to situation model processing and their mapping onto existing psychological models of narrative comprehension.