Publications by authors named "Jessey Wright"

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study.

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Open data allows researchers to explore pre-existing datasets in new ways. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates.

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In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results.

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Arguments in support of open science tend to focus on confirmatory research practices. Here we argue that exploratory research should also be encouraged within the framework of open science. We lay out the benefits of 'open exploration' and propose two complementary ways to implement this with little infrastructural change.

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The argument from inductive risk (AIR) is perhaps the most common argument against the value-free ideal of science. Brian MacGillivray rejects the AIR (at least as it would apply to risk assessment) and embraces the value-free ideal. We clarify the issues at stake and argue that MacGillivray's criticisms, although effective against some formulations of the AIR, fail to overcome the essential concerns that motivate the AIR.

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Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing.

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Functional magnetic resonance imaging research is often associated with images of brains overlaid with patterns of color that indicate significant activity. These images are one of the most salient and recognizable pieces of evidence neuroscientists appeal to as justification for claims about the relationship between cognitive processes and human behavior. The strongest critics of neuroimaging research argue that the technology possesses little, if any, scientific value, in part because of the assumptions implicit in the complex analysis procedures used to transform the data into interpretable data patterns.

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A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context.

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Evidence from a large body of research suggests that perirhinal cortex (PrC), which interfaces the medial temporal lobe with the ventral visual pathway for object identification, plays a critical role in item-based recognition memory. The precise manner in which PrC codes for the prior occurrence of objects, however, remains poorly understood. In the present functional magnetic resonance imaging (fMRI) study, we used multivoxel pattern analyses to examine whether the prior occurrence of faces is coded by distributed patterns of PrC activity that consist of voxels with decreases as well as increases in signal.

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