Mark A. Pitt

PsychologyComputer ScienceNeuroscience

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 4 papers. Top paper: “Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm”.

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Scientific Reports(2020)10.1038/s41598-020-68587-x

N/A

0DataRank · unranked

47 citations

Hairong Gu, Yitong Shen, Nathaniel Haines, Hunter Hahn, Julie Teater +3 more

Anxiety Modulates Preference for Immediate Rewards Among Trait-Impulsive Individuals: A Hierarchical Bayesian Analysis

Clinical Psychological Science(2020)10.1177/2167702620929636

N/A

0DataRank · unranked

26 citations

Theodore P. Beauchaine, Matthew Galdo, Andrew H. Rogers, Hunter Hahn, Mark A. Pitt +4 more

Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox

PsyArXiv (OSF Preprints)(2020)10.31234/osf.io/xr7y3

N/A

0DataRank · unranked

131 citations

Peter D. Kvam, Louis Horowitz Irving, Colin Tucker Smith, Theodore P. Beauchaine, Mark A. Pitt +3 more

A tutorial on using generative models to advance psychological science: Lessons from the reliability paradox.

Psychological Methods(2025)10.1037/met0000674

N/A

0DataRank · unranked

13 citations

Peter D. Kvam, Louis Horowitz Irving, Colin Tucker Smith, Theodore P. Beauchaine, Mark A. Pitt +3 more