Arash Khodadadi und Jerome - Info zur Person

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Toward a Unified View of the Speed-Accuracy Trade-Off: ...

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Arash Khodadadi*, Pegah Fakhari and Jerome R. Busemeyer Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA Edited by: ...

[ ] Learning to Allocate Limited Time to Decisions with...

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Authors: Arash Khodadadi, Pegah Fakhari, Jerome R. Busemeyer. (Submitted on 18 Jul 2016). Abstract: The goal of this article is to investigate how human ...

Neurons and Cognition authors/titles Aug 2016

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Authors: Alan Paris, George Atia, Azadeh Vosoughi, Stephen Berman Authors: Arash Khodadadi, Pegah Fakhari, Jerome R Busemeyer. Subjects: Neurons ...

Quantitative Biology authors/titles 2016

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EP); High Energy Astrophysical Phenomena (astro-ph.HE); Atmospheric Authors: Arash Khodadadi, Pegah Fakhari, Jerome R. Busemeyer. Subjects: Neurons Journal-ref: International Conference On Music Perception And Cognition.

Cognitive Psychology | Vol 95, Pages (June 2017) |...

www.sciencedirect.com

... to allocate limited time to decisions with different expected outcomes. Arash Khodadadi, Pegah Fakhari, Jerome R. Busemeyer. Pages : Download PDF .

Alle Infos zum Namen "Arash Khodadadi"

Learning to allocate limited time to decisions with different expected ...pubmed.ncbi.nlm.nih.gov › ...

pubmed.ncbi.nlm.nih.gov

Authors. Arash Khodadadi , Pegah Fakhari , Jerome R Busemeyer. Affiliations. 1 Indiana University, Department of Psychological and Brain Sciences, ...

Learning to maximize reward rate: a model Europe PMC

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von A Khodadadi · · Zitiert von: 23 — Arash Khodadadi,* Pegah Fakhari, and Jerome R. Busemeyer. Author information Article notes Copyright and License information Disclaimer. › pmc › pm...

[PDF] Mechanisms Underlying Decision-Making as Revealed ...

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3 Authors (Arash Khodadadi, ..., Jerome R. Busemeyer). 10 Citations. Read Later. Overcoming Indecision by Changing the Decision Boundary. › papers

Learning to maximize reward rate: a model based on semi-Markov...

www.ncbi.nlm.nih.gov

Learning to maximize reward rate: a model based on semi-Markov decision processes. Arash Khodadadi,* Pegah Fakhari, and Jerome R.

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