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Daniel J Schad

Biographic Data

ID1941387
NAMEDaniel J Schad
GIVEN NAMESDaniel J
FAMILY NAMESchad
SIGNATURESCHAD D J
AFFILIATIONSUniversity of Potsdam
ORCID0000-0003-2586-6823
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS4
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2008
LATEST PUBLICATION YEAR2024
H-INDEX1
  • Pavlovian-to-instrumental transfer and outcome-devaluation effects in individuals with gaming experience

    Open Access•Lin-xuan Xu, Jialin Zhang et al.•ARTICLE•Computers in Human Behavior•2024

  • Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

    Open Access•Başak Bağcı, Başak Bağci et al.•ARTICLE•Frontiers in Psychiatry•2022

    Our data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP

  • Divergence point analyses of visual world data

    Open Access•Kate Stone, Silvia Lago et al.•ARTICLE•Bilingualism Language and Cognition•2021

    Much work has shown that differences in the timecourse of language processing are central to comparing native (L1) and non-native (L2) speakers. However, estimating the onset of experimental effects in timecourse data presents several statistical problems including multiple comparisons and autocorrelation. We compare several approaches to tackling these problems and illustrate them using an L1-L2 visual world eye-tracking dataset. We then present…

  • How to capitalize on a priori contrasts in linear (mixed) models

    Open Access•Daniel J Schad, Shravan Vasishth et al.•ARTICLE•Journal of Memory and Language•2020

    Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each…

  • Dissociating neural learning signals in human sign- and goal-trackers

    Open Access•Daniel J Schad, Michael A Rapp et al.•ARTICLE•Nature Human Behaviour•2019•References: 62

  • The reliability of a Picture Story Exercise measure of implicit motives

    Open Access•Oliver C Schultheiss, Scott H Liening et al.•ARTICLE•Journal of Research in Personality•2008•Cited by: 4•References: 28

  • The reliability of a Picture Story Exercise measure of implicit motives

    Open Access•Oliver C Schultheiss, Scott H Liening et al.•ARTICLE•Journal of Research in Personality•2008•Cited by: 4•References: 28

  • The reliability of a Picture Story Exercise measure of implicit motives

    Open Access•Oliver C Schultheiss, Scott H Liening et al.•ARTICLE•Journal of Research in Personality•2008•Cited by: 4•References: 28

  • Dissociating neural learning signals in human sign- and goal-trackers

    Open Access•Daniel J Schad, Michael A Rapp et al.•ARTICLE•Nature Human Behaviour•2019•References: 62

  • How to capitalize on a priori contrasts in linear (mixed) models

    Open Access•Daniel J Schad, Shravan Vasishth et al.•ARTICLE•Journal of Memory and Language•2020

    Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each…

  • Divergence point analyses of visual world data

    Open Access•Kate Stone, Silvia Lago et al.•ARTICLE•Bilingualism Language and Cognition•2021

    Much work has shown that differences in the timecourse of language processing are central to comparing native (L1) and non-native (L2) speakers. However, estimating the onset of experimental effects in timecourse data presents several statistical problems including multiple comparisons and autocorrelation. We compare several approaches to tackling these problems and illustrate them using an L1-L2 visual world eye-tracking dataset. We then present…

  • Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

    Open Access•Başak Bağcı, Başak Bağci et al.•ARTICLE•Frontiers in Psychiatry•2022

    Our data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP

  • Pavlovian-to-instrumental transfer and outcome-devaluation effects in individuals with gaming experience

    Open Access•Lin-xuan Xu, Jialin Zhang et al.•ARTICLE•Computers in Human Behavior•2024

Psychology (5 works) · Artificial Intelligence (3 works) · Cognitive psychology (3 works) · Computer Science (3 works) · Developmental psychology (3 works) · Mathematics (3 works) · Mental Health Research Topics (2 works) · Social Psychology (2 works) · Statistics (2 works) · A priori and a posteriori (1 work)

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