Modeling causal conditional reasoning data using SDT: caveats and new insights.
about
When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias.Concerns with the SDT approach to causal conditional reasoning: a comment on Trippas, Handley, Verde, Roser, McNair, and Evans (2014).Fluency and belief bias in deductive reasoning: new indices for old effects.Alleviating the concerns with the SDT approach to reasoning: reply to Singmann and Kellen (2014).
P2860
Modeling causal conditional reasoning data using SDT: caveats and new insights.
description
2014 nî lūn-bûn
@nan
2014 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի մարտին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@ast
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@en
type
label
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@ast
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@en
prefLabel
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@ast
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@en
P2093
P2860
P356
P1476
Modeling causal conditional reasoning data using SDT: caveats and new insights.
@en
P2093
Dries Trippas
Jonathan St B T Evans
Michael F Verde
Nicolas A McNair
P2860
P356
10.3389/FPSYG.2014.00217
P407
P577
2014-03-12T00:00:00Z