.png){kind=icon}
.png){kind=icon}
Return
Dimensions in data: testing psychological models using state-trace analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.tics.2008.04.009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cognitive science is replete with fertile and forceful debates about the need for one or moire underlying mental processes or systems to explain empirical observations. Such debates can be found in many areas, including learning, memory, categorization, reasoning and decision-making. Multiple-process models are often advanced on the basis of dissociations in data. We argue and illustrate that using dissociation logic to draw conclusions about the dimensionality of data is flawed. We propose that a more widespread adoption of 'state-trace analysis' - an approach that overcomes these flaws could lead to a re-evaluation of the need for multiple-process models and to a re-appraisal of how these models should be formulated and tested.
Keywords:
INFERENCE
TASK
RECOLLECTION
FAMILIARITY
JUDGMENTS
SYSTEMS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
17.2
Papers:
3.6K
Citations:
3.5W