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Thus, the result from Experiment 1 could speak to a more general relationship between a tendency to be model-based and a tendency to be goal-directed on separate "tasks", rather than to the more specific hypothesis that associations learned in model-based fashion are more sensitive to subsequent devaluation.
We discuss the relationship between this architecture and learning in model-free and model-based systems, episodic memory, imagery, and planning, including some open questions and directions for further experiments.
From the opposite perspective, assessment is typically identified as one of the contextual factors that impact on learning in models of learning (Biggs 1987; Ramsden 1984; Ross et al. 2003; Vermunt 1996).
We also discuss in some detail the lessons learned in this modeling and standardization effort.
Understanding the nature and integrity of boundaries is key; and boundaries are learned in communities that model respect for them.
As we look at other states who continue to be mired in partisan redistricting there are a many lessons learned in the California model.
Workers actively involved in developing dengue vaccines may benefit from lessons learned in the FIPV model.
For example, my dissertation was about the interaction of game dynamics and learning in a model of language and evolution.
* p < 0.05 indicates a negative association with learning in the model and indicates a positive association with learning.
Remarkably some of these agents facilitate learning in animal models of cognitive dysfunction.
Learning in Graphical Models is the product of a mutually exciting interaction between ideas, insights, and techniques drawn from the fields of statistics, computer science, and physics.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com