The International Conference on Cognitive Modeling brings together researchers who develop computational models to explain and predict cognitive data. The core theme of the 2004 conference was "Integrating Computational Models," encompassing an integration of diverse data through models of coherent phenomena; integration across modeling approaches; and integration of teaching and modeling. This text presents the proceedings of that conference. The International Conference on Cognitive Modeling 2004 sought to grow the discipline of computational cognitive modeling by providing a sophisticated modeling audience for cutting-edge researchers, in addition to offering a forum for integrating insights across alternative modeling approaches in both basic research and applied settings, and a venue for planning the future growth of the discipline. The meeting included a careful peer-review process of 6-page paper submissions; poster-abstracts to include late-breaking work in the area; prizes for best papers; a doctoral consortium; and competitive modeling symposia that compare and contrast different approaches to the same phenomena.

The chapters in Thinking With Data are based on presentations given at the 33rd Carnegie Symposium on Cognition. The Symposium was motivated by the confluence of three emerging trends: (1) the increasing need for people to think effectively with data at work, at school, and in everyday life, (2) the expanding technologies available to support people as they think with data, and (3) the growing scientific interest in understanding how people think with data.

What is thinking with data? It is the set of cognitive processes used to identify, integrate, and communicate the information present in complex numerical, categorical, and graphical data. This book offers a multidisciplinary presentation of recent research on the topic. Contributors represent a variety of disciplines: cognitive and developmental psychology; math, science, and statistics education; and decision science. The methods applied in various chapters similarly reflect a scientific diversity, including qualitative and quantitative analysis, experimentation and classroom observation, computational modeling, and neuroimaging. Throughout the book, research results are presented in a way that connects with both learning theory and instructional application.

The book is organized in three sections:

• Part I focuses on the concepts of uncertainty and variation and on how people understand these ideas in a variety of contexts.
• Part II focuses on how people work with data to understand its structure and draw conclusions from data either in terms of formal statistical analyses or informal assessments of evidence.
• Part III focuses on how people learn from data and how they use data to make decisions indaily and professional life.

The chapters in Thinking With Data are based on presentations given at the 33rd Carnegie Symposium on Cognition. The Symposium was motivated by the confluence of three emerging trends: (1) the increasing need for people to think effectively with data at work, at school, and in everyday life, (2) the expanding technologies available to support people as they think with data, and (3) the growing scientific interest in understanding how people think with data.

What is thinking with data? It is the set of cognitive processes used to identify, integrate, and communicate the information present in complex numerical, categorical, and graphical data. This book offers a multidisciplinary presentation of recent research on the topic. Contributors represent a variety of disciplines: cognitive and developmental psychology; math, science, and statistics education; and decision science. The methods applied in various chapters similarly reflect a scientific diversity, including qualitative and quantitativeanalysis, experimentation and classroom observation, computational modeling, and neuroimaging. Throughout the book, research results are presented in a way that connects with both learning theory and instructional application.

The book is organized in three sections:

• Part I focuses on the concepts of uncertainty and variation and on how people understand these ideas in a variety of contexts.
• Part II focuses on how people work with data to understand its structure and draw conclusions from data either in terms of formal statistical analyses or informal assessments of evidence.
• Part III focuses on how people learn from data and how they use data to make decisions in dailyand professional life.

The International Conference on Cognitive Modeling brings together researchers who develop computational models to explain and predict cognitive data. The core theme of the 2004 conference was "Integrating Computational Models," encompassing an integration of diverse data through models of coherent phenomena; integration across modeling approaches; and integration of teaching and modeling. This text presents the proceedings of that conference. The International Conference on Cognitive Modeling 2004 sought to grow the discipline of computational cognitive modeling by providing a sophisticated modeling audience for cutting-edge researchers, in addition to offering a forum for integrating insights across alternative modeling approaches in both basic research and applied settings, and a venue for planning the future growth of the discipline. The meeting included a careful peer-review process of 6-page paper submissions; poster-abstracts to include late-breaking work in the area; prizes for best papers; a doctoral consortium; and competitive modeling symposia that compare and contrast different approaches to the same phenomena.