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Cognitive
Data Science

Welcome! I am Dr. Jana Jarecki,
a data scientist and researcher currently
working at the University of Basel

Hi, I’m Jana Jarecki,
Data Scientist & Cognitive
Researcher — I analyze human
behavior by combining statistics
with machine learning.

I worked for leading research institutions, e.g., the global top-100 University of Basel and Europe’s top 25 Aarhus University, Denmark, with a Ph.D. in Computational Choice Modeling from the Max Planck Institute in Berlin and a double-degree at Germany’s most prestigious university LMU Munich, I have a strong publication record in peer-reviewed journals, international conference presentations.

I have generated data-based insights into behavior (health, diseases, investments, consumer products) and perception (categories, features, sensory-motor, risk) by integrating different types of data (laboratory data, test results, sensor ratings, hand movements, web contents) collected by institutions, experiments, online surveys, web applications, mobile sensors, or financial providers and citizen science.

Data
Scientific
Pro-
jects

Jarecki, JB, Meder, B, & Nelson, JD (2013). The Assumption of Class-Conditional Independence in Category Learning. Proceedings of the 35th Annual Conference of the Cognitive Science Society. 2650–2655

Jarecki, JB, Meder, B, & Nelson, JD (2018). Naïve and robust: Class‐conditional independence in human classification learning. Cognitive Science 42(1). 4-42

Jarecki, JB & Wilke, A (2018). Into the black box: Tracing information about risks related to 10 evolutionary problems. Evolutionary Behavioral Sciences 12(3). 230-244

Gluth, S, & Jarecki, JB (2019). On the Importance of Power Analyses for Cognitive Modeling. Computational Brain & Behavior 2*, 266–270

Jarecki, JB, & Rieskamp J (2020) Prospect Theory and Optimal Risky Choices with Goals. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 43-49

Jarecki, JB, & Seitz, FI (2020). Cognitivemodels: An R Package for Formal Cognitive Modeling. In Proceedings of the 18th International Conference on Cognitive Modeling 100-106

Jarecki, JB & Rieskamp J (2020) Prospect Theory and Optimal Risky Choices with Goals. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 43-49

Jarecki, JB, & Wilke, A (2020) The Relevance of Subjective Benefits in Risky Choice Across ten Domains of Life. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 3126-3132

Jarecki, JB, Tan, JH, & Jenny, MA (2020). A framework for building cognitive process models. Psychonomic Bulletin & Review 27(6), 1-12.

Hoffart, JC, Jarecki, JB, Dutilh, G, & Rieskamp, J. (2021) The influence of sample size on preferences from experience. Quarterly Journal of Experimental Psychology. 75(1). 1-17

Marbacher, L, Jarecki, JB, & Rieskamp, J (2021). How Goals Erase Framing Effects in Risky Decision Making. Proceedings of the 43th Annual Conference of the Cognitive Science Society. 993–999

Seitz, FI, & Jarecki, JB (2021). People are Insensitive to Within-Category Feature Correlations in Categorization. Proceedings of the 19th International Conference on Cognitive Modelling. 255–256

Albrecht, R, Jarecki, JB, Meier, DS, & Rieskamp, J (2021). Risk preferences and risk perception affect the acceptance of digital contact tracing. Humanities and Social Sciences Communications 8(1)*. 1-9

Jarecki, JB, & Rieskamp, J (2022). Comparing attribute-based and memory-based preferential choice. DECISION 49(1). 65-90

Peer-
Reviewed
Publica-
tions

Jarecki, JB, & Rieskamp, J (2022). Comparing attribute-based and memory-based preferential choice. DECISION 49(1). 65-90

Albrecht, R, Jarecki, JB, Meier, DS, & Rieskamp, J (2021). Risk preferences and risk perception affect the acceptance of digital contact tracing. Humanities and Social Sciences Communications 8(1)*. 1-9

Seitz, FI, & Jarecki, JB (2021). People are Insensitive to Within-Category Feature Correlations in Categorization. Proceedings of the 19th International Conference on Cognitive Modelling. 255–256

Marbacher, L, Jarecki, JB, & Rieskamp, J (2021). How Goals Erase Framing Effects in Risky Decision Making. Proceedings of the 43th Annual Conference of the Cognitive Science Society. 993–999

Hoffart, JC, Jarecki, JB, Dutilh, G, & Rieskamp, J. (2021) The influence of sample size on preferences from experience. Quarterly Journal of Experimental Psychology. 75(1). 1-17

Jarecki, JB, Tan, JH, & Jenny, MA (2020). A framework for building cognitive process models. Psychonomic Bulletin & Review 27(6), 1-12.

Jarecki, JB, & Wilke, A (2020) The Relevance of Subjective Benefits in Risky Choice Across ten Domains of Life. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 3126-3132

Jarecki, JB & Rieskamp J (2020) Prospect Theory and Optimal Risky Choices with Goals. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 43-49

Jarecki, JB, & Seitz, FI (2020). Cognitivemodels: An R Package for Formal Cognitive Modeling. In Proceedings of the 18th International Conference on Cognitive Modeling 100-106

Jarecki, JB, & Rieskamp J (2020) Prospect Theory and Optimal Risky Choices with Goals. In Proceedings of the 42th Annual Conference of the Cognitive Science Society. 43-49

Gluth, S, & Jarecki, JB (2019). On the Importance of Power Analyses for Cognitive Modeling. Computational Brain & Behavior 2*, 266–270

Jarecki, JB & Wilke, A (2018). Into the black box: Tracing information about risks related to 10 evolutionary problems. Evolutionary Behavioral Sciences 12(3). 230-244

Jarecki, JB, Meder, B, & Nelson, JD (2018). Naïve and robust: Class‐conditional independence in human classification learning. Cognitive Science 42(1). 4-42

Jarecki, JB, Meder, B, & Nelson, JD (2013). The Assumption of Class-Conditional Independence in Category Learning. Proceedings of the 35th Annual Conference of the Cognitive Science Society. 2650–2655

Areas of
Expertise

Evidence Requirements

Which questions to ask? Which evidence is needed? Ways to integrate evidence from multiple sources.

ML and Statistical Modeling

Train and test models, predict outcomes, and simulate processes, check for biases.

Data Management

Data integration from servers, data bases, spreadsheets, or real-world evidence from e.g., the internet or social media.

Translation to Models

What do verbal questions mean as models? Which co-variates are necessary? Which analyses are needed?