Lynne M. Reder
Lynne M. Reder is an American psychologist whose research contributed to our understanding of human memory.
Career
Reder received her undergraduate degree in Psychology at Stanford University in 1972, graduating as a member of Phi Beta Kappa. In 1976, she earned her PhD in Psychology from the University of Michigan. After a two-year NIMH post-doctoral fellowship at Yale University, she joined the faculty at Carnegie Mellon University and retired as full professor in 2021.Her contributions to psychological science and experimental psychology have been recognized through multiple honors and elected positions:
- 1999: Elected Fellow, American Psychological Association, Division 3
- 2001: Elected Fellow, American Association for Advancement of Science
- 2005: Elected Fellow, Association for Psychological Science
- 2007: Elected to the Society of Experimental Psychologists
- 2010: Elected to the Memory Disorders Research Society
- 2011 - 2016: Elected to the Governing Board of The Psychonomic Society
- 2013 - 2017: Elected Member at Large, Section J, American Association for the Advancement of Science
Selected research and publications
Role of Elaborations and Summaries in Memory Retention
Reder's early work explored the effects of elaborations and summaries on learning. She found that people often learned more from summaries than original texts and that self-generated elaborations improve retention better than elaborations provided by the author*
Strategy Selection and Question Answering
Reder showed that people do not default to direct retrieval when attempting to answer a question but rather dynamically choose strategies based on intrinsic question features and base rates of success.Role of Hippocampus in Memory
Reder showed that both implicit and explicit memory tasks can rely on the hippocampus, depending on whether the task requires the formation of new associations.Working Memory and Cognitive Resources
Reder’s contributions to working memory include the development of the Modified Digit Spantask, which predicts cognitive performance across domains. She expanded her SAC model to
incorporate the role of working memory in knowledge construction, emphasizing resource limitations in memory processes showing that resources are consumed/depleted as an inverse function of chunk familiarity and rate of replenishment depends on the rate of input and familiarity of the information to be processed.