About

Pernille Hemmer is a Professor in the Department of Psychology at Rutgers University. She received her Ph.D. from the Department of Cognitive Science at the University of California, Irvine, and completed a post-doctoral fellowship in the Department of Psychology at Syracuse University before joining Rutgers University in 2012. Her research focuses on episodic and semantic memory, as well as decision making in naturalistic environments. Specifically, she investigates how people make real-world decisions in complex environments where prior knowledge can be utilized. Her work explores how individuals integrate noisy and incomplete information stored in episodic memory with their prior knowledge of the environment, experimentally quantifying prior knowledge across various domains such as size, height, scenes, and time. Hemmer's studies examine how people use their knowledge and expectations to interpret their environment and whether they do so optimally across different cognitive tasks. Additionally, her research explores individual differences in rational models of cognition, employing Bayesian analysis methods to infer the use of informative priors at the individual level.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Cognitive psychology
• Political Science
• Cognitive science
• Developmental psychology
• Law
• Biology
• Data science
• Neuroscience
• Paleontology
• Mathematics
• Epistemology

Selected publications

• A Hierarchical Bayesian Model of Memory for Spatial Memory

  2026-01-16

  articleOpen access1st authorCorresponding

  Episodic memory and schema knowledge are known to interact when we recall everyday events – such as the location of an object in a scene. Recent Bayesian models of memory have assumed that this interaction is a function of the trade-off between the strength of episodic memory and schema knowledge. Ramey et at. (2022) empirically quantified this relationship using the recollection-familiarity paradigm as a proxy for memory strength. They also manipulated the congruency/incongruence of object locations in natural scenes as a proxy for schema strength. Here we replicate their findings and then model the effects using a Hierarchical Bayesian model of memory. We model familiarity/recollection as memory strength, and the congruence versus incongruence as having different priors - with the congruent prior linked to accuracy for congruent new scenes in the experimental data, and the incongruent prior linked to accuracy for incongruent new scenes. The model successfully captures 1) the greater accuracy for schema-congruent versus incongruent object locations 2) the decreasing difference in accuracy between congruent and incongruent scenes across familiarity confidence, and 3) the elimination of the accuracy difference for recollected scenes. We also evaluated the dual-process signal detection theory claim that recollection responses reflect a distinct recollection process. We found that the best fitting parameters for memory precision had a curvelinear relationship where memory precision gradually improved with increasing familiarity and then substantially changed for recollected responses.

  PublisherDOI

• Insufficient evidence for a hierarchical model of a sense of agency in joint actions: A commentary and re-analysis of Zapparoli et al. (2022; Cortex)

  2026-05-14

  article

  A commentary on Zapparoli et al. (2022; Cortex)

  PublisherDOI

• A commentary and re-analysis of Zapparoli et al. (2022)

  OSF Preprints (OSF Preprints) · 2026-05-09

  other1st authorCorresponding
  Publisher

• Evidence for individual differences in the temporal binding effect.

  Journal of Experimental Psychology Human Perception & Performance · 2025-09-24

  articleOpen access

  The sense of agency is a fundamental aspect of human experience. Temporal binding, the subjective compression of the perceived time interval between an action and its outcome, has previously been assumed to be an implicit measure of the sense of agency. Here, we investigate whether the characteristic directionality of the temporal binding effect is consistently present at the individual level. We first deaggregated the data from three temporal binding data sets and systematically reanalyzed and revisualized these effects at the individual level. This analysis revealed consistent differences in the directionality of the temporal binding effect at the individual level. We next implemented a validated Bayes factor mixed-method modeling approach (Rouder & Haaf, 2021), which simulated individual true effects in two additional data sets and determined that the observed differences in directionality remained after accounting for sampling noise. Model comparison determined that the least constrained model, that is, the one that allowed for individual differences in the magnitude and directionality of the effect, was the best fitting model. These results provide strong support for the presence of qualitative differences in the temporal binding effect. Implications for both the theoretical and applied future of this literature are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

  PublisherDOI

• A Hierarchical Bayesian Model of Memory for Spatial Memory

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Towards a Generalized Bayesian Model of Reconstructive Memory

  Computational Brain & Behavior · 2024-09-27 · 2 citations

  articleOpen access

  Abstract Prior knowledge has long been known to shape reconstruction from memory. An individual stimulus from a category is often remembered to be closer to the center of that category than its true location. This effect, together with more complex memory effects that involve prior knowledge at multiple levels of abstraction, has been successfully explained by the Category Adjustment Model (CAM; Huttenlocher et al. 2000) and its extensions. However, recent experimental results diverge from CAM’s predictions showing that reconstructive memory for atypical category examples is influenced by the category center less than that of typical category examples. To unify these findings, we propose a generalized Bayesian model of reconstructive memory, called the generalized CAM model (g-CAM). We demonstrate through simulations that g-CAM can account for previously known effects of reconstructive memory, while additionally capturing recent empirical findings involving atypical category examples.

  PublisherOA PDFDOI

• Towards a Generalized Bayesian Model of Reconstructive Memory

  Research Square · 2024-09-02

  preprintOpen access
  PublisherOA PDFDOI

• The influence of functional components of natural scenes on episodic memory

  Scientific Reports · 2024 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Cognitive science

  Prior expectation for the structure of natural scenes is perhaps the most influential contributor to episodic memory for objects in scenes. While the influence of functional components of natural scenes on scene perception and visual search has been well studied, far less is known about the independent contributions of these components to episodic memory. In this investigation, we systematically removed three functional components of natural scenes: global-background, local spatial, and local associative information, to evaluate their impact on episodic memory. Results revealed that [partially] removing the global-background negatively impacted recall accuracy following short encoding times but had relatively little impact on memory after longer times. In contrast, systematically removing local spatial and associative relationships of scene objects negatively impacted recall accuracy following short and longer encoding times. These findings suggest that scene background, object spatial arrangements, and object relationships facilitate not only scene perception and object recognition, but also episodic memory. Interestingly, the impact of these components depends on how much encoding time is available to store information in episodic memory. This work has important implications for understanding how the inherent structure and function of the natural world interacts with memory and cognition in naturalistic contexts.

  PublisherDOI

• Correction: Towards a Generalized Bayesian Model of Reconstructive Memory

  Computational Brain & Behavior · 2024-11-06

  articleOpen access
  PublisherOA PDFDOI

• Counterintuitive Concepts Across Domains: A Unified Phenomenon?

  Cognitive Science · 2023-04-01 · 3 citations

  articleOpen accessSenior author

  The minimally counterintuitive (MCI) thesis in the cognitive science of religion proposes that supernatural concepts are prevalent across cultures because they possess a common structure-namely, violations of intuitive ontological assumptions that facilitate concept representation. These violations are hypothesized to give supernatural concepts a memorability advantage over both intuitive concepts and "maximally counterintuitive" (MXCI) concepts, which contain numerous ontological violations. However, the connection between MCI concepts and bizarre (BIZ) but not supernatural concepts, for which memorability advantages are predicted by the von Restorff (VR) effect, has been insufficiently clarified by earlier research. Additionally, the role of inferential potential (IP) in determining MCI concepts' memorability has remained vague and only rarely controlled for. In a pre-registered experiment, we directly compare memorability for MCI and MXCI concepts, compared to BIZ concepts, while controlling for IP as well as degree of bizarreness. Results indicate that when IP and bizarreness are controlled for, memorability of counterintuitive and BIZ concepts-relative to intuitive control concepts-is similar across concepts with one, two, and three characteristics. Findings suggest that the MCI and VR effects may be manifestations of the same underlying mechanisms.

  PublisherOA PDFDOI

Recent grants

• CAREER: Applications of Bayesian Inference to Human Memory and Decision-Making

  NSF · $459k · 2015–2021

Frequent coauthors

• Julien Musolino

  Rutgers, The State University of New Jersey

  37 shared

• Joseph Sommer

  35 shared

• Kimele Persaud

  Rutgers, The State University of New Jersey

  16 shared

• Mark Steyvers

  14 shared

• Zihao Xu

  Wenzhou University

  9 shared

• Qiong Zhang

  Rutgers Sexual and Reproductive Health and Rights

  9 shared

• Zihao Xu

  7 shared

• Qiong Zhang

  Stockholm University

  7 shared

Education

• Ph. D., Cognitive Science

  University of California Irvine

  2011