Pernille Hemmer

Integrating episodic memories and prior knowledge at multiple levels of abstraction

Prior knowledge can have a large influence on recall when the memory for the original event is error prone or incomplete. We investigated the interaction between memory and prior knowledge in a recall task involving natural objects such as fruits and vegetables. We first quantified prior knowledge for the sizes of objects in a norming experiment. We then assessed the influence of prior knowledge in a memory experiment in which we compared the actual size of objects shown during a study phase with the reconstructed size of an object during the test phase. Recall was biased both by the mean size of the specific object studied and by the mean size of all objects in the category. This result suggests that the influence of prior knowledge can come from multiple, hierarchically related levels of representation, such as the object-category and superordinate-category levels.

Modeling Experimentally Induced Strategy Shifts

In dynamic decision-making environments, observers must continuously adjust their decision-making strategies. Previous research has focused on internal fluctuations in decision mechanisms, without regard to how these changes are induced by environmental changes. We developed a simple paradigm in which we manipulated task difficulty, thereby inducing changes in decision processes. We applied this paradigm to recognition memory, manipulating task difficulty by changing the similarity of lures to targets. More difficult decision environments caused participants to make more careful decisions, but these changes did not appear immediately. We propose a simple theoretical account for these data, using a dynamic version of signal detection theory fitted to individual subjects. Our model represents a significant departure from existing models because it incorporates subject-controlled parameters that may adjust over time in response to environmental changes.

Interaction between categorical knowledge and episodic memory across domains.

Categorical knowledge and episodic memory have traditionally been viewed as separate lines of inquiry. Here, we present a perspective on the interrelatedness of categorical knowledge and reconstruction from memory. We address three underlying questions: what knowledge do people bring to the task of remembering? How do people integrate that knowledge with episodic memory? Is this the optimal way for the memory system to work? In the review of five studies spanning four category domains (discrete, continuous, temporal, and linguistic), we evaluate the relative contribution and the structure of influence of categorical knowledge on long-term episodic memory. These studies suggest a robustness of peoples’ knowledge of the statistical regularities of the environment, and provide converging evidence of the quality and influence of category knowledge on reconstructive memory. Lastly, we argue that combining categorical knowledge and episodic memory is an efficient strategy of the memory system.

Moving beyond qualitative evaluations of Bayesian models of cognition

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

Reconstruction from Memory in Naturalistic Environments

Abstract Many aspects of our experiences do not have to be explicitly remembered, but can be inferred based on our knowledge of the regularities in our environment. In this chapter, we investigate the interaction between episodic memory and prior knowledge in naturalistic environments. In contrast to previous studies that suggest a detrimental effect of prior knowledge, we show that when using stimuli that are statistically representative of our environment, prior knowledge of the regularities of our environment can lead to very different outcomes. For example, simple “guessing” using prior knowledge alone—without using episodic memory—leads to relatively high accuracy. In addition, we find relatively few intrusion errors in studies involving natural scenes. We argue that it is important to use ecologically valid stimuli in memory studies, because the findings of memory studies using statistically unrepresentative stimulus material are unlikely to give insights about the operation of human memory in more natural settings.

The dynamics of fidelity over the time course of long-term memory.

Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory.

A Bayesian framework for the development of belief-desire reasoning: Estimating inhibitory power

A robust empirical finding in theory-of-mind (ToM) reasoning, as measured by standard false-belief tasks, is that children four years old or older succeed whereas three-year-olds typically fail in predicting a person’s behavior based on an attributed false belief. Nevertheless, when the child’s own belief is undermined by increasing their subjective uncertainty about the truth, as introduced in low-demand false-belief tasks, three-year-olds can better appreciate another person’s false belief. Inhibition is believed to play a critical role in such developmental patterns. Within a Bayesian framework, using meta-data, we present the first computational implementation of inhibition, as specified by the Theory of Mind Mechanism (ToMM) model, to account for both the developmental shift from three to four years of age and the change in children’s performances between high-demand and low-demand false-belief tasks. A Bayesian framework enables us to evaluate the predictive power of the model and infer the underlying psychological parameters. Together with behavioral evidence, we discuss the critical role of inhibitory control, as specified by ToMM, in children’s theory-of-mind development.

Information and Processes Underlying Semantic and Episodic Memory Across Tasks, Items, and Individuals

The development of memory theory has been constrained by a focus on isolated tasks rather than the processes and information that are common to situations in which memory is engaged. We present results from a study in which 453 participants took part in five different memory tasks: single-item recognition, associative recognition, cued recall, free recall, and lexical decision. Using hierarchical Bayesian techniques, we jointly analyzed the correlations between tasks within individuals—reflecting the degree to which tasks rely on shared cognitive processes—and within items—reflecting the degree to which tasks rely on the same information conveyed by the item. Among other things, we find that (a) the processes involved in lexical access and episodic memory are largely separate and rely on different kinds of information, (b) access to lexical memory is driven primarily by perceptual aspects of a word, (c) all episodic memory tasks rely to an extent on a set of shared processes which make use of semantic features to encode both single words and associations between words, and (d) recall involves additional processes likely related to contextual cuing and response production. These results provide a large-scale picture of memory across different tasks which can serve to drive the development of comprehensive theories of memory.

Seeing Colors: Cultural and Environmental Influences on Episodic Memory

Expectations learned from our perceptual experiences, culture, and language can shape how we perceive, interact with, and remember features of the past. Here, we questioned whether environment also plays a role. We tested recognition memory for color in Bolivia’s indigenous Tsimanè people, who experience a different color environment than standard U.S. populations. We found that memory regressed differently between the groups, lending credence to the idea that environmental variations engender differences in expectations, and in turn perceptual memory for color.

The Generalized Quantum Episodic Memory Model

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over-distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over-distribution effect and subadditivity have been widely observed in item and source-memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal-detection theory. A dual-trace model called the over-distribution (OD) model (Brainerd & Reyna, 2008) can explain the episodic over-distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory (GQEM) model, which extends the Quantum Episodic Memory (QEM) model developed by Brainerd, Wang, and Reyna (2013). We test GQEM by comparing it to the OD model using data from a novel item-memory experiment and a previously published source-memory experiment (Kellen, Singmann, & Klauer, 2014) examining the over-distribution effect. Using the best-fit parameters from the over-distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory.