In this paper, we develop a memory model that predicts retrieval characteristics of real-world facts. First, we show how ACT-R’s memory model can be used to predict people’s knowledge about real-world objects. The model assumes the probability of retrieving a chunk of information about an object and the time to retrieve this information depend on the pattern of prior environmental exposure to the object. Second, we use frequencies of information appearing on the Internet as a proxy for what information people would encounter in their natural environment, outside the laboratory. In two Experiments, we use this model to account for subjects’ associative knowledge about real-world objects as well as the associated retrieval latencies. Third, in a computer simulation, we explore how such model predictions can be used to understand the workings and performance of decision strategies that operate on the contents of declarative memory.

Within the literature of psychological and decision sciences, there is a critical difference in the way recognition is defined and studied experimentally. To address this difference, the current experiment examines and attempts to disentangle the influence of two recognition judgment sources (from within an experiment and from an individual’s prior life experiences) upon two different recognition judgments. By presenting participants with a set of related stimuli that vary naturally in environmental occurrence and by manipulating exposure within an experimental context, this experiment allows for a broader and more ecologically valid assessment of recognition memory. Contrasting with the typical word- frequency effect, the results reveal an overall bias to judge high-frequency items as studied on an episodic recognition test. Additionally, the results underscore the role of context by showing that a single study exposure increases the probability that individuals will judge stimuli as presented outside the laboratory.

We present the project aimed at creating a database of detailed architectural process models of memory-based decision models. Those models are implemented in the cognitive architecture ACT-R. In creating this database, we have identified commonalities and differences of various decision models in the literature. The model database can provide insights into the interrelation among decision models and can be used in future research to address debates on inferences from memory, which are hard to resolve without specifying the processing steps at the level of precision that a cognitive architecture provides.