Search

Event-based prospective memory (PM) refers to the cognitive processes required to perform a planned action upon encountering a future event. Event-based PM studies engage participants in an ongoing task (e.g., lexical decision-making) with an instruction to make an alternative PM response to certain items (e.g., items containing "tor"). The Prospective Memory Decision Control (PMDC) model, which provides a quantitative process account of ongoing-task and PM decisions, proposes that PM and ongoing-task processes compete in a race to threshold. We use PMDC to test whether, as proposed by the Delay Theory of PM costs, PM can be improved by biasing decision-making against a specific ongoing-task choice, so that the PM process is more likely to win the race. We manipulated bias in a lexical decision task with an accompanying PM intention. In one condition, a bias was induced against deciding items were words, and in another, a bias was induced against deciding items were non-words. The bias manipulation had little effect on PM accuracy but did affect the types of ongoing-task responses made on missed PM trials. PMDC fit the observed data well and verified that the bias manipulation had the intended effect on ongoing-task processes. Furthermore, although simulations from PMDC could produce an improvement in PM accuracy due to ongoing-task bias, this required implausible parameter values. These results illustrate the importance of understanding event-based PM in terms of a comprehensive model of the processes that interact to determine all aspects of task performance.

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT‐R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT‐R parameters without requiring the modeler to build and fit an entire ACT‐R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT‐R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT‐R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT‐R and LBA lends a more concrete interpretation to ACT‐R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science. The parameters governing our behavior are in constant flux, and capturing these dynamics in cognitive models remains a challenge. We demonstrate how a mapping between ACT‐R's model of declarative memory and the linear ballistic accumulator enables efficient estimation of memory parameters from data. The resulting estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. [ABSTRACT FROM AUTHOR]

Models of free recall describe free recall initiation as a decision-making process in which items compete to be retrieved. Recently, Osth and Farrell (Psychological Review, 126, 578–609, 2019) applied evidence accumulation models to complete RT distributions and serial positions of participants' first recalls in free recall, which resulted in some novel conclusions about primacy and recency effects. Specifically, the results of the modeling favored an account in which primacy was due to reinstatement of the start-of-the-list, and recency was found to be exponential in shape. In this work, we examine what happens when participants are given alternative recall instructions. Prior work has demonstrated weaker primacy and greater recency when fewer items are required to report (Ward & Tan, Memory & Cognition, 2019), and a key question is whether this change in instructions qualitatively changes the nature of the recall process, or merely changes the parameters of the recall competition. We conducted an experiment where participants studied six- or 12-item lists and were post-cued as to whether to retrieve a single item, or as many items as possible. Subsequently, we applied LBA models with various assumptions about primacy and recency, implemented using hierarchical Bayesian techniques. While greater recency was observed when only one item was required for output, the model selection did not suggest that there were qualitative differences between the two conditions. Specifically, start-of-list reinstatement and exponential recency functions were favored in both conditions. [ABSTRACT FROM AUTHOR]

The relative amount of variability in memory strength for targets vs. lures in recognition memory is commonly measured using the receiver operating characteristic (ROC) procedure, in which participants are given either a bias manipulation or are instructed to give confidence ratings to probe items. A near universal finding is that targets have higher variability than lures. Ratcliff and Starns (2009) questioned the conclusions of the ROC procedure by demonstrating that accounting for decision noise within a response time model yields different conclusions about relative memory evidence than the ROC procedure yields. In an attempt to better understand the source of the discrepancy, we applied models that include different sources of decision noise, including both the diffusion decision model (DDM) and the linear ballistic accumulator (LBA) model, which either include or lack within-trial noise in evidence accumulation, and compared their estimates of the ratio of standard deviations to those from ROC analysis. Each method produced dramatically different estimates of the relative variability of target items, with the LBA even indicating equal variance in some cases. This stands in contrast to prior work suggesting that the DDM and LBA produce largely similar estimates of relevant model parameters, such as drift rate, boundary separation, and nondecision time. Parameter validation using data from Starns’s (2014) numerosity discrimination data demonstrated that only the DDM was able to correctly reproduce the evidence ratios in the data. These results suggest that the DDM may be providing a more accurate account of lure-to-target variability evidence ratios in recognition memory. [ABSTRACT FROM AUTHOR]

• Lexical decision modulates the WFE in a subsequent unrelated recognition task. • An increase in hit rates and false alarms for high frequency words was found in RC. • A decrease in false alarms for the low frequency words was also found in RC. • Changes in drift rate and response criterion of the LBA model explain the WFE. • Previous processing history affects memory processing is subsequent tasks. The natural language frequency of a word is known to influence the ability to perform recognition judgments based on either semantic or episodic memory, an effect commonly known as the word frequency effect (WFE). For episodic recognition specifically, the WFE presents a mirrored pattern with higher hit rates and lower false alarm rates for low frequency words compared to high frequency words. Interestingly, the use of certain study tasks such as judgements of concreteness has been shown to reduce or even abolish the hit rate advantage of low frequency words. In the present study we tested the hypothesis that prior exposure to an unrelated lexical decision task leads to a modulation of the WFE in a subsequent episodic recognition test. Results showed an increase in hit rates and false alarm rates for high frequency words and a decrease in false alarm rates for low-frequency words. Analysis of behaviour with the linear ballistic accumulator model furthermore revealed that this effect was driven by changes in the rate of evidence accumulation of the decision process and in the response threshold. We discuss the relevance of these findings for current theories of memory. [ABSTRACT FROM AUTHOR]