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Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or "particles," available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies ("knowledge restructuring"). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles--increasing the number of particles leads to both faster category learning and improved strategy-switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best-fit number of particles for strategy switching. However, no association between WMC and best-fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.

Is consolidation needed to account for retroactive interference in free recall? Interfering mental activity during the retention interval of a memory task impairs performance, in particular if the interference occurs in temporal proximity to the encoding of the to-be-remembered (TBR) information. There are at least two rival theoretical accounts of this temporal gradient of retroactive interference. The cognitive neuroscience literature has suggested neural consolidation is a pivotal factor determining item recall. According to this account, interfering activity interrupts consolidation processes that would otherwise stabilize the memory representations of TBR items post-encoding. Temporal distinctiveness theory, by contrast, proposes that the retrievability of items depends on their isolation in psychological time. According to this theory, information processed after the encoding of TBR material will reduce the temporal distinctiveness of the TBR information. To test between these accounts, implementations of consolidation were added to the SIMPLE model of memory and learning. We report data from two experiments utilizing a two-list free recall paradigm. Modeling results imply that SIMPLE was able to model the data and did not benefit from the addition of consolidation. It is concluded that the temporal gradient of retroactive interference cannot be taken as evidence for memory consolidation.

Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function-learning task, in which people learn a functional relationship between two variables by observing the values of those variables. We varied the total number of observations and the number of those observations that take unique values. We found an effect of the number of observations, with functions transmitted using fewer observations changing form more quickly. We did not find an effect of the number of unique observations, suggesting that noise in perception or memory may have affected learning. (Contains 4 notes and 3 figures.)

Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called 'iterated learning,' in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model (Griffiths & Tenenbaum, 2006) and a recently proposed alternative model that assumes people store only a few instances of each type of event in memory (MinK; Mozer, Pashler, & Homaei, 2008). Although testing these models using standard experimental procedures is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the distributions of everyday quantities, and they are inconsistent with the predictions of the MinK model. The results suggest that accurate predictions about everyday events reflect relatively sophisticated knowledge on the part of individuals. Keywords: Iterated learning; Optimal predictions; Bayesian models of cognition

Is consolidation needed to account for retroactive interference in free recall? Interfering mental activity during the retention interval of a memory task impairs performance, in particular if the interference occurs in temporal proximity to the encoding of the to-be-remembered (TBR) information. There are at least two rival theoretical accounts of this temporal gradient of retroactive interference. The cognitive neuroscience literature has suggested neural consolidation is a pivotal factor determining item recall. According to this account, interfering activity interrupts consolidation processes that would otherwise stabilize the memory representations of TBR items post-encoding. Temporal distinctiveness theory, by contrast, proposes that the retrievability of items depends on their isolation in psychological time. According to this theory, information processed after the encoding of TBR material will reduce the temporal distinctiveness of the TBR information. To test between these accounts, implementations of consolidation were added to the SIMPLE model of memory and learning. We report data from two experiments utilizing a two-list free recall paradigm. Modeling results imply that SIMPLE was able to model the data and did not benefit from the addition of consolidation. It is concluded that the temporal gradient of retroactive interference cannot be taken as evidence for memory consolidation. [ABSTRACT FROM AUTHOR]