Rescuing Basic Equality

In the debate on the basis of moral equality, one conclusion achieves near consensus: that we must reject all accounts that ground equality in the possession of some psychological capacity (Psychological Capacity Accounts). This widely held view crystallises around three objections. The first is the Arbitrariness Objection, which holds that the threshold at which the possession of the relevant capacities places an individual within the required range is arbitrary. The second is the Variations Objection, which holds that there is rational pressure to acknowledge that variations in psychological capacities between individuals are morally relevant. The third is the No Rational Agency Objection, according to which Psychological Capacity Accounts have unpalatable implications for our treatment of humans who do not possess the relevant capacities. We develop a Psychological Capacity Account based on the capacity for a conception of the good and offer a novel defence of the account against these objections.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

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.