Redundancy Repartitions Capacity in Visual Working Memory: The Sample-Size Model of Neural Activation

Visual working memory (VWM) has a limited capacity of around four items, yet recall is also improved by structure in the memory array. We reconcile these views by testing how color redundancy - a low-level structural regularity - alters recall precision and response time in a continuous color-reproduction task. To explain precision, we introduce the attention-weighted sample-size model of neural activation, a highly constrained neural-population account built on Oberauer’s (2023) Signal Discrimination Model. The model imposes a non-parametric power law on tuning amplitude (beta) such that beta = -0.5 indexes the canonical 1/sqrt(N) capacity decline observed as the number of to-be-recalled elements increases. Constrained amplitude fits matched or exceeded free baseline models and captured redundancy gains and non-redundant costs. Individual model fits revealed redundancy gains occurred through attention reallocation away from non-redundant elements in three participants. Redundant and non-redundant item precision improved in one participant, suggesting a shared capacity improvement - a `spill-over' benefit. We conclude that VWM capacity is defined over feature-based units that reflect array regularities which are used strategically by individuals, producing redundancy gains and spill-over effects that may be masked by group statistics.