Memory, perceptual, and motor costs affect the strength of categorical encoding during motor learning of object properties.

Nearly all tasks of daily life involve skilled object manipulation, and successful manipulation requires knowledge of object dynamics. We recently developed a motor learning paradigm that reveals the categorical organization of motor memories of object dynamics. When participants repeatedly lift a constant-density "family" of cylindrical objects that vary in size, and then an outlier object with a greater density is interleaved into the sequence of lifts, they often fail to learn the weight of the outlier, persistently treating it as a family member despite repeated errors. Here we examine eight factors (Similarity, Cardinality, Frequency, History, Structure, Stochasticity, Persistence, and Time Pressure) that could influence the formation and retrieval of category representations in the outlier paradigm. In our web-based task, participants (N = 240) anticipated object weights by stretching a virtual spring attached to the top of each object. Using Bayesian t-tests, we analyze the relative impact of each manipulated factor on categorical encoding (strengthen, weaken, or no effect). Our results suggest that category representations of object weight are automatic, rigid, and linear and, as a consequence, the key determinant of whether an outlier is encoded as a member of the family is its discriminability from the family members.