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

Abstract

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.

Ouvrai: Opening access to remote VR studies of human behavioral neuroscience

“…These involve recording and responding to keyboard, trackpad, mouse, and/or touchscreen inputs. They provide all of the basic functionality that would be needed to implement a variety of motor tasks, such as the serial reaction time task where keyboard button presses are recorded to study motor sequencing 13 , a visuomotor rotation paradigm where mouse/trackpad movements are recorded to study motor adaptation 8,9,10 , or virtual physics tasks where the timing and/or location of clicks and button presses can be used to study learning of the mechanical properties of objects 5,7,6 . Additionally, the touchscreen template experiment imports the cannon-es physics engine in order to simulate realistic 3D collisions, showcasing the extensibility of Ouvrai studies via JavaScript modules.…”

“…Crowdsourcing hubs such as Prolific and Amazon Mechanical Turk (MTurk) give researchers access to a worldwide population of participants-provided that their experiments can be accessed through a web browser. i Some researchers have attempted browser-based studies of human motor learning that rely on standard desktop computer peripherals 5,6,7,8,9,10,11,12,13 . Unfortunately, this approach has significant limitations.…”