Material evidence: interaction of well-learned priors and sensorimotor memory when lifting objects

Abstract

Skilled object lifting requires the prediction of object weight. When lifting new objects, such prediction is based on well-learned size-weight and material-density correlations, or priors. However, if the prediction is erroneous, people quickly learn the weight of the particular object and can use this knowledge, referred to as sensorimotor memory, when lifting the object again. In the present study, we explored how sensorimotor memory, gained when lifting a given object, interacts with well-learned material-density priors when predicting the weight of a larger but otherwise similar-looking object. Different groups of participants 1st lifted 1 of 4 small objects 10 times. These included a pair of wood-filled objects and a pair of brass-filled objects where 1 of each pair was covered in a wood veneer and the other was covered in a brass veneer. All groups then lifted a larger, brass-filled object with the same covering as the small object they had lifted. For each lift, we determined the initial peak rate of change of vertical load-force rate and the load-phase duration, which provide estimates of predicted object weight. Analysis of the 10th lift of the small cube revealed no effects of surface material, indicating participants learned the appropriate forces required to lift the small cube regardless of object appearance. However, both surface material and core material of the small cube affected the 1st lift of the large block. We conclude that sensorimotor memory related to object density can contribute to weight prediction when lifting novel objects but also that long-term priors related to material properties can influence the prediction.

Perceiving and acting upon weight illusions in the absence of somatosensory information

“…A lack of such a forward model in IW explains both the perceptual and sensorimotor prediction findings: he would have no means to use size or material cues to guide his fingertip force rates when lifting novel objects, nor would he have his perceptions of object weight influenced by his prior expectations. This conclusion fits well with what is known about the parameterization of fingertip forces in the context of object lifting: individuals typically prepare to lift objects in a predictive manner using either information provided by visual cues to weight or, if such information is unavailable, by the forces utilized in the previous lift (Baugh et al 2012;Forssberg et al 1992;Loh et al 2010). Studies showing that IW modulated grip forces in response to self-generated dynamic changes in load force suggest he can operate in a predictive fashion (Hermsdörfer et al 2008), and he appears to use a forward model when engaged in a mirror drawing task (Miall and Cole 2007).…”

“…This dissociation between the ability to detect real and illusory weight differences is, to our knowledge, a novel finding which provides the first indication that the mechanisms underpinning weight illusions may be fundamentally different to those underpinning normal weight perception, a proposition in line with neuroimaging work showing left ventral premotor adaptation to manipulations of illusory, but not real, object mass (Chouinard et al 2009). Whereas unimpaired individuals lift heavy-looking objects at a higher rate of force than objects that they expect to feel lighter (Baugh et al 2012;Buckingham et al 2009;Gordon et al 1991), IW showed no such tendency in the context of either size of material cues, despite being able to appropriately rank order the objects in terms of expected weight prior to lifting. Although his levels of sensorimotor prediction did not differ from those of our control sample, we find this lack of feedforward behavior particularly surprising in an individual who, presumably, would seem to be particularly reliant on visual cues.…”

Probabilistic information on object weight shapes force dynamics in a grip-lift task

“…Size cues and an assumed material density are used to estimate an object's weight and to scale the force output adequately (Baugh et al 2012;Buckingham et al 2009). Cues announcing the weight of a target object are also known to influence the kinematics of reach-to-grasp movements (Brouwer et al 2006;Eastough and Edwards 2007).…”

Cognitive attribution of the source of an error in object-lifting results in differences in motor generalization

“…Lastly, the density of the large red block was also set to 1.81 g/cm 3 . Previous research (Baugh et al 2012) has demonstrated that when extrapolating lifting forces to the larger red block, participant's weight predictions would be brought down by the more stable long-term priors related to the apparent material. Therefore, it was anticipated that participants' lifting forces would initially be reduced by previous experiences with lifting the black plastic blocks, allowing for the examination of weight prediction differences between groups of participants without participants over-estimating the weight of the red block.…”

“…Much of the time, this process is driven by error-based learning in which future movements are adapted based on the errors experienced in previous scenarios, such as object lifting (Johansson and Cole 1992;Flanagan et al 2006;Baugh et al 2012;Fercho and Baugh 2014), eye movements (Pelisson et al 2010), reaching (Thoroughman and Shadmehr 2000), and grip force modulation (Flanagan and Wing 1997). At the heart of efficient error-based learning is appropriate credit assignment.…”