Overriding serial dependence: Learning novel cues to update internal predictions of object weight
preprint
OA: closed
Public-Domain
AI-generated summary
This study found that novel visual cues, both on and off objects, rapidly updated predictions of object weight, overriding prior lifting experience, with screen-presented cues proving more influential than object-based ones.
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Abstract
Background: Efficient manual interactions with objects rely on accurate predictions of object heaviness, informed by both visual cues and prior lifting experience (history cues). While familiar visual cues (e.g., object size) are known to guide motor planning, it remains unclear, in comparison, how well arbitrary, novel visual cues can be integrated into predictive force scaling. This study investigates the extent to which familiar and novel visual cues influence anticipatory grip force adjustments, comparing the effects of novel cues across intrinsic (on the object) and extrinsic (on a screen) presentations. Results: In a precision-grip lifting task with three object weights, peak grip force rate (PGFR), an indicator of object heaviness predictions, was measured across four conditions: ‘no-cue’, ‘familiar cue’, ‘novel intrinsic cue’, and ‘novel extrinsic cue’. Intrinsic cues were visual patterns (line orientations) on the surfaces of the objects themselves, while extrinsic cues were diagrammatic depictions of the objects with these cues shown on a monitor. Without visual cues, PGFR was strongly biased by the preceding lift’s weight. All visual cues significantly reduced this bias, with participants weighing novel cues comparably to familiar cues. Surprisingly, extrinsic novel cues exerted a stronger influence than intrinsic ones, contrary to predictions. Conclusions: Novel visual cues can be rapidly deployed for motor planning, highlighting the flexibility of sensorimotor control, with extrinsic cues potentially providing advantages. These findings may offer new avenues for enhancing manual interactions in environments with limited or unconventional weight predictors.
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Source provenance
- europepmc
- last seen: 2026-05-20T01:45:00.602351+00:00
- unpaywall
- last seen: 2026-05-27T02:00:06.600101+00:00
License: Public-Domain