Jimin Ju^1,2 (), Sung-Ho Kim²; ¹University of Iowa, ²Ewha Womans University

In dynamic events, even minimal kinematic information can lead observers to assign different causal roles to the objects involved—agent versus patient—thereby specifying who acts on whom and how. Because such role assignments entail distinct expectations for how each object should continue to move, they should also modulate how precisely brief motion disruptions are detected. We evaluated this claim with a pause-detection task using ambiguous separation displays (Ju & Kim, 2026), in which a line segment with a disc attached at its end elongates and then separates: pre-separation acceleration evokes the impression of a rigid stick pushing the disc, whereas deceleration evokes a disc pulling an elastic band. On half the trials, a 100-ms pause was inserted into the post-separation motion of either the line or the disc (blocked), and observers (N = 30) judged whether a pause occurred. Signal-detection analyses revealed a robust Target × Pre-separation crossover: pause-detection sensitivity was higher for the disc following pre-separation acceleration—when a rigid stick pushing the disc is the dominant percept—than following deceleration—when a disc pulling an elastic band is dominant—whereas the line showed the reverse, with greater sensitivity after deceleration than acceleration; as such, in both cases, detection was enhanced when the probed object was perceived as the causal patient. These findings indicate that objects construed as inertial, patient-like recipients of external forces elicit tighter Newtonian predictions, making brief disruptions more salient as violations of expected motion. In contrast, when an object is interpreted as an active, self-propelled agent whose motion is less tightly constrained by external forces, the same kinematic irregularities become less noticeable. More broadly, within a force-resistance framework, the attribution of agent-patient roles can tune precision of motion predictions, altering sensitivity to transient deviations as dynamic events unfold.