Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”

Bridging computational approaches to speech production: The semantic–lexical–auditory–motor model (SLAM)

Speech production is studied from both psycholinguistic and motor-control perspectives, with little interaction between the approaches. We assessed the explanatory value of integrating psycholinguistic and motor-control concepts for theories of speech production. By augmenting a popular psycholinguistic model of lexical retrieval with a motor-control-inspired architecture, we created a new computational model to explain speech errors in the context of aphasia. Comparing the model fits to picture-naming data from 255 aphasic patients, we found that our new model improves fits for a theoretically predictable subtype of aphasia: conduction. We discovered that the improved fits for this group were a result of strong auditory-lexical feedback activation, combined with weaker auditory-motor feedforward activation, leading to increased competition from phonologically related neighbors during lexical selection. We discuss the implications of our findings with respect to other extant models of lexical retrieval