Computational models that simulate individuals’ movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control. Whilst there is evidence that individuals demonstrate idiosyncratic control-tracking strategies, it remains unclear whether models can be sensitive to these idiosyncrasies. Perceptual control theory (PCT) provides a unique model architecture with an internally set reference value parameter, and can be optimized to fit an individual’s tracking behavior. The current study investigated whether PCT models could show temporal stability and individual specificity over time. Twenty adults completed three blocks of 15 1-min, pursuit-tracking trials. Two blocks (training and post-training) were completed in one session and the third was completed after 1 week (follow-up). The target moved in a one-dimensional, pseudorandom pattern. PCT models were optimized to the training data using a least-mean-squares algorithm, and validated with data from post-training and follow-up. We found significant inter-individual variability (partial η2: .464–.697) and intra-individual consistency (Cronbach’s α: .880–.976) in parameter estimates. Polynomial regression revealed that all model parameters, including the reference value parameter, contribute to simulation accuracy. Participants’ tracking performances were significantly more accurately simulated by models developed from their own tracking data than by models developed from other participants’ data. We conclude that PCT models can be optimized to simulate the performance of an individual and that the test-retest reliability of individual models is a necessary criterion for evaluating computational models of human performance.

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Background Work in humans has shown that impulsivity can be advantageous in certain settings. However, evidence for so-called functional impulsivity is lacking in experimental animals. Aims This study investigated the contexts in which high impulsive (HI) rats show an advantage in performance compared with mid- (MI) and low impulsive (LI) rats. We also assessed the effects of dopaminergic and noradrenergic agents to investigate underlying neurotransmitter mechanisms. Methods We tested rats on a variable inter-trial interval (ITI) version of the 5-choice serial reaction time task (5CSRTT). Rats received systemic injections of methylphenidate (MPH, 1 mg/kg and 3 mg/kg), atomoxetine (ATO, 0.3 mg/kg and 1 mg/kg), amphetamine (AMPH, 0.2 mg/kg), the alpha-2a adrenoceptor antagonist atipamezole (ATI, 0.3 mg/kg) and the alpha-1 adrenoceptor agonist phenylephrine (PHEN, 1 mg/kg) prior to behavioural testing. Results Unlike LI rats, HI rats exhibited superior performance, earning more reinforcers, on short ITI trials, when the task required rapid responding. MPH, AMPH and ATI improved performance on short ITI trials and increased impulsivity in long ITI trials, recapitulating the behavioural profile of HI. In contrast, ATO and PHEN impaired performance on short ITI trials and decreased impulsivity, thus mimicking the behavioural profile of LI rats. The effects of ATO were greater on MI rats and LI rats. Conclusions These findings indicate that impulsivity can be advantageous when rapid focusing and actions are required, an effect that may depend on increased dopamine neurotransmission. Conversely, activation of the noradrenergic system, with ATO and PHEN, led to a general inhibition of responding.

Sensorimotor delays dictate that humans act on outdated perceptual information. As a result, continuous manual tracking of an unpredictable target incurs significant response delays. However, no such delays are observed for repeating targets such as the sinusoids. Findings of this kind have led researchers to claim that the nervous system constructs predictive, probabilistic models of the world. However, a more parsimonious explanation is that visual perception of a moving target position is systematically biased by its velocity. The resultant extrapolated position could be compared with the cursor position and the difference canceled by negative feedback control, compensating sensorimotor delays. The current study tested whether a position extrapolation model fit human tracking of sinusoid (predictable) and pseudorandom (less predictable) targets better than the non-biased position control model, Twenty-eight participants tracked these targets and the two computational models were fit to the data at 60 fixed loop delay values (simulating sensorimotor delays). We observed that pseudorandom targets were tracked with a significantly greater phase delay than sinusoid targets. For sinusoid targets, the position extrapolation model simulated tracking results more accurately for loop delays longer than 120 ms, thereby confirming its ability to compensate for sensorimotor delays. However, for pseudorandom targets, this advantage arose only after 300 ms, indicating that velocity information is unlikely to be exploited in this way during the tracking of less predictable targets. We conclude that negative feedback control of position is a parsimonious model for tracking pseudorandom targets and that negative feedback control of extrapolated position is a parsimonious model for tracking sinusoidal targets.

There is limited evidence regarding the accuracy of inferences about intention. The research described in this article shows how perceptual control theory (PCT) can provide a “ground truth” for these judgments. In a series of 3 studies, participants were asked to identify a person’s intention in a tracking task where the person’s true intention was to control the position of a knot connecting a pair of rubber bands. Most participants failed to correctly infer the person’s intention, instead inferring complex but nonexistent goals (such as “tracing out two kangaroos boxing”) based on the actions taken to keep the knot under control. Therefore, most of our participants experienced what we call “control blindness.” The effect persisted with many participants even when their awareness was successfully directed at the knot whose position was under control. Beyond exploring the control blindness phenomenon in the context of our studies, we discuss its implications for psychological research and public policy.