Continuous psychophysics: Target-tracking to measure visual sensitivity

Abstract

We introduce a novel framework for estimating visual sensitivity using a continuous target-tracking task in concert with a dynamic internal model of human visual performance. Observers used a mouse cursor to track the center of a two-dimensional Gaussian luminance blob as it moved in a random walk in a field of dynamic additive Gaussian luminance noise. To estimate visual sensitivity, we fit a Kalman filter model to the human tracking data under the assumption that humans behave as Bayesian ideal observers. Such observers optimally combine prior information with noisy observations to produce an estimate of target position at each time step. We found that estimates of human sensory noise obtained from the Kalman filter fit were highly correlated with traditional psychophysical measures of human sensitivity (R2 > 97%). Because each frame of the tracking task is effectively a "minitrial," this technique reduces the amount of time required to assess sensitivity compared with traditional psychophysics. Furthermore, because the task is fast, easy, and fun, it could be used to assess children, certain clinical patients, and other populations that may get impatient with traditional psychophysics. Importantly, the modeling framework provides estimates of decision variable variance that are directly comparable with those obtained from traditional psychophysics. Further, we show that easily computed summary statistics of the tracking data can also accurately predict relative sensitivity (i.e., traditional sensitivity to within a scale factor).

Contact lenses can cause the reverse Pulfrich effect and anti-Pulfrich monovision corrections can eliminate it

“…Thus, it is of paramount importance to develop methods that enable the rapid collection of high-quality data, that require little or no training, and that can be used with nontraditional populations including children. We are working to adapt target-tracking methods for continuous psychophysics for this purpose (Bonnen, Burge, Yates, Pillow, & Cormack, 2015).…”

Performance Monitoring for Sensorimotor Confidence: A Visuomotor Tracking Study

“…In theory, this can be studied 105 by examining how knowledge of variability from sensory, motor, and task sources, influ-106 ences motor decision-making (Wolpert and Landy, 2012). The majority of studies support 107 the hypothesis that humans plan actions consistent with accurate knowledge of their sen-108 sorimotor uncertainty (e.g., Augustyn Bonnen et al, 2015), with some exceptions (e.g., Zhang et al, 2013). 110 However, the degree to which this knowledge is consciously available to the person is highly 111 debatable (Augustyn and Rosenbaum, 2005).…”

“…The recency effect548 In the sensorimotor feedback process, incoming error signals inform upcoming action plans 549 and quickly become irrelevant(Todorov, 2004;Bonnen et al, 2015). In contrast, the goal 550 of performance monitoring for sensorimotor confidence is to accumulate error signals across 551 time, much like the accumulation of sensory evidence for perceptual decisions with a fixed 552 viewing time.…”

Temporal dynamics of a perceptual decision

“…Our response-tracking approach follows several recent studies that have used mouse tracking to probe the cognitive processes underlying various choice tasks (e.g., Bonnen, Burge, Yates, Pillow, & Cormack, 2015;Dale, Kehoe, & Spivey, 2007;Farmer, Cargill, Hindy, Dale, & Spivey, 2007;Hehman, Stolier, & Freeman, 2015;Rheem, Verma, & Becker 2018;Spivey, Grosjean, & Knoblich, 2005). In a choice task, a measure is typically made of some function of the choice outcome.…”

“…Analyses of these mouse trajectories and their temporal dynamics can therefore provide insights into these underlying processes (Hehman et al, 2015). Response tracking has been used to examine language processing (Spivey et al, 2005), target selection in visual search (Song & Nakayama, 2008), visual sensitivity (Bonnen et al, 2015), and cognitive load (Rheem et al, 2018).…”