Steering is initiated based on error accumulation

Vehicle control by humans is possible because the central nervous system is capable of using visual information to produce complex sensorimotor actions. Drivers must monitor errors and initiate steering corrections of appropriate magnitude and timing to maintain a safe lane position. The perceptual mechanisms determining how a driver processes visual information and initiates steering corrections remain unclear. Previous research suggests two potential alternative mechanisms for responding to errors: (i) perceptual evidence (error) satisficing fixed constant thresholds (Threshold), or (ii) the integration of perceptual evidence over time (Accumulator). To distinguish between these mechanisms an experiment was conducted using a computer-generated steering correction paradigm. Drivers (N=20) steered towards an intermittently appearing ‘road-line’ that varied in position and orientation with respect to the driver’s position and trajectory. One key prediction from a Threshold framework is a fixed absolute error response across conditions regardless of the rate of error development, whereas the Accumulator framework predicts that drivers would respond to larger absolute errors when the error signal develops at a faster rate. Results were consistent with an Accumulator framework, thus we propose that models of steering should integrate perceived control error over time in order to accurately capture human perceptual performance.

Perceptual Reasons and Defeat

Perceptual evidence about the external world is paradigmatically defeasible. If something looks red to you, it is reasonable to believe that it is red, but if you are wearing rose-tinted glasses, it may not be reasonable at all to believe this, unless you have some independent source of evidence. In this paper, I will compare four models for how to understand this phenomenon. These models differ in their answers to two questions: what evidence we get about the external world through perception, and what our having that evidence consists in. I like one of these models better than the others, but in this paper my primary concern will be to compare their virtues and vices.

A Hierarchical Attractor Network Model of perceptual versus intentional decision updates

Changes of Mind are a striking example of our ability to flexibly reverse decisions and change our own actions. Previous studies largely focused on Changes of Mind in decisions about perceptual information. Here we report reversals of decisions that require integrating multiple classes of information: 1) Perceptual evidence, 2) higher-order, voluntary intentions, and 3) motor costs. In an adapted version of the random-dot motion task, participants moved to a target that matched both the external (exogenous) evidence about dot-motion direction and a preceding internally-generated (endogenous) intention about which colour to paint the dots. Movement trajectories revealed whether and when participants changed their mind about the dot-motion direction, or additionally changed their mind about which colour to choose. Our results show that decision reversals about colour intentions are less frequent in participants with stronger intentions (Exp. 1) and when motor costs of intention pursuit are lower (Exp. 2). We further show that these findings can be explained by a hierarchical, multimodal Attractor Network Model that continuously integrates higher-order voluntary intentions with perceptual evidence and motor costs. Our model thus provides a unifying framework in which voluntary actions emerge from a dynamic combination of internal action tendencies and external environmental factors, each of which can be subject to Change of Mind.

The myth of visual depth cues II: Linear perspective

“Linear perspective,” used to refer to converging lines in an image, is conventionally deemed a “visual depth cue.” Perceptual evidence is not consistent with this view.