Biomedical subjects

Richard F Murray

Publications and source records attributed to Richard F Murray.

4 recordsLinked to original sources

PubMed2004

Troubles with bubbles.

The bubbles method is a recently developed variant of reverse correlation methods that have been used in psychophysics and physiology. We show mathematically that for the broad and important class of noisy linear observers, the bubbles method recovers much less information about how observers process stimuli than reverse correlation does. We also show experimentally that the unusual type of noise used in the bubbles method can drastically change human observers' strategies in psychophysical tasks, which reduces the value of the information that is obtained from a bubbles experiment. We conclude that reverse correlation is generally preferable to the bubbles method in its present form, but we also give suggestions as to how the bubbles method could be modified to avoid the problems we discuss.

Humans↗

PubMed2003

A linear cue combination framework for understanding selective attention.

Using a linear cue combination framework, we develop a measure of selective attention that describes the relative weight that an observer assigns to attended and unattended parts of a stimulus when making perceptual judgments. We call this measure attentional weight. We present two methods for measuring attentional weight by calculating the trial-by-trial correlation between the strength of attended and unattended parts of a stimulus and the observer's responses. We illustrate these methods in three experiments that investigate whether observers can direct selective attention according to contrast polarity when judging global direction of motion or global orientation. We find that when observers try to judge the global direction or orientation of the parts of a stimulus with a given contrast polarity (white or black), their responses are nevertheless strongly influenced by parts of the stimulus that have the opposite contrast polarity. Our measure of selective attention indicates that the influence of the opposite-polarity distractors on observers' responses is typically 65% as strong as the influence of the targets in the motion task, and typically 25% as strong as the targets in the orientation task, demonstrating that observers have only a limited ability to direct attention according to contrast polarity. We discuss some of the advantages of using a linear cue combination framework to study selective attention.

Attention↗

PubMed2003

Saccadic and perceptual performance in visual search tasks. II. Letter discrimination.

Can the oculomotor system use shape cues to guide search saccades? Observers searched for target letters (D, U, or X) among distractors (the letter O in the discrimination task and blank locations in the detection task) in Gaussian white noise. We measured the accuracy of first saccadic responses on each trial and perceptual (i.e., button-press) responses in separate trials with the stimulus duration chosen so that the saccadic and perceptual processing times were matched. We calculated the relative efficiency of saccadic decisions compared with perceptual decisions, eta(rel) = (d'(sac)/d'(per))2. Relative efficiency was low but consistently greater than zero in discrimination tasks (15% +/- 6%) and high in detection tasks (60% +/- 10%). We conclude that the saccadic targeting system can use shape cues, but less efficiently than the perceptual system can.

Adult↗

PubMed2002

Optimal methods for calculating classification images: weighted sums.

In signal detection theory, an observer's responses are often modeled as being based on a decision variable obtained by cross-correlating the stimulus with a template, possibly after corruption by external and internal noise. The response classification method estimates an observer's template by measuring the influence of each pixel of external noise on the observer's responses. A map that shows the influence of each pixel is called a classification image. Other authors have shown how to calculate classification images from external noise fields, but the optimal calculation has never been determined, and the quality of the resulting classification images has never been evaluated. Here we derive the optimal weighted sum of noise fields for calculating classification images in several experimental designs, and we derive the signal-to-noise ratio (SNR) of the resulting classification images. Using the expressions for the SNR, we show how to choose experimental parameters, such as the observer's performance level and the external noise power, to obtain classification images with a high SNR. We discuss two-alternative identification experiments in which the stimulus is presented at one or more contrast levels, in which each stimulus is presented twice so that we can estimate the power of the internal noise from the consistency of the observer's responses, and in which the observer rates the confidence of his responses. We illustrate these methods in a series of contrast increment detection experiments.

Contrast Sensitivity↗