Mapping spikes to sensations

Abstract

Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code's performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research.

Keywords: neurometric; perception; psychometric; psychophysical task; psychophysics; receiver operating characteristic; signal detection theory; single-unit electrophysiology.

Figure 1

Example illustration of a neurometric–psychometric comparison. (A) A typical psychometric curve from a yes/no detection experiment with six stimuli of varying intensity (see Box 1). Smooth line indicates fit of a cumulative Gaussian to the data points. Dotted line indicates the stimulus value at which the psychometric curve reaches 50% of its final height. This value is commonly taken as the psychophysical threshold. (B) Some typical neurometric curves: a single neuron’s spikes were counted during stimulus presentation. The neuron was assumed to “detect” the event when it fired in excess of n spikes during stimulus presentation, where n here encompasses 1, 2, 4, and 8 spikes. The neurometric curve for n = 2 matches the psychometric curve best, as assessed by their common threshold of 175 (arbitrary units). Thus, the NP ratio here is 175/175 = 1.

Figure 2

Sequence of events in four tasks commonly employed in animal psychophysics. (A) Go–NoGo task. (B) Yes/no task. (C) Two-interval forced choice task. (D) Spatial two-alternative forced choice task.

Figure 3

Signal detection theoretical process model of performance in the yes/no task. See main text for details.

Figure 4

Signal detection theoretical process model of performance in the 2-AFC task. See main text for details.

Figure 5

Illustration how different stimulus presentation probabilities and different ROC-analysis strategies may yield disparate estimates of sensory performance. (A) The total stimulus set comprises six different stimuli, five of which correspond to S2 (gray distributions, blue distribution is the sum of five individual ones) and one corresponds to S1 (red). All six stimuli occur with equal probability (means: 100:10:150) and have identical SD (20). Middle panel: depending on the location of the response criterion on the decision axis, different sets of probabilities of a correct response exist. For each possible criterion on the abscissa, the corresponding accuracies for each stimulus can be read off the ordinate. Right panel: overall proportion of correct responses (across all stimuli) as a function of criterion placement. Vertical line indicates optimal criterion placement. (B) As in (A), but probability of S1 and S2 are equal (0.5 each); within the S2 category, all stimuli are equally probable (p = 0.1). For the same set of stimuli as in a, the optimal criterion is shifted considerably to the right, and the overall proportion of correct responses drops from 0.84 to 0.75. (C) As in (A), but showing performance in a two-stimulus yes/no task with S1 and one stimulus out of S2 with the weakest signal strength. (D) As in (A), but showing performance in a two-stimulus yes/no task with S1 and one stimulus out of S2 with strongest signal strength. (E) Psychometric functions for different task conditions: magenta, task as in (A), blue, task as in (B), green: psychometric curve resulting from a sequence of separate yes/no experiments where stimuli are presented pairwise and in blocks (i.e., S2 vs. S1–S1, S2 vs. S1–S2, S2 vs. S1–S3 etc.), red: psychometric curve resulting from a sequence of 2-AFC experiments where stimuli are presented pairwise and in blocks.

Figure 6

Signal detection theoretical process models of performance in the yes/no task with reference stimulus. (A) Stimulus distributions along the decision variable under the optimal strategy. The reference stimulus sample is ignored on every trial, and thus the task reduces to the familiar yes/no task. (B) Stimulus distributions along the decision variable under the suboptimal strategy, when subjects decide on the basis of the difference of the first and the second sample.