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Basing perceptual decisions on the most informative sensory neurons.

ABSTRACT: Single unit recording studies show that perceptual decisions are often based on the output of sensory neurons that are maximally responsive (or "tuned") to relevant stimulus features. However, when performing a difficult discrimination between two highly similar stimuli, perceptual decisions should instead be based on the activity of neurons tuned away from the relevant feature (off-channel neurons) as these neurons undergo a larger firing rate change and are thus more informative. To test this hypothesis, we measured feature-selective responses in human primary visual cortex (V1) using functional magnetic resonance imaging and show that the degree of off-channel activation predicts performance on a difficult visual discrimination task. Moreover, this predictive relationship between off-channel activation and perceptual acuity is not simply the result of extensive practice with a specific stimulus feature (as in studies of perceptual learning). Instead, relying on the output of the most informative sensory neurons may represent a general, and optimal, strategy for efficiently computing perceptual decisions.

SUBMITTER: Scolari M

PROVIDER: S-EPMC2957467 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Basing perceptual decisions on the most informative sensory neurons.

Scolari Miranda M Serences John T JT

Journal of neurophysiology 20100714 4

Single unit recording studies show that perceptual decisions are often based on the output of sensory neurons that are maximally responsive (or "tuned") to relevant stimulus features. However, when performing a difficult discrimination between two highly similar stimuli, perceptual decisions should instead be based on the activity of neurons tuned away from the relevant feature (off-channel neurons) as these neurons undergo a larger firing rate change and are thus more informative. To test this ...[more]

PMID: 20631209

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Project description:Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that the majority of observers were best-fit by either: 1) the Heuristic model, which used dot cloud position, spread, and number of dots as cues; or 2) an Evidence-Strength model, which computed the distance between the sensory measurement and discrimination criterion, scaled according to sensory uncertainty. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our modelling. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.

Dataset Information

Basing perceptual decisions on the most informative sensory neurons.

Publications

Basing perceptual decisions on the most informative sensory neurons.

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