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EEG activity represents the correctness of perceptual decisions trial-by-trial.

ABSTRACT: Performance monitoring is an executive function, which we depend on for detecting and evaluating the consequences of our behavior. Although event related potentials (ERPs) have revealed the existence of differences after correct and incorrect decisions, it is not known whether there is a trial-by-trial representation of the accuracy of the decision. We recorded the electroencephalographic activity (EEG) while participants performed a perceptual discrimination task, with two levels of difficulty, in which they received immediate feedback. Receiver Operating Characteristic (ROC) analyses were used to reveal two components that convey trial-by-trial representations of the correctness of the decisions. Firstly, the performance monitoring-related negativity (PM-N), a negative deflection whose amplitude is higher (more negative) after incorrect trials. Secondly, the performance monitoring-related positivity (PM-P), a positive deflection whose amplitude is higher after incorrect trials. During the time periods corresponding to these components, trials can be accurately categorized as correct or incorrect by looking at the EEG activity; this categorization is more accurate when based on the PM-P. We further show that the difficulty of the discrimination task has a different effect on each component: after easy trials the latency of the PM-N is shorter and the amplitude of the PM-P is higher than after difficult trials. Consistent with previous interpretations of performance-related ERPs, these results suggest a functional differentiation between these components. The PM-N could be related to an automatic error detection system, responsible for fast behavioral corrections of ongoing actions, while the PM-P could reflect the difference between expected and actual outcomes and be related to long-term changes in the decision process.

SUBMITTER: Pardo-Vazquez JL

PROVIDER: S-EPMC3975098 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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EEG activity represents the correctness of perceptual decisions trial-by-trial.

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