Sustained effects of corrupted feedback on perceptual inference.

Feedback is central to most forms of learning, and its reliability is therefore critical. Here, we investigated the effects of corrupted, and hence unreliable, feedback on perceptual inference. Within the framework of Bayesian inference, we hypothesised that corrupting feedback in a demanding perceptual task would compromise sensory information processing and bias inference towards prior information if available. These hypotheses were examined by a simulation and in two behavioural experiments with visual detection (experiment 1) and discrimination (experiment 2) tasks. Both experiments consisted of two sessions comprising intervention runs with either corrupted or uncorrupted (correct) feedback, and pre- and post-intervention tests to assess the effects of feedback. In the tests alone, additional prior beliefs were induced through predictive auditory cues to assess sustained effects of feedback on the balance between sensory evidence and prior beliefs. Both experiments and the simulation showed the hypothesised decrease in performance and increased reliance on prior beliefs after corrupted but not uncorrupted feedback. Exploratory analyses indicated reduced confidence regarding perceptual decisions during delivery of corrupted feedback. Our results suggest that corrupted feedback on perceptual decisions leads to sustained changes in perceptual inference, characterised by a shift from sensory likelihood to prior beliefs when those are accessible.

Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information.

OBJECTIVE: We investigated the potential of computer-based models to decode diagnosis and lifetime consumption in alcohol dependence (AD) from grey-matter pattern information. As machine-learning approaches to psychiatric neuroimaging have recently come under scrutiny due to unclear generalization and the opacity of algorithms, our investigation aimed to address a number of methodological criticisms. METHOD: Participants were adult individuals diagnosed with AD (N = 119) and substance-naïve controls (N = 97) ages 20-65 who underwent structural MRI. Machine-learning models were applied to predict diagnosis and lifetime alcohol consumption. RESULTS: A classification scheme based on regional grey matter attained 74% diagnostic accuracy and predicted lifetime consumption with high accuracy (r = 0.56, P < 10-10 ). A key advantage of the classification scheme was its algorithmic transparency, revealing cingulate, insular and inferior frontal cortices as important brain areas underlying classification. Validation of the classification scheme on data of an independent trial was successful with nearly identical accuracy, addressing the concern of generalization. Finally, compared to a blinded radiologist, computer-based classification showed higher accuracy and sensitivity, reduced age and gender biases, but lower specificity. CONCLUSION: Computer-based models applied to whole-brain grey-matter predicted diagnosis and lifetime consumption in AD with good accuracy. Computer-based classification may be particularly suited as a screening tool with high sensitivity.