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Considerable experimental evidence supports the existence of predictive error signals in various brain regions during associative learning in animals and humans. These regions include the prefrontal cortex, temporal lobe, cerebellum and monoamine systems. Various quantitative theories have been developed to describe behaviour during learning, including Rescorla-Wagner, Temporal Difference and Kalman filter models. These theories may also account for neural error signals. Reviews of imaging studies of depressive illness have consistently implicated the prefrontal and temporal lobes as having abnormal function, and sometimes structure, whilst the monoamine systems are directly influenced by antidepressant medication. It was hypothesised that such abnormalities may be associated with a dysfunction of associative learning that would be reflected by different predictive error signals in depressed patients when compared with healthy controls. This was tested with 30 subjects, 15 with a major depressive illness, using a gambling paradigm and fMRI. Consistent with the hypothesis, depressed patients differed from controls in having an increased error signal. Additionally, for some brain regions, the magnitude of the error signal correlated with Hamilton depression rating of illness severity. Structural equation modelling was used to investigate hypothesised change in effective connectivity between prespecified regions of interest in the limbic and paralimbic system. Again, differences were found that in some cases correlated with illness severity. These results are discussed in the context of quantitative theories of brain function, clinical features of depressive illness and treatments.

Original publication




Journal article



Publication Date





269 - 280


Adult, Aged, Antidepressive Agents, Artifacts, Brain, Depressive Disorder, Major, Dose-Response Relationship, Drug, Drug Therapy, Combination, Echo-Planar Imaging, Female, Games, Experimental, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Male, Mathematical Computing, Middle Aged, Nerve Net, Software, Statistics as Topic