Trends in Cognitive Sciences
ReviewSpecial Issue: Cognition in Neuropsychiatric DisordersComputational psychiatry
Section snippets
The explanatory gap
The idea of biological psychiatry seems simple and compelling: the brain is the organ that generates, sustains and supports mental function, and modern psychiatry seeks the biological basis of mental illnesses. This approach has been a primary driver behind the development of generations of anti-psychotic, anti-depressant, and anti-anxiety drugs that enjoy widespread clinical use. Despite this progress, biological psychiatry and neuroscience face an enormous explanatory gap. This gap represents
Mathematical modeling
To define computational modeling, we must first distinguish it from its close cousin, mathematical or biophysical modeling. Mathematical modeling provides a quantitative expression for natural phenomena. This may involve building multi-level (unifying) reductive accounts of natural phenomena. The reductions involve explanatory models at one level of description that are based on models at finer levels, and are ubiquitous in everything from treatments of action potentials [5] (see also [6] for a
Early connectionist models of mental dysfunction
There is an old idea in brain science, namely, that complex functions emerge from networked interactions of relatively simple parts 27, 28. In the brain, the most conspicuous physical substrates for this idea are the networks of neurons connected by synapses. This perspective has been termed ‘connectionism’. One modern expression of connectionism began with the work of Rumelhart, McClelland and the parallel distributed processing research group [29] (but now see [30]), which applied this
Recent efforts toward computational characterization of mental dysfunction
In this section, we review recent efforts to develop and test computational models of mental dysfunction and to extract behavioral phenotypes relevant for building computationally-principled models of mental disease. The examples discussed are intended to provide insights into healthy mental function but in a fashion designed to inform the diagnosis and treatment of mental disease. Along with the pioneering earlier studies 35, 36, 37, 38, 39, 40, there have been recent treatments and reports of
Computational phenotyping of human cognitive function
Computational models of human mental function present more general possibilities for producing new and useful human phenotypes. These phenotypes can then structure the search for genetic and neural contributions to healthy and diseased cognition. We do not expect such an approach to supplant current descriptive nosologies; instead, they will be an adjunct, where the nature of the computational characterization offers a new lexicon for understanding mental function in humans. Moreover, this
Concluding remarks
If the computational approaches we have outlined turn out to be effective in psychiatry, then what might one expect? The large-scale behavioral phenotyping project sketched above involves substantial aspects of data analysis and computational modeling. The aim of the data analysis will be to link precise elements of the models to measurable aspects of behavior and to molecular and neural substrates that can be independently measured. A strong likelihood here is that the models will offer a set
Glossary
- Cognitive phenotype
- a phenotype is a measureable trait of an organism. Although easy to state in this manner, the idea of a phenotype can become subtle and contentious. Phenotypes include different morphology, biochemical cascades, neural connection patterns, behavioral patterns and so on. Phenotypic variation is a term used to refer to those variations in some trait on which natural selection could act. A cognitive phenotype is a pattern of cognitive functioning in some domain that could be
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