How does the human brain construct intelligent behavior? Computational models have proposed several mechanisms to accomplish this: the most well known is “Hebbian learning,” a process mathematically similar to both principal components analysis and Bayesian statistics. But other neural learning algorithms must exist – how else could the brain disentangle mere correlations from true causation?
Temporal precedence helps to some extent – and does seem to play a large role in Hebbian learning (e.g., spike-timing dependent plasticity). But the smell of rain does not actually cause rain – although it can both precede and follow it – so temporal precedence is not a complete answer to disambiguating causality from correlation.
Environmental interaction is another possibility – and one function of children’s exploratory play does appear to be the resolution of causally-ambiguous events.
But exploratory play is just a specific example of a larger class of learning situations, where the environment itself can provide an “error signal” in testing causal inferences – appropriately known as “error-driven learning” in computational models. Errors in predictions about the environment can be used to better extract causation from correlation.
Although the classic implementation of error driven learning (known as “backpropagation”) has been criticized as biologically implausible from a cellular neuroscience perspective, cognitive and computational neuroscience has clearly converged on the idea that some learning of this sort must be occurring. Randy O’Reilly’s GeneRec algorithm recasts backpropagation (and all other error-driven learning algorithms) in a more biologically-palatable form, but still requires discrete phases of “prediction” and “error signal” to accomplish learning.
A variety of other computational models, as described in a wonderful new paper by Kveraga, Ghuman & Bar, propose similar mechanisms in the domain of visual processing. According to these models, object recognition is facilitated by “top-down” predictions of what the eyes will see next – a streamlining process which not only limits the potential problem space of object recognition, but also allows for momentary experience to be used as an error signal in error-driven learning.
Kveraga et al review how this basic idea pops up again and again – from Adaptive Resonance Theory, to Ullman’s “counter streams” framework, to Mumford’s bidirectional recurrent connectivity framework, and recent Bayesian work by Friston et al. (There are countless more examples, including Churchland’s new work as well as Jeff Hawkins’ “memory prediction” framework.)
An interesting feature of the visual system is it’s asymmetry: lower visual processing areas (V1) project only indirectly to the apex of the processing stream (IT), but higher-order areas do project directly back to the lower areas (V1). Kveraga et al. discuss how this asymmetry suggests that higher-order regions of the brain are “predicting” what they might see next, by biasing early visual cortex in that direction.
However, there’s an interesting discrepancy between this “top-down” view of the visual hierarchy and a characteristic of known error-driven learning algorithms: error signals tend to become diluted as they pass through multiple layers of an artificial network, and so direct connections can be required to backpropagate a strong error signal. Thus, the most parsimonious explanation of the asymmetry in connectivity is that object recognition areas (IT) provide a corrective signal to primary visual cortex (V1) in order to tune-up feature detectors in those areas. Thus, these two pieces of evidence may be more compatible with “top-down” correction rather than “top-down” prediction.
On the other hand, Kveraga et al do review a variety of evidence showing activity in orbitofrontal regions (OFC) preceding that in object recognition (IT) and primary visual areas (V1) by up to 50-85msec – as though OFC is actually accomplishing the “top-down prediction” advocated by computational models (and curiously doing so via information from the magnocellular stream).