The neural mechanisms underlying behavioral improvement in the detection or discrimination of visual stimuli following learning are still ill understood. Studies in nonhuman primates have shown relatively small and, across studies, variable effects of fine discrimination learning in primary visual cortex when tested outside the context of the learned task. At later stages, such as extrastriate area V4, extensive practice in fine discrimination produces more consistent effects upon responses and neural tuning. In V1 and V4, the effects of learning were (...) most prominent in those neurons that can contribute the most reliable information about the trained stimuli. I suggest that, depending on the particulars of the task demands, neurons at various stages of stimulus and task processing can change their tuning and responses, so that execution of the task will produce a higher frequency of reward. I speculate that the sort of changes that will occur depend on the task and on stimulus analysis require- ments, and they may vary from changes in bottom-up stimulus processing ⁄ tuning within early visual areas or more efficient readout of early visual areas to top-down driven changes in response proper- ties of these areas. (shrink)

Computational modeling of the brain holds great promise as a bridge from brain to behavior. To fulfill this promise, however, it is not enough for models to be 'biologically plausible': models must be structurally accurate. Here, we analyze what this entails for so-called psychobiological models, models that address behavior as well as brain function in some detail. Structural accuracy may be supported by (1) a model's a priori plausibility, which comes from a reliance on evidence-based assumptions, (2) fitting existing data, (...) and (3) the derivation of new predictions. All three sources of support require modelers to be explicit about the ontology of the model, and require the existence of data constraining the modeling. For situations in which such data are only sparsely available, we suggest a new approach. If several models are constructed that together form a hierarchy of models, higher-level models can be constrained by lower-level models, and low-level models can be constrained by behavioral features of the higher-level models. Modeling the same substrate at different levels of representation, as proposed here, thus has benefits that exceed the merits of each model in the hierarchy on its own. (shrink)

We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both (...) the psychology of expert performance and the underlying neural mechanisms constitutes one of the most challenging problems in the cognitive sciences. Using perceptual templates we show how the amount of contextual information can change significantly even within a given task, the relationship between local and non-local contexts and finally why there is very little correlation between measures of intelligence and level of expertise in many of the most complex tasks performed by humans. (shrink)