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Li et al. present a purely engineering-based approach to active speaker localization. Their system uses Viola and Jones' object detection algorithm for face detection and cross-correlation for auditory speaker localization.

Sun argues that mechanisms and representations (and thus computational models) are an important and necessary part of scientific theories and that that is true especially in cognitive science.

Sun argues that computational cognitive models describe mechanisms and representations in cognitive science well.

Static, purely psychophysical theories of cognition (computational theories of the mind, in Marr's sense) are weak and descriptive only, as opposed to explanatory.

A deep SC neuron which receives enough information from one modality to reliably determine whether a stimulus is in its receptive field does not improve its performance much by integrating information from another modality.

Patton et al. use this insight to explain the diversity of uni-sensory and multisensory neurons in the deep SC.

Colonius and Diederich argue that deep-SC neurons spiking behavior can be interpreted as a vote for a target rather than a non-target being in their receptive field.

This is similar to Anastasio et al.'s previous approach.

There are a number of problems with Colonius' and Diederich's idea that deep-SC neurons' binary spiking behavior can be interpreted as a vote for a target rather than a non-target being in their RF. First, these neurons' RFs can be very broad, and the strength of their response is a function of how far away the stimulus is from the center of their RFs. Second, the response strength is also a function of stimulus strength. It needs some arguing, but to me it seems more likely that the response encodes the probability of a stimulus being in the center of the RF.

Colonius and Diederich argue that, given their Bayesian, normative model of neurons' response behavior, neurons responding to only one sensory modality outperform neurons responding to multiple sensory modalities.

Colonius' and Diederich's explanation for uni-sensory neurons in the deep SC has a few weaknesses: First, they model the input spiking activity for both the target and the non-target case as Poisson distributed. This is a problem, because the input spiking activity is really a function of the target distance from the center of the RF. Second, they explicitly model the probability of the visibility of a target to be independent of the probability of its audibility.

If SC neurons spiking behavior can be interpreted as a vote for a target rather than a non-target being in their receptive field, then the decisions must be made somewhere else because they then do not take into account utility.

Deco and Rolls introduce a system that uses a trace learning rule to learn recognition of more and more complex visual features in successive layers of a neural architecture. In each layer, the specificity of the features increases together with the receptive fields of neurons until the receptive fields span most of the visual range and the features actually code for objects. This model thus is a model of the development of object-based attention.

Bayesian models cannot explain why natural cognition is not always optimal or predict bahavior in cases when it is not.

Computational models cannot predict non-functional effects, like response timing.

Purely computational, Bayesian accounts of cognition are underconstrained.

Cognitive science must not only provide predictive generative models predicting natural cognitive behavior within a normative framework, but also tie in these models with theories on how the necessary computations are realised.

Computational theories of the brain account not only for how it works, but why it should work that way.

According to Marr, the computational theory of an information-processing system is the theory of what it does, why it does what it does and "what is the logic of the strategy by which" what it does can be done.