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Soltani and Wang propose an adaptive neural model of Bayesian inference neglecting any priors and claim that it is consistent with certain observations in biology.

LIP seems to encode decision variables for saccade direction.

Soltani and Wang argue that their model is consistent with the 'base rate neglect' fallacy.

The base rate fallacy is a fallacy occuring in human decision making in which humans estimate a posterior probability without properly taking account of the prior probability (i.e. solely on the basis of the likelihood).

Soltani and Wang propose an adaptive model of Bayesian inference with binary cues.

In their model, a synaptic weight codes for the ratio of synapses in a set which are activated vs. de-activated by the binary cue encoded in their pre-synaptic axon's activity.

The stochastic Hebbian learning rule makes the synaptic weights correctly encode log posterior probabilities and the neurons will encode reward probability correctly.

LIP and the FEF are usually connected to decision making.

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.

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.

Probability matching is a sub-optimal decision strategy, statically, but it can have advantages because it leads to exploration.

The fact that average neural activity in cortex is decreased if spatial attention is on the location of a non-target out of a target and a non-target compared to when it is on the target supports the notion that inhibition plays an important role in stimulus selection.

Two superimposed visual stimuli of different orientation, one optimal for a given simple cell in visual cortex, the other sub-optimal but excitatory, can elicit a weaker response than just the optimal stimulus.

Utility functions are used in economics to explain people's decisions. They can also be used to examine non-economic decisions, like decisions in sensorimotor control.

Koerding et al. show how a utility function can be inferred from subjects' decisions in a two-alternative-forced-choice task.

LIP has been suggested to contain a saliency map of the visual field, to guide visual attention, and to decide about saccades.