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.⇒
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.⇒