Below is a list of specific issues important to philosophy of neuroscience: Many of the methods and techniques central to neuroscientific discovery rely on assumptions that can limit the interpretation of the data.
Michael Anderson points out that subtraction-method fMRI misses a lot of brain information that is important to the cognitive processes.
The logic is circular if the researcher then uses the appearance of brain region activation as proof of the correctness of their cognitive theories.
Critics were quick to point out that the insula is not a very selective piece of cortex, and therefore not amenable to reverse inference.
Neuroimaging data will always be relegated to the lower level of implementation and be unable to selectively determine one or another cognitive theory.
Recently, researchers have begun using a new functional imaging technique called resting-state functional-connectivity MRI.
By looking at the natural fluctuations in the blood-oxygen-level-dependent (BOLD) pattern while the subject is at rest, the researchers can see which brain regions co-vary in activation together.
Alternatively, one could take a "theory-neutral" approach, and randomly divide the cortex into partitions with an arbitrary size.
Wigs et al. once again warns that inference of functional brain region communities is difficult under ICA.
The problem with graph theory analysis is that network mapping is heavily influenced by a priori brain region and connectivity (nodes and edges).
However, graph theory analysis is still considered extremely valuable, as it is the only method that gives pair-wise relationships between nodes.
Mumford et al. hoped to avoid these issues and use a principled approach that could determine pair-wise relationships using a statistical technique adopted from analysis of gene co-expression networks.
It is assumed that one instance of double dissociation is sufficient proof to infer separate cognitive modules in the performance of the tasks.
In one widely cited study, Joula and Plunkett used a model connectionist system to demonstrate that double dissociation behavioral patterns can occur through random lesions of a single module.
These results suggest that a single instance of double dissociation is insufficient to justify inference to multiple systems.
He argues that double dissociation logic leads one to infer that peanuts and shrimp are digested by different systems.
[34] He claims that it is easy to demonstrate the existence of a true deficit but difficult to show that another function is truly spared.
On a different note, Alphonso Caramazza has given a principled reason for rejecting the use of group studies in cognitive neuropsychology.
This section will begin with a historical overview of computational neuroscience and then discuss various competing theories and controversies within the field.
[41] This work culminated in the theoretical development of so-called Turing machines and the Church–Turing thesis, which formalized the mathematics underlying computability theory.
The second group consisted of Warren McCulloch and Walter Pitts who were working to develop the first artificial neural networks.
By the mid-1980s, a group of researchers began using multilayer feed-forward analog neural networks that could be trained to perform a variety of tasks.
Most cognitive scientists posit that the brain uses some form of representational code that is carried in the firing patterns of neurons.
Computational accounts seem to offer an easy way of explaining how human brains carry and manipulate the perceptions, thoughts, feelings, and actions of individuals.
Lastly, soft constraints and generalization when processing novel stimuli allow nets to behave more flexibly than symbolic systems.
Recently, Nicholas Shea has offered his own account for content within connectionist systems that employs the concepts developed through cluster analysis.
This view has been criticized by Piccinini on the grounds that such a definition makes computation trivial to the point where it is robbed of its explanatory value.
According to the biosemantic account, this swamp-you would be incapable of computation because there is no evolutionary history with which to justify assigning representational content.
[56] Though Piccinini undoubtedly espouses structuralist views in that paper, he claims that mechanistic accounts of computation avoid reference to either syntax or representation.
[55] It is possible that Piccinini thinks that there are differences between syntactic and structural accounts of computation that Shagrir does not respect.