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Below is a list of specific issues important to philosophy of neuroscience: "The indirectness of studies of mind and brain" [1] "Computational or representational analysis of brain processing" [2] "Relations between psychological and neuroscientific inquiries" [3] What constitutes adequate explanation in neuroscience? Philosophers of neuroscience have discussed such assumptions in the use of functional magnetic resonance imaging , [6] [7] dissociation in cognitive neuropsychology , [8] [9] single unit recording , [10] and computational neuroscience.

Localization of function means that many cognitive functions can be localized to specific brain regions. A good example of functional localization comes from studies of the motor cortex. Many philosophers of neuroscience criticize fMRI for relying too heavily on this assumption.

Michael Anderson points out that subtraction method fMRI misses a lot of brain information that is important to the cognitive processes. Some philosophers entirely reject any notion of localization of function and thus believe fMRI studies to be profoundly misguided.

One way to understand their objection to the idea of localization of function is the radio repair man thought experiment. The radio begins whistling loudly and the radio repair man declares that he must have ripped out the anti-whistling tube.

There is no anti-whistling tube in the radio and the radio repair man has confounded function with effect. This criticism was originally targeted at the logic used by neuropsychological brain lesion experiments, but the criticism is still applicable to neuroimaging. These assumptions are necessary to justify their inference of brain localization. The logic is circular if the researcher then use the appearance of brain region activation as proof of the correctness of their cognitive theories.

A different problematic methodological assumption within fMRI research is the use of reverse inference [18] A reverse inference is when the activation of a brain region is used to infer the presence of a given cognitive process. Poldrack points out that the strength of this inference depends critically on the likelihood that a given task employs a given cognitive process and the likelihood of that pattern of brain activation given that cognitive process.

In other words, the strength of reverse inference is based upon the selectivity of the task used as well as the selectivity of the brain region activation. A article published in the NY times has been heavily criticized for misusing reverse inference.

The researchers took insula activation as evidence of feelings of love and concluded that people loved their iPhones. Critics were quick to point out that the insula is not a very selective piece of cortex, and therefore not amenable to reverse inference. The Neuropsychologist Max Coltheart took the problems with reverse inference a step further and challenged neuroimagers to give one instance in which neuroimaging had informed psychological theory [20] Coltheart takes the burden of proof to be an instance where the brain imaging data is consistent with one theory but inconsistent with another theory.

Neuroimaging data will always be relegated to the lower level of implementation and be unable to selectively determine one or another cognitive theory. In a article, Richard Henson suggests that forward inference can be used to infer dissociation of function at the psychological level. One final assumption worth mentioning is the assumption of pure insertion in fMRI.

For example, if you wanted to find the reading comprehension area of the brain, you might scan participants while they were presented with a word and while they were presented with a non-word e. If you infer that the resulting difference in brain pattern represents the regions of the brain involved in reading comprehension, you have assumed that these changes are not reflective of changes in task difficulty or differential recruitment between tasks.

The term pure insertion was coined by Donders as a criticism of reaction time methods. Recently, researchers have begun using a new functional imaging technique called resting state functional connectivity MRI.

By looking at the natural fluctuations in the bold pattern while the subject is at rest, the researchers can see which brain regions co-vary in activation together. They can use the patterns of covariance to construct maps of functionally linked brain areas. The name "functional connectivity" is somewhat misleading since the data only indicates co-variation.

Still, this is a powerful method for studying large networks throughout the brain. There are a couple of important methodological issues that need to be addressed. Firstly, there are many different possible brain mappings that could be used to define the brain regions for the network. The results could vary significantly depending on the brain region chosen. Secondly, what mathematical techniques are best about to characterize these brain regions?

The brain regions of interest are somewhat constrained by the size of the voxels. Rs-fcMRI uses voxels that are few millimeters cubed so the brain regions will have to be defined on a larger scale. Two of the statistical methods that are commonly applied to network analysis can work on the single voxel spatial scale, but graph theory methods are extremely sensitive to the way nodes are defined.

Brains regions can be divided according to their cellular architectural, according to their connectivity, or according to physiological measures. Alternatively, you could take a theory neutral approach and randomly divide the cortex into partitions of the size of your choosing. As mentioned earlier, there are several approaches to network analysis once the your brain regions have been defined.

Seed based analysis begins with an a priori defined seed region and finds all of the regions that are functionally connected to that region. Wig et al. Another approach is to use independent component analysis to create spatio-temporal component maps and the components are sorted by components that carry information of interest and those that are caused by noise.

Wigs et al. ICA also has the issue of imposing orthogonality on the data. The problem with graph theory analysis is that network mapping is heavily influenced by a priori brain region and connectivity nodes and edges , thus the researcher is at risk for cherry picking regions and connections according to their own theories. However, graph theory analysis is extremely valuable since it is the only method that gives pair-wise relationships between nodes.

ICA has the added advantage of being a fairly principled method. It seems that using both methods will be important in uncovering the network connectivity of the brain. Mumford et al. Dissociation in cognitive neuropsychology[ edit ] Cognitive Neuropsychology studies brain damaged patients and uses the patterns of selective impairment in order to make inferences on the underlying cognitive structure.

Dissociation between cognitive functions is taken to be evidence that these functions are independent. Theorists have identified several key assumptions that are needed to justify these inferences: [26] 1 Functional Modularity- the mind is organized into functionally separate cognitive modules.

Anatomical Modularity- the brain is organized into functionally separate modules. This assumption is very similar to the assumption of functional localization. These assumptions differ from the assumption of functional modularity, because it is possible to have separable cognitive modules that are implemented by diffuse patterns of brain activation. This assumption is needed if we are to make any claim about functional organization based on dissociation that extrapolates from the instance of a case study to the population.

It is possible to remove one functional module without significantly altering the overall structure of the system.

This assumption is necessary in order to justify using brain damaged patients in order to make inferences about the cognitive architecture of healthy people.

There are three principal types of evidence in cognitive neuropsychology: association, single dissociation and double dissociation. For example, there are many cases who have deficits in both abstract and concrete word comprehension following brain damage. Association studies are considered the weakest form of evidence, because the results could be accounted for by damage to neighboring brain regions and not damage to a single cognitive system.

This pattern indicates that a the two tasks employ different cognitive systems b the two tasks occupy the same system and the damaged task is downstream from the spared task or c that the spared task requires fewer cognitive resources than the damaged task.

The "gold standard" for cognitive neuropsychology is the double dissociation. Double dissociation occurs when brain damage impairs task A in Patient1 but spares task B and brain damage spares task A in Patient 2 but damages task B. It is assumed that one instance of double dissociation is sufficient proof to infer separate cognitive modules in the performance of the tasks.

Many theorists criticize cognitive neuropsychology for its dependence on double dissociations. 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.

They repeatedly simulated random destruction of nodes and connections in the system and plotted the resulting performance on a scatter plot. The results showed deficits in irregular noun pronunciation with spared regular verb pronunciation in some cases and deficits in regular verb pronunciation with spared irregular noun pronunciation.

These results suggest that a single instance of double dissociation is insufficient to justify inference to multiple systems. Charter uses the example of someone who is allergic to peanuts but not shrimp and someone who is allergic to shrimp and not peanuts. He argues that double dissociation logic leads one to infer that peanuts and shrimp are digested by different systems. John Dunn offers another objection to double dissociation.

As more data is accumulated, the value of your results will converge on an effect size of zero, but there will always be a positive value greater than zero that has more statistical power than zero. Therefore, it is impossible to be fully confident that a given double dissociation actually exists.

On a different note, Alphonso Caramazza has given a principled reason for rejecting the use of group studies in cognitive neuropsychology.

In order to justify grouping a set of patient data together, the researcher must know that the group is homogenous, that their behavior is equivalent in every theoretically meaningful way. In brain damaged patients, this can only be accomplished a posteriori by analyzing the behavior patterns of all the individuals in the group.

Thus according to Caramazza, any group study is either the equivalent of a set of single case studies or is theoretically unjustified. There are other methodological questions including whether neurons represent information through an average firing rate or whether there is information represented by the temporal dynamics.

There are similar questions about whether neurons represent information individually or as a population. Computational neuroscience[ edit ] Many of the philosophical controversies surrounding computational neuroscience involve the role of simulation and modeling as explanation.

Carl Craver has been especially vocal about such interpretations. This section will begin with a historical overview of computational neuroscience and then discuss various competing theories and controversies within the field. Historical overview[ edit ] Computational neuroscience began in the s and s with two groups of researchers. The second group consisted of Warren McCulloch and Walter Pitts who were working to develop the first artificial neural networks.

McCulloch and Pitts were the first to hypothesize that neurons could be used to implement a logical calculus that could explain cognition. They used their toy neurons to develop logic gates that could make computations. Behaviorism had dominated the psychology until the s when new developments in a variety of fields overturned behaviorist theory in favor of a cognitive theory.

From the beginning of the cognitive revolution, computational theory played a major role in theoretical developments. By this point computational theories were firmly fixed in psychology and neuroscience.

By the mids, a group of researchers began using multilayer feed-forward analog neural networks that could be trained to perform a variety of tasks. The work by researchers like Sejnowski, Rosenberg, Rumelhart, and McClelland were labeled as connectionism, and the discipline has continued since then. Connectionism was also condemned by researchers such as Fodor, Pylyshyn, and Pinker. The tension between the connectionists and the classicists is still being debated today.

Representation[ edit ] One of the reasons that computational theories are appealing is that computers have the ability to manipulate representations to give meaningful output. Digital computers use strings of 1s and 0s in order to represent the content such as this Wikipedia page.



Below is a list of specific issues important to philosophy of neuroscience: "The indirectness of studies of mind and brain" [1] "Computational or representational analysis of brain processing" [2] "Relations between psychological and neuroscientific inquiries" [3] What constitutes adequate explanation in neuroscience? Philosophers of neuroscience have discussed such assumptions in the use of functional magnetic resonance imaging , [6] [7] dissociation in cognitive neuropsychology , [8] [9] single unit recording , [10] and computational neuroscience. Localization of function means that many cognitive functions can be localized to specific brain regions. A good example of functional localization comes from studies of the motor cortex.


Brain-wise : studies in neurophilosophy






Brain-Wise: Studies in Neurophilosophy


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