Probleem 4 - Language and the brain Flashcards
The emerge of cognitive neuroscience
In the 1980s, cognitive neuroscience was beginning to emerge as a new field of research as the cognitive revolution began to interact with what were becoming the neurosciences. At the time, behavioral neuroscience was well established as physiological psychology and systems neuroscience was forming out of the interactions between physiology, anatomy and psychology. These subdisciplines formed together what is now known as cognitive neuroscience.
Development of cognitive neuroscience
Within cognitive neuroscience, the single most influential advance for understanding cognition has been the advent and development of brain-imaging techniques and methodologies, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI).
Existence of a range of techniques is valuable because each has different properties with respect to the degree of temporal and spatial localization of neural activity.
Another large development is the rise computational moddeling (connectivism) for use in modeling neurobiological data.
Neuropsychological studies revealed numerous of associations between stimulus-dependent behavior and awareness of stimulus.
Future of cognitive neuroscience
Rate of progress within cognitive neuroscience increases exponentially each year. There is increasing use of techniques to establish effective connectivity within the brain in order to understand interactions within and between brain systems.
Limitations:
- Due to the complexity of brain/mind, approaching a research question from only one perspective limits the inferences that may be made.
- A lot studies have a lack of power»_space; results in multiple interpretations.
- Functional imaging only provides evidence to resolve open theoretical issues.
- The field risks being driven by possibilities»_space; much data»_space; apart from scientific questions/processes/theories. It will be unable to make sense of the increasing masses of brain-based data.
Criticisms of functional neuroimaging»_space; Inferential distance and the objects of imaging
Functional brain images are easily misunderstood as photographs of brain function.
> > Blood vs brain
- The signal measured in fMRI is a characteristic of blood rather than brain tissue. This dependence on hemodynamic proxies for brain activity is commonly cited as a flaw of fMRI.
- The concern that fMRI shows blood oxygenation rather than neural activity directly should be weighted with the fact that in science we often can’t observe the subject matter of interest itself.
- FMRI is currently the best tool for gaining insights into brain function.
> > Functional images as fabrication
- The importance of the decisions that researchers make regarding what aspects of brain activity to represent and how to represent this leads to worries that brain images are more researcher inventions than researcher observations.
Methatheoretical assumptions and goals of neuroimaging.
Functional neuroimaging has been criticized for encouraging certain types of theories while preventing others from being tested.
> > Localization vs explanation
- Neuroimaging has been compared to a form of phrenology; with the research goal being simply to associate a psychological function with a specific part of the brain.
- Localisation = a questionable scientific goal. Once localization is confirmed the ability to detect regional brain activity increases. Researchers can use this method to determine which brain areas are likely to be recruited for a given psychological function.
> > Relevance to psychological theorie; Is functional neuroimaging an effective means of testing psychological theorie?
- Important to remember that decisive experiments are generally not possible in psychology because the processes are to complex and have to many degrees of freedom.
- A fairer more realistic question: Can functional brain imaging contribute to confirming psychological hypotheses in the way behavioral studies do? So can functional brain imaging rule out more straight forward alternative hypotheses and leave more alternatives to support main hypothesis?
> > Biasing hypothesis generation
- The use of imaging constraints the kinds of theories of mind brain relations that will be devised and tested.
- Early approaches only focussed on small brain regions, ignoring the complex interactions between regions.
This problem is minimized by the concurrent use of other methods, they supplement each other.
Wanton reverse inference
Going from an observation of brain activity to an inference about the psychological process that caused it, is called reversed inference. Isn’t always a one-on-one relationship.
Forward interference
Manipulating psychological processes and observing resulting brain activation.
Neuroimaging’s slippery statistics
Functional brain imaging research is particularly dependent of statistics.
Statistical inference vs direct observation
The use of statistics often involves substituting estimated values for raw data (result = fabrication). The individual differences in brain anatomy is problematic when scans from individuals are averaged to produce a single image.
When carried out properly, statistical analyses deepen our understanding of the data and the larger reality from which they are gathered.
Multiple comparisons
There is an enormous number of statistical tests that can be carried out with image data, which leads to a lower reliability. When significance testing is carried out on brain image data a large proportion of tests is significance due to chance (5%).
Circularity
Some researchers first identify most activated voxels and then carry out analysis on same data set. this leads to an increase of the effect.
The undue influence of brain imaging
Functional neuroimaging is criticized as being persuasive or appealing. This is due to public’s lack of scientific literacy.
- Brain images are overly convincing; Research with brain images got higher credibility ratings»_space; Never been replicated.
- Brain images are overly appealing; draw to much attention away from more worthy science.»_space; little evidence.
Overview of criticisms of functional brain imaging
_ FOTO INVOEGEN _
Applications of human neuroscience - ethical, legal and societal impact
Two kinds of problems have emerged from the increasing ability of brain imaging:
- Problems that arise from the current and imminent capabilities.
- Problems that arise from lack of claimed capabilities.
- The extent to which imaging can predict important personal characteristic such as health status, academic achievement and criminal behavior must be managed with care to protect privacy and avoid discrimination.
- Imaging cannot help with high-stakes problems the public should be protected from claims that it can.
Applications of human neuroscience - Treatment of neuropsychiatric disorders
The current diagnostic system is inefficient. Although depression, schizophrenia, autism etc. are considered as disorders of the brain, they are diagnosed exclusively by behavioral signs and symptoms. These criteria do not have a clear relation to the biological processes targeted by medicine.
> > An alternative way of systemizing psychiatric disorders is the NIMH research domain criteria (RDOC) that describes disorders according their impairments in specific functional systems in the brain.
