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It's no longer news, but I want to tell anyway….. The field of cognitive neuroscience, the popular and fashionable close relative of cognitive psychology, has been invaded by a plethora of brain imaging studies that have attempted to identify the brain structures/areas involved in a variety of perceptual, cognitive and behavioral activities. This enterprise has been complex and thorny. One of the most stubborn challenges is of a methodological nature, as the entire enterprise relies on the development of techniques that can be employed to probe the human mind/brain to uncover its secrets. Challenges though start at the conceptual level. Although defining perception, attention, memory, and reasoning may seem easy to a layperson, it is much more arduous to identify and operationally define the specific mental operations that habitually support each of these activities (e.g. memory) in their varied forms (e.g., explicit vs. implicit memory, short-term vs. long-term memory, etc.) and contexts of use. Equally difficult is to develop tasks that engage these mental processes and capture their main functional and structural features. It has been widely recognized that even the most simple laboratory tasks (e.g., recall of a list of words) are not 'process pure', being often contaminated by other processes that may be more or less intertwined with the domain being studied (e.g., attention, motivation, etc.).
Laboratory tasks, which are assumed to rely on specific mental processes, have been nevertheless developed and then administered to a variety of individuals. Often the interest of researchers is to link performance measures, such as response latencies and accuracy, to real-time pictures of participants' brains completing selected tasks. Collection and analysis of behavioral changes and corresponding brain activity changes have come under scrutiny not only because of the noise (e.g., extraneous processes) that accompanies behavioral and brain data purportedly linked to a predefined set of mental operations, but also because of the potentially unique characteristics of the participants and procedures adopted. Not surprisingly, both the internal and external validity of the data collected and the interpretations suggested by individual studies have been questioned at first timidly and then more openly and decisively by a growing number of researchers. Consequently, along with traditional brain mapping studies, relying on techniques such as Event-Related Potential (ERP), functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET) and its more illustrious embodiment, Single Photon Emission Computed Tomography (SPECT), other studies have emerged with a much more ambitious mission. Their goal is to identify the brain structures/areas involved in a specific mental phenomenon (e.g., experiencing a specific emotion, recalling words, hearing voices in one's head, etc.) by statistically summarizing the findings of research examining it. The rationale underlying these meta-analytic studies is rather straightforward to the untrained eye. Each study of a set that investigates a given phenomenon has unique features, involving the participants used and the procedures and materials adopted. Irrespective of whether unique features are the result of an intentional selection or merely an accidental occurrence, they tend to produce non-negligible 'noise' in the data which may obscure the 'signal' (i.e., the phenomenon of interest). If the results of these studies are combined, a large pool of data will increase the researcher's chances of detecting the signal from useless noise, thereby potentially offering a rather accurate description of the processes performed by different brain areas. Regretfully, this rationale and the goal of localizing cognitive and behavioral functions upon which it relies offer a path ridden with obstacles when localization pertains to the human brain, an intricate system of distributed activities, and uses averages of neural activation to identify the locus of discrete processes.
At first blush, aggregates of data may seem to be preferable to individual data sets; but if the individual sets are already problematic, do meta-analyses extract the desired signal or merely provide a misleading picture of it? Consider that the success of a recipe relies on each of its ingredients and that if only one ingredient is rotten or merely of less-than-stellar quality, the final product will be unreliable (unlikely to be replicated) and somewhat distorted (i.e., something that clearly does not represent the intended recipe). Not surprisingly, the first and most common concern regarding the reliability and validity of meta-analyses pertaining to localization of brain functions is the quality of the individual ingredients. To this end, a growing body of scientists has solicited a critical examination of the relative costs and benefits of brain imaging studies which now define a substantial portion of the knowledge base of the field of cognitive neuroscience. Given that such studies often rely on highly expensive data-collection equipment, leading to small numbers of participants, and are still encumbered by time-consuming data processing techniques, this solicitation seems to be reasonable and long overdue. Nevertheless, although it is reasonable to ask cognitive neuroscientists to consider whether the findings of brain imaging studies are reliable (i.e., replicable) and have internal and external validity, also worth noting is that feasible alternatives to existing non-invasive techniques are yet to become available.
Cleverly, William R. Uttal, the author of Reliability in cognitive neuroscience: A meta-meta-analysis, focuses the reader's attention on findings of brain imaging studies to highlight the problematic issue of consistency of data not only across studies that purport to examine the same phenomena, but also within studies when data of different participants or of the same participants at different times are examined (i.e., between-subjects and within-subjects variability). Undoubtedly, Uttal's strategy of examining the quality of the data of meta-analyses by comparing the records of different practical instantiations of purportedly the same phenomenon brings to the forefront information that serves as a fertile ground for the author's critique of the meta-analytic approach. Uttal's critique though goes further. He takes upon himself the even more challenging task of asking whether the level of analysis afforded by current brain imaging techniques is simply too coarse for the purpose of localization of brain functions because such techniques are based on the cumulative activation of millions of neurons. Given that within-subjects, between-subjects, and inter-studies variability of brain activation produced by large numbers of neurons is considerably high, the notion that neural substrates of diverse mental operations can be identified by merely averaging neural activation appears unattainable. Consequently, Uttal gently brings the reader to entertain the notion that a more fine-grained analysis, one based on clusters/networks of neurons, may be preferable. Single-neuron studies appear to be the starting point of his quest for techniques that offer the desired level of analysis.
Although it is rather difficult to overcome human fascination with colorful pictures, their intrinsic value and usefulness for understanding the human mind come even more into question when Uttal analyzes the current popular metaphor of brain functioning as involving localized and separable processes/functions based on distinguishable neural systems. He offers a counterproposal in which brain functioning is conceptualized as relying on broadly distributed functions based on interconnected systems for which plasticity is a defining property. When Uttal then applies the same rigorous analysis to the popular assumption of methodological insensitivity of neuro-imaging data, he is led to conclude that current brain imaging data are unable to resolve theoretical controversies regarding the nature of mental processes. He adds that current neuro-imaging techniques and resulting colorful pictures can mislead observers into seeing organized patterns of activation where instead only quasi-random neural activation reigns. Overall, Reliability in cognitive neuroscience: A meta-meta-analysis is a must-read for scholars, students, and all readers who appreciate a well-reasoned narrative, supported by a comprehensive overview of the available evidence. This book concerns not simply meta-analyses and their application to the field of cognitive neuroscience, but is a strident reminder that more precise definitions of mental operations are at the foundation of any successful future development of techniques that truly uncover the link between mental activity and brain activation.
© 2013 Maura Pilotti
Maura Pilotti, Ph.D., Ashford University