The analytic depth of cognitive neuroscience is, in many ways, a curse. Those aspects of high-level cognition most relevant to real-world applications are the least understood at a neurobiological level, and those mechanisms that are well-understood neurobiologically are too simple to inform real-world practices.
The explanatory gaps between these levels of analysis is a result of hyper-reductionism in science, itself rooted in a lasting preference (reverence?) for the simplistic and "parsimonious." But natural phenomena, like the emergence of behavior from the brain, are ultimately more complex than these reductionistic methods presume, or can account for. Below I review a second domain of cognitive neuroscience in which the shortcomings of reductionism are all too clear, and describe how an integrative reconstructionist approach can help repair the science of higher-level cognition.
Symbol Use
Humans have the remarkable capacity to recognize and use abstract symbols, putatively reflected in behaviors as rich and diverse as representational play, the use of words, metaphors, numbers, pictoral representation, and the solution of algebraic equations.
However, there are clear reasons to believe that symbol use is not a unitary capacity, but rather highly graded and heterogenous.
For example, children acquire an understanding of pictoral representation gradually, showing declining manual exploration of photographs and eventually adult-like regard for photos by 18-months. However, they do not extend this supposed capacity for "symbol use" to the solution of algebraic equations for many years (and even then do so only with great difficulty).
This disunity in symbol use is actually mirrored by deep theoretical disagreements about the nature of symbols. Some suggest that humans only utilize six types of symbols whereas others seem to suggest that almost anything can be a symbol ("something that someone intends to refer to something ..." from DeLoache, 2004). Thus symbol use seems too complex to be an object of direct scientific study, at least at these theoretical and behavioral levels of analysis.
In contrast, neurobiological investigations of symbol use can seem too simplistic: "symbols" are alternately considered to be the arbitrary words or pictures paired with a particular task, the neuronal responses specific to a task but invariant to its stimuli, and the neural representation of categories like "color."
Although interesting and important, some might claim these findings are irrelevant to the larger questions surrounding the behavioral correlates of symbol use, and associated possibilities for applying this understanding. Others might counter that understanding the role of symbols in applied domains requires reducing the construct to these simpler, more tractable, and unfortunately less rich subcomponents.
And here is the crucial problem with hyper-reductionism (what I previously and incorrectly referred to as Occam's Razor). The preference for theories with relatively fewer assumptions (among all those other theories which are identical in explanatory power) ultimately leads us towards highly simplistic models of nature which apply only to one level of analysis, if they apply at all.
Here again, the reconstructionist approach can help to resolve the dilemma between intractable complexity on one hand and over-simplification on the other. For example, it may be that the mechanisms guiding the representation of abstract categories like "color" are the same in principle to those guiding more abstract symbols as in metaphors. Similarly, the neuronal responses to abstract "nonmatch" tasks may be similar in nature to those underlying algebraic rules. We can only know by attempting to reconstruct such high-level behaviors from these lower-level computational models and neurobiological phenomena.
Conclusions
The cognitive neuroscience of high-level cognition is stretched thin with tension between the too-complex and the too-simple, as demonstrated above in the sample domain of symbol use. In seeking to explain complex behavior, neuroscience has isolated myriad neurobiological mechanisms which seem too simple and narrow to explain something like the use of symbols. (And I imagine these tensions are not unique to brain science).
Reconstructionist approaches may provide a solution to the dilemma between the study of intractable complexity on the one hand and the methods of narrow reductionism on the other. Computational modeling can specify how these reduced components give rise to large-scale behavior, and how to actionably and mechanistically understand such behavior in clinical or applied settings.
And this is the blessing of analytic depth - reconstructionist and integrative approaches can pull from multiple levels of analysis to provide a more coherent and actionable understanding of natural phenomena than the "infinite reduction" method dominating mainstream science.
- Log in to post comments
Every body understands that men's life is very expensive, but different people need cash for different stuff and not every person earns big sums cash. Therefore to receive quick personal loans or consolidation loans should be good solution.
References:
Braver T.S., Bongiolatti S.R. (2002). The role of frontopolar cortex in subgoal processing during working memory. NeuroImage 15: 523-536.
Braver, T.S., Gray, J. R., & Burgess, G.C. (2007). Explaining the many varieties of working memory variation: Dual mechanisms of cognitive control. In A. Conway, C. Jarrold, M. Kane, A. Miyake, J. Towse (Eds.), Variation in Working Memory. Oxford University Press.
Brown JW, Braver TS. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science. 2005 Feb 18;307(5712):1059-60.
Bunge, S.A., Dudukovic, N.M., Thomason, M.E., Vaidya, C.J. & Gabrieli, J.D.E. (2002). Immature frontal lobe contributions to cognitive control in children: Evidence from fMRI. Neuron, 33: 301-311, 2002
Carlson, S. M., Davis, A., & Leach, J. G. (2005). Less is More: Executive function and symbolic representation in preschool children. Psychological Science, 16, 609-616.
Cicerone K., Levin H., Malec J., Stuss D., & Whyte J. (2006). Cognitive rehabilitation interventions for executive function: moving from bench to bedside in patients with traumatic brain injury. J Cogn Neurosci. 2006 Jul;18(7):1212-22.
Cohen, J.D. & O'Reilly, R.C. (1996). A preliminary theory of the interactions between prefrontal cortex and hippocampus that contribute to planning and prospective memory. in M. Brandimonte, G. Epstein & M. McDaniel (Eds) Prospective Memory: Theory and Applications. Mahwah, New Jersey: Lawrence Erlbaum Associates.
DeLoache, J. S., (2004). Becoming symbol-minded. Trends in Cognitive Sciences, 8, 66-70.
Duncan, J. and Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10):475-483.
Fincham, J. M. Carter, C. S. van Veen, V., Stenger, V. A. and Anderson, J. R. (2002). Neural mechanisms of planning: A computational analysis using event-related fMRI. PNAS, 99(5): 3346 - 3351.
Lyn, H., Greenfield, P., & Savage-Rumbaugh, S. (2006). The development of representational play in chimpanzees and bonobos: Evolutionary implications, pretense, and the role of interspecies communication. Cognitive Development 21(July-September):199-213
McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457.
OReilly, R.C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press.
O'Reilly, R.C., Noelle, D.C., Braver, T.S. and Cohen, J.D. (2002). Prefrontal cortex in dynamic categorization tasks: Representational organization and neuromodulatory control. Cerebral Cortex, 12, 246-257.
Premack D. (2004) Is language the key to human intelligence? Science. 2004 Jan 16;303(5656):318-20.
Rougier, N.P., Noelle, D., Braver, T.S., Cohen, J.D. & O'Reilly, R.C. (2005). Prefrontal Cortex and the Flexibility of Cognitive Control: Rules Without Symbols. Proceedings of the National Academy of Sciences, 102, 7338-7343.
Suddendorf, T., & Corballis, M. C. (in press) The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral & Brain Sciences.
Unterrainer, J.M., Rahm, B., Kaller, C.P., Ruff, C.C., Spreer, J., Krause, B.J., Schwarzwald, R., Hautzel, H., & Halsband, U. (2004). When planning fails: Individual differences and error-related brain activity in problem solving. Cerebral Cortex, 14, 1390-1397.
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001). Single neurons in prefrontal cortex encode abstract rules. Nature 411: 953-956.
Hey Chris,
I agree that the problem is with that middle ground that seems to be missing. It's either too simple that it cannot explain much, or too complexed that it can only explain that certain happening in the long string of variables.
Reconstructionist efforts i think, will allows us to find out what are the "rules" that subjects adhere to when learning or carrying out activities. I think that certain rules can be obtained from reconstructionist data because biology has always adhered to certain rules when activities are carried out. Perhaps it is those rules that are required to bridge the gap between the too simple and too complexed of theories.
I believe that we're working the same item but just in different perceptions and context. Each new theory that is adopted can be seen as the theory that manages to interpret the most events accurately (like Occam's Razor implies). Thus perhaps, the "rules" that reconstructionist efforts through computational analysis can aid us in formulating theories on a more objective scale.