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A happy marriage between developmental psychology and neuroscience, yes but… with some problems MARÍA-JOSÉ RODRIGO Universidad de La Laguna
Abstract In this response to the commentaries on the target article, “Where developmental psychology and neuroscience meet: A threatening or a felicitous encounter?” I address three nuclear topics. First, I make explicit some underlying assumptions that prevent us from reducing psychological to neurological phenomena. Second, I delve deeper into the relationships between neurological and psychological levels. In so doing, I describe some strengths and shortcomings of neuroimaging techniques in their attempt to analyse brain functioning. Finally, I add some evidence illustrating that Neuroscience can place the Nature/Nurture debate on more productive grounds. Keywords: Reductionism, mind-brain connections, nature-nurture debate, neuroimaging techniques.
Un matrimonio feliz entre la psicología evolutiva y la neurociencia, sí pero… con algunos problemas Resumen En esta respuesta a los comentarios del artículo diana, “Donde la psicología evolutiva y la neurociencia se encuentran: ¿un encuentro amenazador u oportuno?”, se abordan tres tópicos centrales. Primero, se explicitan algunas asunciones que nos permiten prevenir el riesgo de reducir los fenómenos psicológicos a los neurológicos. Segundo, se profundiza en las relaciones entre los niveles neurológicos y psicológicos. Para ello se describen las fortalezas y limitaciones que presentan las técnicas de neuroimagen en su intento de capturar el funcionamiento del cerebro. Finalmente, se añade alguna evidencia para ilustrar cómo la Neurociencia puede situar el debate entre Natura y Nurtura sobre bases más productivas. Palabras clave: Reduccionismo, conexiones mente-cerebro, debate sobre natura-nurtura, técnicas de neuroimagen.
Acknowledgments: The preparation of this paper was supported by the Spanish Ministerio de Ciencia e Innovación, project SEJ2007-67082 and by the Canarian Agency for Research, Innovation and Society of Information (Structuring Project in Cognitive Neuroscience). Author’s Address: Universidad de La Laguna. Facultad de Psicología. Campus de Guajara. 38205 La Laguna (S/C Tenerife). Phone: 922317535; Fax: 922317461. E-mail:
[email protected] © 2010 Fundación Infancia y Aprendizaje, ISSN: 0210-3702
Infancia y Aprendizaje, 2010, 33 (1), 41-46
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Allow me firstly to thank the colleagues who commented on the target article. These contributions from scholars of different fields of expertise are important to clarify positions, introduce new reflections and enrich the target article with further pros and cons arguments. Implicit or explicitly, all commentators have responded to the question that triggers the debate by clearly supporting the idea that the encounter between both disciplines is a felicitous one. However, they healthily fluctuate in a continuum from those enthusiastically endorsing the marriage with neuroscience to those keeping certain emotional distance (thanks to Gómez for the metaphor!). Moreover, commentators in the latter position foresee some theoretical and methodological risks that reduce the enthusiasm about the proposals of the target article. In the following pages I will try to address most of these concerns grouping them around three nuclear tenets. Developmental Psychology and Neuroscience need inferences to reach the explanatory level I tried very hard in my article to avoid the risks of reductionism. But probably, as Martí suggests, there is still some ambiguity in this respect across the text. I found only one statement that could be thus interpreted: “neuroscience data may be very helpful in identifying the specific neurocognitive mechanisms underlying complex behavioral and emotional outcomes, which permits us to move beyond the descriptive level to the process level”. Martí argued that “this statement seems to confer on the biological level alone the power of explaining behavior and its development, contrasting it with the merely descriptive power associated with the psychological level”. However, my first underlying assumption is that both Psychology and Neuroscience have to elaborate evidence-based inferences in order to build an explanatory model. Take the example of Psychology to illustrate the process of theory building: important information about where, why, and how cognitive processes take place in the human mind was obtained well before modern imaging techniques were available. Most psychological studies used mental chronometric techniques to tap the time course of information processing in the human cognitive system. Thus, for decades reaction time was one of the dependent variables most widely used for making inferences about mental processes. Interestingly, the application of a single measure to many cognitive areas and tasks was the origin of a considerable corpus of knowledge. New experimental paradigms and specific tasks were developed to study particular areas, increasing the amount of data about mental functions. But there is always, at a given point, a gap between the description level provided by the data and the explanatory level provided by the construction of functional models of the mind. Piaget’s theory was not an exception. The psychological mechanisms postulated by Piaget (equilibration, awareness or abstraction) were the result of a big leap from raw data, which were quite indirect (mostly children’s verbal reports), to the definition of epigenetic principles operating across stages. With neuroscience, new, powerful techniques may allow researchers to look deeper into the brain’s activity. But I agree with Gómez on his thoughts about the pitfalls of confusing technology with theory. Good comprehensive models may still be necessary to unveil what’s inside the “black box”. The second assumption required to stay away from reductionism is that model building could be performed in better conditions inasmuch as we are able to rely upon data coming from various levels of analyses. Each level imposes its own constraints and therefore the model should satisfy more conditions to qualify as a good model. Neisser (1967) suggested that Psychology should not be viewed just as something to do until electrophysiology comes around to solve the problem. Thus, achieving a more profound analysis depends upon integrating constraints from different sources, not reducing to one source the kind of information upon which the model rests. As Rueda
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Author’s response / M.-J. Rodrigo
clearly states in her commentary, “the ability to image brain function online with performance of particular tasks is of special interest to psychologist because it allows for the direct assessment of multiple dimensions of cognitive function (namely anatomy, physiology, timing and circuitry of activations, etc.) in addition to behavioral measures, which only take into account the final, observable response”. The third assumption required to avoid reductionism entails accepting that a good Neuroscience needs a good Developmental Psychology. Quoting my own article, “looking at the history of our discipline it is clear that we have been able to accumulate solid evidence of developmental changes based on well-designed experimental paradigms and tasks using self-reports, observations and chronometric techniques”. In other words, psychologists are very well equipped with all the necessary assets to produce good neuroscience. We have good functional models, we have good task analyses and we are very skillful in building careful experimental designs relying on our traditional dependent measures. Of course, we have much to learn about the appropriateness of using new technologies to address different research questions, and about their current limitations, in order to avoid naïve and inaccurate interpretations of neurological data (see below). In turn, colleagues from other more biologicallyoriented disciplines should overcome a tendency to create excessively simple tasks and designs to test complex psychological functions. I agree with Rueda that multidisciplinary teams are necessary to implement fine research methods and to connect sensitive data with good functional models in the new field of Developmental Cognitive Neuroscience. Tapping the correspondence between biological and psychological phenomena Let me look back on the early history of the Neuroscience, when localizationists and holistics argued about the nature of the correspondences between biological and psychological phenomena. Localizationists assumed that small cortical areas are fully capable of performing complex cognitive operations. According to this view, a given psychological process (e.g., word comprehension) is restricted to one area, that is, no other areas are assumed to contribute to this specific process. In contrast, a holistic approach would imply that the entire cortex displays equipotentiality with regard to all cognitive operations, and that all cortical areas (or even brain parts) can contribute to complex processes. The Hebbian position emerged as a view radically different from both approaches. It assumed that neurons in different cortical areas may be part of the same distributed functional unit. If neurons in an associative network show correlated activity, there will be a strong functional connection among them. This implies that these neurons will be more likely to act together as a group. Hebb (1949) calls such anatomically and functionally connected neuron groups “cell assemblies.” Many decades after those early proposals, connectivity among regions is considered a fundamental property of the brain. Therefore, we should not expect one-to-one, simple correspondences between the activation of a small portion of the neural substrate and the operation of complex psychological functions. Evidence from neuropsychological, electrophysiological, and neuroimaging studies in humans has revealed that interactions among widespread neural regions in the brain underlie mental processes and organized behavior. Most studies show that the various anatomical regions of the cortex involved in processing a task must be able to effectively communicate and synchronize their processes for the system to function. Accordingly, two new methods have been developed to capture such connectivity (see Rueda’s commentary). Anatomical connectivity can be captured by tracking the directionality and regularity of myelinated neural fibers with Diffusion Tensor Imaging. Functional connectivity can be assessed in fMRI by computing the
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correlation of the activation time series in a given region with the activation time series of another region. The extent to which the activation levels of two regions fall and rise in tandem is taken as a reflection of the degree to which the two regions are functionally connected. To further investigate the complex links between neural substrates and the psychological level a wide variety of techniques are at hand. No technique by itself is capable of capturing the multidimensional nature of neurological phenomena. For instance, electroencephalography (EEG) and magnetoencephalography (MEG) provide exact information about the temporal dynamics of electrophysiological activation and deactivation processes that occur in the millisecond range. They also allow for localization of sources, although such localization is usually much less precise than that obtained through imaging of brain metabolism. Metabolic imaging techniques with high spatial resolution, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), are extremely valuable for localizing brain structures that become maximally active, thereby increasing their metabolic rates during cognitive tasks. However, metabolic methods give only a rough picture of the temporal dynamics of brain processes, and it is therefore important to use both electrophysiological and metabolic imaging techniques, even combined in the same experimental setting, when investigating cognitive functions in the brain. Thus, there is great consensus among researchers that a multi-technique approach is necessary for the study of brain functioning. More extensive uses of neuroimaging techniques in other disciplines can be envisioned. Oliva illustrates the fruitful combination of neuroimaging and molecular genetics techniques to compare the brain activity of genetically different groups of subjects. Gómez also mentions a new branch of neuroscience, comparative neuroscience, devoted to the study of the developing brains in action across different species. Surely, not all problems can be solved with the refinement of neuroimaging techniques or their combined use. As we know more about the circuitry and functional organization of the brain, new shortcomings of our current measurements of human brain activity are discovered. Gómez emphasizes one of these shortcomings following Logothetis’ (2008) critical remarks on the fMRI signal. The fMRI signal “cannot easily differentiate between function-specific processing and neuromodulation, between bottom-up and top-down signals, and it may potentially confuse excitation and inhibition”. Nevertheless, Logotheti also states in the same paper that fMRI is currently the best tool we have for gaining insights into brain functioning and formulating interesting hypotheses. The plausibility of these hypotheses depends on the experimental designs, statistical analyses and insightful models used. Oliva also remarks recent criticisms directed to some methods of imaging data analyses (Vul, Harris, Winkielman, & Pashler, 2009). This being said, we should underscore that cognitive-emotional and behavioral developmental processes are closely connected with the patterns of brain functioning. Mental processes are instantiated in the brain and there is a bidirectional influence between the psychological and neurological levels mediated by environmental influences. Moreover, causality is not confined to the brain level as Martí suggested. I agree that the experience of consciousness (the qualia) is impossible to grasp by neuroimaging or any other neurological measure. No doubt that the subjective aspect of meaning is an important part of our mental life that also escapes those measures. For instance, the way I experience pleasure when looking at a baby smile is impossible to grasp using an ERP measure. It is either impossible to register using reaction times or verbal reports! Pleasure, however, has a biological basis and so my brain reacts in a different way when I see a smile than when I see a crying face. I can postulate a direct connection for such correspondence, though my current methods might not be good enough to accurately map that link.
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Author’s response / M.-J. Rodrigo
The nature-nurture debate is not over but the “living dead” arise less often I am very pleased by the way my colleagues made use of different metaphors to convey their ideas: marriage, windows, walls, black box, shadows, and… the living dead. Definitively, these are powerful weapons targeted to destroy my defenses! Let us concentrate in this final section on my last enemy, the living dead. The general idea I put forward in my target article is that the neuroscience approach has provided important information that allows for a new evaluation of debates which could not be resolved using only behavioral data. I did not mean that the debate was over, but rather I proposed that neuroscience data have placed it on more fertile grounds. Oliva did a very good job in his commentary, explaining how advocates of environmentalist theories and those favoring a genetic view furiously fought against each other during decades; how the dispute flooded into the political arena, and how, recently, the colorful displays of neuroimaging findings helped to break down the walls that separated both positions. However, after having recognized that genes and environment matter, there is a lot of work to do in disentangling gene-environment interactions. I agree with Gómez that morphogenesis is a thorny question and that, in trying to answer it, the living dead rises again. I also agree that it is a legitimate question to be asked. Then, what is my point? My point is that neuroscience can provide relevant pieces of information that reduce the chances of living dead arising. Let us take the example of autism. Gómez argues that understanding if the origins of the disorder lies in the absence of an innate ToM mechanism or rather in a cascading effect of simpler components, such as altered patterns of attention to social cues in early infancy, is an important question to be addressed. A recent study with highly functional autistic children has revealed a dysfunctional mirror neuron system (Ramachandran & Oberman, 2006). To study the mirror neuron system in people with autism, researchers relied on the observation that the firing of neurons in the premotor cortex suppresses the mu component of the EEG, an oscillatory rhythm of the brain in the range of 8-13 hertz. Investigators monitored the mu waves of children with autism and controls as they made voluntary muscle movements (opening and closing the right hand) and watched the same actions on video. Previous research on mirror neurons indicated that motor command neurons fire whenever a person makes a voluntary muscle movement. But mirror neurons in the premotor cortex also fire when a person observes someone else performing an action. Therefore, it was expected that mu suppression might occur in both conditions if the mirror system works properly. Results indicated that mu waves of the control subjects showed a drastic decrease under both conditions. By contrast, mu waves of children with autism showed suppression when real motor action was involved, but not in the condition of observed action. Other researchers have confirmed these results using different techniques for monitoring neural activity (MEG and fMRI). Another interesting piece of information has been recently added. Neuroimaging and behavioral studies have shown that children and adults with autism have impaired face recognition. Individuals with autism also exhibit atypical event-related brain potentials to faces, characterized by a failure to show a negative component (N170) latency advantage to face compared to non-face stimuli. Surprisingly, parents of children with autism also showed the same failure (Dawson et al., 2005). These results raise the possibility that face processing might be a functional trait marker of genetic susceptibility to autism. The genetic basis of face-processing impairments as part of the broader phenotype of autism might be related to abnormalities in social orienting or the affective tagging of social relevant stimuli. Of course, these are not the final words to be said on autism. But these data, besides others indicating problems in gaze following, pointing, joint attention, and imitation of goal-directed actions, support the idea that autistic condition may emerge out of
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failures in different basic components. These early components are probably involved in the complex function known as theory of mind. But neuroscience techniques, as well as other research methods including those used by animal cognition studies, favor the view that this complex function emerges very early in a piecemeal fashion. Incidentally, most of these components are pre-requisites that lie at the foundation of human cooperative communication (Tomasello, 2008). To finish, I would like to thank my colleagues again. Using a computational metaphor, it has been a privilege to have your expert minds working in parallel with mine. Across the distance, we have managed to align our thoughts in the service of a common goal: to acquaint the community of Developmental Psychologists with Neuroscience. I hope that our balanced discussions about the strengths and limitations, the gains and losses that may result from this marriage will encourage developmental researchers to join this new convergent area of studies. Last but not least, on behalf of my colleagues I would like to thank the editors of the journal for promoting this fruitful initiative and leaving us plenty of space and time to develop our thoughts and prepare our discussions.
References DAWSON, G., WEBB, S. J., WIJSMAN,, E., SCHELLENBERG, G., ESTES, A., MUNSON,, J., & FAJA, S. (2005). Neurocognitive and electrophysiological evidence of altered face processing in parents of children with autism: Implications for a model of abnormal development of social brain circuitry in autism. Development and Psychopathology, 17, 679-697. HEBB, D. O. (1949). The organization of behavior. A neurophysiological theory. New York: Wiley. LOGOTHETIS, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869-878. NEISSER, U. (1967). Cognitive Psychology. Englewood Cliffs, NJ: Prentice Hall. RAMACHANDRAN, V. S. & OBERMAN, L. M. (2006). Broken mirrors. A theory of autism. Scientific American, 295, 62-69. TOMASELLO, M. (2008). Origins of human communication. Cambridge, MA: The MIT Press. VUL, E., HARRIS, C., WINKIELMAN, P., & PASHLER, H. (2009) Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science, 4, 274-290.
