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IMPORTANTE

Los modelos de base: la fundamentación teórica del PracticumAcostumbrados a visualizar la actividad docente como una caja negra (los docentesexplican sus lecciones, los estudiantes toman sus apuntes y los estudian

para después reproducirlos en sus trabajos o en los exámenes), poco hemossabido de cómo se desarrolla ese proceso interior que llamamos aprendizaje.

Conocíamos los inputs (lo que nosotros explicamos, los materiales que se manejan)

y los outputs (los productos que elaboran nuestros estudiantes y en los quese basará su evaluación), pero no cómo se llega de unos a otros, ni por qué enunos casos el resultado es exitoso y en otros no lo es. Durante bastante tiempo,la docencia universitaria se ha despreocupado de esta cuestión, convirtiendo «elaprendizaje» en un problema que afecta sólo a los estudiantes y son ellos y ellasquienes deben resolverlo. Enseñanza y aprendizaje se consideraron componentes,estancos e independientes.

Enseñar era la tarea del profesorado y de ellos

dependía que se hiciera bien o mal. Aprender era la tarea de los estudiantes y deellos dependía el que tuvieran éxito o no. Ninguno de los dos supuestos es correcto,obviamente, pero todavía subsiste una notable predisposición a tomarloscomo principio práctico de acción. Esa situación es aplicable al conjunto de las

actividades formativas, pero el vacío que genera la black box es mayor aún en elcaso del Practicum. Aclarar ese agujero negro es lo que tratan de hacer los modelos de aprendizaje.Cuanto más sepamos de los procesos internos que llevan a cada sujetoal aprendizaje, en mejores condiciones estaremos para optimizarlos, para organizarlas cosas de manera que los resultados de aprendizaje resulten efectivos.No debemos olvidar que los «modelos» constituyen formas de representaciónretrospectiva (reflejaren un esquema lo que se está haciendo), pero tambiénun marco de referencia prospectivo (organizar lo que se va a hacer de acuerdo

a un modelo).Resulta tan obvio lo dicho hasta aquí que produce un cierto sonrojo mencionarlo.Pero me da la impresión de que, aún así, generaríamos una notable incomodidadsi preguntáramos a los responsables académicos de nuestras Facultadescuál es el modelo de aprendizaje en el que han basado el Practicum de su carrera.Incluso si esa pregunta se hiciera en Facultades de Psicología o Educacióna las que se supone especialistas en modelos de aprendizaje. Y, sin embargo, laausencia de un modelo claro que defina la organización del Practicum es, en miopinión, la variable que mejor explica la heterogeneidad, dispersión y falta deeficacia de muchos modelos de prácticas actualmente vigentesUno de dichos modelos es el del

aprendizaje experiencial de Kolb (1984)que conjuga y complementa algunos de los enfoques más interesantes surgidos

en los últimos años: el valor de la actividad directa (la práctica) y el valor de lareflexión. El punto de partida de este modelo es una experiencia real y concretaa la que el estudiante se incorpora, en la que participa activamente y en la quetoma notas de lo que va pasando. La experiencia está configurada, por tanto,como un proceso de acción al que acompaña y sigue otro de reflexión, y quese continúa por un periodo de extracción de generalizaciones y de preparaciónpara experimentar la siguiente experiencia. El aprendizaje experiencial se producea través de círculos progresivos: la práctica inicial va seguida de una reflexiónsobre la misma, lo que permite progresar hacia a una práctica más elaboradasobre la que también se reflexiona para así poder progresar de nuevo a unapráctica cada vez mejor fundamentada y mejor adaptada al contexto en el que selleva a cabo. Este tipo de aprendizaje puede ser promovido bien a través de la reflexión

sobre experiencias pasadas, bien a través de la reflexión de experienciasplaneadas con ese fin como es el Practicum y, también, mediante experiencias

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simuladas como el trabajo de laboratorio o el role playingEl aprendizaje es el proceso por el cualse crea el conocimiento a travésde la transformación de la experiencia. Esta definición enfatiza algunosaspectos críticos del proceso de aprendizaje, tal y como éste es analizadodesde la perspectiva experiencial. En primer lugar está el énfasis en elproceso de adaptación y aprendizaje como algo opuesto al contenido oa los resultados. En segundo lugar está la idea de que el conocimiento esun proceso de transformación, que está en continua creación y recreación,y no una entidad independiente que debe ser adquirida o transmitida. Entercer lugar, se considera que el aprendizaje transforma la experiencia tantoen su forma objetiva como subjetiva. Finalmente se insiste en que paraentender el aprendizaje debemos entender la naturaleza del conocimientoy viceversa. (Kolb, 1984, p. 38). Podríamos decir que estas ideas ya están presentes en lafundamentación delos actuales enfoques, o en la mayoría de ellos, que se da al Practicum en lastitulaciones universitarias. Pero, después, no se consigue transferirlas adecuadamentea su desarrollo operativo. Una de las dificultades fundamentales que

se deben enfrentar es, precisamente, la de la reflexión. Reflexionar no es simplemente

contar la propia experiencia (lo que muchos de nuestros estudianteshacen en sus memorias y diarios) sino, justamente, ser capaz de llegar más alláde la experiencia vivida, saber decodificarla, referenciarla, valorarla e integrarlaen nuestros esquemas cognitivosÉse es uno de los aspectos que destacan Oser y Baeriswyl (2001) con suanalogía de las coreografías de la enseñanza. La primera de las 12 coreografías

que describen es la que denominan aprendizaje a través de la experiencia quese acomoda bien a la forma en que puede ser concebido el Practicum. Tambiénel Practicum puede ser analizado en términos similares, como una coreografíadiversa a la académica y donde se busca propiciar el aprendizaje a través de unasecuencia (modelo base) que combina la experiencia directa y la reflexión. Segúnestos autores, la secuencia de operaciones que debe realizar el aprendiz en este

modelo de aprendizaje es la siguiente: a) anticipación del plan de acción a de

sarrollar (pensar en lo que se va a hacer, observar, colaborar, participar, diseñar,

manipular, construir, resolver, etc.) y los problemas previsibles; b) realización

de las actividades previstas en los correspondientes contextos; c) construcción

del significado de la acción a través de un intercambio comunicativo (contar lo

que se ha hecho y por qué); d) generalización de la experiencia a través de laidentificación de los elementos comunes en las experiencias de diversos sujetos;

e) reflexión sobre experiencias similares existentes en la bibliografía, en lo explicadoen clase, en los libros de texto, en bases de datos, Internet, la literatura, etc.Como puede constatarse, la secuencia resuelve perfectamente no sólo la combinación

de experiencia y reflexión, sino también entre teoría y práctica, entreexperiencia realizada, experiencia vivida y experiencia contada. Permite, además,complementar el doble contexto de la formación: el académico y el profesionalTambién han idoconsolidándose otros planteamientos sobre el aprendizajeaplicables al Practicum. Así, por ejemplo, los modelos del «aprendizaje en situación» (situated learning) de Warner y McGill (1996), que destacan el impacto dela interacción entre sujeto y contexto a la hora de definir los aprendizajes. En elcaso del Practicum, esto nos plantea importantes exigencias a la hora de seleccionarlos centros de prácticas y a tomar en consideración la dinámica global enque se produce la estancia del aprendiz en dicho contexto (la forma en que esaceptado, su nivel de integración, el clima institucional, etc.). Hascher et al.(2004)han insistido fuertemente en la importancia de los contextos de prácticas en

el proceso de aprendizaje. Igual consideración merecen los modelos del «focusof concern», interesantes aportaciones didácticas orientadas a la resolución de

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problemas prácticos relevantes. Se ha empleado en estudios de Medicina, peropudiera ampliarse con facilidad a cualquier carrera. Ofrece, además, la ventaja,de que resulta coherente y complementario con las metodologías activas (estudiode casos, resolución de problemas, etc.) que pudieran emplearse en las sesiones

académicas. Progresiva incorporación de las TICLa transformación que se está produciendo en la Educación Superior al socairede las TIC es extraordinaria. Si la función básica de los docentes es la de servirde mediadores entre los estudiantes y los aprendizajes que se espera desarrollen,esa función ya no precisa hacerse de forma presencial, sino que admite otrasmodalidades (McNair y Marshall, 2006). La docencia presencial se complementay enriquece con la virtual. Y esa posibilidad afecta de forma sustancial, por suparticular naturaleza, al Practicum.

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Bridging Psychology and

Mathematics: Can the Brain

Understand the Brain?• Mariano Sigman

Citation: Sigman M (2004) Bridging Psychology and Mathematics: Can the Brain Understand theBrain? PLoS Biol 2(9): e297. doi:10.1371/journal.pbio.0020297

Published: September 14, 2004

Copyright: © 2004 Mariano Sigman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

“It was not only difficult for him to understand that the generic term dog embraced so many unlike specimens of differing sizes and different forms; he was disturbed by the fact that a dog at three-fourteen (seen in

 profile) should have the same name as the dog at three-fifteen (seen

from the front).…Without effort, he had learned English, French,Portuguese, Latin. I suspect, nevertheless, that he was not very capableof thought. To think is to forget a difference, to generalize, to abstract. Inthe overly replete world of Funes there were nothing but details, almost contiguous details.” —Jorge Luis Borges, “Funes the Memorius” 

We are told scientists are divided into experimentalists and theoreticians. The dialectic description of the dynamics of science, with one tribe gathering data and collecting evidence and another tribeproviding form to these observations, has striking examples that argue for the importance of synthesis. The 16th century revolution, which settled the way in which we see the sky today, isprobably one of the best examples of how comparatively ineffective each of these tribes can be in

isolation. Tycho Brahe, the exquisite observer, who built, calibrated, and refined instruments to see inthe sky what no one else could, collected the evidence to prove a theory that Copernicus had alreadystated years before (in a book he dedicated to the Pope). It was only many years later that Galileo

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established the bridge between theory and observation; he understood the data in terms of thetheory and thereby cemented the revolution. Copernicus's statements, showed Galileo, were not onlyfigments of his imagination; they were an adequate description of the universe as he and Brahe hadobserved it.

Since my first steps in biology, after a prompt departure from physics and mathematics, I have

looked for such encounters between theory and experiment. I began studying the visual system andthe series of fundamental works by  Atneave (1954), Barlow (1960), and Atick (1992) on therelationship between our visual world and the way the brain processes it. Their research was basedon a simple hypothesis: (1) the images that we see are highly redundant and (2) the optic nerve is alimited channel, thus the retina has to compress information. Compression, redundancy? How dosuch concepts relate to the biology of the brain?

In the middle of the last century, working on the problem of communications, languages, codes, andchannels, Claude Shannon proposed a very elegant theory that formalized intuitive ideas on theessence (and the limits) of communications (Weaver and Shannon 1949). One of its key aspects wasthat, depending on the code and on the input, channels are not used optimally and thus do not carryall the information they potentially could. When we compress (zip) a file, we are actually rewriting it ina more efficient (though not necessarily more intelligible) language in which we can store the sameamount of information in less space. This compression has, of course, a limit (we cannot convey theuniverse in a single point), and the mere existence of an optimal code is central to Shannon's idea. Attneave was probably the first to think that these ideas could help unravel how the brain worked,and in a long series of works relating these ideas to experimental data, it was shown that the retina'sbusiness is mainly to get rid of the redundancies in the visual world.

 About four years ago, Jacob Feldman published a paper, similar in spirit, proposing a simpleexplanation for a long-standing problem in cognitive science about why some concepts are inherentlymore difficult to learn than others (Feldman 2000). An article whose first reference is to work carriedout 50 years previously makes us suspect that an important gap is being filled. As in the previousexperiments, Feldman borrowed a theory—he did not invent it—to explain longstanding andpreviously unexplained research in this area. Feldman's idea was based on a theory developed byKolmogorov that established formal mathematical grounds to define and measure complexity.Kolmogorov's theory was focused on categories, which are just subsets of a universe, a bunch of 

exemplars that constitute a group. “Dogs” is a category in the universe of animals. Differentstatements can define the same category, for example, “big dogs” and “dogs that are not small” arethe same group, and some information in the statement may be irrelevant because it does not aid indetermining which elements belong or not. In the same way that Shannon spoke of a non-redundantcode, Kolmogorov showed that categories could be defined with an optimal (non-redundant)statement. The length of this statement defines a measure of complexity termed Kolmogorovcomplexity.

To visualize the intuitive nature of his theory it helps to do a thought experiment. Imagine a set of objects defined by, say, three features: form, color, and size. And imagine, for simplicity, that eachfeature is binary, that is, there are only two possible cases. Size is big or small, color is yellow or red,and shape can be either a triangle or a circle. This defines, of course, a universe of eight objects. Wecan now define categories within this universe: for example, all the circles (a category of four 

exemplars), or all the big and yellow objects (two exemplars), or all the triangles that are not red(again two) (see Figure 1). We can also define a category by enumeration, for example, the small redtriangle, the big yellow circle, and the small yellow circle (three exemplars). Some rules (and thus thegroups defined by these rules) are intuitively easier to define than others. “All the circles” is an easier statement to make (and probably to remember) than “small circles and yellow big objects.” Thisnotion of difficulty is what Kolmogorov's theory formalized, stating that complexity was the length of the shortest definition (among all the possible definitions) of a given set. From this thoughtexperiment, we can understand the logic of Feldman's paper, which showed that. Kolmogrovcomplexity is very closely related to our intuitive notion of conceptual difficulty. Feldman presentedsubjects with all possible categories (of a fixed number of exemplars) in different universes andshowed that the critical parameter to rank the difficulty of a given subset was its Kolmogrovcomplexity. Moreover by explicitly presenting all the members and the nonmembers of a category tonaïve subjects, he showed that we can spontaneously reduce a category to its minimal form andremember it without any explicit instruction. Thus, what Feldman found, following the original ideas of 

Shepard, was that our psychological measure of complexity—our difficulty in defining andremembering a category or concept—is also determined by the Kolmogorov complexity thatdescribes it.

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Figure 1. Visualizing Kolmogorov´s ComplexityIntuitive categories can be defined by short statements. The universe: circles and triangles, red andyellow, big and small (A). Examples of easy categories: red objects (B); triangles (C). Example of adifficult category: yellow circles and small red circles (D).doi:10.1371/journal.pbio.0020297.g001

This essay is, in a way, about how we avoid becoming Borges's character Funes, who could notunderstand repeated observations as exemplars of a common rule and thus could not synthesizeand categorize. Simply, he could not think. Probably the most disappointing moment of Feldman'spaper comes at the very end, where it deals with its (somehow unavoidable) recursive quest.Understanding why some concepts are difficult to learn may itself be difficult to learn. Modernmathematics, together with Kolmogorov complexity and information theory, has taught us another fundamental concept that may be relevant when trying to understand the logic of the mind. In a longseries of paradoxes enumerated by Bertrand Russell, Kurt Goedel, and others, we learn that a formal

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system that looks at itself is bound to fail. At the very end of his paper, Feldman writes, “In a sense,this final conclusion [that psychological complexity is Boolean complexity] may seem negative:human conceptual difficulty reflects intrinsic mathematical complexity after all, rather than someidiosyncratic and uniquely human bias.” Who invented mathematics? The Martians? On the contrary,I believe this result supports a more naturalistic and less Platonic conception of mathematics. Formalideas in mathematics are not arbitrary constructions of an arbitrary architecture; rather, they reflectthe workings of the brain like a massive collective cognitive experiment. Mathematics does not onlyserve to help us understand biology; mathematics is biology. We are not less original if our thoughtsresemble our mental constructions, we are just consistent. It is within this loop, this unavoidablerecursion—mathematics understanding the logic of the brain—that we will have an opportunity totest, as some conspire, whether among all the wonders evolution has come out with, the ultimatemight be a brain good enough to avoid the risk of understanding itself.

References

1. 1. Atick JJ (1992) Could information theory provide an ecological theory of sensory

processing? Network 3: 213–251.

2. 2. Attneave F (1954) Informational aspects of visual perception. Psychol Rev 61: 183–193.

3. 3. Barlow HB (1960) The coding of sensory messages. In: Thorpe WH, Zangwill OL, editors.Current problems in animal behaviour. Cambridge: Cambridge University Press. pp. 331–360.

4. 4. Feldman J (2000) Minimization of Boolean complexity in human concept learning. Nature

407: 630–633.

5. 5. Weaver W, Shannon CE (1949) The mathematical theory of communication. Urbana

(Illinois): University of Illinois Press. 117 p.

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Timing the Brain: Mental Chronometry as

a Tool in Neuroscience• Michael I Posner 

Citation: Posner MI (2005) Timing the Brain: Mental Chronometry as a Tool in Neuroscience. PLoSBiol 3(2): e51. doi:10.1371/journal.pbio.0030051

Published: February 15, 2005

Copyright: © 2005 Michael I. Posner. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

Abbreviations: fMRI, functional magnetic resonance imaging; PET, positron emission tomography

How do we relate human thought processes to measurable events in the brain? Mental chronometry,which has origins that date back more than a century, seeks to measure the time course of mentaloperations in the human nervous system [1]. From the late 1800s until 1950, the field was builtalmost entirely around a single method: measuring and comparing people's reaction times duringsimple cognitive tasks. As far back as 1868, Franciscus Donders [ 2] subtracted the time taken tomake a single response to an unvarying stimulus—what he called an instructed reflex—from the time

it took to make the same response to one of two events, obtaining the time required to discriminatebetween the two stimuli. Further, he subtracted the time to discriminate two stimuli from a situation inwhich there were also two possible responses in order to obtain the time required for choice.

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In the 1950s, studies of reaction time were combined with the then-developing mathematical theoryof information [3] to address issues such as the maximum transmission rate of the human nervoussystem [4] and how coding in the brain of stimuli and their responses could influence these limits [5].These studies revealed that reaction time alone was not sufficient to elucidate the exact processesby which the brain achieved the human ability to process information. However, when combined withother methods, the latency of responding can help connect brain studies to the behavior of humansin real situations.

Recording the average event-related electrical potentials from scalp electrodes became a researchtool in the 1960s, with the advent of analog and then digital computers to accomplish the recordingand averaging. It became clear that components of the event-related potential could besystematically related to sensory and motor stages of information processing. For instance, a visualstimulus was found to evoke a short-latency scalp response from the primary visual cortex at about60 milliseconds, followed by positive and negative voltage changes in neighboring visual areas.Similarly, scalp recorded potentials from the frontal cortex could be recorded in relation to motor activity. It was now possible to observe some of the sensory and motor stages that were inferredfrom Donders's subtractive method (see [6] for a review).

Saul Sternberg [7] developed a much-improved method for dividing reaction time into successive or serial stages, called the additive factors method. Subjects were asked to determine whether or not aprobe digit had been present in a just previously presented series of digits. Sternberg argued that thetime to complete the task could be divided into a sensory stage, dependent on stimulus parameterssuch as the intensity or clarity of the probe; a comparison stage, dependent only on the number of items in memory; and a response stage that reflected the difficulty of the specified response. Factorsthat influenced one stage (e.g., stimulus intensity) would be additive with those that influencedanother stage (e.g., motor output). With this simple framework, it was now possible to determine atwhich stage(s) a new factor (e.g., nicotine, sleep deprivation, or Parkinson's disease) had itsinfluence.

In the 1950s, the advent of microelectrode recordings of single neurons from anesthetized monkeysallowed for an even finer resolution of neurophysiological processes and seemed to provide supportfor the view that the brain does indeed process information in serial stages. Hubel and Wiesel [8]argued that successive levels of the visual system could be seen as accomplishing successive

analyses of input. The microelectrode strategy was quickly adopted to alert animals, making itapparent that higher level brain areas involved in operations upon input might feedback their influences on earlier processing stages [9,10]. These control systems, often called attention, posedsomething of a problem for completely serial views of information processing. However, they alsoprovided evidence of localized brain areas within the parietal lobe of the monkey that could besystematically related to processing operations involved in attention—which were then beinginvestigated by mental chronometry in patients with parietal and other lesions [11].

In the late 1980s, neuroimaging experiments made possible the examination of activity in localizedbrain areas, first through the use of injected radionuclides detected by positron emission tomography(PET) [12] and later through the use of an externally imposed magnetic field in functional magneticresonance imaging (fMRI) [13]. Over the last ten years, fMRI has improved in spatial and temporalresolution and can now provide evidence of quite specific brain areas, in the millimeter range, that

are involved in cognitive tasks. Most studies have shown a small number of widely distributed brainareas that must be orchestrated to carry out a cognitive task. Although, as in all sciences, there aredisagreements, the convergence of results in areas of attention and language seem to meparticularly impressive.

When the fMRI method for localization is brought together with methods that can accurately measuretiming of the same activity (i.e., electrical or magnetic event-related potentials) they can provideconsiderable insight into the nature of thought. Consider the simple task of deciding whether apresented digit is above or below five [14]. Dehaene argued that the task could be divided into four stages. The first involves obtaining the identity of the probe input (encoding), the second making acomparison against the stored representation of the digit five, the third selecting a response, andlastly, checking the output for error. According to additive factor theory, a variable that effects overallreaction time by varying the time to complete one stage will be additive with the effects of variablesthat affect other stages. The input or encoding stage was varied by using either Arabic or spelled

digits (e.g., “3” or “three”). The comparison stage was varied by comparing digits close to five (e.g.,six) with those far from five (e.g., nine). The response stage was varied by specifying a response

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from either the dominant or non-dominant hand and error monitoring was examined by comparingerror with correct trials. Each of these variables influenced only the appropriate stage and wasadditive in its effect with each of the other variables (see Figure 1).

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Figure 1. Reaction Time for Various ConditionsPeople were asked to judge whether a presented digit was greater or less than five. The time to

respond (reaction time) varied systematically as a function of notation (Arabic digits vs. spelledout

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numbers), distance (closer or farther in sequence from five), and responding hand. The three effectsare additive, indicating the likelihood of serial stages of cognitive processing. (Adapted from [ 14])doi:10.1371/journal.pbio.0030051.g001

Moreover, scalp-recorded, event-related potentials showed a separate scalp distribution and latencyfor each variable [14]. Subsequent fMRI data has confirmed and increased the precision of the

anatomy proposed for each of these stages.

Of course, not all human activities involve a set of exhaustive and independent serial stages that canbe shown to add up to the overall reaction time. While tasks like the number comparison discussedabove can be usefully divided into stages, some components may deal with simultaneous operationsand may be limited only by a total capacity of central mechanisms. We know that many situationsinvolve parallel processing and feedback loops at many levels. Sternberg has attempted to apply amodified version of additive factor theory to brain systems using neuroimaging that allows for someof these possibilities [15,16].

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Figure 2. Regions of the Brain Involved in a Number Comparison Task Derived from EEG andfMRI StudiesThe regions represented correspond to those showing effects of notation used for the numbers (pinkand hatched), distance from the test number (orange), choice of hand (red), and errors (purple).

(Illustration: Giovanni Maki; adapted from [18])doi:10.1371/journal.pbio.0030051.g002

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Laboratory studies often use the simultaneous execution of two different tasks (dual tasks) tosimulate the more realistic situations where humans time-share activities. In this issue of PLoSBiology , Sigman and Dehaene [17] provide a model that further extends the additive factor logic tothe dual task situation. They propose that for tasks that can be broken down into three consecutivestages—perception, decision based on noisy integration of evidence, and response—the perceptualand motor stages can operate both simultaneously with and independently of stages of another taskand are thus easily amenable to additive factor analysis. The decision stage, however, appears torepresent a kind of “cognitive bottleneck” for which the reaction times of the two tasks becomeinterdependent. The model adds considerably to the range of situations to which an additive factor approach can be applied, allowing investigators to seek more information about how new variablesinfluence hidden processing stages.

Many cognitive and emotional tasks studied with neuroimaging have implicated a small number of brain areas that are consistently active. Mental chronometry plays a role in suggesting the cognitiveoperations that each of these areas performs and how they are organized in real time. The toolkit of new techniques provides the basis for further tests to evaluate whether a chronometric model revealsa crucial set of connected computations (circuit) for carrying out the task. For example, using amagnetic pulse delivered outside the skull, it is now possible to induce a reversible lesion at the timeof a particular computation to determine whether the specific computation assigned to a given area istruly needed to carry out the task. Studies using diffusion tensor imaging can examine whether there

are large-scale connections between neural areas posited by a particular model. In describing thelinks between brain and behavior, mental chronometry is still a cornerstone that binds psychology tothe techniques of neuroscience.

References

1. 1. Posner MI (1978) Chronometric explorations of mind. Hillsdale (New Jersey): Lawrence

Erlbaum Associates. 271 p.

2. 2. Donders FC (1969) On the speed of mental processes. Acta Psychol 30: 412–431.

[Translation of: Die Schnelligkeit psychischer Processe, first published in 1868].

3. 3. Shannon CE, Weaver W (1949) The mathematical theory of communication. Urbana

(Illinois): University of Illinois Press. 117 p.

4. 4. Attneave F (1959) Applications of information theory to psychology. New York: Henry

Holt. 120 p.

5. 5. Posner MI (1966) Components of skilled performance. Science 152: 1712–1718.

6. 6. Rugg MD, Coles MGH (1995) Electrophysiology of mind: Event related potentials and

cognition. Oxford: Oxford University Press. 220 p.

7. 7. Sternberg S (1969) The discovery of processing stages. Acta Psychol 30: 276–315.

8. 8. Hubel D, Wiesel TN (1968) Receptive field and functional architecture of the monkey

striate cortex. Journal Physiol (Lond) 195: 215–243.

9. 9. Mountcastle VB (1978) Brain systems for directed attention. J R Soc Med 71: 14–27.

10. 10. Wurtz RH, Goldberg ME, Robinson DL (1980) Behavioral modulation of visual responses

in the monkey. Prog Psychobiol Physiol Psychol 9: 43–83.

11. 11. Posner MI (1988) Structures and functions of selective attention. In: Boll T, Bryant B,editors. Master lectures in clinical neuropsychology and brain function: Research, measurement, andpractice. Washington (D.C.): American Psychological Association. pp. 171–202.

12. 12. Posner MI, Raichle ME (1994) Images of mind. New York: Scientific American Library.

257 p.

13. 13. Rosen BR, Buckner RL, Dale AM (1998) Event-related functional MRI: Past, present,

future. Proc Natl Acad Sci U S A 95: 773–780.

14. 14. Dehaene S (1996) The organization of brain activations in number comparison: Event-

related potentials and the additive-factors method. J Cogn Neurocsci 1: 47–68.

15. 15. Sternberg S (2001) Separate modifiability, mental modules and the use of pure and

composite measures to reveal them. Acta Psychol 106: 147–246.

16. 16. Sternberg S (2004) Separate modifiability and the search for processing modules. In:

Kanwisher N, Duncan J, editors. Functional neuroimaging of visual cognition—Attention andperformance XX. Oxford: Oxford University Press. 572 p.

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17. 17. Sigman M, Dehaene S (2005) Parsing a cognitive task: A characterization of the mind's

bottleneck. PLoS Biol 3: e37.

18. 18. Dehaene S (1997) The number sense. New York: Oxford University Press. 274 p.

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Synopsis Selected PLOS Biology research articles are accompanied by a synopsis written for ageneral audience to provide non-experts with insight into the significance of the published work.

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A Window into the Brain Demonstrates the

Importance of Astrocytes

Citation: (2004) A Window into the Brain Demonstrates the Importance of Astrocytes. PLoS Biol2(4): e115. doi:10.1371/journal.pbio.0020115

Published: April 13, 2004

Copyright: © 2004 Public Library of Science. This is an open-access article distributed under theterms of the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

Did you ever wish you could peek inside someone's brain and see what was going on in there? Inresearch reported in this issue of PLoS Biology , Hajime Hirase and his colleagues at RutgersUniversity have done just that by focusing their microscope on the brains of living rats in order toexamine how certain cells called astrocytes function in vivo.

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Astrocyte in the cerebral cortexdoi:10.1371/journal.pbio.0020115.g001

In the longstanding quest to understand how the brain works, scientists have focused on neurons.Neurons conduct action potentials, electrical signals that transmit information in the nervous system.But the brain also contains several other types of cells called glia. (Glia is derived from the Latin for “glue”; these cells were thought to “hold it all together.”) One type of glial cell, the astrocyte (namedfor its starlike shape), is the most populous cell in the brain and forms an intimate association withneurons and their synapses. It was thought that these cells played a supporting role in the brain,ensuring the proper chemical environment for synapses.

Recent research, however, has suggested that astrocytes and other glial cells may play a moresignificant role. When examining astrocytes cultured in the lab, scientists have observed behavior suggesting that astrocytes can communicate with neurons. Though astrocytes cannot propagate

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electrical signals like neurons do, they can sense the transmission of such signals at the synapsebetween two neurons. Furthermore, astrocytes are able to propagate a different kind of signal, achemical signal based on the release of calcium ions. Calcium signaling is a mechanism of chemicalsignaling that has been observed in many other cell types. The exact properties of neuron–astrocytecommunication, however, are not clear because different preparations of these tissues have yieldeddifferent results. It has also not been established that this type of communication occurs in the livingbrain.

To explore such questions, Hirase and colleagues have taken the next step by investigating thecalcium signaling properties of astrocytes in the brains of living rats. To accomplish this feat, theresearchers used a combination of two technologies. They monitored calcium signaling using afluorescent dye called Fluo-4, which fluoresces in response to calcium ions. Then they used a specialtype of microscope called a two-photon laser scanning microscope to visualize the dye. Since thistype of microscope uses a lower energy laser, it can image the dye in living tissue without causingharm.

The researchers applied the dye to the brains of anesthetized rats, washed out the excess dye thathad not penetrated into cells, and then imaged the tissue under the microscope. They first confirmedthat they indeed were examining astrocytes and noticed that cells displayed a moderate level of baseline calcium signaling activity. They then used a drug called bicuculline to stimulate neurons andobserved a significant increase in the calcium signaling activity of the astrocytes. Because bicucullineonly affects neurons, this implies that the astrocytes are responding to the activity of the neurons.The researchers also found that neighboring astrocytes often also displayed coordinated calciumsignaling activity, suggesting that the communication among astrocytes is facilitated by increasedneuronal activity.

This research confirms the complexity of astrocyte signaling functions in the living brain anddemonstrates that astrocytes play far more than a supporting role in brain function. It also establishesan important experimental system for scientists seeking to understand how these distinct elements of the brain—neurons and astrocytes—work together. Though this research makes it clear thatsignaling exists both among astrocytes and between neurons and astrocytes, scientists have yet tounderstand the effect of this signaling. Some possibilities include regulation of synapse formation,modification of synaptic strength, or more complicated roles in information processing resulting from

the coordination of neuronal activity. Future research using this and other systems will help revealthese functions.

SYNOPSIS 

Synopsis Selected PLOS Biology research articles are accompanied by a synopsis written for ageneral audience to provide non-experts with insight into the significance of the published work.

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A Neural Seat for Math?• Liza Gross

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Citation: Gross L (2006) A Neural Seat for Math? PLoS Biol 4(5): e149.doi:10.1371/journal.pbio.0040149

Published: April 11, 2006

Copyright: © 2006 Public Library of Science. This is an open-access article distributed under the

terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original author and source are credited.

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 Abstract mathematical reasoning is often treated as a uniquely human endeavor. But many species,from pigeons to primates, show some ability to grasp the concept of number, suggesting that thesenumerical abilities represent the evolutionary building blocks of higher math in humans. Sophisticatedsymbolic number processing in adults recruits a region of the brain called the intraparietal sulcus(IPS). While children can grasp basic math concepts relating to size and number before they knowthe words that describe them, very little is known about the neural basis of these abilities. Does theIPS support the relatively simple numerical tasks of childhood as well as the sophisticated numericalcalculations that adults learn to perform?

Behavioral studies show that adults respond similarly to nonsymbolic numerical stimuli (arrays of dots) and symbolic numerical stimuli (Arabic numerals), suggesting that a common pathway supportsboth tasks. But neuroimaging studies have not resolved whether the same brain pathway is involvedin both symbolic and nonsymbolic number processing. In a new study, Jessica Cantlon, ElizabethBrannon, Elizabeth Carter, and Kevin Pelphrey at Duke University used functional magneticresonance imaging (fMRI) to investigate how the IPS responds to nonsymbolic numerical values inadults and preschool-aged children. They show that the brain circuitry governing nonsymbolicnumber processing is already in place very early in human development.

In the study, adults and four-year-olds lay in a scanner while passively viewing a continuous streamof visual arrays on a computer screen. The arrays were designed to elicit differences in brainresponse to stimuli that were either novel in number or novel in shape. This study design operatesunder the assumption that neurons tuned to a particular stimuli (numbers, for example) will stopresponding when exposed to a standard stimulus (16 circles) over and over, but will respond tostimuli that deviate from the norm (six or 32 circles). Every so often, a deviant number or shape (atriangle or square in place of a circle) was mixed in with the standard stimuli. Participants pressed abutton when a crossbar in the center of the visual display turned red to maintain focus. Cantlon et al.analyzed the fMRI data to determine which brain regions responded to both types of deviant stimuliin the adults and children.

Number deviants produced a much greater bilateral response in the IPS of adults compared withshape deviants, with activity extending into the inferior and superior parietal lobules (SPL). Thisresponse was confirmed by an alternate measure of brain activation based on blood oxygen level,which rose significantly three to 7.5 seconds after the number of elements changed. Brain regions

that responded to shape deviants were concentrated in the ventral temporal-occipital cortex.

fMRI results for the children showed that number deviants produced a significant response in andaround the right IPS and the right SPL. Brain response to shape deviants was similar to thatobserved in adults. The location and pattern of brain activity in the preschoolers resembled thatreported in studies of nonsymbolic numerical processing and basic math ability in adults. By four years old, children's brains already selectively respond to nonsymbolic numerical values, suggestingthat the neural networks for number processing are established early in life.

How to explain the finding that IPS activity was bilateral in adults and concentrated in the righthemisphere in the four-year-olds? It could be that the left hemisphere acquires more sophisticatedmath-related functions over time while the right remains relatively stable. But since some childrenshowed more activity in the left IPS, the researchers warn that future study will have to determine

whether this pattern is unique to kids.

Overall, these results indicate that the brain dedicates a region to cultivating numerical abilities earlyin development. The IPS provides the neurobiological platform for nonsymbolic numerical processingin young children, then supports the expanding capacity for higher-math operations in adulthood. Six-month-olds also have an abstract numerical sense, suggesting that the IPS may even underlienumerical processing in infancy. Much remains to be learned about how children learn to count andmatch words with symbolic representations of numbers, but these results suggest that focusing onthe IPS might help relate biology to behavior to answer some of these questions, and perhaps shedlight on the evolution of numerical cognition.

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The brain circuits for comprehending math are already in place early in development.doi:10.1371/journal.pbio.0040149.g001

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Functional Imaging of Numerical Processing in Adults and 4-y-Old Children

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lizagross@gmail.com Citation: Schmidt MF (2008) Using Both Sides of Your Brain: The Case for 

Rapid Interhemispheric Switching. PLoS Biol 6(10): e269. doi:10.1371/journal.pbio.0060269

Published: October 28, 2008

Copyright: © 2008 Marc F. Schmidt. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by National Institutes of Health Grant RO1 DC006102.

Abbreviations: nXIIts, tracheosyringeal part of the hypoglossal nucleus; RA, robust nucleus of thearcopallium; VRN, vocal-respiratory network

The vertebrate brain is symmetrically organized, with two identical-looking cerebral hemispheres anda brainstem that contains identically named nuclei on both sides of the midline. However tempting itis to think of each brain half as a mirror image of its contralateral counterpart, this is an incorrectoversimplification. Careful anatomical comparison of select anatomical structures and their 

connections reveals asymmetries across sides [1]. These asymmetries are not limited to anatomicalfeatures but also include extensive functional differences between cerebral hemispheres [2]. Theexistence of asymmetries implies that the two hemispheres are not just two massively redundantnetworks, but rather functionally specialized entities that work synergistically to coordinate thebehavior of the organism.

Hemispheric Specialization as a General Feature of BrainSystems

Hemispheric specialization, or lateralization as it is often referred to, was originally thought to be aunique human characteristic but appears to be a general property among vertebrate brains. Thereare many examples of hemispheric lateralization, such as the specialization of the right hemisphere

to process and store visual inputs that are important for imprinting in chicks [3], and the differentauditory processing characteristics in the left and right auditory forebrain of songbirds [4,5]. Thereare also many species of amphibians, reptiles, fish, and birds that show functional lateralization atthe periphery that suggests hemispheric specialization [6]. A striking example is the way in whichmany migratory birds sense the direction of the magnetic field using only their right eye(magnetoperception is detected by specialized ganglion cells in the retina) [7].

It was originally believed that specific behaviors were entirely lateralized to a single hemisphere [8].The emerging view, however, is that individual hemispheres are not necessarily dedicated to a singlebehavior but are instead specialized for specific features of those behaviors [9]. In the context of language, for example, both hemispheres are involved in some aspect(s) of speech processing andproduction, even though the left hemisphere might appear more dominant. The left hemisphere is, for example, more specialized for lexical and syntactical aspects of language, while the right hemisphereis more sensitive to emotional features of speech [10]. There is supportive evidence that properties of the auditory cortex parallel these specializations. The left auditory cortex, for example, is moresensitive to fast temporal features of sound, which are necessary for phonemic-level speech

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processing. In contrast, the right auditory cortex is sensitive to the slow rhythmic patterns in soundsthat are associated with prosody, which is the rhythm, stress, and intonation of speech [ 11]. Even atthe level of speech production, a behavior originally thought to be controlled exclusively by the lefthemisphere, there is now clear evidence that both hemispheres participate in the phonation process[12].

Interhemispheric Switching Can Occur under a Wide Range of Conditions and Time Scales

Many behaviors require the recruitment of specializations from each hemisphere. In the case of language, for example, syntax and prosody need to be combined to produce coherent speechpatterns. Given the known hemispheric specializations for language, this combination requires thecoordinated engagement of both hemispheres. This engagement might be simultaneous, i.e., bothhemispheres are active at the same time, or it might conceivably occur in alternation, i.e.,hemispheric control switches from one hemisphere to the other. Although the concept of hemisphericswitching during the production of a single goal-directed behavior might not seem obvious, switchingin hemispheric activation has been observed in a number of animals, including humans.

Hemispheric switching can be observed under a variety of conditions and on multiple time scales. Atthe slow end of the spectrum, switching back and forth between hemispheres can be observedduring sleep in many aquatic mammals and birds [13]. Sleep-like activity (as measured by EEGrecording) will occur in a single hemisphere for several minutes at a time before switching over to thecontralateral hemisphere [14]. At slightly faster rates, interhemispheric switching can be observedduring eye movement in animals that can independently control each eye, such as chameleons andsandland fish. These animals never generate saccades in both eyes at the same time, but insteadgenerate a run of saccades in one eye before switching to the other eye after 10 to 20 seconds.Because the visual pathways in these animals are entirely crossed, the pattern of eye movementsimplies hemispheric switching at that same rate [15]. At the fast end of the spectrum,interhemispheric switching can occur at a rate of about 1 Hz during specific perceptual rivalry tasks inhumans [16]. In the case of binocular rivalry, where different visual stimuli (horizontal lines in one eyeand vertical lines in the other) are presented one to each eye simultaneously, subjects alternatebetween perceiving either vertical or horizontal lines but rarely perceive both. Use of transmagnetic

stimulation (a noninvasive technique akin to electrical stimulation) to perturb neural activity has beenused to show that interhemispheric switching is synchronous with these observed perceptualswitches [17]. Interestingly, the rate of interhemispheric switching during these tasks is not fixedbecause it can vary significantly with mood shifts and is much slower in subjects with manicdepression [18].

Only a few examples of interhemispheric switching have been demonstrated to date. Nevertheless,the range of conditions over which such switching can be observed suggests that it might be ageneral mechanism of brain function in bilaterally organized brain systems. While the adaptiveadvantage of switching between hemispheres might be apparent in sleeping animals—because itallows animals the possibility of having one hemisphere in an awake state at all times—the role thatsuch switching plays in motor production or perception is not known. In a new study from Hahnloser'sgroup in the current issue of PLoS Biology, Wang et al. provide evidence, during song production in

birds, for the fastest rate of interhemispheric switching yet described [19]. Importantly, because songproduction is controlled by a well-defined neural system and can be quantified with exquisiteprecision, the avian song system might prove to be an ideal model for providing insight into thefunctional implication of interhemispheric switching.

Song Production in Birds: A Highly Asymmetric Behavior 

Song production in passerine birds is one of the most striking and best characterized asymmetricbehaviors at the peripheral level [20]. Each bird has a bipartite vocal organ, known as a syrinx, that isdivided into left and right halves that each contain an independent membranous sound source [21].The vocal output that one hears from a bird's beak is therefore the sum of the sounds generated fromeach “sound box.” In the majority of species studied, individual song syllables can be produced usinga variety of different strategies. In some cases, sound can be generated in both syringeal halves at

the same time, while in other cases, sound can be produced in one side at a time with air flowactively blocked in the non–sound producing syringeal half [22]. The strategies used to produce agiven syllable can vary even within the same song, and there are many cases where alternations

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between sides can occur multiple times during the production of a single syllable (Figure 1).Switching between sides is so perfectly synchronized that the acoustic output is completely smoothacross these transitions. It has been suggested that the ability of birds to use each syringeal half as aseparately controlled “sound box” might be a strategy to increase the complexity of their songs [23].

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Figure 1. Songbirds Can Switch Rapidly between Sides When They SingMany songbirds will use both their left and right syrinx to produce song. In some cases, as illustratedhere by the brown-headed cowbird, they can switch rapidly from producing sound in the left syrinx toproducing sound in the right syrinx. In this example, a cluster of five short song elements is producedwithin a very short period of about 200 ms. The contribution of each syringeal side can be measured

by implanting a small heated microbead thermistor in each primary bronchus. These measure therate of airflow through each side of the syrinx. The bottom portion of the figure shows the airflowthrough each bronchus with airflow from the right side in blue and left side in red. Note that leftairflow has been flipped upside down to better compare with the right side. From thesemeasurements, one can infer the syringeal source of each song element. As shown in the sonogram,cowbirds rapidly alternate between producing a note with the left (red) and the right (blue) sides of the syrinx.(Based on recordings performed in the cowbird by Rod Suthers)doi:10.1371/journal.pbio.0060269.g001

In songbirds, each hemisphere contains a discrete set of brain structures that control songproduction in adult birds [24]. Figure 2 is a schematic of the main descending motor pathwayshowing the connection between the forebrain nuclei HVC (used as a proper name) and RA (robustnucleus of the arcopallium) and the two major projections of RA to the brainstem. One of these

projections goes directly to the ipsilateral brainstem's hypoglossal nucleus (nXIIts, thetracheosyringeal part of the hypoglossal nucleus), a motor output structure that exclusively

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innervates the ipsilateral syringeal muscles. The other major projection from RA innervates a seriesof highly interconnected vocal-respiratory nuclei in the ipsilateral brainstem, several of which sendprojections to respiratory motoneurons in the spinal cord. These nuclei are highly interconnectedacross the midline and can therefore be thought of as a bilaterally organized vocal-respiratorynetwork (VRN).

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Figure 2. Bilateral Organization of the Song Motor Control SystemThis schematic is a simplified representation of the motor portion of the avian song systememphasizing its bilateral organization. Sound is produced in a bipartite vocal organ known as thesyrinx. The syringeal muscles that make up each half of the vocal organ are innervated bymotoneurons in the ipsilateral nXIIts. This nucleus receives its own motor commands from HVC andRA in the ipsilateral forebrain. Therefore, motor commands generated in the left hemisphere(highlighted in red) end up activating muscles on the left half of the syrinx, while those on the rightside (highlighted in blue) activate syringeal muscles on the right side. Motor commands from eachhemisphere are also sent to brainstem nuclei that form part of the bilaterally organized vocal-

respiratory network (VRN), which is highly interconnected across the midline. The VRN sends motor commands to the muscles of respiration. This network also sends major projections back to theforebrain nucleus HVC via the intermediary of the thalamic relay nucleus uvaeformis (Uva). These

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bottom-up projections are thought to synchronize activity in both hemispheres and might play a rolein the rapid hemispheric switching described by Wang et al.doi:10.1371/journal.pbio.0060269.g002

The hypoglossal nuclei (nXIIts) that innervate the syringeal muscles do not connect across themidline, and muscles on each half of the syrinx therefore receive separate sets of motor commands

from each ipsilateral motor pathway. Because song involves the simultaneous activation of muscleson both halves of the syrinx, it is critical that syringeal output be precisely synchronized andcoordinated during song production. Songbirds, however, do not have direct projections between anyof the cerebral song control nuclei in each hemisphere (birds do not have a corpus callosum), and socoordination must occur through a mechanism other than direct hemispheric connectivity. Becausethe brainstem VRN provides, via its bilateral projections back up to HVC (Figure 2), the major way inwhich forebrain song nuclei are functionally connected, it is well positioned to serve a key function insynchronizing song-related motor activity in each hemisphere [25]. Given the new results from Wanget al. [19], these projections might well also play a critical role in switching between hemispheres.

Rapid Interhemispheric Switching Occurs during PreciseTransitions in the Song

One approach to test whether a behavior is being driven by the rapid alternation of motor commandssent by each hemisphere is to briefly stimulate the circuits responsible for producing the behavior and ask whether the ability of such perturbation to alter the behavior is dependent on the specifichemisphere that is stimulated. In songbirds, it has been shown that brief electrical stimuli (a few shortbiphasic pulses at relatively low current levels) delivered directly to HVC, RA, or the VRN caninterrupt the temporal structure of the ongoing song [26]. In zebra finches, whose song is made up of a stereotyped sequence of syllables (known as a motif) that is repeated multiple times, thisinterruption is observed as a truncation of the ongoing syllable (with a delay of approximately 70 ms)followed either by the termination of the song or the start of a new motif [26].

Using this technique, Wang and colleagues implanted electrodes in HVC of each hemisphere andfound that stimulating HVC in one hemisphere caused song disruptions only when these short stimuliwere delivered during certain portions of the motif. Astonishingly, the authors observed that

stimulation of the contralateral HVC interrupted song at precisely the time when stimulation in theother HVC had no effect on song. In other words, left hemisphere stimulation disrupted song duringperiods when the right hemisphere stimulation was ineffective and vice versa. This pattern of left/righthemisphere switching could occur as often as three to four times during the production of a single200 ms syllable. When the authors increased the amount of current, this effect disappeared andstimuli were effective at perturbing song temporal structure throughout the whole motif, suggestingthat the threshold for perturbing syllable sequencing within a motif is what switches rapidly betweenhemispheres. The change in perturbation threshold in a given hemisphere does not necessarily implythat the “nondominant” hemisphere is inactive. Multi-unit neural recordings from HVC during singingin the same species [27] show clear premotor activity in each hemisphere during the entirety of themotif. The ability of electrical stimulation to perturb activity in each hemisphere in rapid alternationtherefore reflects changes that are subtler than simple all-or-none changes in premotor activity.

Given the evidence for alternation between the left and right half of the syrinx, a critical question iswhether the timing of hemispheric switching is synchronized to the switching observed in the syrinx.The authors did not directly measure syringeal dynamics, but they did compare switching patternswith the major acoustic transitions in the song. They did not find any obvious relationship betweenhemispheric switching and transitions in the song (e.g., syllable onset or offset) or acoustic featuretransitions (e.g., pitch or amplitude). While this approach provides a reasonable initialcharacterization, extrapolating syringeal switching patterns from the acoustic signal is likely to beinaccurate. Future experiments combining HVC stimulation with syringeal muscle recordings or airflow measurements, parameters that are typically used to measure syringeal switching [28], aretherefore necessary to unequivocally reveal any relationship between central (hemisphere) andperipheral (syrinx) switching. An additional direction that might be explored to link interhemisphericswitching with song output is to compare interhemispheric switching patterns in birds that have beentutored with, and therefore learned, the same exact song, a process that is possible under specifictutoring paradigms [29]. Having birds sing identical songs would allow analysis across birds of the

general types of song transitions that lead to interhemispheric switching.

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The Switching Mechanism: The Brainstem as a Possible KeyPlayer 

When hemispheres contribute differentially to behaviors that recruit muscles bilaterally, such as for bimanual movement or vocal production, it is sometimes assumed that hemispheric coordination is

achieved exclusively by the corpus callosum, a massive fiber bundle that connects the left and rightcerebral hemispheres. This commissural system is evolutionarily recent and is only observed inplacental mammals, yet hemispheric switching is observed in animals such as fish and birds that donot have a corpus callosum. A possible neural substrate for hemispheric coordination and switchingmight therefore include brainstem neuromodulatory systems. These are interconnected across themidline and project diffusely throughout the hemispheres, and could therefore differentially influenceeach hemisphere. Consistent with this view, a recent study has shown that cortical release of theneurotransmitter acetylcholine is lateralized during asymmetric sleep in fur seals [30]. While thismechanism might be well suited for hemispheric switching during sleep, the generally slow timecourse of action of neuromodulators makes this mechanism somewhat less attractive for rapidhemispheric switching.

For fast switching, alternative mechanisms might include bilaterally coupled networks in thebrainstem that directly “drive” each hemisphere. This is plausible in the song system given known

projections from the brainstem back up to the forebrain via the intermediary of the thalamus, and thedirect influence that these brainstem areas have on neural activity in forebrain song control nuclei[31]. Interestingly, a similar relationship between the brainstem and forebrain has been demonstratedin other motor control systems such as the primate oculomotor system [32]. Further supportiveevidence for a role of the brainstem in hemispheric switching comes from findings that switchingduring binocular rivalry [17] can occur in the absence of a corpus callosum [33] and is modulated byserotonin receptor subtypes that are located largely in the brainstem [34]. For song production, thebrainstem VRN is well placed to play a central role in hemispheric switching, but it is unlikely that itacts as a simple oscillating circuit, given that Wang and colleagues never observed any periodicity inthe pattern of hemispheric switching. In fact, they often observed a wide range of switching intervals(from 4 ms to 150 ms) within a single song motif. An important direction for future work will be toidentify the signal(s) responsible for the transitions that underlie hemispheric switching. In the songsystem, the VRN and the inputs it receives from RA are well placed to show spiking patterns that

might be predictive of switching times.

Functional Implications

One of the primary unanswered questions is why brain systems have evolved to switch rapidlybetween hemispheres. One possibility is that interhemispheric switching is part of a strategy tooptimize processing power within each hemisphere while keeping both sides synchronized. Similar strategies are certainly used computationally in multicore processors. In songbirds, it is certainlyclear from lesion studies that normal adult birds require both hemispheres to produce song [35]. Thequestion of how the elements of song production are divided between hemispheres, however, isunclear. Wang and colleagues failed to f ind any relationship between switching times and features of song acoustics such as syllable onsets, yet they observed a wide range of switching time intervals.Presumably these intervals are not arbitrarily determined but reflect some aspect of song production,

such as switching between sides of the syrinx. It is also important to note that the bilateral nature of the song system appears to have a learned component because unilateral lesions of RA in juvenilebirds causes these birds to sing approximately normal songs as adults [35]. This suggests that thesong system can compensate for whatever functional advantages are conferred by bilateralprocessing. An intriguing question for these “unihemispheric” birds is thus how this compensationoccurs. For example, do unilateral birds also show switching at the level of the syrinx? If so, how issuch switching achieved?

More generally, the purpose of interhemispheric switching may become clearer as more data arecollected through the paradigm developed by the Wang lab, and similar experiments are performedin other motor systems. Functional specialization is found throughout the nervous system butrequires communication among many different areas of the brain to result in cohesive activity.Interhemispheric switching provides a compelling example, and may very well elucidate more

general principles of brain function and the production of temporally complex behaviors.

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Acknowledgments

I wish to thank many members of the Schmidt lab, in particular Chris Glaze and Jon Raksin, for reading various versions of the manuscript. I also wish to thank Martin Wild, Rod Suthers, and JackPettigrew for valuable suggestions about the manuscript.

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