Should Psychology Ignore the Language of the Brain ?

Claims that neuroscientific data do not contribute to our understanding of psychological functions have been made recently. Here I argue that these criticisms are solely based on an analysis of functional magnetic resonance imaging (fMRI) studies. However, fMRI is only one of the methods in the toolkit of cognitive neuroscience. I provide examples from research on event-related brain potentials (ERPs) that have contributed to our understanding of the cognitive architecture of human language functions. In addition, I provide evidence of (possible) contributions from fMRI measurements to our understanding of the functional architecture of language processing. Finally, I argue that a neurobiology of human language that integrates information about the necessary genetic and neural infrastructures will allow us to answer certain questions that are not answerable if all we have is evidence from behavior. KEYWORDS—language; brain; ERP; fMRI From a sociology of science perspective, cognitive neuroscience is a tremendous success. In part due to the enormous technical progress in noninvasive recordings of activity in the living human brain, a whole industry of research on brain and cognition has developed. The number of neuroimaging research centers has grown exponentially over the last decade, as has the number of publications reporting functional magnetic resonance imaging (fMRI) results. However, this success is not without its critics. The criticisms follow roughly the following logic: If you are interested in the genetic and neurobiological infrastructure underlying and implementing human cognition, that is just fine. Go ahead and do your research. However (according to the critics), if you are interested in characterizing human cognition itself, it does not help to know that ‘‘it happens somewhere north of the neck’’ (Fodor, 1999). Despite the vast amount of neuroimaging studies, theories of human cognition have not (yet) profited from measuring the brain. The reason can be principled or practical. The principled reason is that our explanation of the mind ‘‘abstracts away from the biological realizations of cognitive structures’’ (Block, 1990, p. 261). The relation between mind and brain is not transparent enough to result in sufficiently strong constraints for cognitive theories by virtue of our understanding of the brain (Fodor, 1974; Page, 2006). The less severe variant of the critique is that one could potentially derive useful information from neuroimaging but, in practice, no actual examples that successfully distinguish between competing psychological theories have been provided. The first thing to note about this debate is that the critique, at least implicitly, expresses doubts that cognitive neuroscience is a fruitful scientific endeavor. In this context, it is surprising that all of the criticisms focus on fMRI results only (Coltheart, 2006; Page, 2006; Uttal, 2001). The arguments are thus largely based on selective shopping in the toolkit of cognitive neuroscience. This toolkit has a lot more to offer than just fMRI; it also includes event-related brain potentials (ERPs), magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and measurements of brain structure. Moreover, theories in cognitive neuroscience are informed by animal models and studies using singleor multi-unit recordings of action potentials and/or local field potentials (i.e., measures of neuronal activity at the microscale of brain organization). The general issue is, therefore, not what fMRI contributes but, rather, how far cognitive neuroscience is a viable scientific enterprise using whatever research tools are at its disposal. This is the question that I will address here, for one particular, central cognitive skill: our capacity to communicate by means of natural language. But first, we need to specify the criteria for success. To establish the criteria that an adequate neurobiology of language has to meet, we first need to clarify what we take our explanandum—that is, the thing we want to explain—to be. If one is interested not only in the cognitive architecture of language but also in the only machinery that so far has been able Address correspondence to Peter Hagoort, F.C. Donders Centre for Cognitive Neuroimaging, P.O. Box 9101, NL-6500 HB Nijmegen, The Netherlands; e-mail: peter.hagoort@fcdonders.ru. nl. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 96 Volume 17—Number 2 Copyright r 2008 Association for Psychological Science to instantiate natural language (i.e., the human brain), it is obvious that the bridge between psycholinguistics and neurobiology has to be crossed. However, it is a valid position to restrict one’s explanandum to the cognitive architecture of language functions. In that case, the brain facts will only be relevant in so far as they can be used to develop, select, or constrain a cognitive-architecture model for the language function of interest. The cognitive architecture then specifies the levels of representation needed and the processing steps required for accessing representational structures and for performing the necessary computational operations on them. Even in this case, brain facts might be relevant. One example of a brain fact relevant for cognitive models relates to the nature of the flow of information. Strictly feedforward models of language comprehension (e.g., Cutler & Clifton, 1999) predict a fixed spatiotemporal pattern of brain activity that is not seriously modulated by attention or output-related factors. Such models are compatible with a serial model of perception and action, in which a perceptual stage is followed by central cognition (e.g., executive function), which is then followed by appropriate action (cf. Fodor, 1983). One of the arguments for strictly feedforward systems is that only these will guarantee the high speed that characterizes many aspects of human cognition. For instance, we easily recognize and understand three or four words per second. How would this be possible if all information that we have in memory about the entities to which these words refer could, in principle, have an impact on language perception? Despite the design argument in favor of feedforward models, recent findings in cognitive neuroscience raise serious doubts about the general tenability of strictly feedforward serial models. Certainly, the neural architecture of the human brain supports feedback options. From all we know now, recurrent loops are a fundamental characteristic of the mammalian neocortex. All layers of the cortex are heavily back-connected to earlier regions in the neural processing chain. Information flow in the brain is, therefore, not one-way. The cerebral cortex is basically a feedback system, and the lack of top-down influences on perception (informational encapsulation) can thus not be based on the architecture of the cortex. This does not necessarily mean that information flow, in terms of a processing model, cannot be feedforward. If, under conditions of normal operation, the input system provides its input to the next level in the processing hierarchy before feedback can have its effect, informational encapsulation might still be achieved. However, in this case, it is speed that buys the system informational encapsulation, rather than the reverse. Moreover, it is only a soft form of encapsulation (i.e., not hardwired in the cortex), since with additional time, feedback will start to have its effect. Importantly, much recent evidence in cognitive neuroscience suggests that perception is influenced by the observer’s attentional state, the task, and the observer’s strategies. These seem to be relevant considerations for functional-architecture models of human cognition. An adequate neurobiology of language might thus provide data that are relevant for cognitive models of language functions. At the same time, the relevant brain facts can only be obtained in neuroscience research that is guided by state-of-the-art psycholinguistics in terms of theoretical models and experimental materials. The criteria for an integrated neurobiology of language are thus (a) specifications of the neural principles of language functions that are adequate in relation to behavioral data and the cognitive architectures derived from these data (upward adequacy) and (b) specifications of the cognitive architectures that are adequate in the light of our understanding of the principles of brain function (downward adequacy). The underlying assumption is that there is a systematic relation between cognitive states and brain states. Despite claims made in the past that these two levels of description and explanation might not be related in a lawful or transparent way (e.g., Fodor, 1974; Mehler, Morton, & Jusczyk, 1984), many believe that cognitive neuroscience has made sufficient progress to warrant this assumption a certain face validity. Here I give a few examples to indicate where the contribution to psycholinguistics could be seen for two different methods used in cognitive neuroscience—namely, ERPs and fMRI. However, I want to stress that, at the end of the day, it is not a single method that is going to make the difference.