The reasoning behind cooperation and cheating: The prisoner’s dilemma
4.5 Local integration II: Neural activity and the BOLD signal Our second example of a local integration comes from a very different location within
the overall“space” of cognitive science. Whereas the cheater detection module and the experimental results on the psychology of reasoning that it is trying to explain are very high-level, our next example takes us down to the interface between functional neuroi- maging and the physiology of the brain.
As we saw insection 3.4the development of functional neuroimaging technology was a very important factor in cognitive science’s turn to the brain. Functional neuroimaging allows us to study the workings of the brain at the level of neural systems and large-scale neural circuits. In some sense, that is, it allows us to study the behavior of large popula- tions of neurons. But when one is looking at brightly-colored pictures communicating the results of PET or fMRI scans it is only too easy to forget that very little is known about the relation between what those scans measure and the cognitive activity that is going on while the measurements are being made. It is only in the very recent past that
progress has been made on building a bridge between functional neuroimaging and neurophysiology. This is the topic of our second case study.
There are two principal technologies in functional neuroimaging. Insection 3.4 we looked at the PET technology, which measures cerebral blood flow by tracking the movement of radioactive water in the brain. A newer, and by now dominant, technology is functional magnetic resonance imaging (fMRI). Whereas PET measures local blood flow, fMRI measures levels of blood oxygenation. Unlike PET, which can track a direct index of blood flow, fMRI works indirectly. The basic fact underlying fMRI is that deoxygenated hemoglobin (which is the oxygen-carrying substance in the red blood cells of humans and other vertebrates) disrupts magnetic fields, whereas oxygenated hemoglobin does not.
The standard background assumption in neuroimaging is that blood flow to a particu- lar region of the brain increases when cellular activity in that region increases. This increase in blood flow produces an increase in oxygen. The degree of oxygen consump- tion, however, does not increase in proportion to the increase in blood supply (as opposed, for example, to the level of glucose consumption, which does increase in proportion to the increase in blood supply). So, the blood oxygen level increases in a brain region that is undergoing increased cellular activity– because the supply of oxygen exceeds the demand for it. The increase in blood oxygen level can be detected in the powerful magnetic field created by the MRI scanner, since oxygenated and deoxygenated blood have different magnetic properties. This difference is known as the BOLD (blood oxygen level dependent) contrast. It is what is measured by functional magnetic reson- ance imaging.
So, fMRI measures the BOLD contrast. But what does the BOLD contrast measure? In some sense the BOLD contrast has to be an index of cognitive activity– since it is known that cognitive activity involves increased activity in populations of neurons, which in turn results in increased oxygen levels and hence in a more pronounced BOLD contrast. But what exactly is the neuronal activity that generates the BOLD contrast? This problem here is a classic integration problem. We are trying to integrate information about blood flow with information about the behavior of populations of neurons. And we are trying to understand how individual neurons contribute to the behavior of neural populations. In doing this we are trying to integrate two different levels of explan- ation (two different parts of neuroscience), since functional neuroimaging is a very different enterprise from the study of individual neurons.
Neuroscientists study the behavior of individual neurons through single-cell record- ings (to be discussed in more detail inChapter 11). Microelectrodes can be inserted into the brains of animals (and also of humans undergoing surgery) and then used to record activity in individual cells while the animal performs various behavioral tasks.Figure 4.8
below illustrates a microelectrode recording in the vicinity of a single neuron. This type of single-cell recording has been used primarily to identify the response profiles of individual neurons (i.e. the types of stimuli to which they respond).
Response profiles are studied by looking for correlations between the neuron’s firing rate and properties of the environment around the subject. Experimenters can identify
Figure 4.8 A microelectrode making an extracellular recording.
those properties by tracking the relation between the firing rates of individual neurons and where the animal’s attention is directed. They are usually low-level properties, such as the reflectance properties of surfaces. But in some cases neurons seem to be sensitive to higher-level properties, firing in response to particular types of object and/or situations. The basic assumption is that individual neurons are“tuned” to particular environmental properties.
Since the salient property of individual neurons is their firing (or spiking) behavior, it is a natural assumption that the neural activity correlated with the BOLD contrast is a function of the firing rates of populations of neurons. In fact, this is exactly what was suggested by Geraint Rees, Karl Friston, and Christoph Koch in a paper published in 2000. They proposed that there is a linear relationship between the average neuronal firing rate and the strength of the BOLD signal – two variables are linearly related when they increase in direct proportion to each other, so that if one were to plot their relation on a graph it would be a straight line.
This conclusion was based on comparing human fMRI data with single-cell recordings from monkeys. In fact, their study seemed to show a very clear and identifiable relation- ship between average spiking rate and the BOLD response– namely, that each percentage increase in the BOLD contrast is correlated with an average per second increase of nine spikes per unit. If the Rees–Friston–Koch hypothesis is correct, then the BOLD response directly reflects the average firing rate of neurons in the relevant brain area, so that an increase in the BOLD contrast is an index of higher neural firing activity.
Neurons do more than simply fire, however. We can think of a neuron’s firing as its output. When a neuron fires it sends a signal to the other neurons to which it is connected. This signal is the result of processing internal to the neuron. This processing does not always result in the neuron’s firing. Neurons are selective. They fire only when the level of internal activity reaches a particular threshold. This means that there can be plenty of activity in a neuron even when that neuron does not fire. We might think of this as a function of the input to a neuron, rather than of its output. A natural question to ask, therefore, is how cognitively relevant this activity is. And, given that we are thinking about the relation between neural activity and the BOLD contrast, we have a very precise way of formulating this question. We can ask whether the BOLD signal is correlated with the input to neurons, or with their output (as Rees, Friston, and Koch had proposed).
This is exactly the question explored in a very influential experiment by Nikos Logothetis and collaborators. Logothetis compared the strength of the BOLD signal against different measures of neural activity in the monkey primary visual cortex (see
section 3.2for a refresher on where the primary visual cortex is and what it does). The team measured neural activity in an anaesthetized monkey when it was stimulated with a rotating checkerboard pattern while in a scanner. In addition to using fMRI to measure the BOLD contrast, researchers used microelectrodes to measure both input neural activity and output neural activity. At the output level they measured the firing rates both of single neurons and of small populations of neurons near the electrode tip (“near” here means within 0.2 mm or so). InFigure 4.9below these are labeled SDF (spike density function) and MUA (multi-unit activity).
The experimenters measured input neural activity through the local field potential (LFP). The LFP is an electrophysiological signal believed to be correlated with the sum of inputs to neurons in a particular area. It is also measured through a microelectrode, but the signal is passed through a low-pass filter that smoothes out the quick fluctuations in the signal that are due to neurons firing and leaves only the low-frequency signal that represents the inputs into the area to which the electrode is sensitive (an area a few millimeters across).
The striking conclusion reached by Logothetis and his team is that the BOLD contrast is more highly correlated with the LFP than with the firing activity of neurons (either at the single-unit or multi-unit level). This is nicely illustrated in the graph inFigure 4.9. In many cases, the LFP will itself be correlated with the firing activity of neurons (which is why Logothetis’s results are perfectly compatible with the results reached by Rees, Friston, and Koch). But, if Logothetis’s data do indeed generalize, then they show that when spiking activity and LFP are not correlated, the LFP is the more relevant of the two to the BOLD contrast.
This is a very significant example of a local integration. The Logothetis experiments build a bridge between two different levels of organization in the nervous system. The large-scale cognitive activity that we see at the systems level (when we are thinking, for example, about the primary visual cortex as a cognitive system) is more closely tied to neural activity that does not necessarily involve the firing of neurons. They also build a bridge between two different levels of explanation and two different technologies for studying the brain – between studying blood flow as an index of cognitive activity through functional neuroimaging, on the one hand, and through studying the electrical behavior of individual neurons, on the other.
Exercise 4.7 Make a table of relevant similarities and differences between the two case studies, thinking particularly about how they each serve as local solutions to the integration challenge.
0 5 10 15 20 25 30 35 40 45 9.0 6.0 3.0 0 -3.0
BOLD signal change (s.d. units)
9.0 6.0 3.0 0 -3.0
Neural signal change (s.d. units)
BOLD signal: ePts
BOLD LFP MUA SDF
Figure 4.9 Simultaneous microelectrode and fMRI recordings from a cortical site showing the neural response to a pulse stimulus of 24 seconds. Both single- and multi-unit responses adapt a couple of seconds after stimulus onset, with LFP remaining the only signal correlated with the BOLD response. (Adapted from Bandettini and Ungerleider2001)
Summary
This chapter has begun the project of explaining what makes cognitive science a unified and focused field of study with its own distinctive problems and tools. The interdisciplinary study of the mind is a huge field spanning many different levels of explanation and analysis. This raises what I have termed the integration challenge. This is the challenge of providing a unified theoretical framework encompassing the whole “space” of the cognitive sciences. This chapter has introduced the integration challenge and illustrated two local integrations – two cases where cognitive scientists have built bridges across disciplines and across levels of explanation in order to gain a deeper theoretical understanding of a particular cognitive phenomenon. The first local integration brought the psychology of reasoning into contact with evolutionary biology and game theory. The second explores the connections between two different tools for studying activity in the brain – microelectrode recordings and functional neuroimaging.
Checklist
Integration across levels
(1) Cognitive science is an inherently interdisciplinary enterprise.
(2) The hexagonal figure from the Sloan report is not a good representation of the interdisciplinary nature of cognitive science.
(3) Disciplines and sub-fields across cognitive science differ across three dimensions – the type of cognitive activity that they are interested in, the level at which they study it, and the degree of resolution of the tools that they use.
(4) The different branches of psychology vary primarily across the first dimension, while those of neuroscience vary primarily across the second and third.
(5) The integration challenge for cognitive science is the challenge of providing a unified theoretical framework for studying cognition that brings together the different disciplines studying the mind. Integrating the psychology of reasoning with evolutionary biology
(1) Experiments such as those with the Wason selection task have shown that abilities in conditional reasoning are highly context-sensitive.
(2) Subjects are much better at tasks involving permissions and entitlements than they are at abstract reasoning tasks.
(3) Evolutionary psychologists have explained this by hypothesizing that we have evolved a specific module dedicated to detecting cheaters and free riders.
(4) Part of the theoretical justification for this module comes from using heuristic strategies for solving iterated prisoner’s dilemmas to model the evolution of cooperation and altruism.
Integrating the BOLD response with neural activity
(1) Functional magnetic resonance imaging (fMRI) provides a measure of blood flow in terms of levels of blood oxygenation (the BOLD signal). This gives an index of cognitive activity.
(3) One possibility is that cognitive activity detected by fMRI is correlated with the outputs of populations of neurons (as manifested in their firing activity). Another possibility is that the correlation is with the input to populations of neurons (as measured by the local field potential). (4) The experiments of Logothetis and his collaborators seem to show that the correlation is with the
input to neural areas, rather than with their output.
Further reading
Historical background on the Sloan report can be found in Gardner1985and Miller2003(available in the online resources). The report itself was never published. A very useful basic introduction to levels of organization and structure in the nervous system is ch. 2 of Churchland and Sejnowski 1993. For more detail, the classic neuroscience textbook is Kandel, Schwarz, and Jessell 2012. Stein and Stoodley2006, and Purves, Augustine, Fitzpatrick, Hall, Anthony-Samuel, and White
2011are alternatives. Craver2007discusses the interplay between different levels of explanation in the neuroscience of memory. Piccinini and Craver2011is a more general discussion. For opposing perspectives see Bickle2006and Sullivan2009. For more details on general strategies for tackling the interface problem, see the suggestions for further reading inChapter 5.
Evans and Over2004gives a good and succinct overview of the cognitive psychology of conditional reasoning. Also see Oberauer2006, Byrne and Johnson-Laird2009, and Oaksford, Chater, and Stewart’s chapter in The Cambridge Handbook of Cognitive Science (Frankish and Ramsey2012). For the deontic version of the selection task see Griggs and Cox1982, and Pollard and Evans1987. Cosmides and Tooby1992is a clear statement of the reasoning that led them to postulate the cheater detection module. For experimental support for the cheater detection module see Cosmides1989. More recent summaries of Cosmides and Tooby’s research can be found in Cosmides, Barrett, and Tooby2010, and Cosmides and Tooby2013. Alternative explanations of performance on the selection task can be found in Oaksford and Chater1994, and Sperber, Cara, and Girotto1995. For more reading on the massive modularity hypothesis see the end of
Chapter 10.
For specific references on the fMRI technology see the suggestions for further reading in
Chapter 11. For a survey of some of the general issues in thinking about the neural correlates of the BOLD signal see Heeger and Ress2002and Raichle and Mintun2006. Logothetis’s single- authored 2001 paper in the Journal of Neuroscience is a good introduction to the general issues as well as to his own experiments. A more recent summary can be found in Goense, Whittingstall, and Logothetis2012. For the Rees–Friston–Koch hypothesis, see Rees, Friston, and Koch2000. For commentary on Logothetis see Bandettini and Ungerleider2001. For an alternative view see Mukamel et al.2005.