Abstract
Recent advancements in the brain sciences have enabled researchers to determine, with increasing accuracy, patterns and locations of neural activation associated with various psychological functions. These techniques have revived a longstanding debate regarding the relation between the mind and the brain: while many authors claim that neuroscientific data can be employed to advance theories of higher cognition, others defend the so-called ‘autonomy’ of psychology. Settling this significant issue requires understanding the nature of the bridge laws used at the psycho-neural interface. While these laws have been the topic of extensive discussion, such debates have mostly focused on a particular type of link: reductive laws. Reductive laws are problematic: they face notorious philosophical objections and they are too scarce to substantiate current research at the intersection of psychology and neuroscience. The aim of this article is to provide a systematic analysis of a different kind of bridge laws—associative laws—which play a central, albeit overlooked role in scientific practice.
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Notes
As Fodor notes, reductive bridge laws express a stronger position than token physicalism, the view that all events that fall under the laws of some special science are physical events. Statements such as \(R_1\) and \(R_2\) presuppose type physicalism, according to which every kind that figures in the laws of a science is type-identical to a physical kind. Since our focus is not on physicalism per se; the relevant claim is whether the kinds of one science can be reduced to the kinds of a more fundamental science, not necessarily to physics.
In an influential discussion, Marr (1982) argued that information-processing systems should be investigated at three complementary levels. Hypotheses at Marr-level 1 pose the computational problem: they state the task computed by the system. Hypotheses at Marr-level-2 state the algorithm used to compute Marr-level 1 functions: they specify the basic representations and operations of the system. Finally, hypotheses at Marr-level 3 specify how Marr-level 2 algorithms are implemented in the brain: they purport to explain how these basic representations and operations are realized at the neural level.
In general, the amygdala is critically involved in conditioned and unconditioned fear response in animals, including humans. For example, patients with selective damage to the amygdala show no physiological response to a previously fear-conditioned stimulus, although they can explicitly remember the conditioning experience (Kandel et al. 2013, Ch. 48).
Miller and Cohen (2001) present several studies that support the key role of the DLPFC in cognitive control and rule-guided processes. A relevant set of experiments are based on the famous Stroop task, in which subjects are instructed to name the color of the ink of words as they appear on a screen. Famously, reaction times and error rates increase dramatically when subjects read color-terms that differ from the color of their ink. Miller and Cohen present imaging studies which show that, in the misleading cases, subjects who manage to follow the correct rule and name the word’s ink color showed increased activation in DLPFC, compared to subjects who fail the task.
In the classic version of the trolley problem, personal cases are exemplified by the ‘footbridge’ scenario, where five people are saved by throwing a corpulent person on the track. Impersonal cases are exemplified by the ‘switch’ scenario, where five people are saved by pulling a lever that diverts the trolley onto a parallel track where it will kill a single person.
We surmise that the task relativity of reverse inferences is systematically overlooked because methodological discussions (e.g., Poldrack 2006; Phelps 2006) often consider only arbitrary ‘empty’ tasks which do not eliminate any processing possibilities (that is, any bridge laws) for the brain region of interest. Hence, reverse inferences seem intuitively weak. However, once we consider the tasks relevant to each reverse inference, we can eliminate some subset of bridge laws which cover the brain regions of interest, thereby increasing their strength.
An alternative is to reformulate reverse inference in likelihoodist terms (Machery 2014). Consider two competing cognitive hypotheses \(m_1\) and \(m_2\) and neural activation data \(n_1\). On this view, \(n_1\) favors \(m_1\) over \(m_2\) if and only if \(P(n_1|m_1)>P(n_1|m_2)\). In a likelihoodist framework, one only compares cognitive hypotheses that are under dispute, treating reverse inference as an inherently comparative technique that tells us which among the competing hypotheses is favored by some neural evidence. One drawback of this suggestion is that evidence becomes purely comparative. On the other hand, the advantage of this approach is that the relevant likelihoods can be calculated without having to determine the base rates of activation of the brain regions involved. We should note that the main reason why Machery prefers this likelihoodist approach to reverse inference over the Bayesian account is that Eq. 1 cannot, in general, be computed. This is because neuroscientists rarely know the base rates of activation of particular brain regions of interest. It is not clear whether Machery thinks this is still a serious problem for Huzler’s refined Eq. 2, since he does not directly consider that option. However, we should also note that there is a substantial literature on Bayesianism and imprecise probabilities that can be used to address Machery’s concern (Joyce 2011).
Cases where classifiers cannot perform significantly above chance can still be interesting, albeit for different reasons. Suppose that, in task \(t\), a classifier underperforms when using data sets taken from some region \(n_1\), but performs significantly above chance when using data from region \(n_2\). This provides evidence that \(n_2\) carries information relevant to performing \(t\), whereas \(n_1\) does not.
Of course, this does not mean that there are no difficulties in using this method. Appropriate experimental design is crucial, especially since pattern classifiers are designed to use whatever information is available to make better predictions. In addition, there is still a question whether we can extend the reliability of classifiers obtained from the testing phase to cases in which the experiments cannot determine the engagement of the psychological variables, since the latter inevitably involve some variation on the task. There are various studies which suggest that classifiers perform well under task variations. For example, in one study pattern classifiers were used to predict phonemes. The classifiers were still successful when presented with data from voices which were not presented in the learning phase (Formisano et al. 2008). Hence, at least this much variation in the task does not affect performance. In a study of visual working memory, classifiers were trained on data elicited by unattended gratings, and then tested on whether they could also predict which of two orientations was maintained on working memory when subjects were viewing a blank screen. Again, their reliability was maintained despite the substantial difference in stimulus and task (Harrison and Tong 2009). Indeed, testing for this kind of robustness relative to stimuli/task variation is usually taken as evidence that the brain region from which the data was obtained really does provide information about the function of interest (Tong and Pratte 2012).
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Acknowledgments
We are grateful to Max Coltheart, Kateri McRae, Bruce Pennington, and three anonymous reviewers for constructive comments on various versions of this essay. Some of the ideas developed here were presented at the Neuroscience Research Group at the University of Denver, at the 2014 Annual Conference in History and Philosophy of Science at the University of Colorado at Boulder, and at the 2014 Meeting of the Philosophy of Science Association in Chicago: all audiences provided helpful feedback.
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Marco J. Nathan and Guillermo Del Pinal contributed equally to this work.
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Nathan, M.J., Del Pinal, G. Mapping the mind: bridge laws and the psycho-neural interface. Synthese 193, 637–657 (2016). https://doi.org/10.1007/s11229-015-0769-2
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DOI: https://doi.org/10.1007/s11229-015-0769-2