Molecules of the Mind: The story of the psychological discovery that changed the world

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A reviewer sent back the comment: When his work was confirmed, the leading journal Nature warned scientists to beware of "radical psychoimmunologists" who would use Blalock's work to suggest that body and mind were in communication.

Introduction

Thereafter, Pert and her colleagues proudly called themselves radical psychoimmunologists. Pert's own career has often been as controversial as the new science she's helped to create, and she writes of this with candor. She discovered endorphins as a graduate student, according to her account, only by secretly pursuing an experiment her professor had ordered her to drop. When he was later given the prestigious Lasker Award for work she had contributed to mightily, Pert was left out of the prize. She refused to keep quiet about it. The ensuing scandal made her something of a pariah to the establishment.

More recently, Pert and her husband, immunologist Michael Ruff, have devoted years of research to a potentially nontoxic cure for AIDS based on psychoneuroimmunology. They synthesized a peptide that would mimic the part of the virus that attaches to cell receptors and thus block the virus from entering a cell, instead of using toxic conventional drugs to destroy it. But their work has been dismissed, like other early advances in psychoneuroimmunology. It has only lately begun to gain interest and garner some backing among mainstream AIDS researchers.

At its best, Molecules of Emotion is a lucid explanation of new research on the way peptides work to connect all aspects of body and mind in a network of shared information. To cite only a single example, Pert explains: In fact, she writes, "the brain is a bag of hormones. The central theme of Pert's book is that the peptides that flood our bodies are, in fact, the molecules of emotion.

Emotions, largely ignored within the traditional confines of science and medicine, are actually the key to understanding psychoimmunology's emerging picture of how body and mind affect each other. For example, it's through the emotion-modulating peptides that an embarrassing thought can cause blood vessels to dilate and turn a face beet red. In the same way, the molecules of emotion can mobilize immune cells to destroy an incipient tumor. Techniques like meditation or visualization may also act as forces to set those molecules in action.

Pert's mission, as she describes it, is to bridge the gap that exists between the laboratory and the layman. In the process, I virtually cross into another dimension, where the leading edge of biomolecular medicine becomes accessible to anyone who wants to hear it. Much of what Pert has to say is solidly grounded in new research, but she's on shakier ground in her occasional embrace of pop psychology and mysticism.

Now a research professor at Georgetown University, she is as apt to speak at a conference of New Age gurus as at a gathering of molecular biologists. She is trying to open a dialogue between the mind of science and the body of beliefs many people are turning to for alternative healing. Whether these efforts represent critical lapses or welcome leaps of faith must be left to the perspective of each reader.

Paul Trachtman, who resides in New Mexico, is a regular contributor to Smithsonian. Recent philosophers of science have used historical sketches like these to reconstruct the prehistory of current philosophical debates about scientific discovery. The argument is that scientific discovery became a problem for philosophy of science in the 19 th century, when consequentialist theories of scientific method became more widespread. When consequentialist theories were on the rise, the two processes of conception and validation of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes. For Whewell, discovery comprised all three elements: In fact, much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. The previous section shows that scholars like Bacon and Newton aimed to develop methodologies of scientific inquiry.

Whewell, by contrast, was explicitly concerned with developing a philosophy of discovery. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later.

It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark. Having a happy thought is not yet a discovery, however. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. More precisely, colligation works from both ends, from the facts as well as from the ideas that bind the facts together.

Colligation is an extended process. It involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. It is essential that the outcome of the colligation be inferable from the data prior to any testing Snyder His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery.

The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. A colligation, if properly done, has as such justificatory force. Similarly, the process of verification is an integral part of discovery and it too has justificatory force.

To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation Ducasse Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight.

Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules. In recent decades, philosophical attention has shifted to the eureka moment. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. In the early 20 th century, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread but not unanimously accepted.

Alternative conceptions of discovery emphasize that discovery is an extended process, i. Moreover, it was assumed that there is a systematic, formal aspect to that reasoning. Proponents of this view argued that traditional here: It is the task of the logic of discovery to draw out and give a schematic representation of the reasoning strategies that were applied in episodes of successful scientific inquiry.

Early 20 th -century logics of discovery can best be described as theories of the mental operations involved in knowledge generation. Among these mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning. It is argued that these features of scientific discovery are either not or only insufficiently represented by traditional logic Schiller Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions.

These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative Carmichael , One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning Schiller ; Benjamin In the 20 th century, it is widely acknowledged that analogical reasoning is a productive form of reasoning that cannot be reduced to inductive or deductive inferences see also section 9.

However, these approaches to the logic of discovery remained scattered and tentative at that time, and attempts to develop more systematically the heuristics guiding discovery processes were eclipsed by the advance of the distinction between contexts of discovery and justification. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense test, verification of it.

As the previous sections have shown, the distinction among different features of scientific inquiry has a longer history, but in philosophy of science it became potent in the first half of the 20 th century. In the course of the ensuing discussions about scientific discovery, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion. The boundary between context of discovery the de facto thinking processes and context of justification the de jure defense of the correctness of these thoughts was now understood to determine the scope of philosophy of science.

The underlying assumption is that philosophy of science is a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis.

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Philosophy of science, by contrast, is exclusively concerned with the context of justification. It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is.

Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact Reichenbach This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then validated. Rather, conception and validation are two different epistemic approaches to theory: Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory.

The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea. Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery.

In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge.

Its questions are of the following kind. Can a statement be justified? And if so, how? Is it logically dependent on certain other statements? Or does it perhaps contradict them? As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained.

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological theory.

The impact of the context distinction on studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery however construed is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century and is still held by many. The last section shows that there were a few attempts to develop logics of discovery in the s and s.

But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science. Therefore, discovery was not a legitimate topic for philosophy of science. The wide notion of discovery is mostly deployed in sociological accounts of scientific practice.

Until the last third of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only in the s did the interest in philosophical approaches to discovery begin to increase. But the context distinction remained a challenge for philosophies of discovery. There are three main lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens up a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology.

All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one.

Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient. But the different approaches to scientific discovery employ different terminologies. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures.

Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz.


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At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and validation of knowledge. Philosophers who take this approach argue that the process of discovery follows an identifiable, analyzable pattern section 7. Others argue that discovery is governed by a methodology.


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  • The methodology of discovery is a legitimate topic for philosophical analysis section 8. All of these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating and developing the creative thought. These are the processes that can be examined with the tools of philosophical analysis. The third response to the challenge of the context distinction also assumes that discovery is or at least involves a creative act.

    But in contrast to the first two responses, it is concerned with the creative act itself. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis section 9. The first response to the challenge of the context distinction is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis.

    They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

    This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning section 6. The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems section 6.

    One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.

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    This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena. In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention Hanson According to Hanson, the abductive argument has the following schematic form Hanson More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences.

    Notably, some philosophers have even questioned the rationality of abductive inferences Koehler ; Brem and Rips Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis Harman ; see also Blackwell Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

    Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type.

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    The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise Laudan ; Schaffner In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses.

    Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain selective abduction to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

    The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science.

    In this approach, scientific discovery is treated as a form of problem-solving activity Simon ; see also Newell and Simon , whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search e.

    The problem space comprises all possible configurations in that domain e. There are two special states, namely the goal state, i. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached Langley et al.

    A problem solution is a sequence of operations leading from the initial to the goal state. The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space.


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    • These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

      Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related.

      AI-based theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators processes that will change the situation that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined Simon AI-based approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

      As accounts of scientific discoveries, computational heuristics have some limitations. Most importantly, because computer programs require the data from actual experiments the simulations cover only certain aspects of scientific discoveries. They do not design new experiments, instruments, or methods. Moreover, compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated Bechtel and Richardson Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language Hempel Subsequent work has shown that computational methods can be used to generate new results leading to refereed scientific publications in astronomy, cancer research, ecology, and other fields Langley The most recent computational research on scientific discovery is no longer driven by philosophical interests in scientific discovery, however.

      Instead, the main motivation is to contribute computational tools to aid scientists in their research Addis et al. Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. Kuhn identifies a general pattern of discovery as part of his account of scientific change.

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      A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms.

      A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science.

      The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:. Anomaly appears only against the background provided by the paradigm. Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery.

      Kuhn illustrates these points with the discovery of oxygen see Kuhn []: Oxygen had not been discovered before and had been discovered by Even before , Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. In , Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry.

      But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected.

      In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery. In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

      These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories.