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To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
1
Science and Common Sense:
Perspectives from Philosophy and Science Education
Sara Green, Department of Science Education, University of Copenhagen
Sara.green@ind.ku.dk
Abstract
This paper explores the relation between scientific knowledge and common sense intuitions as
a complement to Hoyningen-Huene’s account of systematicity. On one hand, Hoyningen-
Huene embraces continuity between these in his characterization of scientific knowledge as an
extension of everyday knowledge, distinguished by an increase in systematicity. On the other,
he argues that scientific knowledge often comes to deviate from common sense as science
develops. Specifically, he argues that a departure from common sense is a price we may have
to pay for increased systematicity. I argue that to clarify the relation between common sense
and scientific reasoning, more attention to the cognitive aspects of learning and doing science
is needed. As a step in this direction, I explore the potential for cross-fertilization between the
discussions about conceptual change in science education and philosophy of science.
Particularly, I examine debates on whether common sense intuitions facilitate or impede
scientific reasoning. While contending that these debates can balance some of the
assumptions made by Hoyningen-Huene, I suggest that a more contextualized version of
systematicity theory could supplement cognitive analysis by clarifying important organizational
aspects of science.
1. Introduction
Is scientific knowledge mainly an extension and specification of everyday knowledge, or does
science require or lead to a break with common sense intuitions? If there is a discontinuity, how
is it possible to learn science at all? To what extent do common-sense intuitions enable or limit
scientific reasoning? The answers to such questions have important implications for philosophy
of science and science education. The aim of this paper is to explore the potential for cross-
fertilization between the discussions about conceptual change in these two domains, motivated
by the claim made by Hoyningen-Huene that systematicity theory offers a particularly suited
platform in order to investigate the relation of the sciences and common sense.
Hoyningen-Huene’s thesis is that scientific knowledge can be characterized as an extension of
everyday knowledge, distinguished by an increase in systematicity:
Science develops out of common sense of the respective historical time of out of a
nonscientific knowledge practice due to an increase in systematicity. Thus, we can
determine the relationship between science and common sense by investigating what
the effects of this increase in systematicity are, first upon common sense itself and
later during the ensuing scientific development (Hoyningen-Huene 2013. 187)
By clarifying how science grows out of common sense, historically and in contemporary
scientific practice, Hoyningen-Huene emphasizes continuity between everyday knowledge and
scientific knowledge. Yet, he stresses an important difference in the degree of systematicity in
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
2
nine dimensions in science, compared to non-scientific activities. The nine dimensions are: 1)
Descriptions, 2) Explanations, 3) Predictions, 4) The defense of knowledge claims, 5) Critical
discourse, 6) Epistemic connectedness, 7) An ideal of completeness, 8) Knowledge
generation, and 9) The representation of knowledge. The main thesis is that science -
compared to everyday knowledge about the same subject - more carefully considers and
excludes possible alternative explanations, samples more systematically, makes a more
extensive recording and evaluation of data, and that scientific knowledge has a higher degree
of connectedness to other knowledge claims.
Hoyningen-Huene contends that the difference between everyday knowledge and scientific
knowledge is a difference in degree, but he departs from the so-called continuity-thesis in
emphasizing that scientific knowledge often comes to deviate from common sense and our
everyday experiences as science develops. He stresses that many scientific insights are
inaccessible to direct perception or even conflict with what we experience. For instance,
whereas the Aristotelian tradition of physics can be seen as a successful specification of
common sense, he argues that the same cannot be said for examples from modern physical
theories involving claims about anti-matter, string-theory, relativity, universes in multiple
dimensions, space being bent etc. Hoyningen-Huene considers the implications of such
theories as “slaps in the face of common sense” (Hoyningen-Huene 2013: 193). A further claim
is that giving up common sense notions and intuitions is a price that we sometimes have to pay
for the increased systematicity of science. That is, common sense is sometimes a “victim of
science”, resulting from the incumbent increase in systematicity (Hoyningen-Huene 2013: 188,
194). These claims raise important questions about the extent “common sense” can be said to
be a precondition for or a victim of science, and about the role of systematicity in the move
from common sense to scientific knowledge.
Other papers in this special issue critically examine the strength of what Hoyningen-Huene
calls systematicity theory, particularly the attempt to demarcate scientific knowledge from
everyday knowledge with reference to systematicity. My aim is to highlight important cognitive
aspects that are not given much attention in Hoyningen-Huene’s account. Hoyningen-Huene
primarily deals with continuities and breaks in a comparison of the endpoints of conceptual
development, i.e. similarities and difference between scientific and everyday knowledge.
1
With
this choice he leaves unspecified many aspects associated with the cognitive reasoning
processes involved in learning and doing science. I stress how the “bird’s eye” perspective of
systematicity theory can gain from a finer-grained analysis of conceptual development in
science education and philosophy of science.
It may be objected to the framework presented that drawing parallels between conceptual
change in science and science education is of limited use due to differences in the levels of
organization. For instance, Greiffenhagen and Sherman (2008) have argued that drawing
analogies between Kuhn’s view of conceptual change in science (at the level of a community of
scientists) and science education (at the level of individual students) is misleading. The
analogy certainly has limitations, particularly the way that Kuhn’s incommensurability thesis has
been taken up to describe learning processes in science education (Section 2; see also Levine
2000). Importantly, however, the comparison of literatures on conceptual change in the two
domains does not have to rely on a Kuhnian view of conceptual change, or to assume that
1
The terms ‘common sense’ and ‘scientific knowledge’ are not strictly defined or used consistently in the book,
which may seem problematic (Rowbottom 2013). Whereas Rowbottom calls for insights from social epistemology
to illuminate the question about what scientific knowledge is, this paper calls for connections to the literature on
science education and cognitive science in clarifying the role of common sense.
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
3
conceptual change only operates at specific levels in the two contexts (see also Section 4).
The comparison made in this paper is not dependent on a Kuhnian view of science, but is
simply an attempt to explore fruitful connections in the two literatures that can help nuance
common assumptions about the relation between common sense and science. I shall,
however, return to Kuhn in Section 4 to comment on the connection that Hoyningen-Huene
himself draws between systematicity theory and Kuhn’s account.
We begin with an introduction to debates in science education and cognitive science about
what common sense and scientific reasoning entail (Section 2). This will serve as a
background for reexamining Hoyningen-Huene’s assumptions about common sense and
scientific knowledge, and for comparing similar debates about conceptual change and heuristic
strategies in philosophy of science (Section 3). Both literatures stress important roles of
common sense intuitions in scientific reasoning that may only become visible through a finer-
grained examination of the knowledge process. While such studies provide resistance to
assumptions made about discrepancies between science and common sense, Section 4
examines situations where there seems to be a greater distance between common sense
intuitions and scientific theories. In such contexts, I explore a possible role of systematicity
theory in specifying the nature of external support required for learning and doing science.
Specifically, I explore the potential of Hoyningen-Huene’s account to clarify institutional aspects
of science through a more contextualized notion of systematicity. Section 5 offers concluding
remarks.
2. Perspectives on common sense from science education
One of the important aims of philosophy of science is to reflect critically on the hidden
assumptions of scientific practice. But philosophy of science should be equally critical of its
own assumptions when discussing philosophical questions such as the similarities and
differences between science and common sense. Assumptions about what common sense is,
and about what learning and doing science entails, greatly influence the perceived relations
between common sense and science. Moreover, the grain of analysis (temporal perspectives
as well as scope of analysis) can influence the conclusions drawn about the relation between
these. To illustrate these issues, I start by introducing three theories about learning and
common sense in science education (the Theory Theory, the Ontological View, and Knowledge
in Pieces, respectively).
2
All three accounts draw parallels between scientific reasoning of
novices and experts. But whereas the two former emphasize that learning science entails
abandoning common sense “misconceptions”, the latter stresses that expert knowledge is only
possible on the basis of intuitive knowledge. The comparison to the Knowledge in Pieces
account is also interesting because this account explicitly describes the difference between
intuitive and expert knowledge as a difference in the degree of systematicity (diSessa 1993b:
Systematicity section). I highlight the practical implications of each theory while relating these
to the approach taken by Hoyningen-Huene.
2.1. Learning science - unlearning common sense
The Theory Theory is a prominent theory for conceptual change in science education that
draws on developmental psychology and cognitive science. The version of the account that I
2
For more detailed overviews, see (Zimmerman 2000) and (diSessa forthcoming).
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
4
shall focus on here argues that children learn through a process similar to the way that
scientists reason through hypothesis generation and theory revision in response to evaluation
of evidence (Carey 2009; Chi 1992; Gopnik et al. 1997; 1999). The description of theory
revision is inspired by work in philosophy of science, but proponents of the Theory Theory
argue that the analogy can be read both ways. That is, studying cognitive development can
help us understand how science works as well as vice versa. Accordingly, this view is
sometimes called ”the child as scientist view”, “the scientist in the crib” or “the scientist as child”
(Solomon 1996).
3
The Theory Theory emphasizes continuity in the reasoning processes of early development
and scientific research in the sense that “there are common cognitive structures and
processes, common representations and rules, that underlie both everyday knowledge and
scientific knowledge” (Gopnik and Glymour 2002: 117-118). Gopnik and Glymour provide a
version of the Theory Theory that draws on the representational framework of causal maps,
particularly Bayes nets (directed graphs), to specify how theory change in both domains implies
a recovery of an “accurate causal map of the world” (Gopnik & Glymour 2002; Gopnik &
Wellman 2012). A causal map is an abstract representation of the causal, correlational and
counterfactual relations between objects and events in the world. Gopnik and Glymour argue
that also everyday or ‘folk’ theories have the character of causal maps and posit coherent
relations among different events and objects.
Hoyningen-Huene’s description of scientific knowledge as distinguishable from everyday
knowledge by a higher degree of systematicity seems to be in alignment with Gopnik and
Glymour’s description of causal maps in the two domains, but Hoyningen-Huene’s account is
not restricted to the narrow notion of knowledge as causal maps. There is much more to
science than representing the causal structure of the world, and Hoyningen-Huene elaborates
on some of these aspects when clarifying the dimensions of prediction, knowledge generation,
the representation of knowledge, and the role of generalizations in physics and chemistry.
Hoyningen-Huene’s and Gopnik’s accounts thus focus on very different aspects of science and
have complementary virtues, but also complementary limitations. While Hoyningen-Huene’s
account ignores many psychological and social aspects of scientific practice, Gopnik’s account
may overemphasize the sufficiency of developmental psychology for providing an epistemic
epistemology (see, e.g. Solomon 1996). This point is important for evaluating claims made by
the Theory Theory considering the revision of the ‘theories’ when faced with conflicting
evidence.
For the Theory Theory, the continuity of cognitive structures and conceptual development does
not extend to the relation between the “naïve” theories and scientific theory. According to the
Theory Theory, learning science involves a process of theory revision and transformation
according to the strength of evidence. Gopnik and Glymour (2002: 129) for instance argue that
whereas adults in everyday life are rarely forced to revise their causal knowledge, the situation
is very different in cognitive development and science education. In their view, both child
development and science learning involves abandoning prior (intuitive) theories that otherwise
would block conceptual change because these provide a false picture of the causal structure of
the world. Moreover, the naïve ideas (or theories) are taken to be systematically intertwined
and coherent, akin to scientific theories, which means that giving up intuitive notions and
3
Nersessian’s (1989; 1992) account also emphasize similarities in the cognitive processes in the two contexts,
but the development of the two accounts has since gone in different directions (c.f., Gopnik & Glymour 2002;
Nersessian 1996; 2002).
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
5
conceptions is often a hard challenge. Accordingly, some proponents of the Theory Theory
therefore talk about learning as a series of personal scientific revolutions, drawing on Kuhn’s
notion of “local incommensurability” (Carey 1985, 2009; Kuhn 1962/1996).
The Ontological View puts even more emphasis on the challenge to go beyond naïve theories,
arguing that prior ontologies impede progress in students’ learning (Chi & Slotta 1993; Chi
2013; Reif & Larkin 1991). Chi for instance emphasizes how scientific ontologies are
categorically distinct from common sense ontologies. According to this account, the implication
for science education is that teaching science must involve the development of new and
directly instructed ontologies. That is, the naïve ontologies or theories are considered “false”
and need to be replaced by “correct” ones. In this view, learning is equal to “changing naïve
conflicting knowledge to correct knowledge” (Chi 2013). Thus, learning involves abandoning or
bracketing prior naïve knowledge because the naïve ideas conflict with the development of new
ontologies that better support scientific theories. Accordingly, Wiser aims to develop
instructional designs that protect students from the prior common sense notions, providing a
“free-standing network of ideas” that does not borrow from or interfere with naïve theories
(Wiser 1995: 34). Rather than smoothly extending out from the student naïve ideas, here
perceived as “misconceptions”, new theoretical frameworks must be built almost from scratch
(Slotta & Chi 2006: 286).
These views seem to conflict with Hoyningen-Huene’s emphasis on continuities and
differences primarily in the degree in systematicity between everyday knowledge and scientific
knowledge. As we shall see in the following, the Knowledge in Pieces (KiP) theory allows for
more flexible pre-scientific elements of reasoning and can support this aspect of Hoyningen-
Huene’s account. At the same time, the KiP theory suggests a constructivist approach to
learning for which learning is only possible through connections to already established
knowledge. The KiP theory thus raises the question whether common sense can meaningfully
be considered a “victim of science”.
2.2. Learning science – constructing from current knowledge
Whereas the Theory Theory and the Ontological View claim that students cannot make
progress without abandoning prior ontologies and intuitions, constructivist theories of learning
hold that knowledge can only develop by building on current knowledge. In this context, the
notion of ‘constructivism’ implies a rejection of the traditional “transmission view” of teaching
and learning in which objective knowledge is passively transmitted to students and added to
their existing knowledge structure. From a constructivist perspective, new knowledge is always
constructed at the basis of previous experiences (diSessa 1993a; 1993b; Knorr-Cetina 1981;
Piaget 1974; Posner et al. 1982). The claim that accepting scientific knowledge requires giving
up common sense intuitions must therefore confront questions about how learning and doing
science should be possible at all, if not drawing on common sense intuitions.
What motives a constructivist approach to learning? As noted in the previous section, much
research in science education has focused on strategies to replace the ‘misconceptions’ of
students with new scientific explanations or ontologies. However, empirical studies of learning
situations show that the so-called ‘misconceptions’ are very robust. For instance, the beliefs
held by participants in special conceptual change programs compared to control groups display
no significant differences (Lijnse 2000). Similarly, longitudinal studies involving interviews with
a group of students over several years demonstrate the continuous influence of ‘life-world
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
6
knowledge’ shaped by early experiences on reasoning about ecological processes (Helldén &
Solomon, 2004). This suggests that the different knowledge domains are not incommensurable
and that common sense intuitions are not given up (see also Section 4).
Detailed knowledge on how current belief systems of students (or scientists) interact with and
accommodate new ideas and findings is still lacking. But steps towards the development of a
finer-grained analysis are currently taken in both science education and philosophy of science.
The KiP theory illustrates how a different view may result from a more contextualized
‘microgenetic’ analysis of student learning (diSessa 1993a; 2014a; 2014b; forthcoming). The
notion of microgenetic analysis in this context refers to the fine grain of detail in data collected
during a real-time process of conceptual change (see also Siegler & Crowley 1991). Consider
as an illustrative example a recent study by diSessa where a real-time learning situation was
observed (diSessa 2014b; forthcoming). In the study, students in a high school class construct
a model of thermal equilibration at the basis of experiments with hot and cold water in a test
tube. The aim of the exercise is for students to get an understanding of Newton’s description of
temperature change as proportional to the difference of temperatures among two objects, but
the students were not given any prior theoretical information. The study shows how students
were able to draw on intuitive or “primitive” ideas about the behavior of the liquids as
anthropomorphic agents and still arrive at the intended learning goal (see diSessa 2014b: 813).
For the Theory Theory and the Ontological View, the attribution of agency to liquids or
molecules would be considered a “misunderstanding” that needs to be replaced with “correct”
theories and ontologies to enable scientific reasoning. In contrast, diSessa argues that such
“misconceptions” constitute part of scientific learning and understanding (see also Gupta et al.
2014). In the example, the description of the temperature changes in terms of preferences of
liquids as agents facilitate conceptualization about what happens to the liquids outside
equilibrium, and the students spontaneously reformulate their findings to arrive at a formal
description of how the rate of temperature change is driven by temperature differences.
4
The possibility embraced by the KiP theory for a partial reliance on common sense intuitions in
scientific reasoning should be understood against the background of an important difference in
the underlying assumption about our belief systems of the different theories. Whereas the
Theory Theory and the Ontological View emphasize that naïve knowledge make up a coherent
system of beliefs, KiP posits a system of many loosely connected knowledge elements called
“phenomenological primitives”, or p-prims (diSessa 1993a, 1993b). p-prims provide generic
causal schemas for making phenomena and relations sensible to us, such as descriptions of
causal processes in anthropomorphic terms. Another example is what diSessa (2014a) calls
“Ohm’s p-prim”: the idea of a direct relation between agentive “effort” and some kind of result,
e.g. between effort in running and speed. Changing the view of naïve theories from being
misconceptions to p-prims opens for the possibility to see these as essential in scaffolding
learning processes (diSessa 1993b; 2014a; 2014b).
Rather than viewing common sense intuitions as something that get in the way of scientific
reasoning, the task becomes to understand in which contexts they work and in which they are
of limited help. diSessa (1993b) illustrates how different p-prims are “activated” in different
situations as a result of “high cuing-priority” of some p-prims (and not others), depending on the
4
diSessa (personal communication) opens for the possibility that the connection to common sense intuitions
could be maintained in the final encoding, but systematically suppressed in articulation. The option indicates how it
would be difficult to settle this question empirically.
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
7
context. For instance, diSessa (2014a) points out that Ohm’s p-prim provides an intuitive
understanding of situations where greater effects give rise to greater results, but face
limitations when the task for instance is to understand Newton’s law of inertia stating that
motion perpetuates itself if not acted upon by a force. In such cases, however, Ohm’s p-prim is
not “unlearned” but put aside in favor of other ideas that may better scaffold the learning. For
instance, inertia may be understood against the background of opposing situations in which
students draw on “constraint p-prims” (diSessa 1993b, 121) that allow students to understand
unconstrained movement in comparison to constraints experienced by movement in water vs.
air.
Compared to the Theory Theory and the Ontological view, the developmental histories of the
cognitive elements are considered more independent, affording a greater flexibility of naïve
knowledge in learning situations. Rather than abandoning initial conceptual frameworks, this
involves a process in which the student get a feeling (intuitively or more explicitly) of the
contexts in which specific frameworks are useful. Similarly, Mortimer (1995) has argued that
learning science does not necessarily involve conceptual change but changes in the student’s
conceptual profile. From this perspective, learning science in part becomes a way of exploring
the fruitfulness of conceptual tools that are already at hand for understanding new observations
but also in knowing when the tools meet their limitations and need to be supplemented or
“displaced” (diSessa 2014a; see also Helldén & Solomon 2004). Finer-graining the analysis
thus opens a space for the use of “misconceptions” as access-points to scientific theories,
rather than only obstacles to progress. Moreover, the emphasis of the flexibility of p-prims and
the development of more systematic knowledge systems raises an important question about
whether scientific knowledge is developed from cognitive systems that do not display the same
degree of systematicity and coherence as the products of science (diSessa 1993b).
In summary, whereas the KiP theory seems to support Hoyningen-Huene’s emphasis on a
continuity between common sense and science, it is difficult from a constructivist perspective to
make sense of how common sense could be a “victim” of more advanced research
(Hoyningen-Huene 2013: 188, 194). Against this challenge it may be objected there are
important differences between the learning situations in science education and the cognitive
demands for navigating in the theoretical spaces of theoretical physics that we do not have
intuitive perceptual access to. I shall return to Hoyningen-Huene’s examples of “breaks” with
common sense in Section 4. First, I highlight connections to the points taken up by diSessa
and recent discussions on conceptual change and heuristic strategies in philosophy of science.
3. Philosophy of science and biased heuristics
As the previous section illustrates, the idea that theories are revised through rejection of
particular conceptual frameworks or ontologies has been contested by researchers in science
education. Recently, philosophers of science have taken issue with the ways in the Theory
Theory has been justified with reference to work in philosophy of science. For instance,
Glennan (2005) characterizes the view of the Theory Theory on conceptual development
through theory revision as an odd mixture of Popper and Kuhn. On one hand, conceptual
development is taken to happen through a process akin to Popper’s idea of falsification of
hypothesis in view of conflicting evidence. But this process is described through a radical
replacement of common sense ontologies akin to a revolution in a Kuhnian sense. This
combination is problematic because, unlike the Theory Theory and the Ontological view, Kuhn
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
8
(1962) objected to the view of the history of science that pictures earlier theories as naïve,
mistaken or underdeveloped precursors to more correct ones. Moreover, Kuhn questioned
Popper’s simplified ideal of falsification with reference to commitments to a set of values,
theories and practices in normal science. In contrast, the Theory Theory and the Ontological
view give the impression that evidence uniquely determines theory choice. This view leaves
only a marginal role for the influence of theoretical frameworks required for any observation, as
well as of social and institutional factors (Solomon 1996; see also Section 4).
In summary, the Theory Theory does not seem to find strong support for their view in recent
empirical work in science education, or in Kuhn’s account. Moreover, modern philosophy of
science have moved beyond the traditional view of theory testing in examining how scientists
reason with models as mediators or epistemic artifacts (Morgan and Morrison 1999; Knuutila
2011; Nersessian 2002; 2008). Glennan describes the difference between the Theory Theory
and the model building account in the following way: “On Gopnik’s account, theories face the
tribunal of experience directly, while on the modeler’s account, theories only face that tribunal
as mediated through models” (Glennan 2005: 224). The important difference is whether
scientific products (as causal nets, models or theories) stand in direct representational relations
to the structure of the world or whether this relation is mediated via models. In Glennan’s view,
theories are too abstract to be directly consistent or inconsistent with experience or data. When
models are tested, they are not falsified in the simplistic sense because models are not true or
false. Glennan therefore proposes that the Theory Theory would stand better if drawing on
recent work in philosophy of science on model building. If, however, the Theory Theory buys
into this assumption, theory revision can no longer be understood as simple mismatches
between a real-word target and a mental causal model (see also Nersessian 1996, Solomon
1996). This also has important implications for the view of the relation between common sense
and scientific knowledge in the context of scientific research.
The aforementioned aspects call for greater attention to how scientists (and science students)
evaluate evidence and model explanations, but also of how new knowledge is generated.
Whereas previous philosophical accounts took hypothesis-generation to be a random creative
endeavor outside the scope of philosophy of science (e.g., Popper 1959), philosophers have
recently attended to the important roles of common heuristic strategies that reduce the
unfamiliar scientific operations to more familiar tasks. Heuristic strategies span from the use of
mundane analogies and metaphors to more specialized strategies such as particular
diagrammatic representations or the mechanistic strategies of decomposition and localization
common in molecular biology (Bechtel & Richardson 1993/2010; Hesse 1963; Keller 2002;
Nersessian 1995). Similarly, exploration has recently begun to receive some attention as yet
another strategy of scientific inquiry (see Steinle 1997 in relation to experiments, Gelfert 2016
in relation to models, and Shech 2015 in relation to idealization). Similar to the emphasis on
common sense as leverage for scientific reasoning in science education (Posner et al. 1982),
philosophers and cognitive scientists have argued that common sense intuitions and analogies
play important roles also in advanced scientific reasoning (Carruthers et al. 2002; Nersessian
1992).
These aspects are, however, easily overlooked from a “bird’s eye” perspective examining
similarities and differences of knowledge products. From this perspective, the relation between
common sense and science may appear as discontinuous, for instance that “common sense
knows very little or nothing about bacteria living in symbiosis with us” (Hoyningen-Huene 2013:
194). Considering the specialized theories of microbiology and (the lack of) counterparts in
everyday knowledge, it is indeed difficult to see parallels in the two domains. However,
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
9
although the size and amount of microbial symbionts may be alien to common sense, it may be
the case that the understanding of symbiotic relations in biology can usefully be facilitated
through our notions of collaboration in social contexts. Similarly, diagrams of molecular
factories or lock and key models are often used to leverage the understanding of biological
mechanisms, although these misrepresent the biological reality. It is often assumed that
analogies and metaphors mainly are useful as pedagogic tools in science education but play a
minor role in scientific reasoning of practicing scientists. Such views are, however, challenged
by empirical work in philosophy of science demonstrating that cognitive strategies such as
analogies, metaphors, thought experiments and mental modeling are essential to scientific
reasoning (Dunbar 2002; Nersessian 1992; 2008; see also Green 2013). Drawing on many
years of observational studies of practicing scientists, Dunbar even argues that mundane
analogies are among “the most frequent workhorses” of the scientific mind (Dunbar 2002).
Similar questions thus arise in the context of philosophy of science. Do common sense
reasoning processes such as analogies from the social or engineering domains to living
systems enable or impede scientific reasoning? And to what extent is it possible to do science
without resources from common sense?
3.1. Reasoning with “false” models
A debated issue in philosophy of science is the extent to which the function of heuristics - to
simplify the problem space of scientific analysis - enables or limits conceptual development and
progress in science. The points of criticism can be illustrated with the debate in philosophy of
biology on the use of engineering approaches and optimality assumptions.
5
Analogies between
designed systems and organisms have a strong impact on development of mental models, and
biologists often benefit from the ready-made design language to conceptualize biological
functions. If more is known about the design space for specific functional capacities in
engineering than about the mechanism underlying a functional capacity in biology, analogical
reasoning can enable a transfer of modeling tools or specific hypotheses from engineering to
biology. Nevertheless, some have argued that engineering analogies and metaphors impede
progress in biology because they oversimplify the problem space for analysis and make
researchers blind to other relevant alternatives. Specifically, engineering approaches have
been criticized for drawing a misleading parallel between intentional design and natural
selection as “optimization” (for an overview of the debates, see Green 2014; 2015).
Similarly, Pigliucci and Boudry (2011) argue that machine and design metaphors are “bad for
science and science education”, and should be avoided in order to minimize
misunderstandings. Examples referred to involve the idea of the genome as a program, the
brain as a computer, and the view of organism as a machine consisting of static parts (see also
Boudry & Pigliucci 2013; Nicholson 2013). Likewise, the usefulness of reverse engineering
approaches in biological research has been questioned with references to the plasticity and
dynamic nature of the structure and function of living systems compared to machines (Braun &
Marom 2015). The call to avoid certain heuristics in science education and scientific research
resonates with the view of the proponents of the Ontological View when arguing that:
5
For a more general discussion of influences of prior beliefs on hypothesis testing, reasoning and evaluation of
evidence, see (Evans 2002; Stanovich 1999).
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
10
Teachers should not try to “bridge the gap” between students’ misconceptions and the
target instructional material, as there is no tenable pathway between distinct
ontological conceptions… Indeed, students’ learning may actually be hindered if they
are required to relate scientifically normative instruction to their existing
conceptualizations (Slotta & Chi 2006: 286).
Importantly, however, the question about the productive and problematic aspects of research
heuristics reaches far beyond the issue of the truth-value of the assumptions they rely on. As
diSessa (2014a; 2014b) argues in the context of science education, it would be a category
mistake to discuss whether common sense notions are true or false. Rather, they are
reasoning tools that work in some circumstances and not in others. Similarly, the use of
analogies and metaphors in scientific reasoning should not be evaluated as empirical
statements about the world, but on the extent to which they productively guide these. “False
models” are fruitful means for knowledge generation if systematically calibrated with
independent sources of evidence (Wimsatt 2007), and design heuristics in biology can be
productive despite the reliance on false assumptions if the analysis can uncover to what extent
the systems are similar and different (Knuuttila & Loettgers 2013). Still, it is important to further
investigate whether these enforce certain metaphysical assumptions that frame our ontological
and scientific viewpoints. That is, the debate about biased heuristics raises the deeper question
about the extent to which we can become prisoners of our own abstractions so that these
impede us from seeing and establishing new relevant connections (Levins & Lewontin 1985).
Thus, whereas some have argued that the biases of research heuristics can be accounted for
through testing of the generated hypotheses, this strategy seems insufficient if the problem lies
in the missed opportunities for conceiving other – and better – alternatives (or in the lag time
for reaching alternative options). For instance, relying on comparisons between intentional
design and optimization of traits via natural selection may lead to a neglect of the influence of
other evolutionary forces than natural selection (Green 2014). The Ontological View therefore
seems to have its merits in reminding us that we view the world through a certain theoretical
lens that may affect the way that we evaluate and make sense of evidence. But the Ontological
View takes it too far in the claim that learning requires students or scientists to completely
abandon common sense ontologies and start reasoning with new ones. This may be abandon
tools that are useful or even necessary for scientific reasoning.
Just like abandoning common sense ontologies in the context of science education may not be
a feasible option, it is relevant to examine the extent to which scientific reasoning is possible at
all without relying on common sense. Even scholars arguing against the use of engineering
metaphors and functional or ‘teleological language’ in biology have problems articulating their
research results without these (Krohs 2015). Similarly, even when accepting theories that
conflict with our perception of the world (e.g. special relativity), we do not seem to give up
common sense intuitions (Mortimer 1995). When common sense intuitions turn out to be
counterproductive to scientific reasoning, they may be displaced (or “nudged out”) rather than
replaced (diSessa 2014a). That is, some intuitions may be bracketed when reasoning within
certain problems spaces while remaining functional in other contexts. From this perspective,
one can rephrase the claim of machine metaphors as being “bad for science and science
education” as a point about the constraints on their scope when applied in biology. Following
Wimsatt (2007), we may say that successful reasoning with biased heuristics involves an
awareness of the limitations of our epistemic tools and their context-dependent uses, rather
than a replacement of false models with correct ones.
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In Section 2, we saw that diSessa (2014b) questions the assumption of the Theory Theory and
the Ontological view that intuitive knowledge make up a systematic and coherent, and
therefore inflexible, network of knowledge elements. Similarly, observational studies of
scientific practice suggest that “causal reasoning in science is not a unitary, cognitive process,
as argued by Gopnik and Glymour […], but a combination of very specific cognitive process[es]
that are co-ordinated to achieve a causal explanation” (Dunbar 2002: 157). Dunbar
emphasizes how causal reasoning in science involves dozens of different problem-solving
techniques, suggesting that scientific thinking contains many different interrelated sub-
components.
6
Thus, whereas increasing systematicity may be a hallmark of scientific reasoning
and scientific knowledge, the lack of systematicity (in the sense of coherence) of the intuitive
knowledge may be what facilitates the use of cognitive elements in many different contexts. It
may also supports the survival of common sense notions even when seemingly counterintuitive
theories, such as special relativity, are accepted. To discuss this question further, we now
return to the hard cases where the distance between common sense and science appears to
be great.
4. The systematic organization of science
I begin with some general reflections on the emphasis on continuities or discontinuities of
common sense and science in the current debates. As pointed out by Gopnik and Glymour
(2002), some of the different viewpoints may simply result from differences in perspective –
reflecting a choice of focus on either the similarities or the differences, or on a specific grain of
analysis. That is, more or less dramatic discontinuities between science and common sense
will be visible, depending on the ways in which the topic is framed and investigated.
Importantly, however, such choices have practical implications.
On one hand, emphasizing the difference between common sense and science may
underestimate the ways in which scientific reasoning relies on everyday cognitive processes
and overestimate the difference between the cognitive processes of expert and novices
(diSessa, forthcoming). Attention to the methodological framework and the underlying
assumptions is therefore of key importance. For instance, expert-novice studies have
highlighted the fluency of expert reasoning and limited reliance on common sense notions
compared to novices. Yet, critics have argued that the comparison is based on problem-solving
cases that for the experts do not go beyond routine puzzle-solving. If so, the cases fail to give a
realistic picture of how scientists deal with ill-defined problems in research and are of limited
use as guidelines for science education or (cf. diSessa, forthcoming; Gupta et al. 2010; 2014;
Schauble & Glaser 1990).
7
Rather than accepting the categorical assumptions about naïve and
expert causality – e.g., that agent causality is inappropriate or unnecessary for reasoning in
physics – a more contextually grounded analysis is needed to identify the situations in which
such strategies facilitate or impede scientific reasoning. That is, we must examine scientific
reasoning using methods that account for situated cognition (Nersessian 1992, 1995). As
6
In the context of science education, diSessa refers to similar organized couplings of p-prims as “coordination
classes”. He however cautions against the image of common sense resources as rigid of tools as components like
bricks in a brick-wall (diSessa, personal communication).
7
A similar criticism has been raised against experimental designs that have been used to support arguments in
cognitive science about how common sense notions impede correct inferences about probabilities (Tversky &
Kahneman 1973). Scholars have pointed out that the experiments rely on certain normative assumptions or lack
clarity about what the “right” or “rational” answer would be (Carruthers 2002; Gigerenzer 2000). See also
(Zimmerman 2000), for a review of different theories and experimental studies.
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argued, finer-grained analyses suggest a more central and less problematic role of common
sense in science education and scientific research.
On the other hand, it may be objected that empirical studies of how common sense notions
guide specific reasoning practices in science education cannot be uncritically transferred to
contexts where scientific theories appear to be particularly counterintuitive. Hoyningen-Huene
(2013) distinguishes between three different shades of deviation from everyday knowledge,
namely i) knowledge that results from a specification of common sense knowledge, ii) new
knowledge that is unrelated to common sense knowledge, and iii) new knowledge that breaks
with common sense (Hoyningen-Huene 2013, 190-192). As examples of the latter he mentions
explanations in theoretical physics, such as special relativity or string theory. Considering these
examples, it may be argued that emphasizing a straightforward continuity between common
sense and science may underestimate the difficulties for many to learn theories that are distant
from everyday experiences. Particularly, it may underestimate the extent to which science is
dependent upon external support, formal education and cultural factors (Carruthers 2002;
McCauley 2011). In other words, attention to the differences may help clarify why some
aspects of science are particularly challenging to accept and what is needed to support these
processes.
4.1. Science at the frontiers of common sense
It is commonly known in cognitive psychology and science education that ideas that differ
radically from established knowledge can be very difficult to learn (Ausubel 1963; Helldén &
Solomon 2004; Posner et al. 1982). Modern science often traffics in highly abstract
representations that require a lot of work to accept and master (McCauley 2011). While
attention to evidence is an indispensable prerequisite for early cognitive development as well
as for doing science, the assessment of data and evidence in the two contexts may be
distinguished by the extent to which understanding data at all requires particular training. To
understand evidence in advanced science disciplines, we often need a “theoretical lens” that
cannot easily be acquired. Stanovich (1999) stresses that navigating in science and in the
modern technological society requires decontextualized reasoning skills, and that: “for
intellectuals to argue that the ‘person in the street’ has no need of such skills of abstraction is
like a rich person telling someone in poverty that money is really not important” (Stanovich
1999: 200).
Hoyningen-Huene (2013) points to history of science in highlighting the difficulties of accepting
new theories with greater distance to common sense. As examples he mentions the timeframe
of acceptance of the Copernican picture in physics and of Darwin’s theory of evolution through
natural selection in biology. Likewise, McCauley (2011, 108) observes that although
microorganisms were discovered already in 1674, it took nearly two centuries to take seriously
the idea that these tiny living systems could affect something as disproportional as human
health. The way that insights from science continue to shock and puzzle us indicates that many
aspects of modern science require a break with naïve realism. At the same time, however,
there appears to be historical changes in what is considered common sense. As Hoyningen-
Huene contends, although we are unable to perceive the speed and rotation of the Earth
around the sun, we today accept the Copernican picture as a fact (Hoyningen-Huene 2013:
192). Thus, connections between common intuitions and science are not fixed once and for all,
and common sense resources may be characterized by some degree of adaptive flexibility.
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When Hoyningen-Huene argues for a break with common sense, he points to situations where
science tells us that the natural world is not as it appears to us. Aside from the Copernican
theory that “deprived the celestial motions of their objectivist status”, Hoyningen-Huene (2013:
195) mentions how phenomena such as smells and colors became subjective “secondary”
qualities, and how we today have to accept wave-particle dualism etc. Einstein’s special theory
of relativity is in Hoyningen-Huene’s view an example of how common sense can be a victim of
an increase in overall systematicity, because the common sense notion of (absolute)
simultaneity is rejected. Special relativity is thus “a kind of knowledge that directly contradicts
common sense knowledge” (Hoyningen-Huene 2013: 191).
Not surprisingly, researchers in science education has pointed to the same examples as one of
the particularly difficult learning challenges for college students and also highlight the conflict
between ordinary and scientific metaphysical beliefs (Posner et al. 1982). Interestingly,
however, Posner and colleagues observe how students in their empirical study draw on
analogies to established knowledge and experiences to make special relativity more accessible
as piecemeal accommodations of the new theory. To describe the final step of accommodation,
Posner and colleagues draw an analogy to Kuhn’s account of conceptual change in science,
which has been criticized (Greiffenhagen & Sherman 2008; Levine 2000). However, it is
interesting to note that they describe accommodation as a gradual and piecemeal affair where
established metaphysical beliefs are often protected from rejection. Similarly, Knobe and
Samuel (2013) report from a set of experiments based on questionnaires that both scientists
and non-scientists draw on a conception of innateness considering biological traits that is
influenced by moral judgments. Depending on the framing of the questions, both groups are
however capable of ‘filtering out’ their initial intuitions and to use a more scientific approach.
The aforementioned empirical studies raise an important question about whether the common
sense notions are ever completely given up, or whether they are just bracketed in some
situations (as discussed in Section 2.2.). As Greiffenhagen and Sherman (2008) observe,
scientists do not seem to replace everyday language with scientific language to describe such
phenomena in daily life. We accept and understand that the perceived movement of the sun is
an apparent motion and that space has no absolute direction, and yet we still speak of the sun
rising in East. This suggests that common sense notions survive as compartmentalized notions
that continue their lives in other contexts. Thus, we should perhaps take a more pluralistic
stance and view the different conceptual schemes as tools for understanding different
perspectives of reality, where some dominant common sense notions must be bracketed to
allow for reasoning within new possibility spaces.
One example of how subjects may bracket certain aspects of common sense resources is
illustrated by a study in which the responses of students shift in response to different prompts
that draw on what the researchers categorize as i) life-world concepts or ii) physical terms
(Helldén & Solomon, 2004). The students appear to shift between different “knowledge
domains” and languages rather than adopting a mixture of life-world and physical terms. In
response to similar studies and results, some researchers in cognitive science have suggested
that reasoning is conducted by two different systems (Evans & Over 1996; Evans & Stanovich
2013; Stanovich 1999, see also Kahneman 2011; McCauley 2011). I shall not go into the
details of such accounts or the empirical support for these. However, I wish to highlight that
their emphasis on a slower and rule-based system for decontextualized or reflective cognition
needed for scientific reasoning is suggested as an explanation for the difficulties of learning
science. Because reflective cognition requires greater effort, learning and doing science
requires a great level of external support and needs to be systematically trained (e.g.
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McCauley 2011). Whereas Hoyningen-Huene’s notion of systematicity does not have much to
offer on the finer-grained cognitive aspects of scientific reasoning, it is worth exploring whether
the account can provide insight to methodological and organizational systems that provides
support for scientific activities.
4.2. Possible implications for science education
In this section, I explore the relevance of Hoyningen-Huene’s and also Kuhn’s account for
science education. I begin by considering objections to using these accounts on the
characteristics of science as a source of insights for science education. I have already
mentioned objections to using Kuhn’s incommensurability thesis in the context of science
education. Greiffenhagen and Sherman (2008) argue that “Kuhn’s conceptual schemes are tied
to what scientists do (e.g., performing new experiments and calculations). However, school
pupils are not engaged in work in that sense, since they do not produce new explanations of
the natural world. Pupils are in school to learn what others have discovered” (Greiffenhagen &
Sherman, 2008: 22). In their view, learning science is better seen as a refinement of everyday
knowledge (as, perhaps ironically, Einstein himself stated). Moreover, they stress that whereas
science education focuses on the cognitive development of individual students, Kuhn’s account
was focused on how science functions on the level of a community (see also Section 1).
I have acknowledged the limitations of viewing conceptual development in science education in
analogy with scientific revolutions (Sections 2 and 3), and activities in the two domains differ.
Yet, as clarified in Section 3, there appears to be many overlaps in the cognitive strategies
used by scientists in exploratory phases and strategies for “active learning” in science
education (Gelfert 2016; Nersessian 1995; Shech 2015; Steinle 1997). Science education
should not ideally be about learning theories only. As numerous empirical studies show, deep
learning is better facilitated by “inductive teaching” where students actively work on solving
problems (Prince 2004). Moreover, pointing to science as a refinement of common sense does
not clarify why learning some aspects of science often is particularly challenging and what can
be done about this. Hoyningen-Huene’s account offers a balance between the different views
in the literature by acknowledging how science grows from common sense by an increase in
systematicity, while also pointing to examples where the connection to common sense seems
more complex and partially discontinuous. Greiffenhagen and Sherman may argue that these
discontinuities are only seen in the generation of new and highly abstract knowledge in
science. Yet, as we have seen, the difficulty of reasoning in the framework of special relativity
is a challenge not only in science but also in higher education (Posner et al. 1982).
Moreover, science education can also be approached from a community perspective that goes
beyond the psychology of individual student. The relevance of Kuhn’s account on these
aspects should not be dismissed.
8
What Hoyningen-Huene views as the most fruitful aspects of
Kuhn’s account, also for the comparison to systematicity theory, is not the description of
paradigm shifts but the features associated with normal science.
9
Specifically, Kuhn (1959;
1962) clarifies how the training of students to follow paradigmatic solutions (exemplars) to well-
8
It should also be noted that science education played important inspirational roles for Kuhn’s work in the late
1950s (Andersen 2012), including the development of his account of examplars in science education.
9
See (Hoyningen-Huene 2013: 208) for a clarification of the difference between history of science and his
“systematic philosophy”, and pp. 163-165 for a comparison to Kuhn’s account.
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described research problems enables students to solve new problems. Hoyningen-Huene
(2013: 163-165) explicitly acknowledges the connection to this aspect of Kuhn’s account with
respect to generation of new knowledge (dimension 8). He argues that exemplars provide
systematic guidance for normal science in the sense that they provide a specific orientation
towards paradigmatic problems. Hoyningen-Huene thus seems to suggest that systematicity
can clarify and extend existing accounts on how students are trained. I contend, however, that
the notion of systematicity would be more useful for science (and arguably also for philosophy
of science) if made more concrete and more contextualized. In the following, I motivate this
claim and provide some suggestions for new paths to explore.
4.2.1. Potential gains from a more contextualized account of systematicity
Hoyningen-Huene’s (2013) main aim is to clarify what in general distinguishes science from
everyday knowledge, and the call for a more contextualized account may seem to miss the
point of his project. However, it is debatable whether the abstract nature of systematicity
succeeds in providing even “some tenuous sort of unity” (Hoyningen-Huene 2013: 169).
Hoyningen-Huene explicitly acknowledges that systematicity can be realized in countless ways.
This context-sensitivity stems from the variety and historical context of scientific disciplines and
practices. Hoyningen-Huene gives a few examples of how systematicity may play out
differently in different disciplines due to different aims (e.g., concrete vs. generalized
descriptions and explanations), and he admits that no less than nine dimensions of
systematicity are needed to respond to counterexamples questioning the difference between
everyday knowledge and science (Ibid: 209-210). The pursued level of generality and
abstraction of the notion of systematicity raises important concerns about whether the term has
much substantial content (Ibid: 179), e.g. whether existence of possible counterexamples
would be likely (Ibid: 169). When writing that “scientificity is a notion that is extremely
dependent on the various discipline and time” (Ibid: 206), one may wonder whether
systematicity has merely become a synonym for science, particularly when it is admitted that
systematicity theory offers “nothing concrete that all the sciences will have in common” (Ibid:
169). Accordingly, several scholars have argued that the flexibility of the notion of systematicity
that Hoyningen-Huene sees as a virtue is, in fact, a vice because the theory is too vague and
flexible to be rejected even in principle (Rowbottom 2013).
While I have given Hoyningen-Huene’s book a more charitable reading than Rowbotton’s
(2013) review, I agree that “HH tends to be at his best when he’s working at the level of the
trees, rather than that of the woods”. I see an unexplored potential in a more contextualized
analysis of how the regulative ideal of increasing systematicity is played out and prioritized
differently in different fields. To illustrate how systematicity is realized, Hoyningen-Huene often
provides rather detailed examples, for instance when describing the characteristics of
systematic representations in different fields such as graphical representations in mathematics,
the periodic system and formulas in chemistry, and evolutionary trees and mechanistic
diagrams in biology (Hoyningen-Huene 2013, Section 3.9). Through such descriptions,
systematicity theory reminds us that learning science is not only about “learning what others
have discovered” (Greiffenhagen and Sherman 2008:22) but also about committing to certain
epistemic standards, representational methodologies and systems of knowledge.
Systematicity theory seems well suited for specifying the characteristics of different systems of
knowledge or different practices, by pointing to the specific dimensions for which specific
disciplines prioritize their systematic endeavors (collection of big data, systematic sampling and
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classification, criteria for statistics or coherence etc.). It may be argued that my suggestion
would make systematicity instrumental to norms in science, rather than an end in itself (cf.
Hoyningen-Huene 2013, Section 5.3). While I acknowledge Hoyningen-Huene’s intention to
provide a general description of the difference between scientific and everyday knowledge, I
believe that systematicity theory could potentially reach a broader uptake if explored as a tool
for the comparison of standards in specific situations and in different traditions. For instance,
rather than a regulative ideal implying that more advanced science is characterized by a higher
degree of systematicity, different concrete instantiations of systematized knowledge systems
and practices can help clarify important aspects of what Kuhn (1959) described is the
“essential tension” between commitment to the well-established methods and theories and the
call for new knowledge in science. Similarly, Andersen (2013) points to the “second essential
tension” between disciplinary tradition and interdisciplinary innovation. Since diverging
epistemic standards often impede interdisciplinary collaboration (e.g., Rowbottom 2009; Green
et al., 2015), attention to what is systematized, how and why may be more fruitful way to draw
on systematicity theory.
For instance, systematicity theory could help us understand not only how exemplars provide
“systematic guidance” in science but also how these are established and learned. A more
contextualized notion could potentially be connected to discussions of systematicity in science
education that also points to a connection to Kuhn’s notion of examplars (diSessa 1993b).
diSessa has argued that: “collecting and systematically attaching p-prims as distributed
encodings for physical principles” can account for “a structural and knowledge-based view of
the process that Kuhn identified as central to learning a discipline, the process by which
students learn to see the exemplar outside its initial context while problem solving” (diSessa,
1993b:145). He argues that intuitive knowledge elements facilitate the constitution of
exemplars, such as the harmonic oscillator, by a gradual clustering and organization of p-prims
into what he calls distributed encodings. p-prims, distributed encodings, exemplars and
advanced scientific theories are thus levels in the reasoning process that may be characterized
as increasingly systematic but also increasingly context-dependent due to the rigidity of the
increasingly coherent knowledge system. The KiP theory is thus one suggestion for how the
increase in systematicity may be given a more concrete and dynamic aspect by studying how
specific intuitive notions can interpolate between familiar phenomena and the highly
schematized abstractions of advanced science. Moreover, the KiP theory complements some
aspects of Hoyningen-Huene’s account in examining different dimensions of systematicity such
as mutual use and plausibility of different p-prims (diSessa 1993b).
10
At the same time,
Hoyningen-Huene’s account unpacks some dimensions of systematicity in science that are not
accounted for in cognitive analyses in science education.
Systematicity theory is a useful reminder that doing and learning science is not only about
establishing and learning scientific results, respectively. It is also about establishing and
understanding the systematized structure and the epistemic and social norms that stabilize the
scientific enterprise, whether understood as a disciplinary matrix (Kuhn, 1962) or an epistemic
culture (Knorr-Cetina 1999). As Hoyningen-Huene points out, the organization of scientific
practice and knowledge is often overlooked by philosophers of science. Attention to these
issues is, I would argue, also important for science education because learning science is also
about navigating in more systematically constrained methodological and theoretical spaces.
10
diSessa (personal communication, 2016) has later questioned the utility of systematicity based on the problem
also in science education that the level of grain and empirical implications of the theory is not sufficiently specified.
This concern applies equally to Hoyningen-Huene’s account in the context of both philosophy of science and
science education.
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Examples are the ways in which students are trained to more carefully consider and exclude
alternative explanations when presented with problems to solve (Hoyningen-Huene 2013: 71),
to more systematical sample, record and evaluate data (Ibid: 83), and to reflect upon and
minimize sources of error (Ibid: 89). Similarly, at university level students may be taught to give
different weight to different sources of evidence, as exemplified in the evidence hierarchy in
medicine (Ibid: 102, 197).
11
These institutional frames may greatly influence the ability of
students to adopt a scientific and systematic approach. To explain the persistence of value-
laden conceptions of innateness despite scientific training, Knobe and Samuels emphasize that
what distinguishes scientists from ordinary folks is not a different ontology but that the
“behavior of scientists is molded by characteristic features of their external situations” (Knobe &
Samuels 2013: 84). Among these they mention how scientists are typically confronted “with
situations that encourage them to think systematically about a whole range of cases” (Ibid).
Organizational aspects of science support a more rigorous criticism and defense of knowledge
claims (dimension 3) that is characteristic of higher education as well as science. Although
operating on a different level (Greiffenhagen and Sherman, 2008), science education is ideally
not only about teaching students scientific knowledge but to learn to use abstract
representations, make mathematical derivations, reflect upon arguments, establish connections
to different pieces of knowledge and compare evidence. These are what Stanowich calls
decontextualized reasoning skills and what McCauley (2011) argues are in need of external
support as they need to be trained. The systematic methodologies exemplified by Hoyningen-
Huene may help understand how these skills become institutionalized in both science and
science education. Moreover, a more contextualized approach to systematicity may help us
understand the implications of increased systematicity in different contexts. For instance,
increasing systematicity in experimental biology may result in increasingly detailed descriptions
and explanations, whereas increasing systematicity in theoretical biology inspired by
engineering and physics may consist in abstraction from such details for the purpose of
generalization (Rowbottom 2009; Green et al., 2015). In other words, a more contextualized
notion of systematicity can not only help us go beyond the focus on theories but also help us
understand how systematicity plays out in different contexts.
Attention to the finer-grained cognitive aspects of conceptual change and iterations between
everyday and scientific reasoning could also help substantiate some of the claims made about
the role of systematicity for theory choice and broader claims about the role of systematicity in
scientific development (Hoyningen-Huene 2013, Section 5.3). Considering Einstein’s special
theory of relativity, Hoyningen-Huene (2013: 193) argues that the change of perspective
resulted from “the realization that a relativized notion of simultaneity would increase the
coherence and thus the systematicity of explanations for some class of phenomena”. Thus,
Hoyningen-Huene considers a more systematic account necessary to “obtain maximal
coherence of all the relevant data”. Hoyningen-Huene does not account for the details of the
process of conceptual change, and against this background it is difficult to examine whether
increased systematicity really explains the most salient features of the shift. The historical case
seems much more complex. Examining the role of systematicity in such contexts requires
closer attention to the process of conceptual development. The comparison of the end-
products of different historical periods in science tends to neglect the continuity and finer
grained aspects of the process of conceptual development. In the context of Hoyningen-
Huene’s specific example, the development of relativity theory should also be understood in the
11
However, the current controversy on the evidential status of systematic meta-analysis may also challenge the
view that increased systematicity is always a good thing (Stegenga 2011; see also below).
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historical context of work on electromagnetism and atom theory that makes the discontinuity to
Newtonian mechanics less apparent (Nersessian 1992). Thus, while the notion of systematicity
has potential for clarifying important difference between everyday knowledge and scientific
knowledge (and between different scientific practices), bridging the gap to cognitive analysis in
science education and HPS can help substantiate and nuance many of the claims made in
Systematicity: The Nature of Science.
5. Concluding remarks
Hoyningen-Huene (2013) argues that systematicity theory is particularly well suited for
clarifying the relation between common sense and scientific knowledge. Yet, by focusing on
rather abstract and static conceptual categories (knowledge products and general categories of
activities in everyday and scientific practice), he leaves unspecified many aspects associated
with the reasoning processes involved in learning and doing science. Hoyningen-Huene points
to gaps between ordinary and scientific knowledge on selected topics, e.g. that hermeneutics
certainly is “alien to common sense” (2013: 193) or that common sense has little to say about
microbiology. Yet, because the comparison is made on this general and rather abstract level,
the important role of common sense notions to make these aspects intelligible is largely
neglected. That is, the degree of discontinuity is sensitive to the grain of analysis. The general
comparison says little about whether giving up common sense intuitions is required for the
piecemeal process by which we accept or learn scientific knowledge. What both constructivist
theories of learning and the cited literature in philosophy of science highlights is that we must
not only analyze the state of differences between common sense and scientific knowledge, but
also the change itself from pre-scientific to scientific ideas and methods. Accordingly, I have
argued that while the “bird’s eye” perspective in Systematicity can gain from a more
contextualized cognitive analysis.
I have argued that fine-graining the analysis reveals a more prominent role of common sense
in scientific reasoning. Whereas Hoyningen-Huene argues that giving up common sense is a
price we sometimes have to pay for increased systematicity, empirical studies in science
education suggest that common sense are very robust and survive the scientific theories that
supposedly break with these. To clarify this robustness, despite the acceptance of scientific
theories, scholars have suggested that our cognitive systems and common sense notions (or
p-prims) are more flexible and compartmentalized than previously assumed. This raises
interesting questions about whether systematic scientific knowledge is made possible by a
lower degree of epistemic connectedness (dimension 6) of common sense. Accordingly, we
may question whether an increase in systematicity is always a good thing or even an end in
itself (cf. Hoyningen-Huene 2013, Section 5.3). Systematicity in the nine dimensions affords
more efficient and robust analyses, but it does so by relying on some degree of methodological
and theoretical rigor. This rigor may provide tensions between tradition and innovation (Kuhn,
1959), and between disciplinary standards and interdisciplinary innovation (Andersen, 2013).
Attending to such issues, I have suggested that there is an unexplored potential of
systematicity theory in a more contextualized analysis of how students become familiar with the
organizational aspects of science, and how increasing systematicity is realized and prioritized
in different fields.
It may be objected that my suggestion is at odds with Hoyningen-Huene’s aim to determine
what in general distinguishes science from everyday knowledge. However, systematicity may
not be most useful as a notion describing the general difference between science and everyday
To appear in a special issue in Synthese edited by Karim Bschir, Simon Lohse and Hasok Chang, on Hoyningen-
Huene’s book Systematicity: The Nature of Science.
19
knowledge, but in specifying how scientific reasoning and collaboration are externally
supported by systematically structured activities and institutions.
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