Constructing Preferences in the Physical World: A Distributed-Cognition Perspective on Preferences and Risky Choices

Article (PDF Available)inFrontiers in Psychology 2:302 · November 2011with27 Reads
DOI: 10.3389/fpsyg.2011.00302 · Source: PubMed
Abstract
Psychological research has firmly established that risk preferences are transient states shaped by past experiences, current knowledge, and feelings as well as the characteristics of the decision environment. We begin this article with a brief review of evidence supporting this conception as well as different psychological theories explaining how preferences are constructed. Next, we introduce the distributed perspective on human cognition and show how it may offer a promising framework for unifying seemingly incompatible accounts. We conclude by suggesting new directions for better capturing the essence of preference construction in laboratory research.
Constructing preferences in the physical world: a distributed-
cognition perspective on preferences and risky choices
Gaëlle Villejoubert* and Frédéric Vallée-Tourangeau
Department of Psychology, Kingston University London, Kingston Upon Thames, UK
*Correspondence: g.villejoubert@kingston.ac.uk
Psychological research has firmly estab-
lished that risk preferences are transient
states shaped by past experiences, current
knowledge, and feelings as well as the char-
acteristics of the decision environment. We
begin this article with a brief review of evi-
dence supporting this conception as well as
different psychological theories explaining
how preferences are constructed. Next, we
introduce the distributed perspective on
human cognition and show how it may
offer a promising framework for unify-
ing seemingly incompatible accounts. We
conclude by suggesting new directions for
better capturing the essence of preference
construction in laboratory research.
On the PsychOlOgy Of human
Preferences and risky chOices
Psychologists have long assumed that core
cognitive processes such as memory, percep-
tion, and attention are inherently construc-
tive – they are the product of the content
of thoughts and the situation within which
people are embedded when they think
(Bartlett, 1932; Neisser, 1967). Risk prefer-
ences are no exception. As Lichtenstein and
Slovic (2006, p. 1) put it: “in many situations
we do not really know what we prefer; we
must construct our preferences as the situa-
tion arises. Scholars often situate the origins
of the concept of preference construction in
Simons (1956, 1990) focus on the bounded
capacities of the human information-pro-
cessing system on the one hand, and the
shaping properties of the environment
within which decisions are made, on the
other. In Simons words “Human rational
behavior is shaped by a scissors whose
two blades are the structure of task environ-
ments and the computational capabilities of
the actor” (Simon, 1990, p. 7).
The notion that preferences are con-
structed is supported by a body of evidence
that is both vast and varied. Lichtenstein
and Slovic’s (1971) work on preference rever-
sals demonstrated the key role of response
mode – bidding for a bet vs. choosing a
bet – in shaping preferences for risky gam-
bles. The work of Tversky and Kahnemans
(1981) on choice framing illustrated how
the superficial framing of the description
of options can cause a reversal in risk prefer-
ences – from risk-seeking preferences in a
choice between options framed with losses
to risk-averse preferences when the same
choice is framed with gains. More recently
research has shown that preferences may
also depend on how outcomes are expe-
rienced – either as a descriptive summary,
or through actual sampling (Hertwig et al.,
2004). Meanwhile, the impact of transient
states such as affect and feelings on risk
judgments (Slovic et al., 2002) further cor-
roborates the conception of preferences as
situated in time and space.
Several theories have been proposed
to explain how preferences and associ-
ated decisions may be constructed. Some
conceive preference construction as result-
ing from the impact of the environment
on individuals’ strategy choice or repre-
sentations. The ecological approach (e.g.,
Brighton and Todd, 2009) proposes that
the mind is endowed with an “adaptive
toolbox” containing purpose-built simple
decision heuristics that exploit the struc-
ture of the information in the immediate
environment. The choice goals framework
(Bettman et al., 1998) also assumes that
individuals possess a repertoire of choice
heuristics, acquired through experience or
training. From these perspectives, an envi-
ronment with a particular information
structure will shape cognition by inviting
the application of the decision heuristic that
is most adapted to this structure. Similarly,
accounting for risky choice framing, pros-
pect theory (Kahneman and Tversky, 1979)
suggests that the environment affects risk
preferences and decisions through its
impact on individuals’ representations – as
opposed to its impact on strategy selection
– as decisions outcomes may be repre-
sented as gains or losses, depending on the
reference point made salient in the task
environment. Meanwhile, other theories
characterized preference construction as
an internal process where the role of the
individual’s immediate environment is less
prominent. Svensons (1996) differentiation
and consolidation (DiffCon) theory posits
that construction occurs through cycles of
alterations of the decision task’s mental
representation in order to single out the
alternative of choice. Search for dominance
structure (SDS) theory (Montgomery,
1998) offers a similar conception where
preferences are assumed to arise from the
restructuring of the mental representation
of attribute information to identify the
dominant alternative. Svenson (1996) does
note that context and decision structure may
influence the decision rules that are elicited.
Montgomery (1998) adds that “individual
may also intervene in the external world to
increase the support for the to-be-chosen
alternative” (p. 287, emphasis added) but
he does not specify what those interventions
may be, what might be intervened upon, or
by which mechanisms such interventions
may result in increased support.
While these theories stress important
features of the constructive process of
preferences, we also believe that they offer
an incomplete view of this process because
they omit an essential aspect of how people
may naturally construct their preferences:
through their actions on their immediate
environment. In the next section of this
article, we present a theoretical framework
that places interactivity at the forefront of
efforts to understand choice preferences.
BeyOnd situated cOgnitiOn:
cOgnitiOn distriButed
A group of cognitive scientists, initially
drawn from cognitive ergonomics and
anthropology, have lobbied for a shift in
the main unit of analysis to understand
thinking (e.g., Hollan et al., 2000). They
reject a traditional model of the mind
where cognition is sandwiched between
perceptual inputs and behavioral outputs
www.frontiersin.org November 2011 | Volume 2 | Article 302 | 1
OpiniOn Article
published: 15 November 2011
doi: 10.3389/fpsyg.2011.00302
from and transform their representation
of the information; whereas a dynamic one
may better support the development of a
productive representation of the problem
information.
Concretely, better understanding how
preferences may be constructed in the
physical world will involve designing exper-
imental settings where participants are no
longer limited to alter the information
presented to them mentally. This, we sur-
mise, will lead to a revision of the amount
of information that people are actually
capable of computing when constructing
preferences. For example, a canonical rep-
resentation of the information in choice
framing tasks such as the Asian Disease
problem (Tversky and Kahneman, 1981)
requires taking into account all outcomes
of concurrent decisions. Such a bias-free
representation has been previously ruled
out as psychologically implausible, assum-
ing that it would exceed human computa-
tional capabilities (Kahneman and Tversky,
1984). Maule and Villejoubert (2007)
surmised that participants might instead
mentally switch between a gain-framed
representation and a loss-framed repre-
sentation, in a similar manner to the per-
spective-switching occurring when people
are presented with ambiguous figures such
as the Necker cube. Choice behavior would
then be determined by the dominant repre-
sentation at the moment of choice. Taking
a distributed-cognition approach to study
choice framing, one could use playing cards
presenting a positive or negative outcome
associated with each of two alternatives.
Probabilities of outcomes would be pre-
sented as the relative proportion of positive
and negative outcomes. This would enable
participants to manipulate, spread, arrange
and rearrange the cards, and perhaps con-
trast losses and gains while constructing
their preference. Importantly, rather than
constrain thinking, the manipulability
afforded by the material presentation of
the information would instead support –
if not augment – people’s computational
abilities. In such a situation, the mental
switch of focus between a gain-framed
and a loss-framed representation (Maule
and Villejoubert, 2007) could then be sup-
ported by the physical presentation of the
information and thus, considerably reduc-
ing the mental efforts required for switch-
ing focus. Moreover, this would make the
expressions into true equations. Moreover,
whereas numeracy predicted performance
in the paper-and-pencil group, perfor-
mance was best predicted by visuo-spatial
reasoning skills in the interactive group.
These results suggest that different types of
resources and skills were recruited in the
interactive and non-interactive versions of
the task, respectively.
The distributed-cognition perspective
may also offer a novel way to conceive the
role of the environment in the construc-
tion of preferences. The theoretical frame-
works reviewed earlier assume that the
environment shapes cognitive activity. In
experiments used to test these approaches,
however, the environment is often pre-
sented in a two-dimensional, fixed presen-
tation akin to the non-interactive version
of the matchstick algebra task, offering
linguistic or numerical information that
is presented in essentially inflexible and
intangible formats. These environments
severely limit individuals’ natural tendency
to think with their eyes and hands. The
distributed-cognition perspective could
offer a new window onto the process of
preference construction, focusing on the
coupling between peoples cognition and
the strategic and opportunistic manipula-
tion of the information populating their
immediate physical space. As Weller et al.s
(in press) study illustrates, adopting a dis-
tributed perspective on cognition does not
necessitate studying cognitive activities in
naturalistic settings. In fact, we believe that
the potential of this approach resides in its
promise to better capture the essence of
cognitive processes in general, and pref-
erence construction in particular, within
laboratory settings.
Adopting a distributed-cognition per-
spective also highlights a potentially inva-
lid assumption underpinning alternative
accounts of preference construction, such
as SDS theory (Montgomery, 1998) and the
DiffCon theory (Svenson, 1996) reviewed
above – and more generally, numerous the-
ories accounting for higher level cognitive
processes – namely, the assumption that
the mental restructuring of a rigid pres-
entation of the informational landscape
is equivalent to the physical restructur-
ing of this landscape, in the individual’s
immediate environment. It is not: an
inflexible physical problem presentation
exerts gravity on people’s effort to depart
(to adapt Hurley, 2001). Instead they argue
that cognition is the product of a distributed
system that reflects the dynamic meshwork
of resources internal to the reasoner (such
as cognitive capacities, acquired knowledge)
as well as resources external to the reasoner
(such as artifacts, people, cultural beliefs;
Kirsh, 2009, 2010; Hutchins, 2010). A key
notion in the systemic perspective is that
people interact with external resources to
augment and facilitate thinking. From a
distributed-cognition perspective, think-
ing is the product of embodied and embed-
ded mental and physical activities. In other
words, people do not just think with their
heads, they also think with their eyes and
hands” in an environment that affords inter-
action. This results in an extended cognitive
system (Wilson and Clark, 2009), akin to
an ecological niche (cf. Laland et al., 2000)
enabling people to exceed the capacities of
their unaided, non-extended mind.
People act upon their environment
when they think, and more specifically
when they evince a preference, in a rich
and varied manner; yet this activity is rarely
the focus of research. People, generally, do
not choose their homes or their cars from
written descriptions. Rather, they walk in
potential flats, project and sketch furniture
placement, open and close wardrobes, sit on
the terrace to help simulate what it would be
like to live in the place. In other words, they
do not only adapt to their environment,
they actively shape, manipulate, and inter-
act with it to support their decision-making.
The distributed perspective has been
the subject of ethnographic analysis “in
the wild” (Hutchins, 1995), but it can also
guide more controlled experimental work
(Fioratou and Cowley, 2009; Weller et al., in
press). For example, we recently examined
performance on matchstick algebra prob-
lems which present participants with a false
algebraic equation made of matchsticks
and require them to move one matchstick
to form a true equation (Knoblich et al.,
1999). Adopting a distributed-cognition
perspective, we compared performance on
the traditional paper-and-pencil version of
the task with performance in an interactive
version where participants could physically
manipulate the matchsticks, using a modi-
fiable, three-dimensional, physical presen-
tation of the equation. Participants in the
interactive group were significantly more
likely to achieve insight to transform these
Villejoubert and Vallée-Tourangeau Distributed cognition and risk preferences
Frontiers in Psychology | Cognition November 2011 | Volume 2 | Article 302 | 2
Preference, eds S. Lichtenstein and P. Slovic (New York,
NY: Cambridge University Press), 1–40.
Maule, J., and Villejoubert, G. (2007). What lies beneath:
reframing framing effects. Thinking Reasoning 13,
25–44.
Montgomery, H. (1998). “Decision making and action:
the search for a dominance structure, in eds Personal
Control in Action, M. Kofta, G. Weary, and G. Sedek
(New York, NY: Plenum Press), 279–298.
Neisser, U. (1967). Cognitive Psychology. New York, NY:
Meredith.
Simon, H. A. (1956). Rational choice and the structure of
the environment. Psychol. Rev. 63, 129–138.
Simon, H. A. (1990). Invariants of human behavior. Annu.
Rev. Psychol. 41, 1–19.
Slovic, P., Finucane, M. L., Peters, E., and Macgregor, D.
G. (2002). “The affect heuristic, in Heuristics and
Biases: The Psychology of Intuitive Judgment, eds T.
Gilovich, D. Griffin, and D. Kahneman (New York,
NY: Cambridge University Press), 397–420.
Svenson, O. (1996). Decision making and the search for
fundamental psychological regularities: what can be
learned from a process perspective? Organ. Behav.
Hum. Decis. Process. 65, 252–267.
Tversky, A., and Kahneman, D. (1981). The framing of
decisions and the psychology of choice. Science 211,
453–458.
Weller, A., Villejoubert, G., and Vallée-Tourangeau, F. (in
press). Interactive insight problem solving. Think.
Reason.
Wilson, R. A., and Clark, A. (2009). “How to situate cogni-
tion: letting nature take its course, in The Cambridge
Handbook of Situated Cognition, eds P. Robbins and
M. Aydede (Cambridge: Cambridge University Press),
55–77.
Received: 07 July 2011; accepted: 11 October 2011; published
online: 15 November 2011.
Citation: Villejoubert G and Vallée-Tourangeau F
(2011) Constructing preferences in the physical world: a
distributed-cognition perspective on preferences and risky
choices. Front. Psychology 2:302. doi: 10.3389/fpsyg.2011.00302
This article was submitted to Frontiers in Cognition, a
specialty of Frontiers in Psychology.
Copyright © 2011 Villejoubert and Vallée-Tourangeau.
This is an open-access article subject to a non-exclusive
license between the authors and Frontiers Media SA, which
permits use, distribution and reproduction in other forums,
provided the original authors and source are credited and
other Frontiers conditions are complied with.
Bettman, J. R., Luce, M. F., and Payne, J. W. (1998).
Constructive consumer choice processes. J. Consum.
Res. 25, 187–217.
Brighton, H., and Todd, P. M. (2009). “Situating ration-
ality: ecologically rational decision making with
simple heuristics, in The Cambridge Handbook of
Situated Cognition, eds P. Robbins and M. Aydede
(New York, NY: Cambridge University Press),
322–346.
Fioratou, E., and Cowley, S. (2009). Insightful thinking:
cognitive dynamics and material artifacts. Pragmat.
Cogn. 17, 549–572.
Hertwig, R., Barron, G., Weber, E. U., and Erev, I. (2004).
Decisions from experience and the effect of rare events
in risky choice. Psychol. Sci. 15, 534–539.
Hollan, J., Hutchins, E., and Kirsh, D. (2000). Distributed
cognition: toward a new foundation for human-com-
puter interaction research. ACM Trans. Comput. Hum.
Interact. 7, 174–196.
Hurley, S. L. (2001). Perception and action: alternate
views. Synthese 129, 3–40.
Hutchins, E. (1995). Cognition in the Wild. Cambridge,
MA: MIT Press.
Hutchins, E. (2010). Cognitive ecology. Top. Cogn. Sci.
2, 705–715.
Kahneman, D., and Tversky, A. (1979). Prospect theory:
an analysis of decision under risk. Econometrica 47,
263–291.
Kahneman, D., and Tversky, A. (1984). Choices, values,
and frames. Am. Psychol. 39, 341–350.
Kirsh, D. (2009). “Interaction, external representation
and sense making, in Proceedings of the Thirty First
Annual Conference of the Cognitive Science Society, eds
N. A. Taatgen and H. V. Rijn (Austin, TX: Cognitive
Science Society), 1103–1108.
Kirsh, D. (2010). Thinking with external representations.
AI Soc. 25, 441–454.
Knoblich, G., Ohlsson, S., Haider, H., and Rhenius, D.
(1999). Constraint relaxation and chunk decomposi-
tion in insight problem solving. J. Exp. Psychol. Learn.
Mem. Cogn. 25, 1534–1555.
Laland, K. N., Odling-Smee, J., and Feldman, M. W.
(2000). Niche construction, biological evolu-
tion, and cultural change. Behav. Brain Sci. 23,
131–175.
Lichtenstein, S., and Slovic, P. (1971). Reversals of prefer-
ence between bids and choices in gambling decisions.
J. Exp. Psychol. 89, 46–55.
Lichtenstein, S., and Slovic, P. (2006). The construction
of preference: an overview,” in The Construction of
process of restructuring directly accessible
to the researcher, through the observation
and coding of the actions and eye-gazes
executed by the decision-makers.
To conclude, Simons (1956, 1990)
emphasis on the major shaping role played
by the environment within which decisions
are made has often been used to explain
how preferences are constructed. Simons
argument has often been summarized
as focusing on the “interaction between
individuals’ mental activities and their
immediate environment (e.g., Brighton
and Todd, 2009, p. 339; Lichtenstein and
Slovic, 2006, p. 23; Bettman et al., 1998, p.
187). However, interactivity as such never
figures in either Simons (1956, 1990)
account or in subsequent theoretical efforts.
Some have developed theories explaining
how decision-makers may select choice
heuristics that are fitted to the structure of
the environment. Others have stressed the
importance of the mental restructuring of
the information in preference construction.
In this article we sought to illustrate how
neither approaches can fully account for the
essence of preference construction as it may
occur in natural settings. We propose that
this is because past research has neglected
an important aspect of cognition – viz., how
interactions with the world may influence
and support mental processes. Whether,
under what conditions, and by which pro-
cesses, freeing up decision-makers hands
may indeed affect the way they construct
their preferences, may thus prove to be an
important new avenue for research.
references
Bartlett, F. C. (1932). Remembering. Cambridge:
Cambridge University Press.
Villejoubert and Vallée-Tourangeau Distributed cognition and risk preferences
www.frontiersin.org November 2011 | Volume 2 | Article 302 | 3
    • "documented the many biases that both individuals and teams face when making decision (Kahneman & Tversky, 1982; Manktelow, 2012). Recently, researchers in the field of cognitive psychology have recognized the distributed nature of human cognition, and have shown how cognitive biases can be limited by an effective use of specific material artifacts (e.g., Vallee-Tourangeau, Abadie, & Tourangeau, 2015; Villejoubert & Vallée Tourangeau, 2011). We still do not know, however, the extent to which the use of material artifacts, such as big data decision tools, could exacerbate or, on the contrary, limit some of these cognitive biases (and if so, which ones?); or, if and when decision-making practices associated with the use of big data systems lead to new types of cognitive biases. "
    [Show description] [Hide description] DESCRIPTION: Despite the undeniable evidence that firms often engage with more than one business model at the same time, research has mainly overlooked the implications of diversification into business model portfolios. This paper offers a review and theoretical conceptualization of business model portfolios and distinguishes it from other traditional types of diversification, as well as from multi-sided business models. By appreciating the importance of looking at complementarities and substitution effects within portfolios, we suggest that business model portfolios can be of at least three types: independent, complementary, and nested. Our discussion contributes to the literature on strategic diversification by advancing a set of propositions on business model portfolio implications for firm competitive advantage, as well as by introducing the concepts of business model relatedness and isomorphism. Lastly, we discuss implication for future research.
    Full-text · Working Paper · Jan 2016 · Adaptive Behavior
  • [Show abstract] [Hide abstract] ABSTRACT: Recognition of the importance of autopoiesis to biological systems was crucial in building an alternative to the classic view of cognitive science. However, concepts like structural coupling and autonomy are not strong enough to throw light on language and human problem solving. The argument is presented though a case study where a person solves a problem and, in so doing relies on non-local aspects of the ecology as well as his observer’s mental domain. Like Anthony Chemero we make links with ecological psychology to emphasize how embodiment draws on cultural resources as people concert thinking, action and perception. We trace this to human interactivity or sense-saturated coordination that renders possible language and human forms of cognition: it links human sense-making to historical experience. People play roles with natural and cultural artifacts as they act, animate groups and live through relationships drawing on language that is, at once, artificial and natural. Thus, while constrained by wordings, interactivity is able to fine-tune what we do with action-perception loops. Neither language nor human problem solving reduce to biological sense-making.
    Full-text · Article · Jun 2013
  • [Show abstract] [Hide abstract] ABSTRACT: In solving a variety of problems people interact with their external environment, often using artefacts close at hand to supplement and augment their problem solving skills. The role of interactivity in problem solving was investigated using a river-crossing problem. All participants performed the task twice, once in a high interactivity condition and once in a low interactivity condition. Moves to completion were higher in the high interactivity condition but latency per move was much shorter with high than with low interactivity. Moves in the world were easier to implement than to simulate mentally and acted as epistemic actions to facilitate thinking. In addition, when participants experienced the low interactivity version of the task second, their performance reflected little learning. However, when the high interactivity version was completed second, latency to solution and latency per move were substantially reduced. These results underscore the importance of investigating problem solving behaviour from a distributed cognition perspective.
    Full-text · Conference Paper · Jul 2013 · Adaptive Behavior
Show more