A Bottom-Up Investigation of Emotional
Modulation in Competitive Scenarios
Lola Ca˜namero and Orlando Avila-Garc´ıa
Adaptive Systems Research Group
School of Computer Science, University of Hertfordshire
College Lane, Hatfield, Herts AL10 9AB, UK
L.Canamero@herts.ac.uk, newoavila@hotmail.com
Abstract. In this paper, we take an incremental, bottom-up approach
to investigate plausible mechanisms underlying emotional modulation of
behavior selection and their adaptive value in autonomous robots. We
focus in particular on achieving adaptive behavior selection in competitive
robotic scenarios through modulation of perception, drawing on
the notion of biological hormones. We discuss results from testing our
architectures in two different competitive robotic scenarios.
1 Introduction
One of the main problems for autonomous robots is behavior selection or “what
to do next” [12]. Motivation-based architectures [12,4,17,2] integrate a combination
of internal and external factors to select the appropriate behavior and
satisfy the robot’s needs in real time. However, these architectures are not always
sufficiently adaptive to rapid environmental changes. Previous work [5]
postulated the use of second-order mechanisms, akin to some of the functions
of emotions in biological systems, that act on other elements in the architecture
for improved performance in dynamic, unpredictable, and dangerous environments.
In that architecture and others that have followed a similar approach,
the adaptive functions of emotions are predefined by the designer. While this
nowadays widespread design practice can produce efficient behavior selection, it
leaves unanswered the question of which are the underlying mechanisms and how
they integrate and interact with other elements to achieve adaptive behavior. In
the work presented here, we take an incremental approach to investigate plausible
mechanisms underlying emotional modulation of behavior selection and
their adaptive value. We are particularly interested in how such modulation can
achieve different functionalities from the same architecture by interacting with
other elements, rather than including emotions as additional components. In
this paper, we focus on discussing how behavior selection can be made adaptive
(i.e., its output biased) to different environmental situations (two different competitive
robotic scenarios) by modulating different sensory channels—perception
of external and internal stimuli. Drawing on the notion of biological hormones,
 Currently with OpenCanarias S.L.
A. Paiva, R. Prada, and R.W. Picard (Eds.): ACII 2007, LNCS 4738, pp. 401–412, 2007.
c Springer-Verlag Berlin Heidelberg 2007
402 L. Ca˜namero and O. Avila-Garc´ıa
Fig. 1. Experimental setups used to carry out the studies: a Competitive Two-Resource
Problem scenario (left), and a “prey-predator” scenario showing the prey robot inside
the nest and the predator outside (right)
we have modeled two of the functionalities ascribed to them in order to improve
the adaptation of motivation-based architectures to different problems.
To achieve different functionalities from the same architecture, we have taken
inspiration from neuroscience models of hormonal control [9,10], in particular regarding
the following ideas: (a) Sensory inputs enhance the release of hormones
that act at different levels of the nervous system; (b) they act as gain-setting
sensitization processes that bias the output of the organism in particular directions;
and (c) after modulation, the organism responds to particular sensory
stimuli with an altered output appropriate to the new situation. We have tested
our “hormone-like” mechanisms in two dynamic and unpredictable competitive
robotic scenarios depicted in Figure 1, and show how they improve adaptation
and performance using quantitative indicators based on the notion of viability.
Finally, we analyze the results in terms of interesting behavioral phenomena that
emerge from the interaction of these artificial hormones with the rest of architectural
elements and the environment, and that resemble “emotional” behavior
in biological systems confronted to similar situations.
2 Behavior Selection Architecture
Following [5], in our architecture behavior selection results from the interactions
of a number of elements integrated through an artificial physiology and in
interaction with the environment.
The physiology consists of (1) survival-related, homeostatically controlled
essential variables and (2) hormones. Essential variables are abstractions representing
the level of internal resources that the robot needs in order to survive.
They must be kept within a range of permissible values for the robot to remain
viable or “alive,” thus defining a physiological space [14] or viability zone [1,13]
within which survival (continued existence) is guaranteed, whereas transgression
A Bottom-Up Investigation of Emotional Modulation 403
of these boundaries leads to “death.” Hormones can be seen as second-order control
mechanisms that affect the behavior of other elements of the arhitecture.
Motivations are abstractions representing tendencies to act in particular
ways as a function of internal and external factors [18]. Internal factors are
mainly (but not only) physiological deficits (0 ≤ di ≤ 1) or bodily needs—
traditionally known as “drives”—that set urges to action to maintain the state
of the controlled physiological variables within the viability zone. External factors
are environmental stimuli, commonly termed “incentive cues” in Ethology,
(0 ≤ ci ≤ 1) that allow to satisfy bodily needs through behavior execution. In
our implementation, each motivation performs homeostatic control of one physiological
variable. We have used the equation proposed in [2] to combine cue and
physiological deficit when computing motivational intensities:
mi = di + (di × αci) (1)
In addition to physiological deficits (di) and incentive cues (ci), this equation
introduces a weighting factor (0 ≤ α ≤ 1) that affects the relevance given to the
external cue.
Behaviors are coarse-grained subsystems (embedding simpler actions) that
implement behavioral competencies similar to [12,5]. Following a classical distinction
in ethology [15], motivated behavior can be consummatory—“goalachieving”
and needing the presence of an incentive stimulus to be executed—or
appetitive—“goal-directed” search for a particular incentive stimulus. In addition
to modifying the external environment, the execution of a behavior has an
impact on (increases or decreases) the level of specific physiological variables.
Therefore, behaviors take part in the homeostatic control to maintain the state
of the physiological variables within the viability zone.
Behavior Selection is performed in a continuous loop consisting of three
main steps: (1) The deficit of the physiological variables (internal needs) and
the intensity of the external stimuli are calculated; (2) motivational intensities
are computed combining (perception of) deficits and external stimuli ponderated
by the weight α, following equation 1; (3) the behavior that (best) satisfies the
motivation with the highest intensity is executed, modifying the physiology and
possibly the position of the robot relative to external stimuli in the environment.
3 Competition for Resources
In previous work [2] we analyzed different motivation-based behavior selection
architectures within a static Two-Resource Problem (TRP), in which a single
robot must maintain appropriate levels of two internal variables by consuming
two resources available in the external environment. The TRP constitutes the
minimal scenario to test behavior selection mechanisms, and it has become a
standard testbed for behavior selection both in animals—see e.g., [17]—and autonomous
agents and robots—e.g., [4,7,3]. Its simplicity, although not devoid of
problems, favors a systematic analysis of results. The particular implementation
of the TRP in [2] used a Lego Mindstorms robot (see Figure 1, left, for a similar
404 L. Ca˜namero and O. Avila-Garc´ıa
arena, although the TRP uses only one robot), with the need to maintain temperature
and energy levels by consuming heat (white gradients on the floor of the
arena) and food (black gradients), respectively. The robot had two motivations:
mcold to increase temperature, which can be satisfied by executing the consummatory
behavior bwarmup, and mfatigue to increase energy, which can be achieved
by executing the consummatory behavior bfeed. In addition, the robot had a reflex
obstacle avoidance behavior bavoid, and the appetitive behavior bsearch. The
execution of all behaviors affects both essential variables1.
To measure results in TRP, we used different performance indicators based
on the notion of viability, in particular: Life Span, defined as the time that the
robot survived in each run (LS = tlife/trun); Overall Comfort, the average level
of satisfaction of the physiological variables during a run (OvC = tlife
i=1 (1 −
di)/tlife); and Physiological Balance, the homogeneity with which physiological
needs are satisfied during a run (PhB = tlife
i=1 (1−σ2(di))/tlife). We also noted
that, when doing behavior selection in TRP, the robot executed regular cycles of
activities rather than isolated behaviors, and those activity cycles were reflected
in the physiological space of the robot, as shown in Figure 2: from the initial
state, the robot would start looking for a given resource, e.g. heat (arrow noted
as A in the figure), then consume it until satiated (B), then start looking for the
other resource (C), consume it until satiated (D), and start all over again. The
position of the cycles in the physiological can be changed: the same cycle (i.e.,
with the same shape and duration of each activity) would be executed closer
to the ideal state, therefore preserving viability “better”, or father away from it
(and therefore in a “less viable” way) depending on the value of α, the parameter
that weighed the significance of external stimuli in equation 1, as depicted on
Figure 2 (right). The regular shape of those activity cycles reflects the fact that
behavior selection in TRP was static and highly predictable.
The Competitive Two-Resource Problem2 (CTRP) is an extension of this
problem that consists in the introduction of two robots in the same environment
simultaneously performing their own TRP, as depicted in Figure 1 (left). The
robots do not explicitly communicate or compete; however, the fact that they
have to use the same resources to satisfy their needs introduces competition
for those resources, as both robots might need access to the same resource at
the same time. Therefore, new forms of environmental complexity—availability
and accessibility of resources—appear due to the interaction between robots,
breaking the predictability and symmetry of TRP. The question that needs to
be examined here is to what extent the architecture used for the TRP can solve
the CTRP.
1 At each execution cycle, bwarmup increases temperature by 0.3 units while decreasing
energy by 0.1 units, bfeed increases energy by 0.3 units while decreasing temperature
by 0.1 units, and bavoid and bsearch decrease each variable by 0.2 units.
2 We refer the reader to [3] for an in-depth technical quantitative analysis of this
scenario, while here we focus on a qualitative discussion of the adaptive value of
hormonal modulation and its significance from the point of view of emotion.
A Bottom-Up Investigation of Emotional Modulation 405
Fig. 2. Activity cycles in TRP. Left: cycle as reflected in the physiological space. Right:
position of cycles in the physiological space as a function of α.
Fig. 3. Decreased performance in the CTRP, as measured by viability indicators (left)
and activity cycles (right)
Analysis showed that the new forms of complexity dramatically decrease the
performance of that behavior selection architecture, as clearly reflected by the
different viability indicators and the activity cycles depicted in Figure 3. In
particular, analysis of the activity cycles shows that the cycles easily loose
the regularity and symmetry they showed in TRP, as illustrated in Figure 3
(right), and that the robot very often dies from two problems that the behavior
selection mechanism used within the TRP presents when used in the CTRP.
First, the robot can fall in a pathological sequence of opportunistic activities—
consuming the same resource—that eventually can drive it to death due to overopportunism.
Second, when one robot is located on top of a resource—i.e., consuming
it—the other robot might bump into it and push it out of the resource.
This will result in the interruption of the ongoing consummatory activity and
to death due to goal interference.
The next step in our incremental design approach is to analyze what needs
to be added to the architecture to be able to solve those problems. A solution
to the “over-opportunism” problem requires shifting attention away from less
406 L. Ca˜namero and O. Avila-Garc´ıa
needed resources when the robot is in a high risk of death (RoD), that we define
as the inverse of the distance between physiological state (dtemper, denergy) and
lethal boundaries. A solution to the “goal interference” problem requires that
the robot in need of an occupied resource does not avoid the “intruder” as if
it were a mere obstacle. Both problems can be solved by altered perception of
external stimuli, i.e., by modulation of exteroception.
3.1 Modulation of Exteroception
Rather than adding more structural elements to our architecture, our solution
consists in trying to achieve additional functionality from the same architecture.
A single “hormone-like” modulatory mechanism can alter perception in both
cases, with a twofold effect. First, by acting on the parameter α of equation 1—
i.e., by biasing the relevance given to external cues—the hormone reduces the
perception of both incentive cues, therefore reducing opportunistic activities
when there is any risk of death. Second, by cancelling the perception of obstacles
sobstacle (carried out using the bumper sensor), and hence the avoidance reflex
behavior, when the robot is facing the competitor, the hormone potentiates the
competition skills of the robot by enhancing its capacity to push the other robot
out the resources and not to be interrupted. To achieve this twofold functionality,
the concentration of hormone will be a function of the risk of death (RoD)
and the perception of the competitor, given by 0 ≤ scompetitor ≤ 1. Hormone
concentration is computed as:
cg = RoD + scompetitor (2)
The relation between hormone concentration and the cancellation of the perception
of incentive cues and obstacles is as follows. To achieve the first functionality,
the cancellation of α is directly proportional to the increment in hormone
concentration, i.e., when RoD increases, α decreases: α = min(1 − cg, 0) The
second functionality is obtained by cancelling the perception of sobstacle—i.e.,
bumpers—when the competitor is in front of the robot. For this mechanism to
be efficient, two conditions must be fulfilled to make a coherent pushing of the
other robot. First, the robot must avoid getting engaged in fights when it has
high RoD. Second, it must only bump blindly into the other robot, not against
the walls of the arena. To produce that effect the cancellation of the bumpers
must be at hormonal levels cg  1 and cg  2.
It is worth noting that the motivation-based behavior selection architecture
has suffered no modification; the only difference with respect to the TRP is the
fact that now one of its parameters (α, cfr. equation 1) is modulated by the
hormonal feedback mechanism.
3.2 Experiments and Results
We tested the robots for a total of 16 runs of 1200 steps (approximately 5 minutes)
each, one step representing a loop of the behavior selection mechanism
that takes 260ms in the 16MHz onboard microcontroller. As shown in Figure 4,
A Bottom-Up Investigation of Emotional Modulation 407
Fig. 4. Activity cycles in CTRP in unmodulated architecture (left) and with modulation
of exteroception (right)
the robot with hormone-like mechanism recovers the stability and viability of
activity cycles. We refer the reader to [3] for a detailed quantitative analysis,
while we focus here on various interesting functionalities that emerged as a result
of modulating the exteroception of the robot. The first functionality is to
stop consuming resources when the robot detects its competitor approaching.
This could be interpreted by an external observer as abandonment of a situation
(waiting for the other robot at the resource) in which competing is disadvantageous.
Instead, the robot will leave the resource and go straightforward towards
the competitor until it reaches it; at that moment, two things can happen. If
there is some level of RoD, the bumpers of the robot will not be cancelled and it
will avoid the competitor, showing a behavior that an observer could interpret
as “fear” after evaluating the competitor. On the contrary, if there is no RoD,
the hormonal system will cancel the bumpers and the robot will push the competitor
unconditionally—as if it showed some sort of “aggression” against it. If
we study the whole picture as external observers, such behavioral phenomena
could well be interpreted as some sort of “protection of resources”.
4 Prey-Predator Scenario
The previous scenario involved no active interaction between the two robots. It
therefore seems natural to ask whether an active relation between the robots
would introduce additional complexity, and how the previous behavior selection
architecture would cope with it. We thus designed a prey-predator scenario
(Figure 1, right) that we call the Hazardous 3-Resource Problem (H3RP). In
H3RP, a “predator” robot actively chases and can damage a “prey” robot by
hitting a home-made contact sensor in the form of a ring. To make this interaction
possible, we had to introduce new elements in the environment—a nest in
one of the corners of the arena, in which the prey can “hide” and recover from
damage—and in the architecture of the prey, namely: (a) a third physiological
variable, integrity, which is a metaphor of the essential need any organism has
408 L. Ca˜namero and O. Avila-Garc´ıa
to keep its tissue—the boundary between the organism and its environment—
intact and that is unpredictably reduced by the attacks of the predator; (b) a
new motivation mdamage to decrease the integrity deficit; and (c) an appropriate
consummatory behavior brecover to satisfy the new need.
Initial experiments showed very quickly that a purely motivation-based behavior
selection mechanism does not perform well within the new framework,
since the prey invariably died as a consequence of predator attack (see the right
graph of Figure 6 for quantitative results of additional experiments). The main
cause seemed to be the inability of the prey to react timely to the attack of
the predator, which was perceived in close proximity only. In other words, the
behavior selection mechanism paid low attention to the new motivation to recover
integrity, even when the predator is in sight. The probability to lose integrity
rises when the predator is around, therefore it would be advantageous for the
prey robot to “anticipate” that loss and start “preparing in advance” to recover
integrity.
In the animal world, exposure to predators triggers what has been termed
“predator-induced stress” or “predator-stress” for short, characterized by high
levels of corticoids or “stress hormones” and a number of responses related to
increased attention to and avoidance of the predator. Such reactions occur not
only in the presence of a predator. Prey animals use unconditioned and conditioned
predator cues to assess risk of predation, and they even seem to be
able to perceive risks in the absence of such cues [6]. An example of the latter
is the phenomenon known as “risk of permanence”—maintained levels of vigilance
after predator’s disappearance. Risk of predation strongly influences prey
decision-making (for example, when and where to feed, vigilance, or the use of
nest), which in this circumstances can be considered as a mechanism to allow
an animal to manage predator-induced stress [11]. Risk of predation has been
proposed to increase the animal’s level of “apprehension,” i.e. the reduction in
attention to other activities (e.g. foraging) as a result of increasing the time
spent executing defense-related activities such as vigilance or refuge use [8].
4.1 Modulation of Interoception
We have again applied “hormonal” modulation to our behavior selection architecture
to achieve such “anticipatory” behavior, this time exploiting the temporal
dynamics of hormonal decay to produce long-term modulatory effects triggered
by short-term exposure to a stimulus [10].
To achieve this, a simple solution consists in using one of the existing sensors
of the prey robot to detect the predator from a distance. Given the morphology
of the robot, this sensor must be the same as that used to locate the nest.
The problem of using that sensor is that it is fixed, pointing forwards. Since the
predator does not pass in front of the prey very often and only does it for very
brief periods, the additional stimulus (spredator) will be too weak to make any
difference. However, long-term hormonal modulation acts as a mechanism for
predation risk assessment in the absence of predator cues. Hormone concerntration
makes the system more sensitive to integrity deficit after the detection of
A Bottom-Up Investigation of Emotional Modulation 409
the predator. Hormonal secretion follows the detection of the stimulus spredator
and increases the perceived integrity deficit. Due to the hormone’s temporal dynamics,
modulation will be acting in the system long time after the predator has
disappeared. Hormone concentration modifies again one of the sensory inputs of
the architecture—interoceptive in this case—biasing behavior selection.
We have modeled hormonal temporal dynamics—release and dissipation—
using an artificial endocrine system similar to that proposed in [16] and described
by equations 3 and 4. A gland g releases hormone as a function of the intensity
of the external stimulus predator (spredator) at a constant releasing rate βg:
rg = βg · spredator (3)
Hormone concentration3 suffers two opposite forces over time: it increases
with the release of hormone by the gland, and dissipates or decays over time at
a constant rate γg:
c(t + 1)g = max[(c(t)g · γg) + rg, 100] (4)
In this implementation, the hormone increases the perception of the integrity
deficit (dintegrity ), i.e., the higher the hormone concentration, the higher the
reading of the dintegrity interoceptor:
dnew
integrity = max(dintegrity + δg · cg, 1) (5)
Factor δg determines how susceptible to hormonal modulation the interoceptor
(dintegrity) is. We use δg = 0.005, which implies that the level of perceived
dintegrity is increased by 0.5 when hormonal concentration is maximum
(cg = 100). In other words, although the level of integrity is at its ideal value
(dintegrity = 0), the interoceptor will perceive a level of 0.5 if hormone concentration
is maximum. Note that there is a constraint to avoid the level of integrity
deficit to be perceived beyond the maximum possible value (dintegrity = 1).
4.2 Experiments and Results
We tested the robot for 16 runs of 1600 steps each, i.e., each architecture (nonmodulated
and modulated) was tested for almost two hours in H3RP.
The prey robot presented higher viability levels in terms of life span, at the
cost of overall comfort, when equipped with the modulatory mechanism, as
shown in Figure 5. Long-term hormonal modulation acts as a mechanism for
predation risk assessment in the absence of predator cues. It can be regarded as
increasing the level of “apprehension” of the prey robot after short-term predator
exposure, and this is reflected in an increment of the motivation to recover and of
the execution time of recover-related (consummatory and appetitive) activities—
the robot spends more time looking for the nest and recovering integrity in it—at
the cost of other activities, namely feed and warmup, as reflected in Figure 6
3 We constrained hormonal concentration to a maximum of cg = 100 in order to keep
more control on the hormone’s dynamics and thus facilitate the analysis of results.
410 L. Ca˜namero and O. Avila-Garc´ıa
Life Span
0.0
0.2
0.4
0.6
0.8
1.0
Overall Comfort
0.30
0.35
0.40
0.45
0.50
0.55
0.60
Physiological Balance
0.50
0.55
0.60
0.65
0.70
Fig. 5. Average performance of non-modulated (light bars) and modulated (dark bars)
architecutres in terms of LifeSpan, Physiological Balance, and Overall Comfort. Bars
show standard error of the mean.
Damage Subsystem Execution
10%
15%
20%
25%
30%
% steps
Consuming Interruption
0%
10%
20%
30%
40%
50%
% consummatory phases
Cause of Death
16
10
0
2
4
6
8
10
12
14
16
run
Integrity
Energy
Temper
Survive
Fig. 6. Comparison between non-modulated (bars on the left of each graph) and modulated
(bars on the right) architectures in terms of execution time of recover subsystem
(left), average number of interruptions of consummatory feed and warmup behaviors
(center), and causes of death in the 16 runs (right). Bars show standard mean error.
(left). This increment in the execution time of recover-related activities is statistically
highly significant. Another important phenomenon is the interruption of
ongoing consummatory feeding or warming-up activities (Figure 6, center). When
the robot is under the effect of the hormone it will abandon the resource and
go to the nest before the motivation has been satiated. The prey robot, when
equipped with the hormonal mechanism, presents statistically higher levels of
interruption of ongoing feeding or warming-up activities. Finally, analysis of the
causes of death (Figure 6, right) shows substantial differences with respect to
the non-modulated architecture.
5 Conclusion
We have discussed a bottom-up study of plausible mechanisms underlying emotional
modulation of behavior selection and their adaptive value, in particular
A Bottom-Up Investigation of Emotional Modulation 411
Motors
External Stimuli
Physiological Deficits
Motivations
Behaviors
ch
cf
so
dt de
bs
bw
ba
bf
mc
mf
A
S
M
g
Hormone
sc
Physiological Deficits
Motors
External Stimuli Behaviors
ch
cf
cn
so
dt de di
bs
bw
bf
ba
br
mc
mf
md A
S
M
sp
g
Hormone
Motivations
Fig. 7. Hormonal modulation of exteroception (left) and of interoception (right)
how such modulation applied to a motivation-based architecture can achieve different
functionalities found in biological emotions, to face different emotionallyrelevant
problems posed by different competitive scenarios.We have considered a
first scenario in which obtaining resources in competition with others is the main
survival-related problem, and a second scenario in which the attack of a predator
constitutes the main threat. Drawing on the notion of biological hormones, we
have focused on achieving adaptive behavior selection in these different competitive
robotic scenarios by modulating perception of external stimuli in the first
case, and of internal stimuli in the second, as depicted in Figure 7. In addition
to improving behavior selection performance and adaptation, modulation has
given rise to some emergent behavioral phenomena that could be interpreted
by an external observer as “emotional,” such as aggressive/defensive behavior
in the first, “fleeing” and “apprehension” in the second. We suggest that such
modulatory mechanisms provide a more principled integration of different behavior
selection elements and functions, in addition to improving the adaptation
of a robot to changing environments. The type of adaptation fostered by such
mechanisms is different from other mechanism such as learning or evolution, for
which “past solutions” are “overwritten” by new ones.
Current and future work includes the integration in the same architecture of
both types of hormonal modulation presented here, to face a more complex preypredator
problem requiring interactions among both mechanisms. We will also
continue our incremental study of plausible modulatory mechanisms underlying
emotions by changing and complexifying the environment to give rise to other
behavior selection problems.
Acknowledgments
Support was provided partly by the European project HUMAINE (FP6-IST–
507422) and partly by a University of Hertfordshire studentship to Orlando
Avila-Garc´ıa.
412 L. Ca˜namero and O. Avila-Garc´ıa
References
1. Ashby, W.R.: Design for a Brain. Chapman & Hall, London (1952)
2. Avila-Garc´ıa, O., Ca˜namero, L., te Boekhorst, R.: Analyzing the Performance of
“Winner-Take-All” and “Voting-Based” Action Selection Policies within the Two-
Resource Problem. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim,
J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 733–742. Springer, Heidelberg
(2003)
3. Avila-Garc´ıa, O., Ca˜namero, L.: Using Hormonal Feedback to Modulate Action Selection
in a Competitive Scenario. In: Proc. Eight Intl. Conf. Simulation of Adaptive
Behavior (SAB04), pp. 243–252. MIT Press, Cambridge, MA (2004)
4. Blumberg, B.: Old Tricks, New Dogs: Ethology and Interactive Creatures. Unpublished
PhD Thesis. MIT Media Laboratory, Cambridge, MA (1997)
5. Ca˜namero, L.D.: Modeling Motivations and Emotions as a Basis for Intelligent
Behavior. In: Johnson, W.L. (ed.) Proc. First Intl. Conf. on Autonomous Agents,
pp. 148–155. ACM Press, New York (1997)
6. Curio, E.: Proximate and Developmental Aspects of Antipredator Behavior. Adv.
Study Behav. 22, 135–238 (1993)
7. Girard, B., Cuzin, V., Guillot, A., Gurney, K.N., Prescott, T.J.: Comparing a
Brain-Inspired Robot Action Selection Mechanism with “Winner-Takes-All”. In:
Proc. Seventh Intl. Conf.on Simulation of Adaptive Behavior, MIT Press, Cambridge
(2002)
8. Kavaliers, M., Choleris, E.: Antipredator Responses and Defensive Behavior: Ecological
and Ethological Approaches for the Neurosciences. Neuroscience and Biobehavioral
Reviews 25, 577–586 (2001)
9. Kravitz, E.A.: Hormonal Control of Behavior: Amines and the Biasing of Behavioral
Output in Lobsters. Science 241, 1175–1781 (1988)
10. Levitan, I.B., Kaczmarek, L.K.: The Neuron: Cell and Molecular Biology, 3rd edn.
Oxford University Press, Oxford, UK (2002)
11. Lima, S.L.: Stress and Decision Making under the Risk of Predation: Recent Developments
from Behavioral, Reproductive, and Ecological Perspectives. Adv. Study
Behav. 27, 215–290 (1998)
12. Maes, P.: A Bottom-Up Mechanism for Behavior Selection in an Artificial Creature.
In: Proc. First Intl.Conf. on Simulation of Adaptive Behavior (SAB90), pp. 238–
246. MIT Press, Cambridge, MA (1991)
13. Meyer, J.-A.: The Animat Approach to Cognitive Science. In: Roitblat, H.L.,
Meyer, J.-A. (eds.) Comparative Approaches to Cognitive Science, pp. 27–44. MIT
Press, Cambridge, MA (1995)
14. McFarland, D. (ed.) Motivational Control Systems Analysis. Academic Press, London
(1974)
15. McFarland, D.: Animal Behaviour, 3rd edn. Addison Wesley Longman Ltd., Redwood
City, CA, USA (1999)
16. Neal, M., Timmis, J.: Timidity: A Useful Emotional Mechanism for Robot Control?
Informatica 27, 197–204 (2003)
17. Spier, E., McFarland, D.: Possibly Optimal Decision Making under Self-Sufficiency
and Autonomy. J. Theor.Biol. 189, 317–331 (1997)
18. Toates, F.: Motivational Systems. Cambridge Univ. Press, Cambridge (1986)

:: بازدید از این مطلب : 146

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

1. What is action-oriented perception?

ZOE DRAYSON

Abstract. Contemporary scientific and philosophical literature on perception often focuses

on the relationship between perception and action, emphasizing the ways in which

perception can be understood as geared towards action or ‘action-oriented’. In this paper

I provide a framework within which to classify approaches to action-oriented perception,

and I highlight important differences between the distinct approaches. I show how talk

of perception as action-oriented can be applied to the evolutionary history of perception,

neural or psychological perceptual mechanisms, the semantic content or phenomenal

character of perceptual states, or to the metaphysical nature of perception. I argue that

there are no straightforward inferences from one kind of action-oriented perception to another.

Using this framework and its insights, I then explore the notion of action-oriented

perceptual representation which plays a key role in some approaches to embodied cognitive

science. I argue that the concept of action-oriented representation proposed by

Clark and Wheeler is less straightforward than it might seem, because it seems to require

both that the mechanisms of perceptual representation are action-oriented and that

the content of these perceptual representations are action-oriented. Given that neither

of these claims can be derived from the other, proponents of action-oriented representation

owe us separate justification for each claim. I will argue that such justifications are

not forthcoming in the literature, and that attempts to reconstruct them run into trouble:

the sorts of arguments offered for the representational mechanisms being action-oriented

seem to undermine the sorts of arguments offered for the representational content being

action-oriented, and vice-versa.

1 Introduction

Contemporary scientific and philosophical literature on perception often focuses on the

relationship between perception and action, emphasizing the ways in which perception

can be understood as geared towards action. Research in psychology, for example,

sometimes characterizes perception as “action-specific” (Ca˜nal-Bruland & van der

Kamp, 2009; Witt, 2011), and talk of perception as being “of affordances for action” is

found in both psychology and philosophy (Richardson, Shockley, Fajen, Riley, & Turvey,

1

2008; Chemero, 2011). Descriptions of perception as “active” are found in vision science

(Whitehead & Ballard, 1990), artificial intelligence (Weyns, Steegmans, & Holvoet,

2004), psychology (Aloimonos, 2013), and philosophy (No¨e, 2004); and as “enactive” in

robotics (Morse & Ziemke, 2007), cognitive science (Froese & Spiers, 2007), and philosophy

(Thompson, 2007). In psychology (Fajen, 2005) and also in sports science (Pijpers,

Oudejans, & Bakker, 2007), perception is described as being “for action”; while the term

“action-oriented” is applied to perception in philosophy (Gallagher & Zahavi, 2014),

computer science (Gora & P., 2014), and neuroscience (Ridderinkhof, 2014).

In what follows, I’ll use the term ‘action-oriented’ as an umbrella-term to include approaches

like these that share the idea of perception as oriented or geared towards action,

in some sense. My first aim in this paper is to provide a framework within which to

classify approaches to action-oriented perception, and to highlight important differences

between distinct approaches.

My framework classifies action-oriented approaches to perception into one of five categories:

1. The evolution of perception as action-oriented: these approaches claim that perception

evolved to guide action, or that perception was selected for its actionoriented

capacities.

2. The mechanisms of perception as action-oriented: these approaches claim that the

mechanisms of perception overlap with, or are closely coupled to, the mechanisms

of action.

3. The contents of perception as action-oriented: these approaches claim that the contents

of perception present the world to the perceiver in terms of possible actions.

4. The phenomenal character of perception as action-oriented: these approaches claim

that a perceptual state is qualitatively experienced as encouraging or demanding a

certain action.

5. The nature of perception as action-oriented: these approaches claim that perception

has a necessary connection to action, that to be a perceiver is essentially to

have certain agentive capacities or skills.

Part of my motivation for providing this framework is to clarify the discussions of actionoriented

perception across philosophy and the sciences. I will show that each approach

within the framework is logically independent of the others: none of the approaches can

be derived from the others without further justificatory assumptions. We should thus not

conclude from the fact that perception is action-oriented in one respect that it is actionoriented

in any of the other respects.

This paper also has a second aim, which is to use this framework to explore the role of

action-oriented perception in embodied cognitive science. In particular, I will focus on

the claim that perceptual representations can be action-oriented. I will show that Clark

(1997) and Wheeler (2005) put forward a concept of action-oriented perceptual repre-

2

sentations which is committed to both the representational mechanisms of perception

being action-oriented, and to their contents being action oriented. But given that neither

of these claims can be derived from the other, proponents of action-oriented representation

owe us separate justification for each claim. I will argue that such justifications are

not forthcoming in the literature, and that attempts to reconstruct them run into trouble:

the sorts of arguments offered for the representational mechanisms being action-oriented

seem to undermine the sorts of arguments offered for the representational content being

action-oriented, and vice-versa.

In Sections 2-6, I introduce each of the distinct respects in which perception can be said

to be action-oriented. Section 7 explores the use of action-oriented perceptual representations

in embodied cognitive science.

2 The evolution of perception as action-oriented

There is an evolutionary sense in which perception can be described as action-oriented,

that is often expressed with the claim that perception is “for action”. Proponents of this

view emphasise that our perceptual capacities evolved to guide our bodily interactions

with the world, specifically those interactions which increased our adaptive fitness. Talking

about visual perception, for example, Briscoe claims that:

“[f]rom an biological or evolutionary standpoint, it is reasonable to think

that vision is for action, that its preeminent biological function is to adapt

an animal’s bodily movements to the properties of the environment that it

inhabits” (Briscoe, 2014, p. 202, my italics).

On one interpretation of such claims, the idea that perception is “for action” in an evolutionary

sense seems trivially true, and it’s not clear whether any believer in evolution

would deny it. But the evolutionary claim is often put forward in a stronger way: the

assumption is sometimes that perception evolved directly to guide action and not (or at

least not directly) to present the perceiver with an action-neutral description of the objective

world. If perception evolved for its action-guiding properties, so this line of thought

goes, then it did not evolve to enable us to have beliefs about how the world is, independently

of our own actions. Proponents of this view either conclude that we do not

perceive the objective world in an action-neutral way, or that if we do, this is not the

primary function of our perceptual systems: perception did not evolve in order to provide

inputs to the human capacity to think and reason about the world. Representative

versions of this view can be found in the works of philosophers like Patricia Churchland

and Kathleen Akins:

“looked at from an evolutionary point of view, the principle function of nervous

systems is to get the body parts where they should be in order that the

organism may survive. [. . . ] Truth, whatever that is, definitely takes the

hindmost.” (Churchland, 1987, p. 548)

3

“evolution will favor sensory solutions that package the information in efficient

and quickly accessible formats, in ways that match the particular physical

form of the motor system, its motor tasks, and hence informational requirements.

[. . . ] the elegant solutions that evolution eventually selects need

not involve any straightforward (to our eyes) ‘veridical’ encoding of sensory

information.” (Akins, 1996, p. 353)

Notice that this involves a strong commitment to a particular evolutionary story. Proponents

of this view have to rule out the possibility that the development of amodal

cognition could have been more adaptive than a faster but less flexible system in which

specific sensory inputs drove specific motor outputs. Some scientists claim exactly this,

arguing that game theory demonstrates that selection pressures would make objective

representations of the world an unlikely outcome (Hoffman, Singh, & Mark, 2013), but

this is often assumed rather than argued for. Notice that even if one accepts that evolution

selected for action-guiding perception, this is consistent with the claim that evolution

also selected for the kind of perception that can yield reasoning capacities and abstract

thought. It might be the case, in other words, that we have two forms of perception. This

is a possibility raised by the dual-visual system theory of perception, discussed in the

next section.

3 The mechanisms of perception as action-oriented

According to a traditional picture of the mind, perceiving and acting are distinct mechanisms

which are separate from each other and from our thinking mechanisms: perception

provides input for thought, and action emerges as the output of thought. (This is what

Susan Hurley (1998) terms the ‘classical sandwich’ picture of the mind.) This picture

is challenged by empirical work which suggests that the mechanisms of perceiving and

acting are closely intertwined, overlapping, or even co-constituting. If this is true, then

there is a sense in which the mechanisms of perception can be action-oriented regardless

of their evolutionary history.

One example of this comes from the literature on so-called ‘mirror neurons’: in macaque

monkeys, neurons known to control hand and mouth movements fire both when the monkey

is manipulating an object (e.g. reaching for a piece of food) and when the monkey

is watching someone perform a similar manipulation. In creatures like humans, where

single-cell recordings are not possible, there is similar data showing that areas of the

brain associated with movement are active during perceptual processing: one and the

same neural mechanism seems to be involved in both perception and action. Perception

is action-oriented in the sense that a perceptual mechanism seems to overlap with a

mechanism for action.

In visual psychology, Milner and Goodale (1995) have advanced the ‘dual visual systems’

hypothesis, which concerns the way that visual perception builds up information

4

about the world. They have demonstrated that sensory input to the visual system of primate

brains can be processed by two independent pathways. The processes in the ventral

pathway reflect the traditional picture of perception as the input to thought: they produce

conscious perceptual states that we can categorize, memorize, and combine with

thoughts to produce a broad range of actions. The processes in the dorsal pathway, however,

do not seem to be the inputs to thought: these visual inputs instead lead only to the

specific motor outputs involved in reaching and grasping objects with our hands. The

dorsal processes seem to be action-oriented in the sense that visual input results in the

appropriate motor output without the intermediary of conscious thought.

This sort of ‘close coupling’ between sensory input and motor output is also found in

robotics and artificial intelligence. The traditional view of perception and action as distinct

mechanisms separated by central thought processes creates engineering problems

for designers of intelligent systems by causing bottlenecks to occur: the sorts of thinking

required to update plans and amend instructions slows down the system’s ability to

respond to sensory stimuli. Roboticists like Rodney Brooks produced systems that could

interact with their environments in real-time, by cutting out the central ‘mind’ focusing

instead on using specific input sensors to generate specific outputs. Brooks (1991) proposes

that arranging these sensory-motor couplings in ‘layers’ in the appropriate way,

surprisingly intelligent behavior can be produced. One layer might govern general locomotion,

such that a robot will move around at random until it hits an obstacle. Another

layer might then take over and turn the robot until the way is clear, before control reverts

to the first layer. A third layer might sense red light and keep the robot on track to reach

the light, thus overriding the first layer’s random movement. Each sensory process in

these robotic architectures is thus action-oriented in the sense that that each layer has

its own sensors which operate exclusively for one kind of behavior. There is no amodal

cognition or control: the communication between the layers is minimal, and amounts to

just switching each other on and off. Such architectures can be used to create systems

that display a remarkable amount of seemingly intelligent flexible behavior from purely

reflex-like action-oriented sensory-motor couplings. It remains an empirical question

whether such architectures can simulate higher-level behaviours, such as deciding between

actions, without the addition of central thought mechanisms.

The three examples I’ve considered – mirror neurons, the dorsal visual stream, and

sensory-motor architectures – are examples of ways in which the mechanisms of perception

could be said to be action-oriented. Notice that claims about action-oriented

perceptual mechanisms are independent from the previously considered claim that perception

is action-oriented in the sense of having evolved to guide action. First, consider

the robotic architectures just described. The fact that an optimal engineering solution

uses action-oriented perceptual mechanisms (in the sense of reflex-like couplings between

sensory inputs and motor outputs) doesn’t tell us that human perceptual systems

actually evolved such couplings: evolutionary solutions are often the satisfactory but suboptimal

ones, rather than the optimal solutions. Second, while the dorsal visual stream’s

use of sensory input to directly guide action seems to support the evolutionary claim,

it does so only with respect to a certain subset of perceptual processing: the guiding

5

of fine-grained motor control, rather than action more generally. And the ventral visual

stream seems to have evolved to generate the kind of perceptual states that can input into

thought, which is exactly what the proponents of the stronger evolutionary claims want to

deny. Third, despite many claims about the evolutionary purpose mirror neurons might

have served, it’s not clear that we can draw any conclusions about the action-oriented nature

of perceptual evolution from the empirical data. Cecilia Heyes (2009), for example,

has persuasively argued that mirror neurons are a byproduct of our general capacity for

associative learning and not the result of evolutionary adaption.

4 The contents of perception as action-oriented

Many of the claims about action-oriented perception in philosophy concern the contents

of perception: how the world is presented to us in experience. It is traditionally assumed

that perception presents the world to us in a way that is entirely neutral with respect to

the actions one might perform: while the contents of beliefs might present the world to us

in terms of how we can act on it (presenting food as edible or stairs as climbable, for example),

such contents are not available in perception, according to the traditional view of

perceptual content as action-neutral. Proponents of action-oriented perceptual content,

on the other hand, propose that action-involving properties like edibility or climbability

can be presented to us in the contents of perception: “we see objects as edible, and

do not just believe that they are” (Nanay, 2012, p. 430). Following Gibson (1979), we

can use the term ‘affordance’ to describe properties like edibility and climbability, and

characterize the action-oriented view of perceptual content as the claim that we perceive

affordances. Action-oriented views of perceptual content come in different strengths,

depending on how they characterize the idea of affordances. To perceive the world as

affording a certain action might, on a weak reading, mean that we perceive the possibilities

for action: the sorts of actions we are capable of performing. A stronger reading

would claim that action-oriented perceptual content presents the world to us in a way that

solicits or encourages us to act in a certain way; and an even stronger reading views affordance

properties in perceptual content as mandating or demanding a particular action.

Action-oriented perceptual content need not be consciously experienced by the perceiver,

but there is a way of extending the idea of action-oriented perceptual content to include

the phenomenal character of perception, which I’ll discuss in the next section.

As I am using the notion of perceptual content here in a weak way, according to which

talk of perceptual content is talk of the world as presenting itself to the perceiver in experience.

Such talk is not committed to the stronger claim that perceptual experience

fundamentally consists in the subject perceptually representing their environment as being

a certain way. The weaker notion of perceptual content is therefore not committed to

representationalism about perception, but is compatible with at least some forms of relationalism

(Siegel, 2014). Furthermore, proponents of action-oriented perceptual content

might conceive of content as propositional or non-propositional; as structured conceptually

or non-conceptually; as Russellian or Fregean, or in terms of possible worlds; and

6

as environment-dependent or independent.

It is important to emphasise that since action-oriented perceptual content is not committed

to this content being carried by a particular neural or psychological mechanism, then

a fortiori it is not committed to the existence of action-oriented perceptual mechanisms.

This will be further discussed later in Section 7.

5 The phenomenal character of perception as action-oriented

Philosophers draw a distinction between the content of perception and its phenomenal

character. To talk of perceptual content is to talk of how perception presents the world;

whereas to talk of the phenomenal character of perception is to talk of the qualitative

properties of the perceptual state, or what it feels like to undergo the perceptual experience.

If we assume that perceptual states need not be consciously experienced, then

is possible for a perceptual state with the sort of action-oriented content outlined above

to lack phenomenal character altogether. And where a perceptual state has phenomenal

character and action-oriented content, that action-oriented content might be “nonsoliciting”

(Siegel, 2014): one could consciously perceive something as affording action

without experiencing the motivation to perform that action. Similarly, Prosser (2011)

proposes that the phenomenal character of a perceptual state correlates with its actionoriented

perceptual content, but doesn’t claim that the phenomenal character itself is

action-oriented.

But it looks like there could be cases where the phenomenal character of a perceptual

state is itself action-oriented. Siegel (2014), for example, claims that perceiving what

the environment affords can sometimes be accompanied by a felt quality of solicitation,

where the perceptual experience has a qualitative feel of inviting or prompting us to act. If

we think of this as action-oriented phenomenal character, then it looks like phenomenal

character might be action-oriented to differing degrees: some perceptual experiences

might not just feel like invitations or prompts, but more like urges that motivate us to

act. Siegel (2014) proposes that this particular subset of perceptual states with actionoriented

phenomenal character have a “feeling of answerability” and are experienced as

mandates for action.

Notice that how one understands the relation between action-oriented content and actionoriented

phenomenal character will depend on one’s attitude more generally to the relation

between perceptual content and phenomenal character. A proponent of strong intentionalism

about phenomenal character, for example, would presumably claim that any

action-oriented aspects of phenomenal character reduce to the action-oriented properties

of the perceptual content. At the other end of the spectrum, one might think that phenomenal

character is entirely independent of perceptual content, and thus that it is possible to

have two perceptual experiences with the same content but distinct characters.

7

6 The nature of perception as action-oriented

I have suggested that claims about perception being action-oriented can be understood

as making a variety of different claims about the evolution, mechanisms, content, or

character of perception. But there remains a further question about the modal strength

of the claims being made. Should we understand any of these claims as proposing that

perception is necessarily action-oriented in any of these senses? And if so, what kind of

necessity is involved?

I take it that most of those making action-oriented claims concerning the evolution of perception

or the mechanisms of perception are making largely empirical claims about features

of perception in the actual world. But at least some proponents of action-oriented

perception seem to be making modal claims, which can be interpreted in at least two

ways. On one hand, the necessity at play might be nomological necessity. To say that perception

is necessarily action-oriented in the nomological sense is not to say that perception

is action-oriented in all possible worlds, but rather to say that the laws of nature in the

actual world make it physically impossible for perception not to be action-oriented in the

actual world. Alternatively, the necessity in question might be metaphysical necessity,

concerning the nature of perception across possible worlds. To posit a metaphysically

necessary connection between perception and action is to claim that there is no possible

world in which perception is not action-oriented in some appropriate sense.

The claims made by some proponents of sensorimotor theories of perception, such as

Alva No¨e (2004) and Susan Hurley (1998), seem to suggest that there is a constitutive

dependence of perception on our capacity for action: that to be a perceiver is necessarily

to be an agent. It is not clear what kind of necessity is involved, but their reliance

on empirical evidence suggests that they are primarily concerned with making a case

for nomological necessity. The case for action being metaphysically necessary for perception

is perhaps found in Schellenberg (2010) exploration of the relationship between

perception, self-location, and spatial know-how. Her key claim, that perception requires

the capacity to know what it would be to act in relation to objects, seems to rely on our

intuitions about how perception could be in possible worlds rather than how it is in the

actual world.

Notice that even where claims of nomological necessity are made and supported by empirical

evidence about the mechanisms of perception, there is not direct entailment from

action-oriented perceptual mechanisms to the claim that perception is necessarily actionoriented.

We cannot simply read off claims of nomological necessity from empirical

facts (Callender, 2011). And claiming that perception is necessarily action-oriented in

the sense that Schellenberg intends does not entail that either perceptual content or phenomenal

character is action-oriented. In fact, Schellenberg proposes that perception’s dependence

on action capacities is what allows us to perceive more than just the relational

or perspectival properties associated with affordances: she claims that the action-oriented

nature of perception can account for our access to the intrinsic, observer-independent

properties of the objects perceived.

8

7 What about action-oriented representation?

I’ve differentiated several different approaches to perception that fall under the umbrellaterm

‘action-oriented’, and demonstrated that each approach makes claims that are independent

from the others. There is no entailment from one variety of action-oriented

perception to another without the addition of further premises.

While I’ve talked about the mechanisms of perception and the content of perception, I

have said little about the matter of perceptual representations: content-bearing internal

states of the perceiver. In fact, many people working in action-oriented perception want

to play down the role of representations. This is because in cognitive science, representational

mechanisms are traditionally posited to account for the flexibility of intelligent

human behaviour: our capacity to respond differently to similar stimuli, and similarly

to different stimuli, in a way that can’t be easily accounted for in terms of reflex-like

responses. Positing internal representations allows cognitive science to say that the same

stimulus can be represented in different ways: the same extensional content can be carried

by different representational ‘vehicles’ which play distinct causal roles within the

cognitive mechanism.1 These vehicles are just internal representations individuated by

the non-semantic properties in virtue of which they are causally efficacious: the formal,

functional, or physical properties to which the cognitive mechanisms are sensitive. Thus

two representations are tokens of the same vehicle type when they are treated similarly

by the cognitive processes at play (Shea, 2007).

Traditionally, cognitive science suggests that these representational mechanisms are genuinely

cognitive in the sense of being non-perceptual: they are distinct from the mechanisms

of perception, and they are what allow the same perceptual input to result in

different action outputs. Proponents of embodied cognitive science, however, deny that

explanations of flexible and context-specific behaviour require this sort of mediating representational

mechanism. They argue instead that if perceptual mechanisms are actionoriented

in the sense discussed in Section 3, then these sorts of coupled mechanisms can

account for the behaviour.

But not everyone agrees that the insights from action-oriented approaches to perception

should lead to the rejection of internal representational states. There are many within

embodied cognitive science who allow that there are internal representations, but deny

that these are the sort of amodal cognitive representations in a central cognitive architecture.

Andy Clark (1997), for example, argues that behaviour is not mediated by “actionindependent

inner states; ones which require additional cognitive operations to drive

appropriate behavior”, but rather by action-oriented representations which are “poised

between pure control structures and passive representations of external reality” (Clark,

1997, p. 49). A similar approach is put forward by Michael Wheeler, who understands

1For naturalistic reasons, cognitive science is generally committed to extensional content.

9

the idea of action-oriented perceptual representations as “special-purpose adaptive couplings”

that are “tailored to a particular behavior” (Wheeler, 2005, p. 196). Both Clark

and Wheeler propose that the sorts of action-oriented mechanisms of perception discussed

in Section 3 are best understood as representational mechanisms: as containing

perceptual states which are both causally efficacious and semantically evaluable. They

argue that these perceptual representations themselves, and not just the mechanisms in

which they occur, should be understood as action-oriented because in virtue of the way

that they are causally coupled to certain motor processes.

To claim that a mechanism is representational is to be committed to vehicles of content.

But this does not commit one to any particular views about the nature of that content:

what it is or how it is determined, evaluated, or structured. (We might agree that a list of

items is a representation, while disagreeing over whether it is a description or an instruction,

for example; we might believe a drawing to be a map without knowing what it is

of; we might know that someone’s utterances are linguistic without knowing what they

mean.) To understand an internal state as a representational vehicle does not, therefore,

entail a particular view of its content. A fortiori, it does not entail that its content is actionoriented

in the sense of presenting the world in action-relevant ways. The reverse is also

true, as already discussed in Section 4: action-oriented perceptual content is neutral with

regard to the mechanisms of perception, and therefore compatible with a range of cognitive

architectures. Action-oriented perceptual content does not entail action-oriented

perceptual mechanisms, and a fortiori does not entail that there are action-oriented vehicles

of representation bearing the action-oriented perceptual content.

Interestingly, however, both Clark and Wheeler seem to assume that their action-oriented

vehicles of perceptual representation have action-oriented perceptual content. Clark

claims that action-oriented perceptual representations “simultaneously describe aspects

of the world and prescribe possible actions”, with the result that “to thus know the world

is at once to know what possibilities it affords for action and intervention” (Clark, 1997,

p. 49), while Wheeler claims that action-oriented perceptual states “represent the world

in terms of specifications for possible actions” with “bearer-relative content” (Wheeler,

2005, p. 196). Their action-oriented representations, in other words, seem to be examples

of action-oriented perceptual mechanisms with action-oriented perceptual contents.

Neither Clark nor Wheeler explain why they think that action-oriented vehicles will have

action-oriented contents. Any attempt to reconstruct their arguments will, I suggest, encounter

the following problem: reasons to think of perceptual vehicles as action-oriented

seem to undermine our reasons to think of perceptual contents as action oriented, and

vice-versa.

To see this, consider the role played by representational vehicles. Recall that vehicles

are just internal representations individuated by the non-semantic properties in virtue

of which they are causally efficacious within the representational mechanisms. Vehicles

provide a naturalistic way of understanding how the same extensional content can

play distinct cognitive roles, such as when the same heavenly body is thought about in

10

different ways according to the time of day it is observed. The extensional content is

the same, but the representational vehicles have distinct causal properties in each case

and thus interact differently with other states of the mechanism. If we claim, as Clark

and Wheeler do, that representational vehicles should be individuated according to their

action-guiding properties, then this enables us to show how the same extensional content

(e.g. a mountain) can be represented as climbable in one context and unclimbable in

another context: distinct vehicles, with different causal relations to action, can carry the

same extensional content. But if this is the role of representational vehicles, to provide

a naturalistic means of accounting for modes of presentation, then it removes the need

to think of the content itself as action-oriented. The perceptual content can be actionneutral

because the relevant action-guiding information is supplied by the perceptual

mechanisms.

One might, however, have independent motivation for thinking that affordance properties

are part of the perceptual content itself: we perceive the climbability of the mountain, for

example, rather than perceptually representing the mountain as climbable. But if we

don’t need to perceptually represent the mountain as being as certain way, then it’s not

clear what motivation we’d have for positing vehicles of representation, rather than nonrepresentational

mechanisms. This, I take it, is the essence of Gibson’s (1979) view of

perception. And even if we had reason to posit representational mechanisms, we’d then

need a further argument for individuating the representational vehicles according to their

action-guiding properties.

In summary, the arguments for taking perceptual vehicles to be action-oriented seem to

undermine the arguments for taking perceptual contents to be action-oriented, and the

arguments for taking perceptual contents to be action-oriented seem to undermine the

arguments for taking perceptual vehicles to be action-oriented. None of this rules out

that there may be arguments from the action-oriented status of either vehicles or contents

to the other, but it suggests that the onus is on proponents of such views to provide

them.

11

Bibliography

Akins, K. (1996). Of sensory systems and the “aboutness” of mental states. Journal of

Philosophy, 93(7), 337–372.

(2013). In Y. Aloimonos (Ed.), Visual navigation: from biological systems to unmanned

ground vehicles. Psychology Press.

Briscoe, R. (2014). Spatial Content and Motoric Significance. Avant: Trends in Interdisciplinary

Studies, 2, 199–216.

Brooks, R. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–

159.

Callender, C. (2011). Philosophy of Science and Metaphysics. In S. French & J. Saatsi

(Eds.), Continuum Companion to the Philosophy of Science (pp. 33–54). Continuum.

Ca˜nal-Bruland, R. & van der Kamp, J. (2009). Action goals influence action-specific

perception. Psychonomic Bulletin & Review, 16(6), 1100–1105.

Chemero, A. (2011). Radical embodied cognitive science. MIT Press.

Churchland, P. S. (1987). Epistemology in the age of neuroscience. Journal of Philosophy,

84(10), 546–553.

Clark, A. (1997). Being There: Putting Brain, Body, and World Together Again. MIT

Press.

Fajen, B. R. (2005). Perceiving possibilities for action: On the necessity of calibration

and perceptual learning for the visual guidance of action. Perception, 34(6), 717–

740.

Froese, T. & Spiers, A. (2007). Toward a phenomenological pragmatics of enactive perception.

Gallagher, S. & Zahavi, D. (2014). Primal impression and enactive perception. Subjective

Time: The Philosophy, Psychology, and Neuroscience of Temporality, 83–99.

Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin.

Gora, P. & P., W. (2014). Adaptive System for Intelligent Traffic Management in Smart

Cities. In International Conference on Active Media Technology (pp. 525–536).

Springer International Publishing.

Heyes, C. (2009). Where do mirror neurons come from? Neuroscience and Biobehavioral

Reviews.

12

Hoffman, D. D., Singh, M., & Mark, J. (2013). Does evolution favor true perceptions? In

Proc. SPIE 8651, Human Vision and Electronic Imaging XVIII (Vol. 865104).

Hurley, S. (1998). Consciousness in Action. Harvard University Press.

Milner, M. A., A.D. & Goodale. (1995). The Visual Brain in Action. Oxford University

Press.

Morse, A. & Ziemke, T. (2007). Cognitive robotics, enactive perception, and learning in

the real world. Cognitive Science.

Nanay, B. (2012). Action-oriented Perception. European Journal of Philosophy, 20, 430–

446.

No¨e, A. (2004). Action in perception. MIT Press.

Pijpers, J. R., Oudejans, R. R., & Bakker, F. C. (2007). Changes in the perception of

action possibilities while climbing to fatigue on a climbing wall. Journal of sports

sciences, 25(1), 97–110.

Prosser, S. (2011). Affordances and Phenomenal Character in Spatial Perception. Philosophical

Review, 120(4), 475–513.

Richardson, M. J., Shockley, K., Fajen, B. R., Riley, M. A., & Turvey, M. T. (2008).

Ecological psychology: Six principles for an embodied-embedded approach to behavior.

In Handbook of cognitive science: An embodied approach (pp. 161–187).

Ridderinkhof, K. R. (2014). Neurocognitive mechanisms of perception–action coordination:

A review and theoretical integration. Neuroscience & Biobehavioral Reviews,

46, 3–29.

Schellenberg, S. (2010). Perceptual Experience and the Capacity to Act. In M. M. Gangopadhay

& F. Spicer (Eds.), Perception, Action, and Consciousness (pp. 145–

159). Oxford University Press.

Shea, N. (2007). Content and Its vehicles in connectionist systems. Mind and Language,

22(3), 246–269.

Siegel, S. (2014). Affordances and the Contents of Perception. In B. Brogaard (Ed.),

Does Perception Have Content? (pp. 39–76). Oxford University Press.

Thompson, E. (2007). Mind in life: Biology, phenomenology, and the sciences of mind.

Harvard University Press.

Weyns, D., Steegmans, E., & Holvoet, T. (2004). Towards active perception in situated

multi-agent systems. Applied Artificial Intelligence, 18(9–10), 867–883.

Wheeler, M. (2005). Reconstructing the cognitive world: the next step. MIT Press.

Whitehead, S. D. & Ballard, D. H. (1990). Active perception and reinforcement learning.

Neural Computation, 2(4), 409–419.

Witt, J. K. (2011). Action’s effect on perception. Current Directions in Psychological

Science, 20(3), 201–206.

Author biography. Zoe Drayson is Assistant Professor of Philosophy at the University

of California, Davis. She previously held positions at the Australian National University

and the University of Stirling, and gained her PhD from the University of Bristol. She

works in philosophy of mind and cognitive science, with a focus on the nature of psy-

13

chological explanations across philosophy and the mind sciences. Her published work

explores the distinction between personal and subpersonal approaches to the mind; the

commitments of embodied and extended cognitive science; the relation between perception,

cognition, and action; the metaphysics and epistemology of explanation; and

various issue surrounding the nature of mental representation.

14

:: بازدید از این مطلب : 131

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

  در واپسین لحظات حیات، عباراتی مشهور از دهان شخصیتهای نامدار جهان خارج شده است که هیچکس نمیداند آیا این عبارات تحت تاثیر احساسات،ترس، هیجان و نظایر آن بیان شده است و یا آنکه آخرین حرف زندگی شان را بطور ناخودآگاه بر زبان رانده اند؟ پاره ای از آنان نیز دنیای پس از مرگ را بچشم دیده اند و از مرگ بعنوان یک پدیده تاریک نام برده اند.

 

:: بازدید از این مطلب : 814

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 

یک روز وقتى کارمندان به اداره رسيدند، اطلاعيه بزرگى را در تابلوى اعلانات ديدند که روى آن نوشته شده بود:

:: بازدید از این مطلب : 817

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 استاد ایرانی فیزیک و نجوم رصدی دانشگاه «ریورساید» کالیفرنیا با بیان اینکه از مهبانگ (انفجار بزرگ) و ابتدای پیدایش جهان، حدود 13 میلیارد سال گذشته است، گفت: طبق محاسبات، جهان 35 میلیارد سال پس از پیدایش یعنی تا حدود 21 میلیارد سال دیگر نابود خواهد شد.

دکتر بهرام مبشر، کیهان‌شناس بزرگ ایرانی دانشگاه «ریورساید» کالیفرنیا و کاشف دورترین خوشه کهکشانی جهان که در دومین همایش عظمت کائنات در دانشگاه صنعتی امیرکبیر سخن می‌گفت، اظهار کرد: هدف من این است که برای شما دانشجویان، سوال ایجاد کنم تا متوجه شوید که این مسائل قابل تفکر است.

:: بازدید از این مطلب : 749

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 برای دیگر کاربردهای شیر به صفحه ابهام زدایی بروید. 
شیر جانوری قوی با سری بزرگ، پاهای بزرگ و قوی و دمی بلند از جنس پلنگ‌شکلان (Panthera) است. اندازهٔ بدن او ۱۴۰ تا ۲۰۰ سانتیمتر و اندازهٔ دم او ۶۷ تا ۱۰۰ سانتیمتر است. نوع نر بزرگتر از ماده‌است و نرها بر روی گردن و شانهٔ خود دارای یال نیز می‌باشند. رنگ بدن شیرها بین قهوه‌ای مایل به زرد و قهوه‌ای مایل به سرخ متغیّر است. رنگ یال شیرهای نر نیز از زرد روشن تا سیاه متغیّر است. همچون دیگر گربه سانان بزرگ شیرها نیز می‌توانند به سرعتهای بالا دست یابند و آن را برای مدّت کوتاهی حفظ کنند. در گذشته در آفریقا، خاورمیانه و جنوب آسیا به وفور یافت می‌شدند ولی اکنون فقط در جنوب صحرای بزرگ در آفریقا و در منطقهٔ حفاظت شده‌ای در شمال غربی هند زندگی می‌کنند. شیر برخلاف مشهور بودن به سلطان جنگل در بیشهها زندگی می‌کند. این جانور ۲۰ ساعت در روز به استراحت می‌پردازند. شیرها معمولاً در طول روز به شکار می‌پردازند ولی در مناطقی که آنها را شکار می‌کنند تنها در هنگام شب فعّال هستند. آنها گروهی زندگی می‌کنند و هر گروه متشکّل از سه شیر نر، پانزده ماده و توله هایشان در یک قلمرو مشخّص است. شیرها از قلمرو خود در مقابل بیگانگان که گاهی شیرهای نر غریبه هستند دفاع می‌کنند. گروه‌های دیگری نیز هستند که فقط از شیرهای نر مجّرد تشکیل شده‌اند. شیرها جانورانی مانند گوراسب و غزال را برای تغذیه شکار می‌کنند. آنها جانوران بزرگتر مانند بوفالوها و زرّافه‌ها را نیز به صورت دست جمعی و به کمک یکدیگر شکار می‌کنند. حتّی پرندگان و گاهی کروکدیلها نیز به دست آنها گرفتار می‌شوند. شکارکردن بیشتر به عهدهٔ شیرهای ماده‌است.

:: بازدید از این مطلب : 812

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 سنگی با نام " چشم گربه":

 

این سنگ یک سنگ گرانبهاست که برای تزئینات و جواهرات ازش استفاده می شه.

اسامي ديگر اين سنگ: چشم گربه را در زبان سانسكريت كينوزن، به پنجابي لهسينا، به هندي ديدوريه، در زبان عربي عين الهر يا عين الهريره و در زبان انگليسي cat's eye گويند.

 

 

خواص طبي سنگ چشم گربه:

اين سنگ مفرح القلوب بوده، اگر از سرمه آن استفاده گردد. بیماریهای چشم را درمان می کند. از پودر و کشته آن مسواک زده شود برای دندان ها مفید می باشد. اگر کشته آن را بر هر زخمی بگذارید، زخم را درمان می کند. استفاده از آن گلودرد و سینه درد را درمان میکند. چرک و ریه و بلغم را از بین می برد.

و اما می رسیم به یه مورد فوق العاده زیبا بازهم به نام " چشم گربه ".  اینبار

" سحابی چشم گربه "

 

درواقع سحابی چشم گربه با عمر تخمینی 1000 سال تصویرجالبی ازیک ستاره درحال مرگ  ویا به احتمال زیاد یک منظومه دوتایی ستاره ای نا شناخته را پیش چشمان ما قرارداده است. براساس تحقیقات صورت گرفته تاثیرات حرکتی دوستاره که به دوریکدیگردرگردشند می تواند دلیل بسیار خوبی برای ایجاد ساختاری چنین پیچیده و متقارن درسحابی باشد،ساختاری که معمولا درسحابی های سیاره ای دیده نمی شود. هنگامی که  NGC 6543برای اولین بار با طیف سنجی مشاهده گردید طیف آن حاوی خطوط نشری  بود که علاوه بروجود ستاره های چند گانه دراین ناحیه ،وجود ابرهای گازی پراکنده را نیز نشان می داد.

 

 

:: بازدید از این مطلب : 603

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 گربه همانند انسان دارای ديد دوچشمی بوده و اجسام را سه بعدی می بيند با اين وجود , چشم گربه که برای ثبت حرکات طراحی شده است تفاوت های زيادی با چشم انسان ها دارد, مردمک چشم گربه برخلاف مردمک چشم ما , گرد نيست بلکه بيضوی شکل است . مردمک های بيضوی شکل نسبت به انواع گرد , خيلی سريعتر باز و بسته شده و موجب می شود که اندازه بزرگتری داشته باشد , به اين دليل يک گربه می تواند مردمک خود را به اندازه قرنيه اش ( تقريباً تا 90 % سطح چشم خود) باز کند پس موجب می شود که نور بيشتری وارد چشم شود . در چشم گربه بر خلاف چشم ما تعداد سلول های استوانه ای نسبت به سلول های مخروطی بسيار بيشتر است , بنابراين چشم گربه برای ديدن اجسام در تاريکی سازگاری بيشتری نسبت به چشم انسان ها دارد . اين جانوران رنگ های ارغوانی , آبی , سبز و زرد را می بينند اما نسبت به رنگ قرمز , نارنجی و قهوه ای کوررنگ هستند گربه ها اين رنگ ها را بصورت خاکستری می بينند . 

 

 

    نکته جالب درخشش چشم گربه ها در تاريکی است . در پشت شبکيه چشم گربه ها  لايه ای از سلول های منعکس کننده نور وجود دارد که tapetum  lucidum  ناميده می شود اين لايه ی آينه مانند ,  نور تابيده شده به شبکيه را منعکس می کند و موجب می شود که سلول های استوانه ای دو بار در معرض نور قرار گيرند , بنابراين در تاريکی بسيار بهتر از انسان ها می تواند ببينند . وجود اين لايه به همراه داشتن مردمک بيضی شکل , موجب شده است که گربه ها فقط  به یک ششم نوری که ما برای ديدن نياز داريم به وضوح اجسام را ببينند , بطوريکه در يک شب نسبتاً ابری با تعدادی ستاره , نور لازم برای ديدن گربه فراهم می شود 

:: بازدید از این مطلب : 593

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

 انسان، به سبب ویژگی های بی نظیرش در میان همه موجودات، عنوان اشرف مخلوقات را به خود اختصاص داده است. او استعداد خاص تفکر و یادگیری دارد که به او اجازه می دهد اعمال خاص انجام دهد. نتایج اعمالش را پیش بینی و ارزیابی کند. تغییر و تحولات گوناگون و سریعی را در عرصه علم و فناوری به وجود آورد. آن چنان که به قول تافلر دنیا را به دهکده ای تبدیل کند که اطلاعات را در سریع ترین زمان ممکن از قاره ای به قاره دیگر انتقال دهد.

بنابراین بر اثر پیشرفت سریع و غیر قابل انتظاری که در قلمرو دانش و فناوری طی چند قرن اخیر نصیب انسان شده است حجم اطلاعات و دانسته های بشری روز به روز به طور سر سام آوری در حال افزایش است. بر دانش آموزان و دانشجویان لازم است که هرچه سریعتر خود را با این تغییر و تحولات همگام سازند. در این راستا، بسیاری از دانش آموزان و دانشجویان کوشش زیاد می کنند. بسیار مطالعه می کنند اما به دلیل عدم توانایی در تمرکز حواس، هنگام مطالعه نمی توانند به نتیجه دلخواه دست یابند.

تمرکز در لغت یعنی تراکم ، فشردگی مجموعه، چکیده، تمرکز در اصطلاح یعنی حفظ و نگهداری توجه و تمرکز حواس، روی موضوعی معین، بدون تمرکز حواس، یادگیری مثمر ثمر نخواهد بود. بنابر این همه افراد توانایی تمرکز دارند و تمرکز نسبی است یعنی کسی نمی تواند ادعا کند کاملا حواس پرت است ویا همیشه تمرکز حواس دارد.

تمرکز گاهی ساده است و گاهی مشکل. تمرکز در موضوع هایی که نیاز به تفکرو تجزیه و تحلیل دارند مشکل و تمرکز در موضوع هایی که جنبه ی تفریحی و سرگرمی دارند، بسیار آسان است.

 

:: بازدید از این مطلب : 520

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0

سپاهيان روم با كشتيهاي جنگي به سواحل شهر نزديك شده بودند ، ارشميدوس به كمك نبوغ رياضي خود منجنيقهايي ساخته بود كه از اين منجنيقها با پرتاب سنگهاي بزرگ تعدادي از كشتيهاي دشمن را غرق كرده بودند . ارزشميدس در اتاق محقرش مشغول حل مسائل رياضي و تكميل اختراع خود بود كه سربازان دشمن ارشميدس را پيدا كردند ، فرمانده سپاه به سربازانش دستور داده بود كه هر جا ارشميدس را پيدا كردند ، او را زنده نزد وي بياورند . عدهاي از سربازان به خانه ارشميدس ريختند ، چون ارشميدوس را نمي شناختند از او اسمش را پرسيدند . ارشميدس مشغول حل مسائل رياضي بود و چنان فكرش متمركز بود كه متوجه ورود سربازان نشد و پاسخ آنها را هم نداد زيرا در اثر تمركز فكر قوي اصلاً صداي آنها را نشنيده بود . سربازان دوباره سوال كردند ، ولي جز بي اعتنايي و سكوت جوابي نشنيدند ، لذا عصباني شده و با وارد كردن ضربهٌ شمشيري بر گردنش سرش را از تن جدا كردند . 
شما حواستان باشد به سرنوشت ارشميدس دچار نشويد !!!!!......

:: بازدید از این مطلب : 585

|

امتیاز مطلب : 0

|

تعداد امتیازدهندگان : 0

|

مجموع امتیاز : 0