Emergent Collective Sensing in Human Groups
Peter M. Krafft (pkrafft@mit.edu)*, Robert X.D. Hawkins (rxdh@stanford.edu)†,Alex “Sandy” Pentland (pentland@mit.edu)‡, Noah D. Goodman (ngoodman@stanford.edu)†,Joshua B. Tenenbaum (jbt@mit.edu)**MIT Computer Science and Artificial Intelligence Laboratory, †Stanford Department of Psychology, ‡MIT Media Lab
Abstract
Despite its importance, human collective intelligence remains
enigmatic. We know what features are predictive of collective intelligence in human groups, but we do not understandthe specific mechanisms that lead to the emergence of this distributed information processing ability. In contrast, there isa well-developed literature of experiments that have exposed
the mechanisms of collective intelligence in nonhuman animal species. We adapt a recent experiment designed to study
collective sensing in groups of fish in order to better understand the mechanisms that may underly the emergence of collective intelligence in human groups. We find that humans in
our experiments act at a high level like fish but with two additional behaviors: independent exploration and targeted copying. These distinctively human activities may partially explain
the emergence of collective sensing in our task environment atgroup sizes and on times scales orders of magnitudes smaller
than were observed in fish.
Keywords: collective intelligence; distributed cognition; social cognition; social computation; online experiments

Introduction
Many common examples of collective behavior illustrate apparent failures of collective intelligence. Mobs, market panics, and mass hysteria draw attention because of their perceived irrationality and drastic consequences. However, thesuccesses of collective intelligence are as remarkable as thefailures are devastating. The richness of human culture, theincredible pace of our technological developments, and thegradual progression of our scientific understanding of the universe stand out as both distinctively human and heavily reliant
on the emergent behavior of the interactions of many individuals. Even at a less grandiose level, humans regularly agree
to work together to accomplish tasks that no individual could
accomplish alone via dynamic cooperative interactions thatare hypothesized to be uniquely human (Tomasello, 2014).
Yet little is known about the specific mechanisms underlying
these synergistic processes of self-organization.
Many mathematical and computational of models collective behavior have been proposed. However, as a result of
the logistical difficulties in conducting real-time human experiments involving multiple participants, and as a resultof a broader lack of data analysis aimed at understanding
collective behavior, the quantitative study of collective behavior has largely lacked an empirical basis. Recently, researchers have begun conducting carefully controlled laboratory experiments to test and refine models of collective behavior (Couzin, 2009; Goldstone & Gureckis, 2009). Yet
many of these experiments, with some notable exceptions(Goldstone, Roberts, Mason, & Gureckis, 2008; Kearns,2012), have been conducted using nonhuman animal subjects.
We are therefore quickly developing a better understandingof the collective behavior of ants (Pratt & Sumpter, 2006),
bees (Seeley & Buhrman, 1999), cockroaches (Amé, Halloy, Rivault, Detrain, & Deneubourg, 2006), and fish (Ward,
Sumpter, Couzin, Hart, & Krause, 2008), but our empiricallygrounded quantitative understanding of human collective behavior remains limited.
In the present paper we harness recent technical advancesin running real-time, networked experiments on the web(Hawkins, 2014) to develop and test a model of collective human behavior. We build on a recent experiment designed to
study the collective behavior of a particular species of fish
(Berdahl, Torney, Ioannou, Faria, & Couzin, 2013) that is
one of the clearest illustrations of collective intelligence ina nonhuman animal group. In this previous experiment, theresearchers studied a type of fish called the golden shiner that
prefers to spend time in dark areas of the water, presumably
to avoid predators. Aware of this natural propensity of thefish, the researchers projected time-varying spatially correlated light fields into a fish tank. The researchers then studied
the effectiveness of the fish at finding the darker areas of thetank as a function of the number of fish participating in thetask. The researchers found that average group performanceincreased significantly as a function of group size, and they
identified two simple behavioral mechanisms driving this improvement: First, individual fish tended to move more slowly
in darker areas. Second, individual fish also tended to turntowards conspecifics. The researchers argued that the combination of these mechanisms generated an emergent collective
gradient sensing ability in groups of fish that had been absent
in individual fish.
This experiment provides a beautiful example of a higher
level of intelligence at the group level emerging from minimal intelligence at the individual level. However, while these
simple mechanisms did appear to give rise to surprisingly effective group behavior, they only lead to substantial gains in
performance for large groups of 50 or more fish. In contrast,
we expect humans in a similar task to show significant gains
with much smaller group sizes. In particular, we expect thathumans should be able to make use of theory of mind, an ability to draw inferences about the underlying mental states ofother players, to better utilize social information in a similar
environment.
To elucidate these potential differences between humans
and fish, we developed a version of the gradient-sensing task
for human participants. Specifically, we recreated the environment used by Berdahl et al. (2013) as an online real-time
multi-player game. In our experiment, participants controlledavatars in a virtual world. Every location in this world cor-

Figure 1: Example score fields from the low noise (left) andmedium noise (right) conditions at particular points in time.
Red areas indicate higher scoring areas.
responded to a score value that changed over time, and participants were awarded bonuses proportional to their cumulative scores in the game. The score of a player at a particular point in time was simply determined by the locationof that player in the virtual world. Our incentives for participants to achieve high scores were designed to parallel thefishes’ preferences for darker areas in their environment. The
players either played alone or in groups of varying sizes. We
used this virtual environment to investigate how the gradienttracking performance of human groups changed as group size
increased, and to attempt to identify behavioral mechanisms
underlying collective sensing in human groups.

Methods
Participants We recruited 563 unique participants from
Amazon Mechanical Turk to participate in our experiment.
All participants were from the United States. After excluding 72 participants due to inactivity or latency, and 6 othersfor disconnecting in the first half of the game, we were left
with usable data from 437 participants in 224 groups. These
groups ranged in size from one to six individuals. Since wewere only able to collect one group of size six, we ignored
this group in our analysis.
Stimuli The game scores of the participants in our experiments were determined by underlying “score fields”. Thesescore fields consisted of 480 × 285 arrays of score values
for each 125ms time interval in our game. We generated
these score fields using the method reported by Berdahl etal. (2013). First, a “spotlight” of high value was created thatmoved in straight paths between uniformly randomly chosen
locations. This spotlight was then combined with a field of
spatially correlated noise. This procedure yields a complex
landscape with many transient maxima and a single persistent time-varying global maximum.
We manipulated the weighting between the noise field andthe spotlight to generate different task conditions. We used
two weight values, corresponding to the “low” and “medium”
noise levels reported by Berdahl et al. Examples of scorefields are shown in Figure 1. 113 individuals (63 groups) wereassigned to the low noise condition and 324 individuals (161groups) were assigned to the medium noise condition. To
decrease variability and increase statistical power, we generated only four distinct score fields per noise level, so multiple
groups experienced the same fields. To discourage inactivity,

Figure 2: A screenshot of the interface that participants saw.
The score displayed corresponds to the value of the score fieldat the location that the player’s avatar is occupying.

players were awarded a score of zero, corresponding to zerobonus, if their avatars were touching a wall.
We attempted to give our participants perceptual and motor
capabilities in this environment similar to the capabilities that
Berdahl et al. observed in the fish in their experiments. In
terms of perception, we restricted the information that participants received about the underlying score fields in the games.
We allowed participants to see only the scores at their avatars’
locations. The participants could not see the scores that otherplayers were obtaining or the scores at any other locations
besides their own. However, the positions, directions, andspeeds of all other players were visible to each player. Allof this information was updated in real-time every eighth of asecond. A screenshot of the interface we used for the game isshown in Figure 2.
Players controlled their avatars using the left and right arrow keys to turn (at a rate of 40◦ per second) and could holdthe spacebar to accelerate. The avatars automatically moved
forward at a constant velocity of 136 pixels per second whenever the spacebar was not depressed. The avatars instantaneously increased to a constant velocity of 456 pixels per
second for the duration of time that the spacebar was held
down. We chose these speed values to match the speeds that
Berdahl et al. reported observing in their fish, and we also
matched the playing area dimensions and game duration to
the parameters of their experiments. Each participant played
in a single continuous game lasting for 6 minutes.
Procedure After agreeing to participate in our experiment,
participants were presented with a set of instructions. These
instructions simply described the mechanics of the game. The
participants were not informed about the nature of the underlying score fields and were not encouraged to work together.
After successfully completing a comprehension test, participants were then redirected to a waiting room. In the waiting
room participants would wait for up to 5 minutes or until a

Results
We find that group size is positively related to group performance in this game in the low noise condition. However, wefind that there was little effect of group size in the medium
noise condition. Average performance as a function of groupsize in each of these conditions is shown in Figure 3. A linear regression on the individuals in the low noise condition
produces a significant positive slope of 0.0238 and a 95%
confidence interval (CI) of [0.006, 0.041]. A linear regression on the individuals in the medium noise condition produces a marginally significant positive slope of 0.0068, 95%
CI [−0.001, 0.015], and this trend is weakened substantiallywith the inclusion of the single 6-person group. Moreover,the marginally significant result in the medium noise condition is driven entirely by the effect of group size in one of
the four distinct score fields we used. This particular scorefield displays a significant effect of group size with a positive
slope of 0.0306, 95% CI: [0.015, 0.046], while none of theothers do. Qualitative inspection revealed that this particularscore field seemed to share spatial properties more similar to

0.90
0.85

Mean Score

pre-assigned number of other players joined the game. Whilein the waiting room, participants could familiarize themselveswith the controls of the game. Players were not shown anyscore in the waiting room unless the participant was against
a wall, in which case the displayed score would change froma dashed line to a red “0%”. We found no evidence for theamount time a player spent in the waiting room having any
effect on individual performance in the game (linear regression slope 1.993e-06, with 95% confidence interval [-1e-05,
1.4e-05]). As in the actual game, participants in the waiting
room would be removed for inactivity if the player’s browser
was active in another tab for more than 15 seconds or if theplayer’s avatar was unmoving against a wall for 30 seconds.
We also removed players if their ping response latencies weregreater than 125ms for more than 36 seconds. We paid participants 50 cents for reading our instructions, and the participants could receive a bonus of up to $1.25 during the six
minutes of gameplay. Final bonuses were computed to be theplayers’ cumulative scores divided by the total length of thegame times the total possible bonus. Following the current
convention on Mechanical Turk, each participant was also
paid 12 cents per minute for any time spent in the waiting
room, minus any time that player spent against a wall. These
numbers were chosen so that the participants were expected
to receive at least the U.S. federal minimum wage of $7.25
per hour for the totality of their time active in the experiment.
We implemented this experiment using the MWERT
framework (Hawkins, 2014). The MWERT framework uses
a set of recent web technologies capable of handling the challenges of real-time, multi-player web experiments, including
Node.js, the Socket.io module, and HTML5 canvases. Since
MWERT was originally used for two-player games, we had
to extend the MWERT framework in several ways to handle
the challenges posed by hosting larger groups of players.

0.80
0.75

Noise Level

0.70

Low
Medium

0.65
0.60
0.55

1

2

3

4

Number of Players

5

Figure 3: Mean performance as a function of group size in the
low and medium noise levels. Error bars are 95% bootstrap
confidence intervals using the group as the primary bootstrap
unit. All points are averages over at least two groups. This
plot excludes the single group we were able to collect of size
six. Including this group weakens the trend in the medium
noise condition.
the low noise score fields, which may explain the strength ofthe effect in that particular score field. Overall these results
indicate that larger groups do tend to perform systemically
better on our task than those in smaller groups, at least in the
low noise condition.1
In order to understand the factors that may have contributed
to the increases in performance achieved by larger groups inthe low noise condition, we examine the behavior of the players in our games. We assume a simple state-based representation of player behavior. We then attempt to identify how participants choose to occupy particular behavioral states at eachpoint in time, and we examine the relationship between theplayers’ decisions to occupy particular states and the performance of those players. Specifically, we assume that at any
particular point in time a player is either “exploring”, “exploiting”, or “copying” (see Rendell et al., 2010, for a similar classification). Conceptually, a player is exploring if that
player is looking for a good location to exploit, a player is
exploiting if that player has found a location where the player
wants to remain, and a player is copying if that player is intending to move to the location of another player.
We empirically determine the state of each player at eachpoint in time using a set of hand-tuned filters. All of thesefilters depend only on information that is observable to any
player in the game (i.e., the filters do not depend directlyon the scores of any individuals), and hence we can use theinferred states of players as proxies for what other players
might infer as the states of those players. Also, since thestates are not defined in terms of scores, we can meaningfully
quantify the relationship between state and performance.
1 Results were similar using a mixed-effects regression includinggroup and score field as random effects, and also revealed larger
variability due to score field in the “medium” noise condition thanthe “low” noise condition.

We use these filters to analyze how players behave in ourgame. First, we compute the probability of a player being
in a particular state conditional on the current score that theplayer is receiving. We find that the probability of a player
occupying a particular state is closely related to that player’sscore. Specifically, players in higher scoring locations are
more likely to be exploiting than exploring or copying, but
the probability that a player is exploring or copying increases
as the player’s score decreases. These results, which are visualized in Figure 4, suggest that players are choosing theirstates relatively rationally. Players will tend to remain in goodareas and will leave bad areas quickly either by exploring independently or by copying other individuals.
Second, we find substantial variation in the types of copying behavior that different individuals display. Some individuals appear to focus their copying behavior on other players who tend to have higher scores, whereas other individuals appear to be less discriminating in their copying behavior.
Moreover, as shown in Figure 5, groups that contain individuals who focus their copying behavior on higher scoring individuals achieve significantly higher performance in our task
(slope: 0.2639, 95% CI: [0.145, 0.383]). This result, thoughsubject to the confounding of correlation and causation, could
be explained by theory of mind assisting in individual andgroup performance. A player who is able to accurately infer
whether another player is receiving a high score may be able
to achieve higher performance on our task by leveraging these

0.9

Probability

exploring
0.8 exploiting
copying
0.7

0.6
0.5
0.4
0.3
0.2
0.1
0.00.0

0.2

0.4

0.6

Score

0.8

1.0

Figure 4: The probability of an individual being in a particular behavioral state as a function of the individual’s score.
1.0

2 Players

3 Players

4 Players

5 Players

0.8Group Performance

We now define the three states: exploiting, copying, andexploring. Exploiting a particular location in the environment
is not completely trivial for players since the avatars always
move at least at a slow constant velocity. In order to attempt tostay in a single location, a player can either meander arounda particular location or can persistently hold down one of thearrow keys while moving at a slow speed, which creates atight circular motion around a particular location. We callthis second activity “spinning” because of its distinctive appearance. We then classify a player as exploiting if the player
is spinning for 500ms or if the player moves at the slow speed
for 3 seconds and has not traveled more than two thirds of thepossible distance that the player could have traveled in thattime. The second condition is supposed to capture the meandering behavior of individuals who have not discovered how
to spin. Copying behavior is more difficult to identify, but
appears to often be characterized by fast directed movements
towards other players. We thus classify a player as copying if
the player is moving in a straight line at the fast speed towards
any particular other player consistently for 500ms. We classify a player as moving towards another player if the second
player is within 60◦ on either side of the first player’s straightline trajectory. Finally, we classify a player as exploring if theplayer is neither exploiting nor copying. Thus a player will beclassified as exploring if that player is either moving slowly
but not staying in the same general location, if the player ismoving quickly but not towards any particular person, or if
the player is moving quickly and turning.

0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.00.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

Proportion of Copying that is of Higher Scores

1.0

Figure 5: Average group performance as a function of thefraction of copying in the group that consists of “intelligent copying”—copying of an individual with a higher score.
Lines are individually fitted regression lines.
inferences to more effectively copy others.

Behavioral Model
The trends we observe suggest a potential set of behavioral
mechanisms that effective human groups may use in our task.
We propose that each player in an effective group chooses astate based on the following rules:
1. If the player is in a good area, the player will remain in thatarea exploiting.
2. If the player in not in a good area and the player perceives
another person as possibly having a higher score, the player
may choose to copy that person.
3. Otherwise the player will explore independently.
According to this model, players in bad locations improvetheir scores by copying exploiting individuals instead of wasting time by copying low scoring players or wasting time by
exploring many poor quality areas. The model also has interesting emergent collective properties. When any individual

finds a good area, that player will attract the other players tothat location by exploiting. Then, when all the players are together in a group exploiting a particular area, one of the players will start to lose bonus as the score field shifts. This player
will then either move closer to the others who are still exploiting or will shift to an exploring state. If that player starts exploring but doesn’t find any good locations, the player will
return to the group if the group is still exploiting. If that
player does find a new good area, though, the player will start
exploiting that area. The rest of the group will then follow
after the highest scoring region shifts to where the exploiting
player is. This mechanism creates a kind of gradual crawling that effectively tracks the moving score field. Thus, byusing this mechanism players are improving both their own
performances directly and also that of the entire group by participating in this process of emergent collective sensing. An
example of this process occurring in participant gameplay isshown in Figure 6.

Perhaps an even more interesting difference that emerged
between humans and fish has to do with the time scale overwhich the collective intelligence mechanism evolved. For
fish, the ability to gain from group performance in these collective sensing tasks is likely based on innate behaviors, selected over many generations of fish facing exactly this problem over their whole lifespans. In contrast, some of ourhumans groups, facing this particular problem for the firsttime, appear to have discovered reasonable collective sensing
strategies in just a matter of minutes.

Discussion

Beyond the recent literature on collective intelligence innonhuman animal groups, there has been a long line of work
studying the factors that predict the performance of humangroups in various scenarios (Kerr & Tindale, 2004). Our findings are consistent with previous work suggesting that having
a larger group is beneficial in complex, uncertain environments (Stewart, 2006). Unlike much of this previous work,
however, we focus here on the possibility in larger groups of
new emergent group abilities and behaviors, and on the mechanisms leading to these emergent properties.

In our experiment, we observed that humans were able toachieve increases in performance at much smaller group sizes
than fish. Fish exhibited mild improvements in group performance at groups of 16 and more substantial improvements atgroups of 64 and 128. However, we see significant improvements in human performance at just five players. This difference may be at least partially explained by the differencesin the mechanism that humans appear to use in this task ascompared to fish.
Interestingly, the mechanism we identify in humans is similar to that of fish in some ways, but it is also distinct important ways. Similar to the behavior of humans in choosing appropriate states based on current score, fish modulated
their speeds based on the level of darkness that they wereexperiencing. Fish moved slower in their preferred darker
areas and faster in lighter areas. Similar to the copying behavior we observe, fish had a tendency for turning towards
other fish. However, Berdahl et al.’s model of the behavior of
their fish did not require any reference to the kind of discerning social awareness that we see in humans. Whereas fish
appear to equally weight information from all nearby conspecifics, effective humans appear to modulate their copying behavior based on the inferred scores of other players.
The strategic use of independent exploration (a form of asocial learning) was also key to the mechanism enabling humansuccess. These key differences support recent work in social
learning (Wisdom, Song, & Goldstone, 2013; McElreath etal., 2008), which find an impressive flexibility in the strategic
deployment of imitation in humans. Of course, it is difficult
to compare human performance directly to that of fish given
the differences between the perceptual and motor abilities offish in an actual fish tank and the abilities of the participantsin our simulated environment. Nevertheless, our comparison
hints at a superior capacity for distributed cognition in humans, possibly enabled by our ability for theory of mind.

Our work therefore may shed light on one of the pressing puzzles of human collective intelligence and human distributed cognition. What are the specific mechanisms bywhich humans establish effective coordinated distributed information processing agents that can accomplish more than
any individual alone, and how do our abilities play a rolein these mechanisms? The perspective of group behavior
as distributed processing (Hutchins, 1995) suggests the importance of communication for collective intelligence because of the importance of communication in distributed systems. Moreover, theory of mind—an enabler of implicit
communication—has been shown to be predictive of collective intelligence (Woolley, Chabris, Pentland, Hashmi, &Malone, 2010; Engel, Woolley, Jing, Chabris, & Malone,2014). While our work does not have a powerful enoughexperimental design to be definitive, our work at least further suggests that one of the roles that theory of mind plays in
the emergence of collective intelligence is facilitating implicit
communication that allows for coordination on good collective actions. Moreover, our work also suggests that the benefitof a group’s coordinating on good actions could be more thansimply the benefit to each individual independently. By combining a natural human tendency for independent exploration
with a discerning social awareness, humans appear to be able
to fluctuate between exploiting known good actions, independently exploring new options, and intelligently copying thepromising choices of other individuals. A simultaneous combination of these activities by a cohesive group appears tolead to a collective memory of recently good actions from individuals who continue to exploit, and a collective movement
towards actions that promise to be good in the near future
driven by independently exploring individuals. The reactive
distributed sensing ability that appears to emerge from thisprocess may confer a unique benefit to working together intightly knit groups.

Figure 6: Reconstructions of actual gameplay in a five-person group illustrating both failed exploration leading to intelligent
copying and successful exploration leading to collective movement. Colors indicate the individuals’ scores, with red being
higher and orange/yellow being lower. The player labels indicate both player IDs and also the player states our feature extraction
procedure inferred. Other annotations are provided to give a sense for the game dynamics. At 34 seconds, in the first panel,most of the group has converged on exploiting a particular area while one individual is exploring independently. To the right, at
36 seconds, the exploring individual appears to have failed to find a good location and ceases exploring by copying the group.
At 40 seconds, the final panel in the first row, the score field has shifted and some of the group begins exploring while others
continue to exploit. By 49 seconds, the first panel in the second row, one of the exploring individuals found a good location,and other players have begun to move towards that individual. At 54 seconds, the entire group is exploiting the new area. In
the final panel, at 55 seconds, the background has shifted enough again that one of the individuals begins to explore.

Acknowledgments
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.
1122374 to PK and Grant No. DGE-114747 to RXDH. Any opinion, findings, and conclusions or recommendations expressed in thismaterial are those of the authors(s) and do not necessarily reflect theviews of the National Science Foundation. This material is based
upon work supported by the Center for Minds, Brains and Machines(CBMM), funded by NSF STC award CCF-1231216.

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