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Ant groups optimally amplify the effect of transiently informed individuals.

Gelblum A, Pinkoviezky I, Fonio E, Ghosh A, Gov N, Feinerman O - Nat Commun (2015)

Bottom Line: A downside of behavioural conformism is that it may decrease the group's responsiveness to external information.Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions.Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7610001, Israel.

ABSTRACT
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

No MeSH data available.


Related in: MedlinePlus

Transient guidance.(a) The information in the angular spread of the load's direction of motion immediately following the attachment of a new ant at t=0 (N=134 attachments). Errors were calculated from the entropy of artificially generated histograms with added binomial noise. (b) A half-polar histogram of the angles between the attachment/detachment point of an ant and the change in velocity (relative impact direction) that follows different events (N=252). (c) Relative impact direction as a function of time (blue line N=134 attachments) and difference between distributions of time since attachment of ants in the leading and trailing edge of the load (turquoise line). The insets illustrate this process for a sample newly attached ant (marked by a yellow circle). Load velocity (green arrow) and the acceleration caused by this ant (dashed arrow) are overlaid. (d) An example of a series of switches between steering ants along a trajectory. Overlaid colours mark trajectory segments where different ants steered the load. Scale bar, 10 cm. e) Mean magnitude of velocity change caused by newly attached ants (denoted by  on y axis) as a function of number of ants already attached (N=134 attachments). Error bars are standard error of the mean.
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f2: Transient guidance.(a) The information in the angular spread of the load's direction of motion immediately following the attachment of a new ant at t=0 (N=134 attachments). Errors were calculated from the entropy of artificially generated histograms with added binomial noise. (b) A half-polar histogram of the angles between the attachment/detachment point of an ant and the change in velocity (relative impact direction) that follows different events (N=252). (c) Relative impact direction as a function of time (blue line N=134 attachments) and difference between distributions of time since attachment of ants in the leading and trailing edge of the load (turquoise line). The insets illustrate this process for a sample newly attached ant (marked by a yellow circle). Load velocity (green arrow) and the acceleration caused by this ant (dashed arrow) are overlaid. (d) An example of a series of switches between steering ants along a trajectory. Overlaid colours mark trajectory segments where different ants steered the load. Scale bar, 10 cm. e) Mean magnitude of velocity change caused by newly attached ants (denoted by on y axis) as a function of number of ants already attached (N=134 attachments). Error bars are standard error of the mean.

Mentions: Freely moving ants are well informed of the correct nest-bound direction (Supplementary Note 6; Supplementary Fig. 3a). When such ants attach to the load they steer it so that it moves more accurately towards the nest. Quantitatively, we found that within the several seconds that follow their attachment, ants inject about 0.5 bits of directional information into the system (see Fig. 2a, Supplementary Note 6 and Supplementary Fig. 3b). This causal effect implies that newly attached ants adopt an influential role.


Ant groups optimally amplify the effect of transiently informed individuals.

Gelblum A, Pinkoviezky I, Fonio E, Ghosh A, Gov N, Feinerman O - Nat Commun (2015)

Transient guidance.(a) The information in the angular spread of the load's direction of motion immediately following the attachment of a new ant at t=0 (N=134 attachments). Errors were calculated from the entropy of artificially generated histograms with added binomial noise. (b) A half-polar histogram of the angles between the attachment/detachment point of an ant and the change in velocity (relative impact direction) that follows different events (N=252). (c) Relative impact direction as a function of time (blue line N=134 attachments) and difference between distributions of time since attachment of ants in the leading and trailing edge of the load (turquoise line). The insets illustrate this process for a sample newly attached ant (marked by a yellow circle). Load velocity (green arrow) and the acceleration caused by this ant (dashed arrow) are overlaid. (d) An example of a series of switches between steering ants along a trajectory. Overlaid colours mark trajectory segments where different ants steered the load. Scale bar, 10 cm. e) Mean magnitude of velocity change caused by newly attached ants (denoted by  on y axis) as a function of number of ants already attached (N=134 attachments). Error bars are standard error of the mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525283&req=5

f2: Transient guidance.(a) The information in the angular spread of the load's direction of motion immediately following the attachment of a new ant at t=0 (N=134 attachments). Errors were calculated from the entropy of artificially generated histograms with added binomial noise. (b) A half-polar histogram of the angles between the attachment/detachment point of an ant and the change in velocity (relative impact direction) that follows different events (N=252). (c) Relative impact direction as a function of time (blue line N=134 attachments) and difference between distributions of time since attachment of ants in the leading and trailing edge of the load (turquoise line). The insets illustrate this process for a sample newly attached ant (marked by a yellow circle). Load velocity (green arrow) and the acceleration caused by this ant (dashed arrow) are overlaid. (d) An example of a series of switches between steering ants along a trajectory. Overlaid colours mark trajectory segments where different ants steered the load. Scale bar, 10 cm. e) Mean magnitude of velocity change caused by newly attached ants (denoted by on y axis) as a function of number of ants already attached (N=134 attachments). Error bars are standard error of the mean.
Mentions: Freely moving ants are well informed of the correct nest-bound direction (Supplementary Note 6; Supplementary Fig. 3a). When such ants attach to the load they steer it so that it moves more accurately towards the nest. Quantitatively, we found that within the several seconds that follow their attachment, ants inject about 0.5 bits of directional information into the system (see Fig. 2a, Supplementary Note 6 and Supplementary Fig. 3b). This causal effect implies that newly attached ants adopt an influential role.

Bottom Line: A downside of behavioural conformism is that it may decrease the group's responsiveness to external information.Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions.Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7610001, Israel.

ABSTRACT
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

No MeSH data available.


Related in: MedlinePlus