This research was funded by the German Initiative of Excellence (Institutional Strategy: https://www.uni-goettingen.de/en/32632.html). We thank Margarita Neff-Heinrich for her English proofreading.

Data collection and data analysis in this project have been approved by the Ethics Committee of the Georg-Elias-M&#252;ller Institute for Psychology of the University of G&#246;ttingen (proposal 039/2012).
1. Experimental Setup
<ol>
	<li>Choose a location that is away from a high-traffic area, such as in a computer lab or other specified area with individual workstations that can be configured as an LAN (local area network).</li>
	<li>Arrange for 10 to 12 notebooks of the same type to be used for the experiment as well as one computer to function as the server (Figure 6). The server program as well as client programs need a JAVA runtime environment, which is available on all common operation systems (a Raspberry Pi as client can suffice).</li>
	<li>Equipment configuration
	<ol>
		<li>Arrange notebooks on individual workstation tables with chairs as shown in Figure 7.</li>
		<li>Connect notebooks to the server computer via Ethernet cables and a network switch to create a local area network.</li>
		<li>Install partition walls between individual workstations to prevent visual sensory communication (eye contact, hand gestures, facial expressions, etc.) between neighboring participants.</li>
		<li>Acquire earplugs (for one-time use) to be distributed to all participants to prevent audible communication between participants.</li>
	</ol>
	</li>
</ol>
2. Participant Recruitment
<ol>
	<li>Choose a recruiting location where there are a large amount of people, such as the entrance hall of an auditorium.</li>
	<li>Address potential recruits using the standardized text that explains the purpose and background of experiment, experiment duration, maximum payment calculated according to performance, and requirement for participation in a multiplayer game on institution-owned laptops.</li>
	<li>Ensure that participants can understand the English and German instructions and questionnaires related to the experiments.</li>
	<li>If the experimental design includes the use of colors, ensure that participants are free of any color blindness that may prevent them from differentiating the colors used.</li>
	<li>Do not recruit previous participants, as participants should be na&#239;ve to the experiment.</li>
	<li>Lead willing recruits to a waiting area away from the recruiting area. Kindly request that they await completion of the group recruitment without talking to each other. Explain that this restriction is related to integrity of the experimental results.</li>
	<li>Once 10 to 12 participants have been recruited, lead them into the pre-arranged computer lab or specified area where the experiment will take place.</li>
	<li>Before participants take their places in the partition-encased workstations, have the participants sign a form designating informed consent.</li>
	<li>Distribute the hygienic, one-time use earplugs to all participants. Inform them that audio-visual communication with other participants is prohibited. Therefore, the use of earplugs and partition-encased workstations is mandatory.</li>
	<li>Have participants take their places in the partition-encased workstations.</li>
</ol>
3. Experimental Procedure
NOTE: In this experimental procedure, the game used by Boos et al.1 is described as an application example.
<ol>
	<li>Preparation phase
	<ol>
		<li>The program itself is formatted as a zip-file HC.zip containing 1) the runnables HC.jar, 2) three files for configuration, namely hc_server.config, hc_panel.config, and hc_client.config, and 3) two subfolders named intro and rawdata.</li>
		<li>Create a shared folder on the server computer and unzip the HC.zip into this folder.</li>
		<li>On each client computer, mount and access this shared folder and open a terminal (Linux, Mac OS X: spotlight | search | terminal) or a prompt (Windows: search &#34;cmd&#34;), respectively. Use the command &#8220;dir&#8221; or &#8220;ls&#8221; so that the unzipped files appear on each terminal.</li>
		<li>Execute the command &#8220;java -version&#8221; on each terminal to ensure that a java runtime environment is available. If not, install java before continuing.</li>
		<li>Look in the three configure files.
		<ol>
			<li>Edit hc_server.configure to configure the 1) number of players, 2) minimum numbers and maximum numbers of moves each player can make, 3) values of the so-called nuggets, and 4) perception radius condition (local or global). 
			&#8203;NOTE: The two perception conditions are the global condition (player can see positions of avatars of all participants) and local condition (player can see only those avatars adjacent to their avatar; see Figure 3)</li>
			<li>Edit hc_client.configure to tell the clients the server&#39;s IP.</li>
			<li>In hc_panel.config, adjust the size of the hexagons according to the screen&#39;s resolution.</li>
		</ol>
		</li>
		<li>First, start the server program HC_Gui.jar (Figure 8) using the command &#8220;java -jar HC_Gui.jar&#8221;. Then, start the client programs on each workstation using the command &#8220;java -jar HC_ClientAppl.jar&#8221;. 
		NOTE: The clients&#39; screens should display the message, &#34;Please wait. The computer is connecting to the server.&#34; In the server GUI, a line appears displaying the IP address of each of the clients. When all clients are connected, the server program displays the message, &#34;All Clients are connected. Ready to start?&#34; 
		&#8203;NOTE: The experimenter can prepare the session up to this point.</li>
		<li>When all participants have taken their places, give the final instructions before they insert the earplugs.</li>
		<li>Click &#34;OK&#34; to start the session. Hereon, the experiment is controlled only by the instructions on the screens visible to participants. Instructions for a single experiment require multiple screen pages, and reading is made possible by the participants paging back and forth as necessary. 
		NOTE: Each participant indicates, by clicking a designated button on the screen, that he/she has read the instructions. The experiment cannot commence until all participants are finished reading the instructions.</li>
	</ol>
	</li>
	<li>Testing phase
	<ol>
		<li>Observe whether the participants are mouse-controlling their avatar dot (twice as big as the visible avatar dots of the other participants) on the HoneyComb virtual 97-hexagon playfield (see Figure 1).</li>
		<li>Have participants start the testing phase in the center of the field, then move on the HoneyComb virtual playfield according to the previously provided instructions on screen.
		<ol>
			<li>All instructions on how to play the game are placed as editable html-files within the program folder of the HoneyComb game. See subfolders intro/de and intro/en for German and English instructions, respectively.</li>
			<li>Have players left-click into the adjacent small hexagon of their choice to move their avatar dot. Only adjacent fields can be chosen for the initial and subsequent moves. 
			NOTE: After each move, a small tail appears for 4000 ms for each participant, indicating the last direction from which he/she hailed.</li>
		</ol>
		</li>
		<li>Allow each participant to partake only once in order to avoid possible biases. 
		NOTE: The game described here requires 5-10 min, including the reading of instructions. Overall, 400 participants in 40 ten-person groups were tested by Boos et al.1.</li>
		<li>Do not restart the experiment with the same participants if there is a technical breakdown or if a participant fails.</li>
	</ol>
	</li>
</ol>
4. Post-Test Phase
<ol>
	<li>Once the game is completed, have participants fill out questionnaires assessing demographic data, Big Five personality factors, perceived levels of stress or calmness, and pay satisfaction (to be paid upon completion of the experiment). These questionnaires can be offered as stand-alone html-files.</li>
	<li>While participants fill out questionnaires, prepare anonymous envelopes with the appropriate amount of money earned in the HoneyComb game just completed. The game&#8217;s HoneyComb-computed amounts to be paid to each player are stipulated on the server screen.</li>
	<li>Distribute the earned payments to participants as they exit the testing area.</li>
	<li>Close the server program, then close the client programs once the server program has finished closing.</li>
	<li>Transfer the data, in the form of 2 text files marked by day- and time-stamp of the experiment, to a USB stick.</li>
</ol>

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C., Manocha, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Least-effort+trajectories+lead+to+emergent+crowd+behaviors.">Least-effort trajectories lead to emergent crowd behaviors.</a> Physical Review E: Statistical, Nonlinear, and Soft Matter Physics. 85 (1), 016110 (2012).</li><li>Shao, W., Terzopoulos, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autonomous+pedestrians.">Autonomous pedestrians.</a> Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. , (2005).</li><li>Reynolds, C. 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The authors have nothing to disclose.

One fundamental question in using multi-client virtual environments as a research paradigm to investigate human collective behavior is whether the results are applicable to actual scenarios. In other words, does the methodological approach yield results with sufficient ecological or external validity? Representing human participants as avatars on a virtual playfield and letting them move via mouse-clicks reduces social cues. Additionally, keeping communication to a minimum allows experimenters to investigate which tacit behavioral cues are transmitted among humans that may affect human group coordination and leadership behavior and under which environmental affordance (e.g., rescue, competition, evacuation) these behaviors are affected by more and to what extent. As long as there is strict adherence to the two pre-testing phases in the protocol and testing procedures, this reductionist approach guarantees internal validity. In order to allow the transfer of results to &#34;real&#34; group and crowd dynamics, the experimental setup and test phases may be gradually modified to become more complex (e.g., allowing for additional communication beyond mere transmitting/reading movement behavior, adding information on individual characteristics embedded semantically into various real-world scenarios, etc.) and&#160;described in the on-screen instructions read by the participants before the game starts.
To address the matter of external validity, the hexagon playfield [initially chosen to standardize player&#39;s movements to standardized, two-dimensional hexagonal coordinates due to (pre-tested) usability and reduction of confounding factors] can be varied. A two-dimensional grid with free movement choice would enable players to create more continuous and complex movement data. A three-dimensional environment created by Unity- or Unreal-Engine, for example, can also heighten the ecological/external validity. However, with each step towards lessoning the restriction of movement, a problem arises. With rising complexity of freedom-of-movement in the simulated scenario, the influence of confounding factors (e.g., interpersonal differences such as computer experience, familiarity with spatial orientation in three-dimensional games) increases, which can lead to biased results and reduce internal validity.
The advantage of the method outlined in the HoneyComb protocol is that it can be combined with computer simulation models and used as a paradigm to empirically test if collective patterns found in the computer simulations also hold for behavior in groups of humans. To enhance the external validity of such tests, participants should be asked in the post-test phase questionnaire if they felt sufficiently and humanly represented by their avatars and whether they were able to perceive their co-players as human actors. The protocol specifies the physical presence of the co-players sitting in workstations beside each other (even though the protocol parameters preclude sensory auditory or visual communication) in order to enhance these feelings of human embodiment.
In sum, the methods applied by the HoneyComb approach outlined in the protocol&#39;s pre-test, test, and post-test phases provide a novel paradigm to investigate basic mechanisms of collective phenomena such as group coordination, leadership, and intra-group differentiation. The method&#39;s most important limitation is its vulnerability to human error by the recruiters, particularly if they are not stringent enough in ensuring that participants do not communicate with each other during the pre-test and test phases.

The computer-based multi-agent game HoneyComb1 offers a methodological paradigm to experimentally investigate how collective human movement patterns and group structures emerge from individual behavior. Human participants are visually represented as avatars (black dots) on a hexagonal virtual playfield resembling a honeycomb (Figure 1). Participants move their avatars via mouse-click to reach goal hexagons, spend move resources (Video 1), and maximize their monetary rewards by building cohesive groups (Video 2). Spatial conditions (e.g., vision radius), reward structures (e.g., monetary goal fields), and communication channels can be manipulated in order to discover which and to what extent these condition rules impact coordination and leadership in collective movement.
The game&#39;s procedural/condition rules, goals, and reward motivators have been designed by social psychologists to investigate human collective movement. In animal swarms as well as human crowds, one can observe emergent phenomena (i.e., global patterns) transpiring from individual behavior that follows local rules. For example, schools of fish and flocks of birds seem to move as coherent entities towards a spatial goal2,3,4, despite large group sizes that reduce their capacity for global or inter-individual communication. Empirical research5,6,7, behavioral modeling8,9,10, and computer simulations11,12,13 have shown that in diverse species, including humans14,15,16, complex patterns at the group level emerge without internal control or external supervision. Local individual movement and, often times, simple rules on the microscopic level are sufficient to generate orderly movement on the macroscopic level. Such experiments contribute to increasing evidence2,6 that not only large swarms but also small groups (human groups as well as other animal groups) are coordinated by local interaction rules1.
Our novel approach using computer-based multi-user avatar games shows one main advantage in researching dynamic human collective phenomena. Using the HoneyComb avatar platform1,17,18,19, spatio-temporal data of individual movement behavior (movement governed by actual individuals) can be fully collected by the server, and the development of behavioral patterns and collective structures can be analyzed with an accuracy of 50 ms (Table 1). As visual and auditory sensory communication can be restricted by requiring participants to use earplugs and encasing their workstations with partition walls, swarm and other crowd behavior conditions can be approximated experimentally. In several experiments1,17,18,19, we manipulated vision radius (global vs. local, Figure 2), monetary incentives (Figure 3a,b), subgroups (Figure 4), and the co-presence of other players (Figure 5) in order to test the impact of these variables on the emergence of collective behavioral patterns such as human flocking behavior17, leadership1, and competition18. To collect the data, a setup of ten to twelve notebooks and one server was used (Figure 6).
The self-organized coordination of individual activities in group-living species has attracted much scientific attention, particularly within the last decade. Examinations are wide-ranging, from simple trail formation and path selection in ants to the complex emergence of vortex structures in fish shoals, and even the segregation of bidirectional flows of pedestrians2.
With our HoneyComb paradigm, we contribute a methodological approach to empirically investigate the impact of varied situational options/constraints, diverse behavioral rules, and individual characteristics on the microscopic level on the emergence of macroscopic behavioral structures in humans. An important advantage is that the paradigm offers strictly controllable experimental settings defined by experimenters, making it possible for manipulation to measure the outcomes of a single experiment or compare multiple experiments. The virtual playfield can be configured according to the requirements of the study design, and sensory communication channels between the participants can be eliminated or reduced according to the experiment parameters. Additionally, environmental affordances can be shaped (e.g., competitive, non-competitive consensus, and rescue settings). Thus, our platform enforces internal validity (i.e., matching the study design as closely as possible to the research questions) by offering the possibility to manipulate/control variables relevant for the specific research question, using human-governed movement data to examine human movement. Field experiments render benefits in terms of external validity (generalizability) of results15,20,21 to the real world, because they do not preclude effects of unknown uncontrollable/insuppressible social cues as well as non- and para-verbal behaviors in humans1.
The computer-based multi-agent game HoneyComb has served to investigate the emergence of coordination and leadership patterns of human players moving their avatars on the virtual playfield. Participants were only provided local information about monetary incentives obtainable on goal hexagons, which included the incentive for group cohesion based on the multiplication of monetary rewards by the number of co-players who also ended up on the same goal hexagon. In our first series of studies, we restricted the experiment setup to two simple parameters of swarming behavior (alignment and cohesion) and reduced mutual information transfer to &#34;reading/transmitting&#34; of only movement behavior of the other participants. We then varied the sight radius of other participant movement behavior to either a global or local view of the virtual playfield, which consists of 97 smaller hexagons, and limited the expendable movement resources (possible moves) of the players.
The shape and the elements of the virtual platform and the experimenter-defined parameters of games able to be played on said platform can be designed according to the specific research questions. Depending on the research goal, the size of the playfield can be changed; the colors, shapes, and meanings of the avatars can be adapted; resources can be implemented; and the reward structure and content can be varied. More or less information, uncertainty, and conflicting preferences can also be implemented22. Varying global player-view information and control are also possible. Therefore, via experimental instructions, the environmental affordances of the experiment can be altered (e.g., a consensus vs. escape scenario). In the next section, we will clarify how these variables can be applied by describing a real study that used some of these parameters to answer specific study questions.

<table><tbody><tr><td>Notebooks</td><td></td><td></td><td></td></tr><tr><td>Partition walls between work stations</td><td></td><td></td><td></td></tr><tr><td>Earplugs</td><td></td><td></td><td></td></tr><tr><td>Equipment for LAN installation</td><td></td><td></td><td></td></tr></tbody></table>

the honeycomb paradigm for research on collective human behavior

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent literature has revealed that these patterns, often visible at the global level of the group, are based on self-organized, individual behaviors that follow several simple local parameters. Understanding the dynamics of human collective behavior can help to improve coordination and leadership in group and crowd scenarios, such as identifying the ideal placement and number of emergency exits in buildings.
In this article, we present the experimental paradigm HoneyComb, which can be used to systematically investigate conditions and effects of human collective behavior. This paradigm uses a computer-based multi-user platform, providing a setting that can be shaped and adapted to various types of research questions. Situational conditions (e.g., cost-benefit ratios for specific behavior, monetary incentives and resources, various degrees of uncertainty) can be set by experimenters, depending on the research question. Each participant's motions are recorded by the server as hexagonal coordinates with timestamps at an accuracy of 50 ms and with individual IDs. Thus, a metric can be defined on the playfield, and movement parameters (e.g., distances, velocity, clustering, etc.) of participants can be measured over time. Movement data can in turn be combined with non-computerized data from questionnaires garnered within the same experiment setup.
The HoneyComb paradigm is paving the way for new types of human movement experiments. We demonstrate here that these experiments can render results with sufficient internal validity to meaningfully deepen our understanding of human collective behavior.

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The HoneyComb Paradigm for Research on Collective Human Behavior

Here, we present the computer-based, multi-agent game HoneyComb, which enables experimental investigations of collective human movement behavior via black-dot-avatars on a virtual 2D hexagonal playfield. Different experimental conditions, like variable&#160;incentives on goal fields or vision radius, can be set, and their effects on human movement behavior can be investigated.

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent ...

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9.3K Views.  University of Goettingen. De computer-gebaseerde multi-agent spel HoneyComb, biedt een experimenteel paradigma voor het onderzoeken van menselijke beweging coördinatie, leiderschap en andere vormen van collectief gedrag. Met behulp van het HoneyComb-paradigma kunnen ruimtelijke temporele gegevens van individueel bewegingsgedrag worden verzameld en kan de ontwikkeling van gedragspatronen en collectieve structuren worden geanalyseerd. Onze visie is dat HoneyComb nieuwe strategieën zou bieden voor het beschermen van me...

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Behavioural Pharmacology in Classical Conditioning of the Proboscis Extension Response in Honeybees (Apis mellifera)

Honeybees (Apis mellifera) are well known for their communication and orientation skills and for their impressive learning capability1,2. Because the survival of a honeybee colony depends on the exploitation of food sources, forager bees learn and memorize variable flower sites as well as their profitability. Forager bees can be easily trained in natural settings where they forage at a feeding site and learn the related signals such as odor or color. Appetitive associative learning can also be studied under controlled conditions in the laboratory by conditioning the proboscis extension response (PER) of individually harnessed honeybees3,4. This learning paradigm enables the study of the neuronal and molecular mechanisms that underlie learning and memory formation in a simple and highly reliable way5-12. A behavioral pharmacology approach is used to study molecular mechanisms. Drugs are injected systemically to interfere with the function of specific molecules during or after learning and memory formation13-16.
Here we demonstrate how to train harnessed honeybees in PER conditioning and how to apply drugs systemically by injection into the bee flight muscle.

Behavioural Pharmacology in Classical Conditioning of the Proboscis Extension Response in Honeybees Apis mellifera

We demonstrate how to implement a behavioral pharmacology method in an appetitive olfactory conditioning paradigm in honeybees (Apis mellifera) by systemic application of drugs. This method allows investigation of the mechanisms underlying learning and memory formation in a simple and reliable way.

Honeybees (Apis mellifera) are well known for their communication and orientation skills and for their impressive learning capability1,2. Because the ...

Neuroscience

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood3. The antennae are extraordinary multisensory feelers and play a pivotal role in various tactile mediated tasks4, including hive building5 and pattern recognition6. Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates7. Bees use different floral signals like colors or odors7, 8, but also tactile cues from the petal surface9 to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae10, 11, 12, 13. This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees14. The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker ...

A Proboscis Extension Response Protocol for Investigating Behavioral Plasticity in Insects: Application to Basic, Biomedical, and Agricultural Research

Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g., food, mates, threats). There are several behavioral mechanisms through which an insect learns salient associations and relates them to these events. It is important to understand this behavioral plasticity for programs aimed toward assisting insects that are beneficial for agriculture. This understanding can also be used for discovering solutions to biomedical and agricultural problems created by insects that act as disease vectors and pests. The Proboscis Extension Response (PER) conditioning protocol was developed for honey bees (Apis mellifera) over 50 years ago to study how they perceive and learn about floral odors, which signal the nectar and pollen resources a colony needs for survival. The PER procedure provides a robust and easy-to-employ framework for studying several different ecologically relevant mechanisms of behavioral plasticity. It is easily adaptable for use with several other insect species and other behavioral reflexes. These protocols can be readily employed in conjunction with various means for monitoring neural activity in the CNS via electrophysiology or bioimaging, or for manipulating targeted neuromodulatory pathways. It is a robust assay for rapidly detecting sub-lethal effects on behavior caused by environmental stressors, toxins or pesticides.
We show how the PER protocol is straightforward to implement using two procedures. One is suitable as a laboratory exercise for students or for quick assays of the effect of an experimental treatment. The other provides more thorough control of variables, which is important for studies of behavioral conditioning. We show how several measures for the behavioral response ranging from binary yes/no to more continuous variable like latency and duration of proboscis extension can be used to test hypotheses. And, we discuss some pitfalls that researchers commonly encounter when they use the procedure for the first time.

The Proboscis Extension Response (PER) conditioning protocol, developed for the honey bee (Apis mellifera), provides an ecologically-relevant and easily quantifiable means for studying several different mechanisms of learning in many insect species.

Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g., food, mates, threats). ...

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often not fully captured in its emergent qualities by the available research methods. In this paper, the Collective Trust Game (CTG) is presented, a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust. The CTG builds on previous research on interpersonal trust and adapts the widely known Trust Game to a group setting in the HoneyComb paradigm. Participants take on the role of either an investor or trustee; both roles can be played by groups. Initially, investors and trustees are endowed with a sum of money. Then, the investors need to decide how much, if any, of their endowment they want to send to the trustees. They communicate their tendencies as well as their final decision by moving back and forth on a playfield displaying possible investment amounts. At the end of their decision time, the amount the investors have agreed upon is multiplied and sent to the trustees. The trustees have to communicate how much of that investment, if any, they want to return to the investors. Again, they do so by moving on the playfield. This procedure is repeated for multiple rounds so that collective trust can emerge as a shared construct through repeated interactions. With this procedure, the CTG provides the opportunity to follow the emergence of collective trust in real time through the recording of movement data. The CTG is highly customizable to specific research questions and can be run as an online experiment with little, low cost equipment. This paper shows that the CTG combines the richness of group interaction data with the high internal validity and time-effectiveness of economic games.

The Collective Trust Game is a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust and related constructs, such as fairness, reciprocity, or forward-signaling. The game allows detailed observations of group processes through movement behavior in the game.

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often ...

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

In both mammals and insects neuronal information is processed in different higher and lower order brain centers. These centers are coupled via convergent and divergent anatomical connections including feed forward and feedback wiring. Furthermore, information of the same origin is partially sent via parallel pathways to different and sometimes into the same brain areas. To understand the evolutionary benefits as well as the computational advantages of these wiring strategies and especially their temporal dependencies on each other, it is necessary to have simultaneous access to single neurons of different tracts or neuropiles in the same preparation at high temporal resolution. Here we concentrate on honeybees by demonstrating a unique extracellular long term access to record multi unit activity at two subsequent neuropiles1, the antennal lobe (AL), the first olfactory processing stage and the mushroom body (MB), a higher order integration center involved in learning and memory formation, or two parallel neuronal tracts2 connecting the AL with the MB.&#160;The latter was chosen as an example and will be described in full. In the supporting video the construction and permanent insertion of flexible multi&#160;channel wire electrodes is demonstrated. Pairwise differential amplification of the micro&#160;wire electrode channels drastically reduces the noise and verifies that the source of the signal is closely related to the position of the electrode tip. The mechanical flexibility of the used wire electrodes allows stable invasive long&#160;term recordings over many hours up to days, which is a clear advantage compared to conventional extra&#160;and intracellular in vivo recording techniques.

Simultaneous extracellular long term recordings from two different brain neuropiles or two different anatomical tracts were established in honeybees. These recordings allow the investigation of temporal aspects of neuronal processing across different brain areas at the single neuron as well as at the ensemble level in a behaving animal.

In both mammals and insects neuronal information is processed in different higher and lower order brain centers. These centers are coupled via convergent ...

Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System

Honeybee workers are engaged in various tasks related to maintaining colony activity. The tasks of the workers change according to their age (age-related division of labor). Young workers are engaged in nursing the brood (nurse bees), while older workers are engaged in foraging for nectar and pollen (foragers). The physiology of the workers changes in association with this role shift. For example, the main function of the hypopharyngeal glands (HPGs) changes from the secretion of major royal jelly proteins (MRJPs) to the secretion of carbohydrate-metabolizing enzymes. Because worker tasks change as the workers age in typical colonies, it is difficult to discriminate the physiological changes that occur with aging from those that occur with the role shift. To study the physiological changes in worker tissues, including the HPGs, in association with the role shift, it would be useful to manipulate the honeybee colony population by preparing single-cohort colonies in which workers of almost the same age perform different tasks. Here we describe a detailed protocol for preparing single-cohort colonies for this analysis. Six to eight days after single-cohort colony preparation, precocious foragers that perform foraging tasks earlier than usual appear in the colony. Representative results indicated role-associated changes in HPG gene expression, suggesting role-associated HPG function. In addition to manipulating the colony population, analysis of the endocrine system is important for investigating role-associated physiology. Here, we also describe a detailed protocol for treating workers with 20-hydroxyecdysone (20E), an active form of ecdysone, and methoprene, a juvenile hormone analogue. The survival rate of treated bees was sufficient to examine gene expression in the HPGs. Gene expression changes were observed in response to 20E- and/or methoprene-treatment, suggesting that hormone treatments induce physiological changes of the HPGs. The protocol for hormone treatment described here is appropriate for examining hormonal effects on worker physiology.

Here we describe our detailed protocol for the preparation of single-cohort honeybee colonies&#160;&#8211; a useful tool for analyzing the role-associated worker physiology. We also describe detailed protocols for treating workers with juvenile hormone and ecdysone to evaluate the involvement of these hormones in the regulation of worker behavior and/or physiology.

Honeybee workers are engaged in various tasks related to maintaining colony activity. The tasks of the workers change according to their age (age-related ...

Neuropharmacological Manipulation of Restrained and Free-flying Honey Bees, Apis mellifera

Honey bees demonstrate astonishing learning abilities and advanced social behavior and communication. In addition, their brain is small, easy to visualize and to study. Therefore, bees have long been a favored model amongst neurobiologists and neuroethologists for studying the neural basis of social and natural behavior. It is important, however, that the experimental techniques used to study bees do not interfere with the behaviors being studied. Because of this, it has been necessary to develop a range of techniques for pharmacological manipulation of honey bees. In this paper we demonstrate methods for treating restrained or free-flying honey bees with a wide range of pharmacological agents. These include both noninvasive methods such as oral and topical treatments, as well as more invasive methods that allow for precise drug delivery in either systemic or localized fashion. Finally, we discuss the advantages and disadvantages of each method and&#160;describe common hurdles and how to best overcome them. We conclude with a discussion on the importance of adapting the experimental method to the biological questions rather than the other way around.

Neuropharmacological Manipulation of Restrained and Free-flying Honey Bees, Apis mellifera

This manuscript describes several protocols for administering pharmacological agents to honey bees, including simple noninvasive methods for free-flying bees, as well as more invasive variants that allow precise localized treatment of restrained bees.

Honey bees demonstrate astonishing learning abilities and advanced social behavior and communication. In addition, their brain is small, easy to visualize ...

Investigating Collective Behaviors in Nematodes Using Dog Food Agar Medium

The Large-Scale Cultivation of Nematodes to Study Their Collective Behaviors

Animals exhibit dynamic collective behaviors, as observed in flocks of birds, schools of fish, and crowds of humans. The collective behaviors of animals have been investigated in the fields of both biology and physics. In the laboratory, researchers have used various model animals such as the fruit fly and zebrafish for approximately a century, but it has remained a major challenge to study large-scale complex collective behavior orchestrated by these genetically tractable model animals. This paper presents a protocol to create an experimental system of collective behaviors in Caenorhabditis elegans. The propagated worms climb on the lid of the Petri plate and show collective swarming behavior. The system also controls worm interactions and behaviors by changing the humidity and light stimulation. This system allows us to examine the mechanisms underlying collective behaviors by changing environmental conditions and examining the effects of individual-level locomotion on collective behaviors using mutants. Thus, the system is useful for future research in both physics and biology.

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Here, a system is reported for studying the collective behaviors of nematodes by culturing them in bulk using dog food agar medium. This system allows researchers to propagate large numbers of dauer worms and can be applied to Caenorhabditis elegans and other related species.

Animals exhibit dynamic collective behaviors, as observed in flocks of birds, schools of fish, and crowds of humans. The collective behaviors of animals ...

Biology

Group Behavior

<h4>Life in a Population</h4>
Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, and mates. These interactions are costly since individuals invest energy to acquire the resource, therefore there are a variety of strategies that organisms adopt to gain an advantage over their competitors. This is observed clearly with polymorphic mating systems, in which one sex, predominantly males, display multiple mating strategies. For example, dominant male sea lions defend harems of females up on the beach, whereas non-dominant males try to increase their chances of mating by staying in or near the water, where they mate with females that have left the beach temporarily 1. Other types of strategies may determine how often an individual will fight for a resource, or how willing they are to cooperate with others.
<h4>Game Theory and Evolutionarily Stable Strategies</h4>
In order to understand how different behavioral strategies evolve, ecologists turn to the game theory, which is a mathematical modeling approach that investigates the outcomes of multi-individual interactions, in which the payoff for any one individual depends its own strategy as well as the strategies of the others 2. In this approach, the relative cost of the interaction and the benefits obtained from the resource determines the net gain or, in some cases, loss incurred by the organism. Different strategies can be assigned costs and benefits based on those faced by an organism. For example, fighting for control of a resource may provide a large benefit, but it also comes with costs that reduce an organism&#8217;s net gain. On the other hand, a strategy to avoid conflict will reap fewer benefits but incurs no costs.
The organisms with the best interaction strategy maximize their net gain, which in turn will contribute to their fitness. Therefore, over the course of evolutionary time, one strategy may arise that outcompetes all others in a population. This is called an evolutionary stable strategy (ESS) 2. Populations evolve to adopt this strategy once it arises through mutation or is introduced through migration. Therefore, these strategies are mostly genetic or adopted at a young age, and changes in the strategies used by a population over time are determined by the action of natural selection. This concept is often illustrated using the Hawk-Dove game, which compares the success of two strategies for obtaining resources 3. In this example, &#8220;hawks&#8221; are aggressive, and always fight for resources. &#8220;Doves,&#8221; on the other hand, are passive, and never fight for resources. In an interaction between two doves, resources are split evenly. When a hawk and a dove interact, the hawk always wins and obtains all of the resources. However, when two hawks interact, they split resources evenly and suffer the cost of their conflict as well 3. Assessing these interactions over consecutive interactions allows modeling of how competing strategies in an evolving population fare against each other, thus emergence of ESS in an experimental setting.
As seen with the doves in the Hawk-Dove example, organisms not only compete against each other, but also display cooperative behaviors. The risk of being a population of 100% doves is the arrival of a cheater, or an individual that does not cooperate 4. Cheaters can invade and outcompete the residents, therefore many cooperating populations have strategies in place to prevent invasion, including the ability to switch strategy as necessary or to identify and cheaters, and in some cases, to communicate this information to others in their group to decrease the chances of cheater success 4.
<h4>Social Interaction in Nature</h4>
The existence of altruism, or a reduction in an organism&#8217;s immediate fitness in order to benefit another&#8217;s, in wild populations has been held up as a counterpoint to the theory of natural selection, however game theory shows how altruism can evolve under certain conditions. Assuming organisms can identify one another or reasonably expect to interact again in the future, acts of apparent altruism may actually be beneficial over time, as an organism can count on the favor being returned. This is seen when flocks of birds or herds of mammals feed collectively &#8211; one individual may sound an alarm call when they spot a predator, making them more vulnerable to attack 5. The net benefit, however, of others frequently doing the same makes this act adaptive. Similarly, a vampire bat may regurgitate its food to feed hungry individuals. When it is unable to find food in future, it may benefit from the same behavior of another vampire bat 6.
In addition to the interactions within a single species, the evolution of social interactions can take place between species as well. Besides predator-prey interactions, different species can compete for the same resources and develop strategies to gain advantage over others. However, individuals of a species may also interact cooperatively with members of another species. Interspecies interaction that require cooperation include mutualisms, or situations in which two organisms provide mutual benefits to one another. Many plants form mutualisms with nitrogen-fixing bacteria in the soil, whereby the plants provide complex sugars to bacteria in exchange for nitrogen 7. Should bacteria fail to provide nitrogen, then plants can reduce the amount of sugars available.
Since cooperative interactions depend on either being able to identify or retaliate against cheaters, it is possible for invaders to take advantage of existing mutualisms in new environments. Invasive species have the potential to be closely related enough to local species that they are able to form mutualisms with other local species, but distantly related enough that existing recognition or defense mechanisms are ineffective. In Hawaii, an invasive dinoflagellate that forms a mutualism with coral takes more resources than local dinoflagellates. This has a negative impact on coral instead of the expected beneficial one 8. Therefore, studying intra- and interspecies interactions not only allows understanding the development of behavioral strategies in evolving populations but is also essential to assess the behavioral phenotypes of invasive organisms to develop effective strategies against them.
<h4>References</h4>
<ol>
	<li>P&#246;rschmann, UlrichUniBi, et al. Male reproductive success and its behavioural correlates in a polygynous mammal, the Gal&#225;pagos sea lion (Zalophus wollebaeki). Molecular Ecology. 2010, Vol. 19, 12 (2574-86).</li>
	<li>Price, J. M Smith &#38; G. R. The Logic of Animal Conflict. Nature. 1973, 246 (15-18).</li>
	<li>Sigmund K, Nowak MA. Evolutionary game theory. Curr Biology. 1999, Vol. 9, 14 (R503-5).</li>
	<li>Rose, M. R. Cheating in Evolutionary Games. J. Theor. Biol. 1978, 75 (21-34).</li>
	<li>Trivers, R. L. Evolution of reciprocal altruism. Q. Rev. Biol. 1971, Vol. 46, 1 (35-57).</li>
	<li>Wilkinson, G. S. Reciprocal food sharing in the vampire bat. . Nature. 1984, 308(181-84).</li>
	<li>Francis M. Martin, St&#233;phane Uroz, David G. Barker. Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. Science. 2017, Vol. 356, 6340 (eaad4501).</li>
	<li>Tracy D.Ainsworth, Rebecca VegaThurber, Ruth D.Gates. The future of coral reefs: a microbial perspective. Trends in Ecology &#38; Evolution. 2010, Vol. 25, 4 (233-40).</li>
</ol>

Group Behavior: Hawk-Dove Game and Trading Exercise - Concept

Life in a Population
Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, ...

JoVE Lab Manual

Ecology

Creating the Minimal Group Paradigm

Source: Julian Wills &#38; Jay Van Bavel&#8212;New York University
The study of intergroup relations, such as prejudice, conflict, and discrimination, has always been a central topic in social psychology. Does discrimination stem from competition with other groups, a history of conflict, or derogatory stereotypes? Despite an abundance of real-world examples, the ingredients that lead to intergroup discrimination are often unclear. 
To help solve this problem, a group of psychologists created "minimal groups" to strip away confounds like monetary self-interest and a history of conflict that are normally involved in intergroup discrimination. In minimal groups, participants are randomly assigned to completely novel groups. Thus, any consequences emerging from this minimal group induction must stem from identifying with a social group and separating the social world into "us" and "them." Research using minimal groups has shown that, despite the arbitrary nature of group membership, participants willingly discriminate by favoring members of their in-group over members of the out-group.
The minimal group paradigm is widely used in social psychology to study the most basic elements of intergroup relations. This method was first introduced in a 1971 paper called Social Categorization and Intergroup Behaviour by Henri Tajfel and colleagues.1 Across three experiments, the authors documented the in-group favoritism that emerges from a rather minimal group induction. This video will demonstrate how to produce the minimal group induction in a manner similar to the first experiment, where groups were ostensibly created based on dot estimation tendencies.

Source: Julian Wills &#38; Jay Van Bavel&#8212;New York University
The study of intergroup relations, such as prejudice, conflict, and discrimination, has ...

The HoneyComb Paradigm for Research on Collective Human Behavior

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