We thank Stefan Schierhuber and David Lang for their assistance in the production of this video, Christoph R&#246;ssler for his help with technical drawings, and Poppy Lambert for proofreading this manuscript. This publication was funded by the Austrian Science Fund (FWF; START project Y01309 awarded to A.A.). The presented research was funded by the Austrian Science Fund (FWF; projects P29075 and P29083 awarded to A.A. and project J 4169-B29 awarded to M.O.) as well as the Vienna Science and Technology Fund (WWTF; project CS18-023 awarded to A.A.).&#160;Moreover, this research was partially funded by the Austrian Science Fund (FWF) &#34;DK Cognition and Communication 2&#34;: W1262-B29 [10.55776/W1262].

This study was approved by the Ethics and Animal Welfare Committee of the University of Veterinary Medicine Vienna in accordance with good scientific practice guidelines and national legislation. The experiment was purely appetitive and strictly non-invasive and was therefore classified as a non-animal experiment in accordance with the Austrian Animal Experiments Act (TVG 2012). The part of the experiment conducted in Indonesia was approved by the Ministry of Research, Technology and Higher Education (RISTEK) based on a meeting by the Foreign Researcher Permit Coordinating Team (10/TKPIPA/E5/Dit.KI/X/2016) who granted the permits to conduct this research to M.O. (410/SIP/FRP/E5/Dit.KI/XII/2016) and B.M. (411/SIP/FRP/ E5/Dit.KI/XII/2016).
1. Preconditions/prerequisites
<ol>
	<li>Basics
	<ol>
		<li>Ensure that subjects can be identified individually. The study species may have distinct individual patterns or individuals can be marked (e.g., with color rings or non-toxic paint). 
		NOTE: For more information on ringing as well as catching and releasing wild Goffins see Capture-release Procedure in Supplementary Information of32.</li>
		<li>Ensure a visually occluded room is available for testing to avoid social learning between subjects.</li>
		<li>Identify a highly preferred reward for the study species and group by testing multiple different, available treats (see33 or Food Preference Test in Supplementary Information of reference32).</li>
		<li>Consider whether feeding time substantially differs between the groups. If so, consider a protocol that ensures the feeding time does not heavily reduce the time available to solve tasks for one of the groups (see step 4.8 for more information). 
		NOTE: In this study, there was a preference of the long-term captive group for cashews and for dried corn in short-term captive group.</li>
	</ol>
	</li>
	<li>Designing the Innovation Arena 
	NOTE: The full apparatus, i.e., the Innovation Arena, consists of 20 different puzzle boxes, arranged in a semi-circle on a wooden platform.
	<ol>
		<li>Design the basic outline of the boxes in a size applicable for the study species. Use transparent boxes having trapezoid shape (for easy alignment in a semi-circle), removable lids (to allow baiting in between sessions), and detachable bases (see Figure 1). 
		NOTE: Each base will later stay in a permanent position while the rest of the boxes will change positions. In the presented study, the size of the boxes was chosen to assure that each puzzle is easily accessible by the cockatoos. The dimensions can be adjusted for each study species.</li>
		<li>Design a platform to hold the 20 puzzle boxes.</li>
		<li>Design a fixation system that will keep the lids of the boxes in place during testing and, therefore, cannot be removed by the subjects during test sessions. 
		NOTE: It has to be detachable from the apparatus as the lids of the boxes need to be taken off for baiting.</li>
		<li>For the front of each box, design 20 different tasks, each of which will constitute a different technical challenge (see Figure 2). 
		NOTE: The tasks for this experiment were designed with the aim that solutions fall within the morphological range of many different species. For comparative strength, it would be ideal to use tasks as similar to these as possible but keep in mind that it is of even greater importance that the tasks are novel to the subjects. See the Table of Materials for exact measurements and the Supplementary Technical Drawing for a more detailed illustration of the tasks.</li>
		<li>Acquire all material needed for the apparatus.</li>
		<li>Ensure to have a wide-angle camera, a coding software (recommended, e.g., Behavioral Observation Research Interactive Software, BORIS34) and software for statistical analysis (recommended, e.g., R35). 
		NOTE: For field studies, ideally, design the arena before leaving for the study site and bring as much as possible of the essential equipment, such as pre-cut acrylic glass, along.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63026/63026fig01.jpg" /> 
Figure 1: Diagram of a basic three-sided box. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63026/63026fig02.jpg" /> 
Figure 2: Tasks of the Innovation Arena with a corresponding description of the motoric action required for solving (<img alt="Equation 1" src="/files/ftp_upload/63026/63026eq01.jpg" style="margin: auto;" />&#160; = reward; red arrows indicate directions of actions required to solve tasks; yellow arrows indicate reward trajectories). Tasks are arranged according to their mean difficulty (left to right, top to bottom). Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Preparations
<ol>
	<li>Glue together three sides of the boxes: left, back, and right side but not the front, top and base.</li>
	<li>Position each three-sided box on top of each base and evenly align them in a semicircle on the platform (Figure 3). The front section of each box should sit 1 m from the center. 
	NOTE: The mechanisms constituting the task (front faces of boxes and possible contents) will be added at a later point during the experiment.</li>
	<li>Draw a line from each box 20 cm toward the center of the arena and connect the lines, resulting in a proximity grid (Figure 3). 
	NOTE: Depending on the size of the study species, a different distance might be more appropriate. For the study presented here, 20 cm was chosen as it is roughly the length of a Goffin (tail feathers excluded).</li>
	<li>Remove everything except the bases of the boxes and attach them permanently to the platform. This will ensure that the boxes will stay in place during the experiment.</li>
	<li>Attach a wide-angle camera on the ceiling above the arena.</li>
	<li>Prepare a schedule for the position of each box per session and subject. Each subject will always be confronted with all boxes, but with a new arrangement each session. The location (positions 1 to 20) of each task should be randomly assigned with the restriction of no box being at the same position twice per subject. 
	NOTE: This is the ideal situation. If one cannot plan the test order of subjects (which is more likely in field studies) this randomization limitation (no box at the same position twice) between sessions (but not within subject) must suffice.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63026/63026fig03.jpg" /> 
Figure 3: The Innovation Arena. Tasks arranged in a semi-circle; the positions of the 20 tasks are exchangeable. A proximity grid (20 cm in front of each box) is marked in black. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Habituation
NOTE: The purpose of habituation is to reduce influences of neophobic reactions toward the arena. Ensure a minimum habituation level for all subjects through a habituation procedure that requires each individual to reach two criteria.
<ol>
	<li>Habituation to non-functional arena (until criterion I)
	<ol>
		<li>Position all three-sided boxes on the bases, add the lid of each box and hold them in place with the fixation system (without the subject present). 
		NOTE: Consider habituating the subjects in stages that are appropriate for each species, for example, by incrementally adding more boxes to the platform, presenting the arena in their home area, placing rewards at any position of the platform such as around, on top, and with the boxes or confront them with the apparatus in bigger groups first and gradually minimize the group size.</li>
		<li>Familiarize subjects with separate elements of the tasks that might elicit neophobic reactions. 
		NOTE: These separate elements (i.e., everything but the basic boxes, platform, and fixation system) must not be combined into functional mechanisms at this stage.</li>
		<li>Place one reward inside the box (center). Bring the subject into the compartment.</li>
		<li>Wait for the length of a session without interfering. Subjects are now supposed to eat the rewards. 
		NOTE: The duration of these habituation sessions differed in the experiment: long-term captive birds received 10 min, while short-term captive cockatoos had 20 min to eat the rewards. This was necessary to account for a substantially longer feeding time due to different reward types. This issue was addressed differently later in the test sessions (see step 4.8).</li>
		<li>Repeat for each subject (one session per test day) until criterion is reached: Each individual consumes all the rewards from three-sided boxes (one reward per box) within three consecutive sessions while being visually isolated from the group.</li>
	</ol>
	</li>
	<li>Habituation to functional arena (until criterion II)
	<ol>
		<li>Glue and permanently attach all the necessary elements to the boxes to make them functional puzzle boxes. 
		NOTE: At this point, the arena is fully functional as in the test sessions.</li>
		<li>Place the boxes randomly on the platform (they will be kept in place by the bases) and secure the lids to the boxes.</li>
		<li>Place one reward on the lid of each box on the edge closest to the center of the arena.</li>
		<li>Bring the subject into the compartment.</li>
		<li>Wait for the length of a session without interfering. 
		NOTE: Subjects are supposed to now eat the rewards.</li>
		<li>Repeat for each subject (one session per test day) until the criterion is reached. 
		NOTE: Criterion II: Individual consumes all the rewards from the top of the functional puzzle boxes (one reward per box) within one session while being visually isolated from the group. This criterion II will ensure that the subjects are not afraid of the arena, even when new parts are attached. They should however not interact with the mechanisms and should be interrupted if they do so.</li>
	</ol>
	</li>
</ol>
4. Testing
<ol>
	<li>Place the boxes on the platform according to the randomization schedule.</li>
	<li>Bait each task at the appropriate location inside the boxes (see Figure 2). 
	NOTE: The exact location of each reward depends on the specific task and can be seen in the video.</li>
	<li>Attach the lids to the boxes and secure them with the fixation system (to make sure subjects cannot pull them off).</li>
	<li>Separate one individual subject and bring it into the test compartment. Subjects are tested one at a time to avoid interference of social learning.</li>
	<li>Either position them on the start position (i.e., the point which is at equal distance to all the tasks at the center of the platform) or place an incentive (e.g., a reward) at the start position to ensure that the subject begins there.</li>
	<li>Start the timer and wait for 20 min (session duration) without interfering or interacting with the subject. The subject can solve as many tasks as possible.</li>
	<li>If the subject gets distracted with non-apparatus related objects, the experimenter is allowed to place them back at the start position of the arena (if possible).</li>
	<li>If the subject feeds for longer than 3 s on the reward, stop the timer, wait until the feeding is finished, and then resume timing. 
	NOTE: This is done to ensure that the maximum time available to solve tasks is not reduced by feeding time and therefore equal for both groups.</li>
	<li>If the subject does not interact with any task within the first 3 min and is also not agitated, apply a motivation protocol (see section 5).</li>
	<li>Once the 20 min have elapsed (maximum duration of one session) or the participant has solved all the tasks, the subject is done with the testing for the day and can be released back into the home area.</li>
	<li>On the next test day, repeat this procedure.</li>
	<li>Continue testing each individual until it either does not solve any new task&#160;in the last five sessions or does not solve any task&#160;at all in 10 consecutive sessions.</li>
</ol>
5. Motivation protocol
NOTE: As described above (step 4.9), a motivational protocol can be implemented if an individual does not interact with any task within the first 3 min of a session.
<ol>
	<li>Place three rewards on top of the boxes (choose a box on the left, middle, and right side for this). If the subject starts interacting with any task 3 min after consuming the rewards, resume the session (the 20 min duration starts at this point).</li>
	<li>If not, place five rewards dispersed on the approach line (i.e., proximity grid). If the subject starts interacting with any task 3 min after consuming the rewards, resume the session (the 20 min duration starts at this point).</li>
	<li>If not, place five rewards at the starting position. If the subject starts interacting with any task 3 min after consuming the rewards, resume the session (the 20 min time frame starts at this point).</li>
	<li>If not, place a handful of rewards at the start position and terminate the test session for this day (but give the subject some time to consume the rewards).</li>
</ol>
6. Analysis
<ol>
	<li>Behavioral coding
	<ol>
		<li>Before analyzing the videos examine the coding protocol in detail (Table 1) and consider whether adjustments are necessary for the species being tested. 
		NOTE: The descriptions of the coding variables should be as specific as possible in order to avoid coding differences between various researchers.</li>
		<li>Annotate point events of: Number of different tasks touched (TasksTouched; Note that the maximum number of tasks touched is 20), number of tasks solved (TasksSolved), contact with baited tasks (BaitedContact), and contact with solved tasks (SolvedContact).</li>
		<li>Annotate durations for latency until the subject crosses the outer boarder of the grid line (LatencyGrid) and time spent within the grid (GridTime).</li>
	</ol>
	</li>
	<li>Statistical analysis
	<ol>
		<li>Determine whether measures for apparatus-directed behaviors (LatencyGrid, GridTime, TasksTouched, BaitedContact, SolvedContact) are correlated.</li>
		<li>If yes, then extract the principal components using a Principal Component Analysis before including them in the model as predictors.</li>
		<li>If they are not correlated, include them separately in the model as predictors.</li>
		<li>Run a Generalized Linear Mixed Model with binomial error structure and logit link function36. To predict the probability of success (i.e., the response variable being SolvedTasks), fit the model with maximal random slope structure and include random intercepts for subject, task, a combined factor of subject and session (SessionID), and a combined factor of subject and task (Subj.Task) to avoid pseudo-replication. Use the comparison of interest (e.g., species) and the Principal Components as predictor variables and control for session. Consider possible interactions.</li>
		<li>To avoid cryptic multiple testing37 first compare the model with a model lacking all fixed effects of interest before testing individual predictors.</li>
		<li>To test for an overall difference of difficulty in tasks between groups, compare the (full) model with one lacking the random slope of group within task.</li>
	</ol>
	</li>
</ol>

The authors declare no conflicts of interest.

The Innovation Arena is a new protocol to test innovative, technical problem-solving. When designing the tasks of the Innovation Arena, we carefully considered that the tasks should be possible to solve given a range of species&#39; morphological constraints (e.g., using beaks, snouts, paws, claws, or hands). To enable broader comparability between species already tested and species to be tested in the future, we encourage the use of these tasks, if feasible with the respective model. However, we are aware that some tasks might need to be adjusted to specific morphological limits of a species. Most importantly, the tasks need to be novel to the subjects, which may require new, alternative designs. One advantage of the Innovation Arena is that, due to the number of different tasks, comparisons will still be possible and informative even if some tasks need to be adjusted or changed in future studies.
While planning the study, it should be considered that the pre-testing phase (e.g., designing and constructing the apparatus) might require a considerable amount of time. Further, it is important to thoroughly habituate the subjects to the apparatus. Different groups can differ substantially in their explorative approach and neotic reactions40,41,42. The elimination (or reduction) of neophobic reactions will make comparisons more reliable and allow the role of motivation to be identified. To measure the individual emergence of innovations over time and to avoid social learning, it is crucial that subjects are tested repeatedly and individually, which may be challenging under field conditions. For many species, wild-caught subjects will need extensive time to habituate to the new environment, human presence, and interaction and to develop a working separation procedure. Furthermore, it might not be practically possible to strictly adhere the randomization schedule for each individual per session. While the long-term captive cockatoos in our study were trained to enter the test compartment when called by their individual name, we needed to be more opportunistic with regard to which individual enters the test room in the field. Aside from levels of motivation, we encountered another factor that could influence the results of a comparative study using the Innovation Arena. Due to feeding preferences and food availability, we used different reward types for the two groups, which increased the feeding times of wild cockatoos compared to the lab birds. We accounted for these differences by adding feeding duration (if it exceeded 3 s) to the total amount of time an individual was confronted with the arena. This protocol ensured that the time to interact with the arena was not reduced in one group due to feeding time. Future studies should consider this potential issue and might aim to implement this protocol already in the habituation phase.
The strength and novelty of this method includes the combination of a greater variety of tasks, simultaneous presentation of these tasks, multiple rewards per encounter with the apparatus, and repeated exposure to the apparatus for each subject.
Further, individuals are tested until they do not solve any new tasks. In contrast to a fixed number of sessions, this maximum (or asymptotic level) of solution discovery, together with the number of tasks solved per session, can be informative about the potential adaption of a group to a changing environment.
An example of an alternative method is the Multi Access Box (MAB), in which it is possible to solve a task through four different solutions but only one reward can be retrieved per encounter with the apparatus18 and thus the estimation of innovation rate over time is significantly limited. Moreover, difficulties with single tasks, which might be species-specific, can strongly influence the comparison of performances with respect to cognitive abilities. To our knowledge, the simultaneous presentation of tasks with motorically distinct solutions has been limited to a maximum of six tasks in previous studies (Federspiel, 6-way MAB on mynah birds, data so far unpublished). While the MAB is a very useful tool to uncover exploration techniques, we think the Innovation Arena is better suited for the comparison of the ability to innovate itself. A wider range of tasks, which also vary in difficulty, can be more informative about an overall technical problem-solving competence29.
In our first study, we successfully compared two groups of the same species, the Goffin&#180;s cockatoo, which differed substantially in their experience. With this comparison, we specifically targeted the question of whether long-term captivity influences problem-solving abilities. Previous studies have suggested that a prolonged captive life style enhances those abilities (see30,43) but direct comparisons through controlled experimental approaches have been rare (but see44,45). By using the Innovation Arena, we were able to target this question and found no support for a captivity effect on the overall capacity of Goffins to find novel solutions, but rather an effect on a motivational level32.
Additionally, the Innovation Arena can be used to address questions focusing on different aspects of innovative problem-solving. Further steps could include investigations targeting the effects of divergence and convergence. For example, comparisons between closely-related species that differ in their ecologies (e.g., island species vs. non-island species), but also distantly-related species, such as a parrot and a corvid representative or avian and primate species that previously showed similar performances in individual physical problem solving46. The Innovation Arena was developed to compare many different species, even those that are distantly related.
That said, this method could very well also be used to investigate inter-individual differences. For example, one could use personality scores as predictors to estimate their influence on innovation rate. We believe the presented method can be used by research groups studying animal and human innovation, and/or collaboratively by labs that specialize in the study of different species.

A great tit (Parus major) is confronted with a milk bottle, but it cannot access the milk directly as the bottle is closed by an aluminum foil. It finds a solution to this problem by pecking through the foil so it can drink the cream. This situation describes one of the most widely known examples of animal innovation1.
Solving such problems can be advantageous, especially in environments that are subject to frequent change. Kummer and Goodall2 have broadly defined innovation as finding &#34;a solution to a novel problem, or a novel solution to an old one&#34;. A more detailed definition of innovation was postulated by Tebbich and colleagues3 as &#34;the discovery of a new behavioral interaction with the social or physical environment, tapping into an existing opportunity and/or creating a new opportunity&#34;.
Witnessing spontaneous innovations demands thorough and time-consuming observations, which is often not feasible in a framework that includes a wide variety of species. In order to deal with this challenge, researchers have conducted rigorous literature reviews to estimate innovation rate4,5 and have uncovered correlations between the propensity to innovate and other factors such as neurological measures6,7,8 and feeding ecology9,10,11. Experimental tests, however, can elicit innovative behavior in a controlled environment. For this reason, performances in technical problem-solving tasks are often used as a proxy for innovative capacities in animals (see review in12).
A variety of different approaches have been used to investigate innovative problem-solving: for example, different groups of animals can be compared by their performance on a particular task. Such studies are typically targeting specific innovations or cognitive abilities (e.g., hook-bending behavior; see13,14,15). This allows researchers to gain detailed information within a specific context, but the interpretation of any similarities or differences is limited by the nature of the task, which might require different innovative strength from different groups (as discussed in13,14).
Other studies have implemented a series of consecutive tasks16,17. A comparison of performances on multiple tasks and an estimation of overall competence within specific domains is made possible by this method. A limitation of such studies, however, is in the successive presentation of the different tasks, which does not allow for an investigation of the emergence of innovations over time.
Yet another approach is to simultaneously offer different options of accessing a single reward. This is frequently achieved by using the Multi Access Box (MAB)18,19,20,21,22,23,24,25,26, where one reward is placed in the center of a puzzle box and is retrievable via four different solutions. Once the same solution is used consistently, it is blocked and the animal needs to switch to another solution to access the reward. Through such an experiment, between and within species preferences can be detected and accounted for but it still limits the expression of innovative behavior to one solution per trial18,19,20,21. In other studies, animals have also been presented with apparatuses containing multiple solutions at the same time, each with separate rewards. This allows for multiple innovations within a single trial, but, so far, tasks have been largely limited to a few motorically distinct solutions. Given it was not the focus of these studies, the experimental setups did not involve repeated exposures to the apparatus, which would allow for a measure of innovation rate per time unit27,28,29.
Here we present a method that, in addition to other approaches, may help us in the aim of comparing different species in their innovative problem-solving abilities. We developed a wider range of tasks within a single setup, which are expected to differ in difficulty per group or species. It is, therefore, less likely that task-specific disparities influence the overall probability of finding solutions. Furthermore, we present all tasks simultaneously and repeatedly to measure the emergence of innovations over time. This measure has the potential to enhance our understanding on the adaptive value of innovative behavior.
A first study using this method has investigated whether long-term captivity influences problem-solving abilities (as suggested by the so-called captivity effect; see30) of the Goffin`s cockatoo (Cacatua goffiniana; hereafter: Goffins), an avian model species for technical innovativeness (reviewed in31).

<table><tbody><tr><td>wooden platform</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: wooden semicircle, radius approx. 1.5m</td></tr><tr><td>&lt;strong&gt;FIXATION SYSTEM&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>5 x metal nut</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8</td></tr><tr><td>5 x rod</td><td></td><td></td><td>(possibly with U-profile)</td></tr><tr><td>5 x threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8; length: 25cm</td></tr><tr><td>5 x wing nut</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8</td></tr><tr><td>&lt;strong&gt;PUZZLE BOXES WITHOUT FUNCTION PARTS&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>20 x acrylic glass back</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 17cm x 17.5cm x 0.5cm</td></tr><tr><td>20 x acrylic glass base</td><td></td><td></td><td>4 holes for screws roughly 2cm from each side&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid : 17.5cm&amp;nbsp; (back) x 15cm (front) x 15cm (sides); 1cm thick</td></tr><tr><td>20 x acrylic glass front</td><td></td><td></td><td>acrylic glass fronts need to be cut differently for each puzzle box (see drawing)&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 17cm x 15cm x 0.5cm</td></tr><tr><td>20 x acrylic glass lid</td><td></td><td></td><td>cut out 0.5cm at the edges for better fit&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid shape: 18.5cm x 16cm x 16cm x 1cm (thick)</td></tr><tr><td>40 x acrylic glass side</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 17cm x 16cm x 0.5cm</td></tr><tr><td>80 x small screw</td><td></td><td></td><td>to attach bases to the platform (4 screws per base)</td></tr><tr><td>&lt;strong&gt;PARTS FOR EACH MECHANSIM PER TASK&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>to assemble the parts use technical drawing InnovationArena.3dm</td><td></td><td></td><td>can be loaded e.g. in 3dviewer.net, which is a free and open source 3D model viewer. github repository: https://github.com/kovacsv/Online3DViewer; please contact authors if you are in need of a different format</td></tr><tr><td>&lt;strong&gt;TASK TWIST&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>5x small nut</td><td></td><td></td><td>to attach glass (punch holes) and acrylic glass cube to threaded rod</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 2cm x 2cm x 1cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>plastic shot glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: height: 5cm; rim diameter: 4.5cm; base diameter: 3cm</td></tr><tr><td>thin threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 10cm</td></tr><tr><td>&lt;strong&gt;TASK BUTTON&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x nut</td><td></td><td></td><td>attach to rod; glue outer nut to rod&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: M8</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>V-cut to facilitate sliding of rod&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 4cm x 3cm x 1cm (0.5cm V-cut in the middle)</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8, length: 5cm</td></tr><tr><td>&lt;strong&gt;TASK SHELF&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>acrylic glass top</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 4cm x 0.3cm</td></tr><tr><td>acrylic glass lower</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 4cm x 1cm</td></tr><tr><td>acrylic glass side 1</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4cm x 3cm x 0.5cm</td></tr><tr><td>acrylic glass side 2</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4.5cm x 3cm x 0.5cm</td></tr><tr><td>thin plastic bucket</td><td></td><td></td><td>one side cut off to fit&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: diameter: approx. 4.5 cm; height: 1cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>&lt;strong&gt;TASK SLIT&lt;/strong&gt;</td><td></td><td></td><td>room to reach in: 2cm in height</td></tr><tr><td>-</td><td></td><td></td><td>recommended: add small plastic barrier behind reward so it cannot be pushed further into the box</td></tr><tr><td>&lt;strong&gt;TASK CLIP&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 1cm x 1cm x 2cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>peg</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 6 cm</td></tr><tr><td>thin threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 6 cm</td></tr><tr><td>&lt;strong&gt;TASK MILL&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x arylic glass triangle</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 10cm x 7.5cm x 7.5cm; thickness: 1cm</td></tr><tr><td>2x plastic disc</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: diameter: 12cm</td></tr><tr><td>4x small nut</td><td></td><td></td><td>for attachment</td></tr><tr><td>7x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4.5cm x 2cm, 0.5cm</td></tr><tr><td>acrylic glass long</td><td></td><td></td><td>position the mill with longer acrylic glass touching lower half of the front (this way the mill can only turn in one direction)&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 6.5cm x 2cm, 0.5cm</td></tr><tr><td>thin threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 4cm</td></tr><tr><td>wooden cylinder</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: diameter: 2cm</td></tr><tr><td>&lt;strong&gt;TASK SWISH&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 2cm x 1cm x 1cm</td></tr><tr><td>4x small nut</td><td></td><td></td><td>for attachment</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 10cm x 2cm x 1cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>thin threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 7cm</td></tr><tr><td>wooden cylinder</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: diameter: 2cm, cut-off slantwise; longest part: 7cm, shortest part: 5cm</td></tr><tr><td>&lt;strong&gt;TASK SHOVEL&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 20cm x 2cm x 1cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 7.5cm x 2cm x 1cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 1cm x 1cm</td></tr><tr><td>small hinge</td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;TASK SWING&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>4x nut</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 7.5cm x 5cm x 1cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>cord strings</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 2x approx. 11cm</td></tr><tr><td>thin bent plastic</td><td></td><td></td><td>bucket to hold reward; positioned on slant</td></tr><tr><td>threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: M8; length: 7cm</td></tr><tr><td>&lt;strong&gt;TASK SEESAW&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 10cm x 1.5cm x 0.5cm</td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4cm x 1.5cm x 0.5cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 10cm x 3cm x 0.5cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4cm x 1.5cm x 1cm</td></tr><tr><td>small hinge</td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;TASK PLANK&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>thin tin</td><td></td><td></td><td>bent approx. 1cm inside box&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 6.5cm x 3cm</td></tr><tr><td>&lt;strong&gt;TASK CUP&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>plastic shot glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: height: 5cm; rim diameter: 4.5; base diameter: 3cm</td></tr><tr><td>&lt;strong&gt;TASK FLIP-BOX&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x acrylic glass triangle</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 7cm x 5cm x 5cm; thickness: 0.5cm</td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4.5cm x 5cm x 0.5cm</td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 7cm x 1cm x 1cm</td></tr><tr><td>small hinge</td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;TASK SLIDE&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>4x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 15cm x 1cm x 0.5cm</td></tr><tr><td>acrylic glass door</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 6cm x 6cm x 0.5cm</td></tr><tr><td>&lt;strong&gt;TASK DJ&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x small nut</td><td></td><td></td><td>for attachment</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>same as box bases&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid : 17.5cm (back) x 15cm (front) x 15cm (sides); 1cm thick</td></tr><tr><td>plastic disc</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: diameter 12cm</td></tr><tr><td>thin threaded rod</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 3cm</td></tr><tr><td>&lt;strong&gt;TASK WIRE&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 9.5cm x 9.5cm x 0.5cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 12cm x 2cm x 1cm</td></tr><tr><td>2x small hinge</td><td></td><td></td><td></td></tr><tr><td>wire from a paperclip</td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;TASK TWIG&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x small hinge</td><td></td><td></td><td></td></tr><tr><td>acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 1cm</td></tr><tr><td>cardboard slant</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid: 17.5cm (back) x 15cm (front) x 17cm (sides)</td></tr><tr><td>white cardboard</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 13cm x 4cm</td></tr><tr><td>Y-shaped twig</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: length: approx. 14cm</td></tr><tr><td>&lt;strong&gt;TASK COVER&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>acrylic glass</td><td></td><td></td><td>same as box bases&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: trapezoid : 17.5cm (back) x 15cm (front) x 15cm (sides); 1cm thick</td></tr><tr><td>thin plastic</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: diameter: 5cm</td></tr><tr><td>&lt;strong&gt;TASK BITE&lt;/strong&gt;</td><td></td><td></td><td>recommended: put tape on sides of platform the keep reward from falling off</td></tr><tr><td>2-3 paper clips</td><td></td><td></td><td></td></tr><tr><td>2x cutout from clipboard</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 10cm x 3cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>hole in middle&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 3cm x 1cm</td></tr><tr><td>toilet paper</td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;TASK DRAWER&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 2.5cm x 0.5cm</td></tr><tr><td>2x acrylic glass</td><td></td><td></td><td>&lt;strong&gt;Dimensions&lt;/strong&gt;: 4cm x 3cm x 1cm</td></tr><tr><td>acrylic glass</td><td></td><td></td><td>hole approx. 2 cm from front&lt;br/&gt; &lt;strong&gt;Dimensions&lt;/strong&gt;: 5cm x 5cm x 1cm</td></tr><tr><td>&lt;strong&gt;OTHER MATERIAL&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>wide-angle videocamera</td><td></td><td></td><td></td></tr></tbody></table>

the innovation arena: a method for comparing innovative problem-solving across groups

Problem-solving tasks are commonly used to investigate technical, innovative behavior but a comparison of this ability across a broad range of species is a challenging undertaking. Specific predispositions, such as the morphological toolkit of a species or exploration techniques, can substantially influence performance in such tasks, which makes direct comparisons difficult. The method presented here was developed to be more robust with regard to such species-specific differences: the Innovation Arena presents 20 different problem-solving tasks. All tasks are presented simultaneously. Subjects are confronted with the apparatus repeatedly, which allows a measurement of the emergence of innovations over time - an important next step for investigating how animals can adapt to changing environmental conditions through innovative behavior.
Each individual was tested with the apparatus until it ceased to discover solutions. After testing was concluded, we analyzed the video recordings and coded successful retrieval of rewards and multiple apparatus-directed behaviors. The latter were analyzed using a Principal Component Analysis and the resulting components were then included in a Generalized Linear Mixed Model together with session number and the group comparison of interest to predict the probability of success.
We used this approach in a first study to target the question of whether long-term captivity influences the problem-solving ability of a parrot species known for its innovative behavior: the Goffin&#180;s cockatoo. We found an effect in degree of motivation but no difference in the problem-solving ability between short- and long-term captive groups.

Nineteen subjects were tested using the Innovation Arena: 11 long-term and 8 short-term captive cockatoos (Figure 4).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63026/63026fig04.jpg" /> 
Figure 4: An overview of the number of tasks solved per session for each individual. a) Field group, b) Lab group. Red lines = female; blue lines = male. Subjects receiving the motivational protocol due to their reluctancy to interact with the apparatus were classified as not motivated and depicted with a gray background. Previously published in Supplementary Information of32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The Principal Component Analysis resulted in two components having Eigenvalues above Kaiser&#180;s criterion38 (see Table 2 for PCA output). PC1 loaded on frequency of contacts with tasks, time spent in proximity (i.e., within the grid) of the tasks, and the number of tasks touched. PC2 was positively affected by the number of contacts with already solved tasks and negatively with the number of tasks touched, not solved. Such task-directed behaviors are frequently used for measuring motivation (see12 for a review). Therefore, we used PC1 and PC2 as&#160;quantitative measures for motivation to interact with the apparatus in our model. Together they explained 76.7% of the variance in apparatus-directed behaviors and both, as well as session, significantly influenced the probability to solve tasks (PC1: estimate = 2.713, SE &#177; 0.588, &#967;2&#160;= 28.64, p &#60; 0.001; PC2: estimate = 0.906, SE &#177; 0.315, &#967;2&#160;= 9.106, p = 0.003; session: estimate = 1.719, SE &#177; 0.526, &#967;2&#160;= 6.303, p = 0.001; see Figure 5; see&#160;Table 4).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63026/63026fig05.jpg" /> 
Figure 5: Influence of control predictors on the probability to solve: (a) PC1, (b) PC2, (c) Session. Points show observed data, area of points indicates the number of observations for each data point, dashed lines show fitted values of model and areas symbolize confidence intervals of model. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Six out of the 19 subjects received the motivational protocol during the experiment (Lab: 1 out of 11; Field: 5 out of 8). PC1 of these birds, which we categorized as not motivated, ranged between -2.934 to -2.2, while positive values were found for all other motivated individuals (Table 3).
With the presented method we found no difference of group on the probability to solve the 20 technical problem-solving tasks of the Innovation Arena (estimate = &#8722;0.089, SE &#177; 1.012, &#967;2&#160;= 0.005, p = 0.945; Figure 5; see Table 4 for fixed effects estimates; all birds included).
A post-hoc comparison of the model with one including an interaction term of group with session (estimate = 2.924, SE &#177; 0.854, &#967;2&#160;= 14.461, p &#60; 0.001) suggests lower probability to solve in the field group in earlier sessions but not in the later. This difference in earlier sessions might be due to the high number of less/not motivated birds in group field (individuals for which testing stopped due to the rule of not solving any task in 10 consecutive sessions received between 10 and 13 sessions).
Further, we found no difference between the groups regarding the overall difficulty of tasks (comparison of full model with all birds included, with a reduced model lacking random slope of Group within Task:&#160;&#967;2&#160;= 7.589, df = 5, p = 0.18). However, visual comparisons of birds that never required a motivational trial, hint to some differences in ability for single individual tasks (see, e.g., the Button task in Figure 6).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63026/63026fig06.jpg" /> 
Figure 6: Observed data of motivated subjects and fitted values of model per task and group: Boxplots show the proportion of successes per task for both groups (green = Field; orange = Lab). Bold horizontal lines indicate median values, boxes span from the first to third quartiles for birds. Boxplots illustrate data from motivated birds only (to improve visual clarity). Individual observations are depicted by points (larger area indicates more observations per data point). Red horizontal lines show fitted values. Fitted values originate from the whole data set. Included are illustrations of Bite (bottom left), Button (top middle) and Seesaw (top right) tasks. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/63026fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
These results demonstrate the feasibility of the methodology for comparative research even if the animals have different experiences and ecological circumstances. A comparison of innovative problem-solving abilities using only a single task, such as the Button task, might have yielded a false conclusion that long-term captive birds are better problem-solvers. This difference could be explained by the lab population&#8217;s experience with stick insertion experiments while the motor action might not be as ecologically relevant for wild populations. Such differences could potentially be more pronounced when different species are compared (see19). We were further able to test how motivation affects problem-solving ability, while at the same time comparing the results of the two groups while controlling for motivation.
The 20 technical problems of the Innovation Arena can therefore be used to detect group differences on particular tasks, but also to estimate the overall innovative ability of groups. In the case of the Goffin`s cockatoo, both groups can, i.e., have the ability to, retrieve many rewards, if they want to, i.e., are motivated to interact with the apparatus.
Table 1: Protocol for coding behaviors: Detailed description of coded behavioral variables. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/JoVe_Table_1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Principal component output: Factor loadings above 0.40 are printed in bold. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/JoVe_Table_2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Details on subjects and values of task-directed behaviors and principal components: Superscripts if measure loads go above 0.40 per PC. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/JoVe_Table_3.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Fixed effects results of the model for probability to solve. Previously published in32. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/JoVe_Table_4.xlsx" target="_blank">Please click here to download this Table.</a>
Supplementary File: Technical drawing of the Innovation Arena (InnovationArena.3dm). Dimensions might deviate slightly. Can be loaded, e.g., in 3dviewer.net, which is a free and open source 3D model viewer39. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63026/InnovationArena.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups at JoVE.com

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena is a novel comparative method for studying technical innovation rate per time unit in animals. It is composed of 20 different problem-solving tasks, which are presented simultaneously. Innovations can be carried out freely and the setup is robust with regard to predispositions on an individual, population or species level.

Problem-solving tasks are commonly used to investigate technical, innovative behavior but a comparison of this ability across a broad range of species is ...

the-innovation-arena-method-for-comparing-innovative-problem-solving

Nanyang Technological University

Research

JoVE Journal

Behavior

5.7K Views.  University of Veterinary Medicine Vienna, University of Vienna, Medical University of Vienna. The Innovation Arena is a novel comparative method for studying technical innovation rate per time unit in animals. It is composed of 20 different problem-solving tasks, which are presented simultaneously. Innovations can be carried out freely and the setup is robust with regard to predispositions on an individual, population or species level.

Video: The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Use of the Puzzle Box as a Means of Assessing the Efficacy of Environmental Enrichment

Environmental enrichment can dramatically influence the development and function of neural circuits. Further, enrichment has been shown to successfully delay the onset of symptoms in models of Huntington&#8217;s disease 1-4, suggesting environmental factors can evoke a neuroprotective effect against the progressive, cellular level damage observed in neurodegenerative disorders. The ways in which an animal can be environmentally enriched, however, can vary considerably. Further, there is no straightforward manner in which the effects of environmental enrichment can be assessed: most methods require either fairly complicated behavioral paradigms and/or postmortem anatomical/physiological analyses. This protocol describes the use of a simple and inexpensive behavioral assay, the Puzzle Box 5-7 as a robust means of determining the efficacy of increased social, sensory and motor stimulation on mice compared to cohorts raised in standard laboratory conditions. This simple problem solving task takes advantage of a rodent&#8217;s innate desire to avoid open enclosures by seeking shelter. Cognitive ability is assessed by adding increasingly complex impediments to the shelter&#8217;s entrance. The time a given subject takes to successfully remove the obstructions and enter the shelter serves as the primary metric for task performance. This method could provide a reliable means of rapidly assessing the efficacy of different enrichment protocols on cognitive function, thus paving the way for systematically determining the role specific environmental factors play in delaying the onset of neurodevelopmental and neurodegenerative disease.

Environmental enrichment provides a potential protective effect against neurodegenerative disorders. Currently, however, there is no easy way of determining the efficacy of enrichment procedures. This protocol describes a simple &#8220;Puzzle Box&#8221; method for assessing an animal&#8217;s cognitive function, in order to reveal the effectiveness of environmental enrichment.

Environmental enrichment can dramatically influence the development and function of neural circuits.  Further, enrichment has been shown to successfully ...

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization through marketing technology. The purpose of this study is to examine how to conduct consumer research using an online survey and analysis of data. This study examines consumers&#39;&#160;perceived benefits while customizing a product as well as emotional product attachment, attitudes toward a customization program, and loyalty intentions in the context of online retailing. In addition, this study investigates how consumer responses are different based on individual characteristics such as fashion innovativeness. An online survey company in South Korea recruited 290 female apparel shoppers who purchased apparel online. To enhance external validity, this study used an existing retail website with a well-established mass customization program. After completing the customization program, participants complete the online questionnaire. Structural equation modeling (SEM) and latent mean analyses (LMAs) are then performed for analyses. This study stresses the importance of testing measurement invariance for mean comparisons. Before the SEM and LMA, this study follows the hierarchy of invariance tests (configural invariance test, metric invariance test, and scalar invariance test), which are not considered by traditional approaches such as ANOVA. These statistical analyses provide applicability of the invariance test procedures and LMA to consumer behaviors. The conclusions of mean differences have integrity and validity because they are guided by a sophisticated statistical procedure to ensure measurement invariance.

Presented here is a protocol to examine consumer responses toward mass customization in the context of online retailing. The protocol details the online survey procedure and how to analyze data using structural equation modeling and group differences using latent mean analyses.

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization ...

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Nowadays, how to encourage students' reflective thinking is one of the main concerns for teachers at various educational levels. Many students have difficulties when facing tasks that involve high levels of reflection, such as on STEM (Science, Technology, Engineering and Mathematics) courses. Many also have deep-rooted anxiety and demotivation towards such courses. In order to overcome these cognitive and affective challenges, researchers have suggested the use of "Problem-Solving before Instruction" (PS-I) approaches. PS-I consists of giving students the opportunity to generate individual solutions to problems that are later solved in class. These solutions are compared with the canonical solution in the following phase of instruction, together with the presentation of the lesson content. It has been suggested that with this approach students can increase their conceptual understanding, transfer their learning to different tasks and contexts, become more aware of the gaps in their knowledge, and generate a personal construct of previous knowledge that can help maintain their motivation. Despite the advantages, this approach has been criticized, as students might spend a lot of time on aimless trial and error during the initial phase of solution generation or they may even feel frustrated in this process, which might be detrimental to future learning. More importantly, there is little research about how pre-existing student characteristics can help them to benefit (or not) from this approach. The aim of the current study is to present the design and implementation of the PS-I approach applied to statistics learning in undergraduate students, as well as a methodological approach used to evaluate its efficacy considering students' pre-existing differences.

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

This protocol guides researchers and educators through implementation of the Problem-Solving before Instruction approach (PS-I) in an undergraduate statistics class. It also describes an embedded experimental evaluation of this implementation, where the efficacy of PS-I is measured in terms of learning and motivation in students with different cognitive and affective predispositions.

Nowadays, how to encourage students' reflective thinking is one of the main concerns for teachers at various educational levels. Many students have ...

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Similarity judgments are commonly used to study mental representations and their neural correlates. This approach has been used to characterize perceptual spaces in many domains: colors, objects, images, words, and sounds. Ideally, one might want to compare estimates of perceived similarity between all pairs of stimuli, but this is often impractical. For example, if one asks a subject to compare the similarity of two items with the similarity of two other items, the number of comparisons grows with the fourth power of the stimulus set size. An alternative strategy is to ask a subject to rate similarities of isolated pairs, e.g., on a Likert scale. This is much more efficient (the number of ratings grows quadratically with set size rather than quartically), but these ratings tend to be unstable and have limited resolution, and the approach also assumes that there are no context effects.
Here, a novel ranking paradigm for efficient collection of similarity judgments is presented, along with an analysis pipeline (software provided) that tests whether Euclidean distance models account for the data. Typical trials consist of eight stimuli around a central reference stimulus: the subject ranks stimuli in order of their similarity to the reference. By judicious selection of combinations of stimuli used in each trial, the approach has internal controls for consistency and context effects. The approach was validated for stimuli drawn from Euclidean spaces of up to five dimensions. 
The approach is illustrated with an experiment measuring similarities among 37 words. Each trial yields the results of 28 pairwise comparisons of the form, "Was A more similar to the reference than B was to the reference?" While directly comparing all pairs of pairs of stimuli would have required 221445 trials, this design enables reconstruction of the perceptual space from 5994 such comparisons obtained from 222 trials.

The protocol presents an experimental psychophysics paradigm to obtain large quantities of similarity judgments, and an accompanying analysis workflow. The paradigm probes context effects and enables modeling of similarity data in terms of Euclidean spaces of at least five dimensions.

Similarity judgments are commonly used to study mental representations and their neural correlates. This approach has been used to characterize perceptual ...

Neuroscience

From Theory to Design: The Role of Creativity in Designing Experiments

Source: Laboratories of <a href="https://www-jove-com.remotexs.ntu.edu.sg/author/Gary_Lewandowski">Gary Lewandowski</a>, Dave Strohmetz, and Natalie Ciarocco&#8212;Monmouth University

Research studies come into being when a researcher speculates about human thought, emotions, or behavior, and has a theory that offers a potential explanation. Often the researcher&#8217;s theory is firmly situated in everyday common experiences that may not naturally lend themselves to direct empirical study. 
 
For example, researchers speculated that perception of a person on Facebook is influenced by the appearances and comments of the person&#8217;s Facebook friends.1 It is difficult to test this theory using real-life Facebook profiles. Instead, researchers must use their creativity to design a study&#8212;in this case, using fake profiles that look highly realistic&#8212;to test their theory.&#160;

This video demonstrates how researchers test a central tenet of a popular social psychology theory. Specifically, this video shows a test of whether engaging in a self-expanding activity leads a person to feel a greater sense of self-efficacy.2

Psychological studies often use higher sample sizes than studies in other sciences. A large number of participants helps to ensure that the population under study is better represented, i.e., the margin of error accompanied by studying human behavior is sufficiently accounted for. In this video, we demonstrate this experiment using just 2 participants, one for each condition. However, as represented in the results, we used a total of 100 (50 for each condition) participants to reach the experiment&#8217;s conclusions.

Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco&#8212;Monmouth University

Research studies come into being when a ...

Education

JoVE Science Education

Psychology

Experimental Psychology

The Multi-group Experiment

Source: Laboratories of <a href="https://www-jove-com.remotexs.ntu.edu.sg/author/Gary_Lewandowski">Gary Lewandowski</a>, Dave Strohmetz, and Natalie Ciarocco&#8212;Monmouth University

A multi-group design is an experimental design that has 3 or more conditions/groups of the same independent variable. This video demonstrates a multi-group experiment that examines how different interethnic ideologies (multiculturalism and color-blind) influence feelings about diversity and actions toward and out-group member. In providing an overview of how a researcher conducts a multi-group experiment, this video shows viewers how to distinguish levels in variables, common types of conditions/groups to use (including placebo and empty-control conditions/groups), the process of conducting the study, the collection of results, and the consideration of their implications.&#160; &#160;

Designing and Conducting Multi-group Experiment

Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco&#8212;Monmouth University

A multi-group design is an experimental design ...

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 ...

Social Psychology

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data&#8212;instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model are commonly employed.
The Kaplan-Meier estimator is a non-parametric tool that estimates survival probabilities over time, producing survival curves that visually display the proportion of subjects surviving past specific time points. These curves are invaluable for comparing survival outcomes between groups, such as patients receiving different treatments. When researchers want to determine whether the observed differences in survival between groups are statistically significant, the log-rank test is often used. This test compares Kaplan-Meier curves without assuming a specific distribution of survival times, making it versatile for various research scenarios.
For more complex analyses, the Cox proportional hazards model provides a powerful framework for examining the relationship between survival time and multiple predictors, such as treatment type, age, or disease severity. This model calculates hazard ratios (HR), which quantify the relative risk of the event occurring in one group compared to another while adjusting for confounding variables. For instance, an HR of 1.5 suggests a 50% higher risk of the event in one group relative to another.
Consider a study comparing survival outcomes for ovarian cancer patients receiving two different chemotherapy regimens. Using the Kaplan-Meier estimator, researchers could visualize survival probabilities for each treatment group over time. If one group consistently shows higher survival rates, the log-rank test can determine whether the difference is statistically significant. To adjust for additional factors like age or cancer stage, the Cox model can be applied, providing hazard ratios that account for these variables and offering deeper insights into the effects of treatment.
Survival analysis is uniquely suited to medical research, offering robust methods for analyzing time-to-event data while accommodating censored observations. These tools enable researchers to compare treatment effectiveness, adjust for confounding variables, and draw reliable conclusions. By focusing not only on whether an event occurs but also on when it happens, survival analysis ensures that medical studies produce accurate, actionable findings critical for advancing patient care.

Porównanie analizy przeżycia dwóch lub więcej grup

Survival analysis evaluates the time until an event, like disease recurrence or death, utilizing techniques that account for censored data, where the ...

JoVE Core

Statistics

Unit 1

Survival Analysis

KEY TERMS AND CONCEPTS

Problem-Solving

Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
<ol>
	<li>Trial and Error: This involves testing multiple solutions until one works. Thomas Edison exemplified this by testing thousands of filaments for the light bulb. It&#39;s not the most efficient process, but it&#39;s often necessary when no clear path exists.</li>
	<li>Algorithms: These are step-by-step procedures guaranteeing a solution. While precise, they can be time-consuming. For instance, arranging the letters in SPLOYOCHYG in all possible ways involves 907,200 permutations.</li>
	<li>Heuristics: These are simpler, quicker strategies that reduce complexity. By recognizing familiar patterns or common combinations, like grouping &#39;CH&#39; and &#39;GY&#39; in SPLOYOCHYG, individuals narrow down possibilities efficiently. Heuristics are faster but can be error-prone.</li>
	<li>Insight: Sometimes, solutions appear suddenly. For example, when trying to solve a complex math problem, a student might suddenly see the correct approach or formula that makes everything clear. Brain scans reveal that insight involves a burst of activity in the right temporal lobe, reflecting a sudden realization.</li>
</ol>

Rozwiązywanie problemów

A problem-solving strategy is a plan of action used to find a solution. It involves trial and error, algorithms, heuristics, and insights. Effective ...