task. Explaining the complex and little understood field of neuroscience with the strangeness of quantum mechanics may seem to not hold much merit. Rather are neurobiological phenomena usually described on a level of abstraction that is measurable and comprehensible. This is a fruitful approach, if a researcher wants to prevent drifting into meta-science and speculation.
Nevertheless, the principles of quantum mechanics explains the microscale of the universe, including the particles that make up the nervous system. Hence, it is crucial for any natural science to not disregard the framework it is operating in. Talking about basic emotions or a contrast of cognition and affect, does not make sense in the roam of quantum systems with their inherent randomness.
Current affective models are theoretical concepts, which should guide research as long as they provide utility. If, however, they are restricting thinking patterns and hence progress, they should be discarded with the notion that they are modelling on a level of abstraction, which is unsuitable for describing newly discovered mechanisms and phenomena (Koch & Hepp, 2006).
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Appendices
A Participant Instructions
Instructions
Imagine that you live in a world without food stores and supermarkets, where your survival depends on your ability to find food. This game is about testing which decisions you would make in such a world. In the game, you will interact with several components of this forager world (foraging is the act of gathering food in the wild). You will move through different forests during the game and in each forest you will get to multiple clearings (the clearings are the areas in the forest where you can find the mushrooms). The goal is to forage mushrooms, which in turn give you food points. You can earn the most mushrooms by foraging successfully and not losing mushrooms during dangerous animal attacks.
Foraging
While walking through a forest, you will get to multiple clearings. At each clearing you will have to decide, if you would like to FORAGE or LEAVE. You can indicate this by clicking the respective button on the screen.
You can have different probabilities of foraging success at clearings. A higher success chance is
depicted by larger mushrooms. The success likelihood is also displayed below the mushrooms in % (e.g.
30%). Successful foraging will yield 1 food point, regardless of the mushroom size.
There is also a chance of not being successful in your foraging efforts. This will yield 0 food points. If you choose not to forage but to leave, you will also get 0 food points. In both cases you will not lose any food points either.
The possible probabilities of foraging success are 15%, 30%, 45% or 60%.
The possible probabilities remain the same throughout the whole experiment and do not change. Of
course, it can happen that your foraging is successful with the smallest possible mushrooms or that it is
not successful with the largest possible mushrooms. But on average, you are more often successful with
Successful foraging is signalled after the decision by
Unsuccessful foraging by
Dangerous Animals
Unfortunately, life in the forager world is also dangerous. If you do not forage but take the safe option and leave, you will not get any precious food points. If you forage, you run the risk of being attacked by a dangerous animal.
If a dangerous animal attacks you, you will be moved to the next forest immediately and lose the food points that you have earned in the current forest.
If for example, you have 1 food point from a previous forest and 1 from the current forest, you will lose only the food point from the current forest during a dangerous animal attack.
Similar to the mushrooms, the symbols are associated with different probabilities, which are depicted by the size of the symbol. The larger the animal symbol the more likely the animal is to attack. Again there is also a a numeric indicator below % (e.g. 20%).
The possible probabilities of attack are 10%, 20%, 30% or 40%. That is, the larger the symbol the worse
the situation is for you.
The possible probabilities remain the same throughout the whole experiment and do not change.
An animal attack after foraging is shown as
Forest
In the actual game, the mushrooms are combined with the dangerous animals within a forest.
That is, there are different combinations.
High probability for foraging success and low probability for dangerous animal.
High probability for foraging success and high probability for dangerous animal.
Low probability for foraging success and high probability for dangerous animal.
Low probability for foraging success and low probability for dangerous animal.
Etc.
In each forest, you remain for up to 4 clearings. You move to the next forest, either after 4 clearings or after a dangerous animal attack.
Clearings
As described above, you are supposed to decide at each clearing whether you want to go FORAGE or whether you want to LEAVE.
For each decision, you have 5 seconds. Please try to answer in time. If you are too slow 1 food point will be deducted from your final payoff. There is no time to spare when you are hungry in the forest.
This is an example of how the screen could look like:
New Forest
The different forests are independent of each other. Food points earned in one forest are therefore safe and cannot be lost in a later forest.
There are 4 forests you can forage in. Unlike for the clearings, this number is fixed and does not depend on your actions.
Payment
The food points earned in all forests will determine your total payoff after the game. The more food points you forage the higher your payoff. Keep in mind that being attacked in a forest means that you lose all food points previously collected in that forest.
Each food point is worth £0.15.
Trails (Videos)
Before entering a new forest you will see a video of animals. They represent animals that you encounter
Buttons
You can chose the FORAGE option by clicking the left button and the LEAVE option by clicking the right button.
Next
B Additional Models
(A) (B)
(Intercept) −0.0738 −0.1690
(0.3229) (0.3928) P(Success) 8.6314∗∗∗ 8.7956∗∗∗
(0.7495) (0.7515) P(Predator) −9.6854∗∗∗ −9.8671∗∗∗
(1.0579) (1.0476)
rnpmDS 0.9640∗∗∗ 0.9581∗∗∗
(0.2181) (0.2205) Negative Induction −0.2219
(0.2727) Positive Induction −0.2076 (0.2443)
Val 0.0050
(0.0049)
Aro −0.0036
(0.0082)
Dom 0.0118∗
(0.0054)
AIC 1059.0813 1052.3112
BIC 1090.5552 1089.0308
Log Likelihood −523.5407 −519.1556
Deviance 1047.0813 1038.3112
Num. obs. 1402 1402
∗∗∗p < 0.001;∗∗p < 0.01;∗p < 0.05;‘p < 0.1
Table 6: Direct Treatment/Affect. Logit Models describing the effect of Treat-ments and Valence on participant foraging choice. SEs are clustered by participant as implemented in the R package miceadds.