Supporting Information
The computer interactive. The interactive whose output is displayed in the main text of the paper
was run on four laptop computers, each with different specifications and peripheral components. It
was not possible to provide an identical user experience on each laptop and instead treatment (1-4, see
main text) was allocated at random on each computer after recruitment of a volunteer. This ensured
that variation in user behaviour with respect to computer was distributed approximately evenly across
treatments. Volunteers consisted predominantly of undergraduate biology students of both sexes and
all volunteers were naive as to the purpose and content of the interactive prior to playing. All users
were English speakers, familiar with the Roman alphabet. The various parts of the interactive game
are shown in Fig. S1. The first screen (Fig. S1A) introduced the game and could be perused by the
user for as long as they wished. The text read as follows: ‘Thanks for agreeing to take part in this
computer interactive. The goal of the interactive is to accumulate as many points as you can by
clicking the letters on the screen. Numerous letters will be flashed on the screen and you can select
one of them with the mouse. The value of the letter you have chosen will be displayed on the screen.
You can also decide not to select any of the letters on the screen. Based on scores you have obtained
previously, try to accumulate the maximum number of points as new screens of letters are presented.
Your accumulated score will be updated and displayed on the top of the screen. Each set of letters will
only be shown for a short time so be quick. The highest possible value of any letter is +10 and the
lowest possible value is -10. The interactive may be a little boring but it would be appreciated if you
could give it your full attention throughout’. After clicking an area at the bottom of the screen, the
game began and the user was taken to a 1.5 second screen (Fig. S1B) preparing them for the first
presentation (Fig. S1C). The letter presentation screen lasted for 3 seconds or until a letter was
clicked, and consisted of a number of letters, n, sampled randomly from the alphabet with n selected
from a uniform random distribution between 1 and 25. After the letter presentation they were
presented with a screen of 1.5 seconds duration that informed them of the score they had just obtained
(Fig. S1D). The cycle shown in Fig. S1B-D was repeated 70 times. Presentations 71-100 consisted of
a standardised task in which there were always 12 letters present with two of these consisting of a
random sample of the ‘host’ (most valuable) letters within presentations 1-70. This letter presentation
screen lasted for three seconds or until a letter was clicked as before and preparatory and score screens
(Fig. S1B and D) appeared around letter presentation screens 71-100 as before. A qualitative
description of strategy developed could be entered by the user at the end of the interactive (Fig. S1F).
The letters chosen to be ‘hosts’ were randomised between users.
Were users sufficiently motivated to develop efficient resource location strategies? We did not
introduce material reward (such as a money) for high scores within the game, however, we
determined whether users were sufficiently motivated to develop efficient resource location strategies
by plotting mean, accumulated number of points for users playing the treatment 4 game (the generalist
treatment where selection of non-hosts is punished –10 points, n = 17) versus a computer generated
randomly striking user playing the same game, within presentations 1-70 (the training part of the
game). It can be seen in Fig. S2 that the trajectories of the real users and the random player differ both
qualitatively and quantitatively. Whereas point accumulation of the random player proceeds on a
straight downwards trajectory into the region of negative cumulative scores, the real users, after an
initial dip in which valuable resources are located, begin to gain points and the trajectory proceeds
into the region of positive scores. This indicates that users are effectively able to locate valuable
resources and avoid harmful ones. We cannot determine if the behaviour of users is optimal, however
the evidence presented here and the general correspondence between the data in the main text and the
neural network model of behaviour of Tosh et al. (2009, discussed in the introduction of the main
text) indicates that the real users are playing the game in a similar manner to a network optimised
using a computational algorithm (see Tosh et al. 2009).
Model predictions when specialists are trained with five valuable letters and generalists with
ten. In the main text of the paper, specialists were trained with two valuable letters and generalists
with six. We repeated the interactive with a different set of 85 participants but now specialists were
trained with five valuable letters and generalists with ten. Five and ten letters were values that the
authors considered initially to be within the range of sensory processing and memorisation of humans,
however, as evidenced below, the absence of significant treatment differences indicated that these
numbers of letters could be potentially inappropriate and so the experiments were rerun with a smaller
number of letters used (results in main text). The interactive was run exactly as described above with
the exception that only a single laptop computer was used. Thus data presented here and in the main
text are not comparable quantitatively, but qualitative comparisons are valid. Fig. S3 shows that none
of the differences between specialists and generalist with respect to punishment/reward are
statistically significant at P = 0.05. Two-way ANOVA interactions between treatments for both
performance parameters in Fig. S3B and C were also non-significant at P = 0.05 (Fig. S3B, F1,75 =
0.277, P = 0.60; Fig. S3C, F1,81 = 0.173, P = 0.68). This indicates that the effects described in the
main text, namely that specialists have a greater tendency to outperform generalists in terms of
decision accuracy when individuals are rewarded rather than punished for mistakes, are dependent on
the absolute value of generalisation and specialisation selected for. It may be that the absolute level of
specialisation and generalisation needs to be relevant to the organism under study to observe
differences in decision accuracy. For example, memorising two letters is probably within the
capabilities of most humans, but quick recall of six may be outwith the abilities of most people. This
difference between two and six letters is, therefore, meaningful to the sensory and decision making
apparatus of humans. If however five letters are used to represent specialists and ten letters to
represent generalists, both specialisation and generalisation may be outwith the capabilities of humans
and so the different is of little relevance in terms of the performance of the sensory and decision
making apparatus. One is tempted to explain the absence of significant effects when five host letters
are used for specialists and ten for generalist as an example of ‘numerosity’: the ability of humans to
be able to accurately relate no more than four objects when simultaneously presented for a short
duration (Atkinson et al. 1976). However, in the present experiments host resources are accumulated
in the memory across numerous presentations of a small (usually < 4) number of hosts, hence the
concept of ‘numerosity’ is inappropriate to explain the present results.
How these additional results are viewed in relation to the importance of the concept of
decision accuracy in the evolution of niche width may depend on the disposition of the reader. To the
positive, we have clarified the circumstances in which this concept is likely to be applicable in nature.
To the negative, specialising to improve decision accuracy does not appear to be a universally
applicable mechanism. To the positive reader we suggest that decision accuracy may have a key role
to play in the evolution of niche width when the number of hosts of at least the specialist under study
is within the processing and memorisation capabilities of the organism under study. Thus a specialist
insect with two hosts may be a better decision maker than a generalist with six, but the decision
making efficiency of a specialist with 100 hosts may differ little from a generalist with 200.
References
Atkinson, J., Campbell, F. W. & Francis, M. R. 1976 MAGIC NUMBER 4 +/- 0 - NEW LOOK AT
VISUAL NUMEROSITY JUDGEMENTS. Perception 5, 327-334.
Tosh, C. R., Krause, J. & Ruxton, G. D. 2009 Theoretical predictions strongly support decision
accuracy as a major driver of ecological specialization. Proceedings of the National Academy
of Sciences of the United States of America 106, 5698-5702.
Fig. S1. The visual appearance of each part of the computer interactive. See text within
Supplementary Information for further details of each part.
Fig. S2. Mean, accumulated number of points for users playing the treatment 4 game (the
generalist treatment where selection of non-hosts is punished –10 points, n = 17) versus a
computer generated randomly striking user playing the same game, within presentations 170 (the training part of the game). The real players are shown by a solid line and the
random player by a dashed one.
Fig. S3. Behaviour of specialists and generalists under different regimes of reward/punishment for selecting a
non-host resource during training. In the computer interactive, users are selected to be specialised by rewarding
five letters and selected to be generalised by rewarding ten letters. Simulations are otherwise as described in the
main text, Fig. 1 main text, and the first section of the Supplementary Information. n (specialist-reward for
selecting non-hosts) = 25, n (specialist-punished for selecting non-hosts) = 19, n (generalist-reward for selecting
non-hosts) = 20, n (generalist-punished for selecting non-hosts) = 21.