ArticlePDF Available

Choosing increases the value of non-instrumental information

Authors:

Abstract and Figures

Curiosity pervades all aspects of human behaviour and decision-making. Recent research indicates that the value of information is determined by its propensity to reduce uncertainty, and the hedonic value of the outcomes it predicts. Previous findings also indicate a preference for options that are freely chosen, compared to equivalently valued alternatives that are externally assigned. Here, we asked whether the value of information also varies as a function of self- or externally-imposed choices. Participants rated their preference for information that followed either a self-chosen decision, or an externally imposed condition. Our results showed that choosing a lottery significantly increased the subjective value of information about the outcome. Computational modelling indicated that this change in information-seeking behaviour was not due to changes in the subjective probability of winning, but instead reflected an independent effect of choosing on the value of resolving uncertainty. These results demonstrate that agency over a prospect is an important source of information value.
This content is subject to copyright. Terms and conditions apply.

Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports
Choosing increases the value
of non‑instrumental information
Matthew Jiwa1*, Patrick S. Cooper1,2, Trevor T.‑J. Chong2,3,4 & Stefan Bode1
Curiosity pervades all aspects of human behaviour and decision‑making. Recent research indicates
that the value of information is determined by its propensity to reduce uncertainty, and the hedonic
value of the outcomes it predicts. Previous ndings also indicate a preference for options that are
freely chosen, compared to equivalently valued alternatives that are externally assigned. Here, we
asked whether the value of information also varies as a function of self‑ or externally‑imposed choices.
Participants rated their preference for information that followed either a self‑chosen decision, or an
externally imposed condition. Our results showed that choosing a lottery signicantly increased the
subjective value of information about the outcome. Computational modelling indicated that this
change in information‑seeking behaviour was not due to changes in the subjective probability of
winning, but instead reected an independent eect of choosing on the value of resolving uncertainty.
These results demonstrate that agency over a prospect is an important source of information value.
Individuals are required to choose whether to receive information about a vast range of topics. From the caloric
contents of our favourite snacks, to the misdeeds of unknown celebrities, and our own genetic make-up, we now
have more information available at the touch of a button—or swipe of a credit card—than ever before. erefore,
how we decide which information to view or avoid is of increasing personal, social, and commercial relevance.
Although information can be used to increase the likelihood or magnitude of future rewards, recent ndings
suggest that the value of information is not determined by this utility alone. Both human and non-human animals
demonstrate a willingness to exchange rewards for information that cannot be used to inuence the probability
or magnitude of future rewards17. Recent neurophysiological data have shown that such non-instrumental
information is encoded by similar neural circuits as primary reinforcers5,810, with unexpectedly informative
signals producing similar neural responses to unexpectedly positive outcomes1,8. Beyond its instrumental value
(the extent to which that information can be used to increase the magnitude or likelihood of future rewards), the
subjective value of information is further determined by both its hedonic value (the expected aective response
to the information), and its cognitive value (the degree of uncertainty that viewing the information is expected
to resolve)5,1012.
Importantly, however, intrinsic biases and contextual factors may aect the contributions that each of these
factors make to the overall value of an informational prospect11. e hedonic value of information may be par-
ticularly susceptible to bias. Typically, it is found that prospects that oer a greater chance of a positive outcome
(or lesser chance of a negative one) elicit a greater willingness to pay to view that information than those with a
more negative outlook5,12,13. However, as ndings in the elds of behavioural economics and cognitive psychology
have repeatedly shown, individuals’ predictions of outcomes are oen open to systematic biases and heuristics
that may, in turn, aect the value of information pertaining to those prospects11,14,15. A common example is
choice-induced preference change—where the act of choosing an option increases its subjective value relative
to alternatives both during and aer the decision making process1623. In addition, choice may not only increase
an the subjective value of a chosen option, but also an individual’s curiosity about it24. However, the evidence
suggesting this does not account for pre-choice preferences, nor can it comment on whether increases in curios-
ity are due to choice-induced modulation, or more indirectly through an increase in the subjective value of the
chosen information.
Critically, individuals predict more favourable outcomes for prospects over which they have agency, rather
than those that are assigned to them—a phenomenon termed the “illusion of control” (IOC)14,25,26. As the prob-
ability of outcomes is crucial to the valuation of prospects, with higher probability of favourable outcomes leading
to a higher valuation of a prospect2729, there are clear theoretical implications of this subjective change for both
the behavioural and neural components of decision-making. As the neural responses to rewards are typically
OPEN

           
           
 *
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
encoded as the signed dierence between an actual reward and an expected reward (reward prediction error;
RPE), we may expect that an increased subjective probability of positive outcomes would lead to an attenuated
RPE in the event of a positive outcome30. However, evidence suggests that neural responses in the striatum may
not be aected by the IOC31. is nding suggests that the probability of winning used in the computation of
the RPE may dier from the probability of winning reported by participants31,32.
e question of whether subjective information value is aected by biases in probability produced by the IOC
remains an open one. In this study, we aimed to test whether agency over an arbitrary choice between alterna-
tive lotteries with identical probabilities of winning systematically increased: (a) participants’ condence in a
winning outcome (measured through self-reported condence levels) and (b) the valuation of receiving early,
non-instrumental information about the outcome of that prospect (measured by participants’ willingness to
exchange rewards for the early knowledge of a lottery’s outcome). In addition, we used computational modelling
to test whether the potential increase in condence in winning the lottery could explain any observed increase
in the information value, or if the increase in information valuation was otherwise better explained by a distinct
eect of agency on the factors determining the subjective value of information.
Results
To assess the inuence of choosing on information valuation, we manipulated participants’ perceived agency
during a simple lottery, in which the decision to play a specic “roulette wheel” could be approved or vetoed. In
a series of trials, participants were presented with three roulette wheels, which they were accurately instructed
each had the same probability of winning, and asked to choose their preferred prospect, similar to Kool etal.31
(see Fig.1a). e participant’s selection was either approved (granting agency over the trial) or vetoed (remov-
ing agency from the trial). ese lotteries utilised scrambled roulette wheels comprising segments of a winning
colour and a non-winning colour. e three wheels on each trial were rotated versions of the same conguration
of otherwise identical colour segments, with the probability to win 0.2, 0.4, 0.6 or 0.8 (see “Methods” for details).
Following the approval/veto stage, we probed the eects of agency on information value using two methods.
First, participants rated their condence to win that trial using a continuous scale. Second, participants were
administered a Becker–DeGroot–Marschak (BDM) auction33 in which they stated the maximum cost they would
be willing to incur in order to reveal the outcome of the lottery immediately. eir bid was then compared to
a random bid made by the computer, and, if the participant’s bid was higher than the computers bid, the latter
would be deducted from the participant’s points total, and they would learn the outcome of the trial. If their bid
was lower, they were instructed that the lottery would still be played out and winnings allocated; however, they
would not learn the outcome immediately. e magnitude of the participants bid therefore served to represent
the maximum value they would be willing to pay in order to view the non-instrumental information about that
trial’s outcome. is auction procedure guaranteed that the most realistic valuation of the non-instrumental
information was obtained on each trial.
To characterise participants’ preferences for acquiring this information, we also constructed a series of com-
putational models which were t to the participants’ trial-wise bidding patterns. ese models included a null
model, which assumed no eect of agency on information-seeking behaviour, as well as a series of models that
characterised dierences in information valuation across agentic and non-agentic trials as due to changes in the
subjective probability of winning, the subjective value of resolving uncertainty, the subjective value of anticipat-
ing a positive outcome, or simply to the desirability of information.
Agency increases win expectancy and information value. To assess whether participants’ subjec-
tive probability of a positive outcome was aected by their agency over the lottery, their condence ratings were
compared across agentic and non-agentic trials. A
2×4
repeated measures analysis of variance (ANOVA) with
within-subjects factors of agency (agentic or non-agentic) and win probability (0.2, 0.4, 0.6 or 0.8) indicated that
condence in positive outcomes was modulated both by the probability of winning,
F(3, 114)=949.90, p<0.001
and possession of agency,
F(1, 38)=30.78, p<0.001
. As shown in Fig.1b, condence ratings were an average
of 2.04% higher when participants had agency over which lottery they played, indicating that this paradigm suc-
cessfully elicited the IOC and replicated earlier ndings14,31.
Next, we examined the inuence of agency on the perceived value of information by assessing participants
willingness to pay for non-instrumental information. A
2×4
repeated measures ANOVA with within-subjects
factors of agency (agentic or non-agentic) and win probability (0.2, 0.4, 0.6 or 0.8) indicated that participants’
bid magnitude was positively predicted by both win-probability,
F(3, 114)=49.92, p<0.001
and agency,
F(1, 38)=14.27, p<0.001
, see Fig.1c. ere was also a signicant interaction eect between the win-probability
and agency,
F(3, 114)=4.41, p=0.006
. A post-hoc comparison of bid-magnitude across probabilities revealed
that the dierence between agentic and non-agentic bids was only signicant for trials in which the probability of
winning was 0.4,
t(38)=3.96, p=0.001
or 0.6,
t(38)=2.92, p=0.023
aer Bonferroni corrections were applied.
ese ndings suggest that participants were willing to sacrice a signicantly higher proportion of their
winnings in order to learn the outcome of a trial if they had agency over the selection of the prospect (compared
to when they did not have agency), with the strongest eect found in scenarios with relatively higher uncertainty.
Agency increases the value of resolving uncertainty. To assess whether the increase in information
desirability could be explained by the increase in the subjective probability of a positive outcome, we constructed
ve computational models. e rst model assumed no eect of agency on information preference. Based on
previous ndings, this null model instead constructed predictions only from a weighted combination of the win-
probability, the uncertainty of the prospect, and a subject-specic constant (see “Methods”).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
We also constructed a series of alternative models to assess dierent characterisations of the contributions
of agency to information value. In the probability-shied model, shis in the subjective probability of winning
across agentic and non-agentic trials were permitted, such that the probability of winning on agentic trials was
shied by the magnitude of an additional subject-specic free parameter.
In addition, we constructed three further models to test for changes in the subjective valuation of each con-
stituent parameter of the null model. ese models use an additional free parameter to allow the contributions
of reward probability (agency reward model), uncertainty (agency uncertainty model), and the constant value of
information (agency constant model) to vary across agentic and non-agentic trials.
To compare model ts, we used the Watanabe-Akaike Information Criterion (WAIC) measure of out-of-
sample prediction error34,35. WAIC calculation involves the subtraction of a measure of model complexity from a
goodness of t measure. It was chosen over other information criteria (e.g., the Deviance Information Criterion)
Figure1. Experimental design and results. (a) On each trial, participants made a selection between three
equivalent roulette wheels. eir choice was either approved or vetoed, in which case the cursor was moved to
and selected an alternative option. ey then rated their condence to win that trial and submitted a bid in a
Becker–DeGroot–Marschak (BDM) auction to determine whether the outcome would be revealed or hidden.
If their bid was successful, the outcome was shown, otherwise, it was hidden. (b) Mean condence ratings (on a
scale from 0 = “sure loss” to 100 = “sure win”) for each possible probability of winning. Condence ratings were
signicantly higher when participants’ selection of lottery was approved. Error bars represent the standard error
of the mean. (c) Mean bid size in points for each possible probability of winning. Bids were signicantly larger
when participants’ selection of lottery was approved. Error bars represent the standard error of the mean.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
as it has a higher power rate and does not assume the posterior distribution to be Gaussian36,37. e WAIC sug-
gests that the agency uncertainty model is preferred, with the null model underperforming compared to each of
the other models, suggesting that dierences in curiosity between chosen and non-chosen lotteries were best
accounted for by an increase in the value of resolving uncertainty, rather than due to the shi in the subjective
probability of a positive outcome (see Table1). Further, the probability-shi parameter of the probability-shied
model demonstrated poor adherence to the reported increase in subjective win probability, as shown by their
weak correlation,
. Posterior predictive checks demonstrated an excellent t of the agency uncer-
tainty model to the data (Supplementary Fig.S1). e nal parameter estimates for the agency uncertainty model
are shown in Supplementary Fig.S2.
Together, these analyses show that subjective information value was increased in agentic, relative to non-
agentic contexts, particularly when uncertainty was maximal. Computational modelling contradicts the notion
that the increased value of information is simply an eect of the increase in the subjective probability of a positive
outcome for agentic prospects. Instead, it suggests that the value of resolving uncertainty is increased insitua-
tions involving agency.
Discussion
In this study, we assessed how choosing a prospect aects information valuation. Each participant was given
the opportunity to bid money in exchange for immediate, but entirely non-instrumental, information about the
outcomes of lotteries. On each trial, the participant could possess agency over which lottery would determine
their winnings, or the lottery could be randomly assigned. First, we replicated earlier ndings that agency over
choosing one’s lottery increased the perceived condence in a positive outcome of the lottery31. Consistent with
previous ndings, we also demonstrated that participants showed a preference for information pertaining to
prospects with high likelihood of revealing positive outcomes, as well as those with high uncertainty5,10,12. Our
results further showed that participants were willing to place higher bids in order to learn the outcome of a trial
over which they had agency, suggesting that they valued the information more under those circumstances. is
was particularly so for trials with a higher degree of uncertainty about the outcome. Computational modelling
analyses indicated that the agency-related subjective change in the probability of a positive outcome did not
provide the best account for the increase in information valuation for agentic choices. e results were best
explained through an increase in the subjective value of resolving uncertainty for agentic prospects. ese nd-
ings are not directly explained by existing theories of information-seeking behaviour11.
e success of the agency uncertainty model over the other competing models may be explained by a tight,
automatic association between agency and the cognitive value of information. e cognitive value of an infor-
mation signal is determined by the extent to which that information signal is able to reduce the uncertainty sur-
rounding one’s own mental model of the world around them11. Outcomes of events produced through agentic
means may be perceived as possessing greater cognitive value for two primary reasons. First, they oen inform
us of the outcomes of our own decisions or actions, and therefore may aect our mental model of concepts
related to our self-perception, including our attitudes towards ourselves and towards other concepts38. More
generally, experiencing the outcomes of our actions may allow us to assess the accuracy of our existing predic-
tions of action-outcome contingencies present in our mental models by either validating or challenging these
predictions. Second, outcomes of our own actions may be associated with a higher cognitive value simply because
we are typically more likely to act upon or interact with objects or concepts that have a greater relevance to us
(and, therefore, have a more signicant presence in our mental model of the world and elicit more curiosity). In
support of this, research has demonstrated that information on topics that are selected by an individual lead to
higher levels of curiosity than those that are randomly selected24. Of course, participants in the current experi-
ment could not expect to extract more cognitive value from information about lotteries under agentic condi-
tions. However, the increased valuation of information arising from agentic decisions may be attributed to the
association between agency and cognitive value being “overlearned”; learned beyond the point of automaticity
such that it is applied dogmatically. e phenomenon of overlearning is typically associated with improvements in
memory retention39, but can also strengthen stimulus-response associations40. If this association between agency
and cognitive value were to be the subject of overlearning, this may result in the higher subjective valuation of
agentic outcomes observed in the present study.
Alternatively, the current ndings could be explained by an overlearning of the perceived contingency
between agency and the utility of information. Under normal, everyday circumstances, information about
decision outcomes can typically be used to inform future decisions41. Conversely, information learned in an
Table 1. Watanabe-Akaike Information Criterion (WAIC) for each of the discussed models. A smaller value
indicates a better t to the data. e WAIC for the best tting model is highlighted in bold.e dierence from
the best-tting model is represented as
WAIC.
Model Free parame ters (per participant) WAIC
WAIC (S.E.M)
Null 4 8065.11 31.60 (11.54)
Probability-shied 5 8040.51 7.00 (14.48)
Agency reward 5 8039.78 6.27 (15.29)
Agency uncertainty 5 8033.51
Agency constant 5 8038.90 5.39 (14.40)
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
environment in which one does not possess agency may not be perceived as useful, because we cannot use such
information to inform future decisions. ough the association between agency and utility was not present in the
current experimental framework, an overlearning of this association may account for the relationship between
agency and the subjective value of resolving uncertainty.
Equally, the failure of the other competing models highlight key shortcomings in the ability of existing
accounts to explain the present ndings. For example, choice-induced preference change1624 alone cannot
account for the present ndings, as it predicts that the value of information should increase uniformly across
agentic prospects (consistent with the agency constant model), or that the value of information should increase
due to the indirect eects of an increase in the subjective value of the associated rewards (consistent with the
probability-shied model). e underperformance of these models relative to the agency uncertainty model indi-
cates that we cannot attribute the present results to choice-induced preference change alone.
Other contemporary accounts argue that agency provides an opportunity for self-enhancement (the motiva-
tion to view oneself in a positive manner), which could supplement information value. Previous studies have
shown that the delivery of outcomes from agentic decisions activates neural circuits that process self-referential
information31,4248. However, information-seeking with the goal of self-enhancement typically involves an active
search for attering (positively-valenced) information49,50. In the current experimental framework, this should
manifest in an increase in the perceived value of information pertaining to agentic prospects with a high prob-
ability of winning, as conceptualised by the agency reward model. e poor t of this model relative to that of
the agency uncertainty model indicates that a self-enhancement explanation alone cannot account for these data.
It should be noted that our conclusions are somewhat limited by the small eect sizes. is highlights the
comparative importance of the primary information-seeking drivers, such as the anticipation of positive out-
comes. Further, in the present study, the agentic condition in which participants chose their own prospect was
contrasted to a condition in which their choice was vetoed31. Arguably, the veto might have evoked cognitive
processes beyond the experience of a lack of agency, for example the feeling of a loss of agency, or dissatisfac-
tion with losing control in general. Future studies could consider a truly passive control condition in which no
agency exists in the rst place; however, this might come with the danger of task disengagement. ese ndings
also leave open further questions about whether subjective changes in reward value and probability inuence
the value of non-instrumental information about those rewards. One example of this is eort discounting, in
which the value of a reward is reduced when it is earned through eortful, as opposed to non-eortful means5155.
While evidence for the IOC has not been demonstrated in neural indices of reward31, the modulation of reward
signals has been demonstrated in tasks requiring eort53,54. Investigating information-seeking behaviour in this
context would provide insight into whether the ndings of the present study are unique to the IOC, or whether
they can be generalised to other, neurally observable subjective alterations to the expected value of a prospect.
Finally, though each of the alternative models outperformed the null model, this is likely because each is able
to make predictions that mimic the performance of the agency uncertainty model. is is exemplied by the poor
correlation between the probability-shi parameter of the probability-shied model and the subjective increase
in win-probability reported by participants. As such, though the success of each alternative model relative to the
null model would promote the conception of hybrid models that include combinations of the agency-modulated
parameters, the inclusion of combinations of these parameters would constitute redundancy. Consequently,
models that include more than one agency-modulated parameter produce divergent model ts, as very dierent
combinations of the parameters lead to similar model performance, making model identication dicult and
inferences about such models unreliable.
In sum, the ndings of the present study demonstrated an increase in the perceived value of resolving uncer-
tainty about the post-decisional outcomes of prospects when those prospects are selected through agentic means,
as opposed to when agency is removed. is increase in value was not explained by a change in the subjective
probability of a positive outcome. Instead, it may be attributed to an overlearning of the association between
choice and the instrumentality or cognitive value of information.
Methods
Participants. Forty-seven participants (30 female, 17 male,
M=23.47, SD =2.69
) completed the experi-
ment. Four were excluded from analyses for failing to meet the pre-determined accuracy threshold for con-
dence ratings of
±20
% from the true probability across all four probability levels, indicating an insucient
understanding of the real probability of winning. A further four were excluded for failing to show any evidence
of information valuation, suggesting a general lack of interest in the lottery’s outcome. e remaining sample
of 39 participants (25 female, 14 male) were aged between 18 and 36 years of age (M = 23.21, SD = 2.65). Par-
ticipants received a reimbursement of AUD $15 for their participation, and were instructed that they could win
an additional reward of up to $5 available depending on the results of the experiment. However, at the end of
the experiment, all participants were awarded the full $20 reimbursement, regardless of performance. Informed
consent was provided by all participants, and research was conducted in accordance with the Declaration of
Helsinki. All study protocols were approved by e University of Melbourne Human Research Ethics Commit-
tee (ID 1954969).
Procedure. All stimuli were presented using the Psychophysics Toolbox56 running on MATLAB R2018a
(e Mathworks, Natick, MA) on a cathode-ray tube (CRT) monitor with a resolution of
1280 ×1024
pixels and
a screen refresh rate of 60Hz.
Before commencing with the experiment, participants were provided with written and verbal instructions for
the task and were permitted to complete a series of practice trials (with a minimum of 20) until they felt condent
to continue to the main task. Participants were instructed that, on each trial, they were to choose from three
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
roulette wheels, each of which had an equal probability of producing a winning outcome. e roulette wheels
comprising segments of red and blue, with the either the red or blue connoting a winning outcome (counter-
balanced across participants). Importantly, participants were fully aware that there was no dierence in the
probability of winning for each of the wheels on each trial. ey were instructed that a spinner was going to be
rotated around one of the wheels and if it landed on their winning colour, they would win 50 points. Losses were
worth 0 points, with their overall total points at the end of the experiment determining the size of the monetary
payment at the end of the experiment. ey were further told that on some trials, their choice of wheel would be
“approved” while on other trials, the computer would “veto” their decision and select a dierent wheel.
e main experiment consisted of ten blocks, each containing twelve trials. In each block, three trials of each
of the four win probabilities (0.2, 0.4, 0.6, 0.8) were presented. On each trial, three possible roulette wheels were
initially shown. e wheels consisted of 40 segments of equal size. To construct the three roulette wheels, the
win probability was multiplied by the total number of segments, and the resultant number of segments were
shaded in the participant’s winning colour, with the remainder shaded in the alternate colour. e order of the
segments was randomly shued. To construct the other two wheels, this roulette wheel was then duplicated,
and rotated
120
clockwise and counter-clockwise. is ensured that the wheels were structurally identical, such
that dierential segment distribution could not produce preferences based on perceived dierences in win prob-
ability. Participants were instructed that each of the three lotteries were equivalent. During the post-experiment
debrief, participants were also asked to describe their method of choosing a lottery, such that any participants
who indicated they believed the lotteries were not equivalent could be excluded. Two participants indicated
that they believed the lotteries had not been equivalent. Both also failed to meet the accuracy requirement for
condence ratings and were excluded.
On each trial, the centre-point of each wheel was positioned along the circumference of an imaginary circle
centred at the mid-point of the screen. e rst wheel was placed randomly along this circumference, with the
remaining two positioned at
120
and
240
clockwise, respectively. e mouse was initially positioned at the centre
of the screen. Participants were instructed to select one of the three options by clicking on it with the mouse
within 2.5s of stimulus onset. If the participant did not respond in time, they received the feedback, “too slow”,
and the trial was restarted with dierent stimuli (with the same win probability).
If the participant’s selection was approved, their selected wheel was highlighted with a green circle. If their
selection was vetoed, the cursor would ash, and was then automatically moved to one of the other two options
by the computer. e newly computer-selected option was then highlighted with the green circle. Approved
and vetoed selections were pseudo-randomised, such that each occurred on half of the trials in each block. e
proportion of approved/vetoed trials was kept equal across all win probabilities, and it was ensured that equal
numbers of approved and vetoed trials resulted in winning outcomes.
Following their selection, there was a 2s delay, aer which participants completed a condence rating to
indicate how likely they perceived a winning outcome to be31. is was completed by moving the mouse along
a continuous scale with markings of “sure loss” and “sure win” at either end. Cursor position was randomised
prior to the appearance of the scale on each trial. Participants were given 4s to complete this rating, with the
position of the mouse at the end of the 4s period recorded as their condence rating.
Aer this, participants completed a procedure that allowed us to assess the value they placed on receiving
immediate information about the outcome of the trial. We used a Becker–DeGroot–Marschak (BDM) auction33
to determine whether they would learn the outcome of the trial or not. In this procedure, the position of the
cursor was randomised before participants had 5s to make a bid of between 0 and 5 points (with increments of
0.1). e size of their bid was then compared against a randomly generated “price” of between 0.1 and 5 points.
If the participant’s bid was equal to or higher than this price, that price would be deducted from their score,
and they were shown the outcome of the lottery at the end of the trial. Otherwise, no points were deducted, but
the outcome of the lottery was kept secret. Participants were explicitly instructed that the outcome of the bid
had no bearing on whether they won or lost on any given trial, only whether they would nd out the outcome
immediately. Any winnings were added to their total score. All bids were made by moving the mouse along a
horizontal scale, with the position of the mouse at the end of the 5s duration determining the size of the bid.
If their bid was successful (i.e., if it was greater than the price), a green bar ashed on the screen for 1s. e
initial three roulette wheels were then redisplayed, with a spinner placed on the (self- or computer-) selected
wheel. e spinner rotated around the selected wheel for 2.5s, stopping on either a winning or losing segment.
If the spinner stopped on a winning segment, the feedback “$$$” was shown. If it stopped on a losing segment,
the feedback “XXX” was shown.
If the participant’s bid was unsuccessful, a red bar ashed on the screen for 1s. e wheels were not shown
with their original segments, but were instead replaced by uniformly grey wheels. e spinner rotated around
the wheel for 2.5s, returning to the top of the wheel, and the feedback “???” was shown, indicating that the
outcome of the trial remained unknown. Participants were explicitly instructed that, when the grey wheel was
shown, the nal position of the spinner in no way reected its position on the selected wheel and was intended
as a ller screen only.
Each trial was followed by an inter-trial interval of 0.4s. Participants were provided with an un-timed break
aer each block of 12 trials.
Computational models. We constructed a series of computational models to characterise the value that
individuals placed on each lottery. All models were t using Hamiltonian Monte Carlo sampling as implemented
in Stan57. Each model was t using four parallel chains with a warm-up period of 1500 samples each followed by
5000 samples drawn from the converged chains.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
Null model. Based on the previously established ndings in this area5,10,12, the null model operationalised the
prediction that participants’ bids were dependent on: (1) the likelihood of a positive outcome, and (2) the uncer-
tainty of the current prospect. Uncertainty was dened as the entropy of the prospect, with entropy calculated
by the Shannon function58, see Eq. (1). To maintain uncorrelated model parameters, this entropy was centred by
subtracting the mean level of entropy across all trials, as shown in Eq. (2).
As a result, the null model predicts that the value of information is determined by the weighted, linear combina-
tion of win probability, uncertainty, and a constant to capture any other sources of information valuation (Eq.3).
Here, the free parameter
βW
dictates the extent to which the probability of winning (P(W)) modulates the subjec-
tive value of information, with greater values of
βW
leading to greater increases in subjective information value
with increasing win probability. Similarly,
βU
corresponds to the magnitude of change in the subjective value of
information associated with the level of uncertainty on the given trial. Finally, the free parameter
φ
is a subject-
specic constant that modulates the value of information uniformly across all trials.
To estimate this set of parameters, we employed a hierarchichal Bayesian estimation strategy that assumes that
each subject’s parameters are drawn from a joint group-level distribution such that the parameters for subject i
are constrained to being drawn from the prior distributions:
Each of these prior distributions was weakly informative, restricting the model to reasonable areas of the possible
parameter space. e half-Cauchy distribution was used as a prior for standard deviations as it restricts away
from large values while still oering some prior information59.
Finally, the outcome variable was also assigned weakly informative group-level hyperparameters and restricted
to a truncated normal distribution with limits of 0 and 5, to match the lower and upper bid limits. e mean of
this distribution,
µV[i]
, was given by Eq. (3).
Probability-shied model. e probability-shied model allowed for the adjustment of the subjective probability
of a winning outcome depending on whether or not participants could choose the lottery, as expected under
the illusion of control. is was achieved by allowing probability to be adjusted by an additional free parameter,
βprob
, such that the probability of winning used in the calculation of entropy and information valuation was
given by Eq. (4). Here, A is a binary variable that equals 1 on trials in which the participant has agency (i.e., when
their selection is approved) and 0 when the participant does not have agency (i.e., when their selection is vetoed).
e entropy equation used to calculate uncertainty was also updated to employ the use of the shied prob-
ability such that it was calculated using Eqs.5 and 6.
e distribution of the
βprob
parameter was truncated at
±0.2
, to prevent the subjective probability of winning
from exceeding 0 or 1. e model otherwise followed the same format as the null model. e full notation for
the probability-shied model is provided below:
(1)
H
(X)=−
n
i=1
P(xi)log2P(xi
)
(2)
¯
H
=H(X)
H(0.2)+H(0.4)+H(0.6)+H(0.8)
4
(3)
µV[i]
=
βU[i]¯
H
+
βW[i]P(W)
+
φi
β
W
Normal
βW
,
σβW
)
β
UNormalβU,σβU)
φ
Normalφ,σφ)
µ
βW
,
µβU
,
µφ
Normal
(
0, 2
)
σ
βW
,σ
βU
,σ
φ
HalfCauchy(0, 2
)
V
i
TruncNormal
V[i]
,σ
V[i]
),∈[0, 5]
σV
HalfCauchy(0, 2)
(4)
P
s
(W)=P(W)+βprob A(t)
(5)
H
s(X)=−
n
i=1
Ps(xi)log2Ps(xi
)
(6)
¯
H
s=Hs(X)
H
s
(0.2)+H
s
(0.4)+H
s
(0.6)+H
s
(0.8)
4
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol:.(1234567890)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
Agency modulation models. Finally, a series of models were constructed that allowed the magnitude of each of
the factors used to compute the value of information in the null model to vary based on the presence or absence
of agency. ese models explored the possibility that agency modulates the value of information via a modula-
tion of the value of the anticipation of a positive outcome, the resolution of uncertainty, or the constant underly-
ing value of information.
e agency reward model predicts that the value of information in agentic contexts diers from that in non-
agentic contexts due to a dierence in the valuation of the anticipation of a positive outcome. is was achieved
by adding the product of a binary parameter, A, and an additional free parameter,
βaW
, to the existing free
parameter
βW
. e full notation for the agency reward model is provided below:
Similarly, the agency uncertainty model predicts that agency aects information valuation due to a modulation
of the value of resolving uncertainty for agentic prospects. is was achieved by adding the product of a binary
parameter, A, and an additional free parameter,
βaU
, to the existing free parameter
βU
. e full notation for the
agency uncertainty model is provided below:
Finally, the agency constant model predicts that agency aects information valuation equally across all levels
of probability and uncertainty. is model conceptualised the possibility that the desirability of information
is modulated equally by the possession of agency irrespective of the probability of winning and uncertainty of
the outcome of the prospect itself. is was achieved by adding the product of a binary parameter, A, and an
additional free parameter,
βaC
, to the existing free parameter
φ
. e full notation for the agency constant model
is provided below:
V
i
TruncNormal
V[i]
,
σV[i])
,
∈[
0, 5
]
µV[i]=βU[i]¯
Hs+βW[i]Ps(W)+φi
σVHalfCauchy(0, 2)
βWNormalβW,σβW)
βUNormalβU,σβU)
φNormalφ,σφ)
βprob TruncNormalβprob ,σβprob ),(0.2, 0.2
)
µ
βW,µβU,µφNormal(0, 2)
µβprob Normal(0.02, 0.05)
σβW,σβU,σφHalfCauchy(0, 2)
σβ
prob
HalfCauchy(0, 0.05)
V
i
TruncNormal
V[i]
,σ
V[i]
),∈[0, 5]
µV[i]=βU[i]¯
H+W[i]+βaW[i]A(t)) P(W)+φ
i
σVHalfCauchy(0, 2)
βWNormalβW,σβW)
βUNormalβU,σβU)
φNormalφ,σφ)
βaW NormalβaW ,σβaW )
µ
βW,µβU,µφNormal(0, 2)
µβaW Normal(0, 1)
σβW,σβU,σφHalfCauchy(0, 2)
σ
βaW
HalfCauchy
(
0, 1
)
V
i
TruncNormal
V[i]
,
σV[i])
,
∈[
0, 5
]
µV[i]=U[i]+βaU[i]A(t)) ¯
H+βW[i]P(W)+φ
i
σVHalfCauchy(0, 2)
βWNormalβW,σβW)
βUNormalβU,σβU)
φNormalφ,σφ)
βaU NormalβaU ,σβaU )
µ
βW,µβU,µφNormal(0, 2)
µβaU Normal(0, 1)
σβW,σβU,σφHalfCauchy(0, 2)
σ
βaU
HalfCauchy(0, 1)
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Vol.:(0123456789)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
Received: 12 January 2021; Accepted: 7 April 2021
References
1. Bromberg-Martin, E. S. & Hikosaka, O. Midbrain dopamine neurons signal preference for advance information about upcoming
rewards. Neuron 63, 119–126. https:// doi. org/ 10. 1016/J. NEURON. 2009. 06. 009 (2009).
2. Bromberg-Martin, E. S. & Hikosaka, O. Lateral habenula neurons signal errors in the prediction of reward information. Nat.
Neurosci. 14, 1209–1216. https:// doi. org/ 10. 1038/ nn. 2902 (2011).
3. Blanchard, T., Hayden, B. & Bromberg-Martin, E. Orbitofrontal cortex uses distinct codes for dierent choice attributes in deci-
sions motivated by curiosity. Neuron 85, 602–614. https:// doi. org/ 10. 1016/J. NEURON. 2014. 12. 050 (2015).
4. Bennett, D., Bode, S., Brydevall, M., Warren, H. & Murawski, C. Intrinsic valuation of information in decision making under
uncertainty. PLOS Comput. Biol. 12, e1005020. https:// doi. org/ 10. 1371/ journ al. pcbi. 10050 20 (2016).
5. Charpentier, C. J., Bromberg-Martin, E. S. & Sharot, T. Valuation of knowledge and ignorance in mesolimbic reward circuitry.
Proc. Natl. Acad. Sci. 115, E7255–E7264. https:// doi. org/ 10. 1073/ pnas. 18005 47115 (2018).
6. Vasconcelos, M., Monteiro, T. & Kacelnik, A. Irrational choice and the value of information. Sci. Rep. 5, 13874. https:// doi. org/ 10.
1038/ srep1 3874 (2015).
7. Bennett, D., Sutclie, K., Tan, N.P.-J., Smillie, L. D. & Bode, S. Anxious and obsessive-compulsive traits are independently associ-
ated with valuation of noninstrumental information. J. Exp. Psychol. Gen. https:// doi. org/ 10. 1037/ xge00 00966 (2020).
8. Brydevall, M., Bennett, D., Murawski, C. & Bode, S. e neural encoding of information prediction errors during non-instrumental
information seeking. Sci. Rep. 8, 6134. https:// doi. org/ 10. 1038/ s41598- 018- 24566-x (2018).
9. Kobayashi, K. & Hsu, M. Common neural code for reward and information value. Proc. Natl. Acad. Sci. US Am. https:// doi. org/
10. 1073/ pnas. 18201 45116 (2019).
10. van Lieshout, L. L. F. et al. Induction and relief of curiosity elicit parietal and frontal activity. J. Neurosci. 38, 2579–2588. https://
doi. org/ 10. 1523/ JNEUR OSCI. 2816- 17. 2018 (2018).
11. Sharot, T. & Sunstein, C. R. How people decide what they want to know. Nat. Hum. Behav. 4, 14–19. https:// doi. org/ 10. 1038/
s41562- 019- 0793-1 (2020).
12. Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M. & Gottlieb, J. Diverse motives for human curiosity. Nat. Hum. Behav. https://
doi. org/ 10. 1038/ s41562- 019- 0589-3 (2019).
13. Iigaya, K., Story, G. W., Kurth-Nelson, Z., Dolan, R. J. & Dayan, P. e modulation of savouring by prediction error and its eects
on choice. Elife 5, e13747. https:// doi. org/ 10. 7554/ eLife. 13747. 001 (2016).
14. Langer, E. J. e illusion of control. J. Pers. Soc. Psychol. 32, 311–328. https:// doi. org/ 10. 1037/ 0022- 3514. 32.2. 311 (1975).
15. Kahneman, D. & Tversky, A. On the psychology of prediction. Psychol. Rev. 80, 237–251. https:// doi. org/ 10. 1037/ h0034 747 (1973).
16. Voigt, K., Murawski, C. & Bode, S. Endogenous formation of preferences: Choices systematically change willingness-topay for
goods. J. Exp. Psychol. Learn. Mem. Cogn. 43, 1872–1882. https:// doi. org/ 10. 1037/ xlm00 00415 (2017).
17. Voigt, K., Murawski, C., Speer, S. & Bode, S. Hard decisions shape the neural coding of preferences. J. Neurosci. 39, 718–726. https://
doi. org/ 10. 1523/ JNEUR OSCI. 1681- 18. 2018 (2019).
18. Izuma, K. et al. Neural correlates of cognitive dissonance and choice-induced preference change. Proc. Natl. Acad. Sci. 107,
22014–22019. https:// doi. org/ 10. 1073/ pnas. 10118 79108 (2010).
19. Ariely, D. & Norton, M. I. How actions create—Not just reveal—Preferences. Trends Cogn. Sci. 12, 13–16. https:// doi. org/ 10. 1016/j.
tics. 2007. 10. 008 (2008).
20. Brehm, J. W. Postdecision changes in the desirability of alternatives. J. Abnorm. Soc. Psychol. 52, 384–389. https:// doi. org/ 10. 1037/
h0041 006 (1956).
21. Sharot, T., Martino, B. D. & Dolan, R. J. How choice reveals and shapes expected hedonic outcome. J. Neurosci. 29, 3760–3765.
https:// doi. org/ 10. 1523/ JNEUR OSCI. 4972- 08. 2009 (2009).
22. Sharot, T., Velasquez, C. M. & Dolan, R. J. Do decisions shape preference? Evidence from blind choice. Psychol. Sci. 21, 1231–1235.
https:// doi. org/ 10. 1177/ 09567 97610 379235 (2010).
23. Lee, D. & Daunizeau, J. Choosing what we like vs liking what we choose: How choice-induced preference change might actually
be instrumental to decision-making. PLoS ONE 15, e0231081. https:// doi. org/ 10. 1371/ journ al. pone. 02310 81 (2020).
24. Schutte, N. S. & Malou, J. M. Increasing curiosity through autonomy of choice. Motiv. Emot. 43, 563–570. https:// doi. org/ 10.
1007/ s11031- 019- 09758-w (2019).
25. Presson, P. & Benassi, V. Illusion of control: A meta-analytic review. J. Soc. Behav. Pers. 11, 493–510 (1996).
26. ompson, S. C., Armstrong, W. & omas, C. Illusions of control, underestimations, and accuracy: A control heuristic explana-
tion. Psychol. Bull. 123, 143–161. https:// doi. org/ 10. 1037/ 0033- 2909. 123.2. 143 (1998).
27. Caplin, A. & Leahy, J. Psychological expected utility theory and anticipatory feelings. Q. J. Econ. 116, 55–79. https:// doi. org/ 10.
1162/ 00335 53015 56347 (2001).
28. Starmer, C. Developments in non-expected utility theory: e hunt for a descriptive theory of choice under risk. J. Econ. Lit. 38,
332–382. https:// doi. org/ 10. 1257/ jel. 38.2. 332 (2000).
29. von Neumann, J. & Morgenstern, O. eory of Games and Economic Behavior (Princeton University Press, 1944).
30. Schultz, W., Dayan, P. & Montague, P. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
V
i
TruncNormal
V[i]
,
σV[i])
,
∈[
0, 5
]
µV[i]=βU[i]¯
H+βW[i]P(W)+i+βaC A(t
))
σVHalfCauchy(0, 2)
βWNormalβW,σβW)
βUNormalβU,σβU)
φNormalφ,σφ)
βaC NormalβaC ,σβaC )
µ
βW,µβU,µφNormal(0, 2)
µβaC Normal(0, 1)
σβW,σβU,σφHalfCauchy(0, 2)
σ
βaC
HalfCauchy(0, 1)
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Vol:.(1234567890)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
31. Kool, W., Getz, S. J. & Botvinick, M. M. Neural representation of reward probability: Evidence from the illusion of control. J. Cogn.
Neurosci. 25, 852–861. https:// doi. org/ 10. 1162/ jocn_a_ 00369 (2013).
32. Tobler, P. N., Christopoulos, G. I., O’Doherty, J. P., Dolan, R. J. & Schultz, W. Neuronal distortions of reward probability without
choice. J. Neurosci. 28, 11703–11711. https:// doi. org/ 10. 1523/ JNEUR OSCI. 2870- 08. 2008 (2008).
33. Becker, G. M., DeGroot, M. H. & Marschak, J. Measuring utility by a single-response sequential method. Behav. Sci. 9, 226–232.
https:// doi. org/ 10. 1002/ bs. 38300 90304 (1964).
34. Watanabe, S., & Opper, M. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular
learning theory.J. Mach. Learn. Res.,11(12) (2010).
35. Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–
1016. https:// doi. org/ 10. 1007/ s11222- 013- 9416-2 (2014).
36. McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2018).
37. Luo, Y. & Al-Harbi, K. Performances of LOO and WAIC as IRT Model Selection Methods (Psychol. Test Assess, 2017).
38. Albarracín, D. & Wyer, R. S. e cognitive impact of past behavior: Inuences on beliefs, attitudes, and future behavioral decisions.
J. Pers. Soc. Psychol. 79, 5–22. https:// doi. org/ 10. 1037/ 0022- 3514. 79.1.5 (2000).
39. Driskell, J. E., Willis, R. P. & Copper, C. Eect of overlearning on retention. J. Appl. Psychol. 77, 615–622. https:// doi. org/ 10. 1037/
0021- 9010. 77.5. 615 (1992).
40. Grol, M. J., De Lange, F. P., Verstraten, F. A., Passingham, R. E. & Toni, I. Cerebral changes during performance of overlearned
arbitrary visuomotor associations. J. Neurosci. 26, 117–125. https:// doi. org/ 10. 1523/ JNEUR OSCI. 2786- 05. 2006 (2006).
41. Sutton, R. & Barto, A. Reinforcement Learning: An Introduction 2nd edn. (MIT Press A Bradford Book, 2018).
42. Fossati, P. et al. In search of the emotional self: An fMRI study using positive and negative emotional words. Am. J. Psychiatry 160,
1938–1945. https:// doi. org/ 10. 1176/ appi. ajp. 160. 11. 1938 (2003).
43. Johnson, S. et al. Neural correlates of self-reection. Brain 125, 1808–1814. https:// doi. org/ 10. 1093/ brain/ awf181 (2002).
44. Johnson, M. et al. Dissociating medial frontal and posterior cingulate activity during self-reection. Soc. Cogn. Aect. Neurosci.
1, 56–64. https:// doi. org/ 10. 1093/ scan/ nsl004 (2006).
45. Rd, L., Gr, F., Pm, C. & Rj, D. Neural activation during selective attention to subjective emotional responses. NeuroReport 8, 3969
(1997).
46. Lane, R . et al. Neural correlates of levels of emotional awareness: Evidence of an interaction between emotion and attention in the
anterior cingulate cortex. J. Cogn. Neurosci. 10, 525–535. https:// doi. org/ 10. 1162/ 08989 29985 62924 (1998).
47. Lou, H. C. et al. Parietal cortex and representation of the mental self. Proc. Natl. Acad. Sci. 101, 6827–6832. https:// doi. org/ 10.
1073/ pnas. 04000 49101 (2004).
48. Maddock, R. J. e retrosplenial cortex and emotion: New insights from functional neuroimaging of the human brain. Tre nds
Neurosci. 22, 310–316. https:// doi. org/ 10. 1016/ S0166- 2236(98) 01374-5 (1999).
49. B eer, J. & Hughes, B. Self-enhancement: A social neuroscience perspective. In Handbook of Self-Enhancement and Self-Protection,
chap 2 1st edn (eds Alicke, M. D. & Sedikides, C.) 49–65 (e Guilford Press, 2020).
50. Strube, M. & Roemmele, L. Self-enhancement, self-assessment, and self-evaluative task choice. J. Pers. Soc. Psychol. 49, 981–993
(1985).
51. Kivetz, R. e eects of eort and intrinsic motivation on risky choice. Market. Sci.https:// doi. org/ 10. 1287/ mksc. 22.4. 477. 24911
(2003).
52. Apps, M. A., Grima, L. L., Manohar, S. & Husain, M. e role of cognitive eort in subjective reward devaluation and risky decision-
making. Sci. Rep. https:// doi. org/ 10. 1038/ srep1 6880 (2015).
53. Botvinick, M. M., Hustetler, S. & McGuire, J. T. Eort discounting in human nucleus accumbens. Cogn. Aect. Behav. Neurosci.
9, 16–27. https:// doi. org/ 10. 3758/ CABN.9. 1. 16 (2009).
54. Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E. & Rushworth, M. F. Eort-based C ost-benet valuation and the human
brain. J. Neurosci. 29, 4531–4541. https:// doi. org/ 10. 1523/ JNEUR OSCI. 4515- 08. 2009 (2009).
55. Atkins, K. J., Andrews, S. C., Stout, J. C. & Chong, T.T.-J. Dissociable motivational decits in pre-manifest Huntington’s disease.
Cell Rep. Med. https:// doi. org/ 10. 1016/j. xcrm. 2020. 100152 (2020).
56. Brainard, D. H. e psychophysics toolbox. Spatial Vis. https:// doi. org/ 10. 1163/ 15685 6897X 00357 (1997).
57. Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Sow. https:// doi. org/ 10. 18637/ jss. v076. i01 (2017).
58. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423. htt ps:// do i . org/ 10. 1002/j. 1538- 7305. 1948.
tb013 38.x (1948).
59. Gelman, A. Prior distributions for variance parameters in hierarchical models (Comment on Article by Browne and Draper).
Bayesian Anal.https:// doi. org/ 10. 1214/ 06- BA117A (2006).
Acknowledgements
T.C. and S.B. were supported by the Australian Research Council (DP 180102383 to T.C. and S.B., DE 180100389
to T.C.).
Author contributions
M.J., P.C., T.C. and S.B. conceived the experiment. M.J. conducted the experiment, analysed the results and wrote
the manuscript. All authors discussed the results and reviewed the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 021- 88031-y.
Correspondence and requests for materials should be addressed to M.J.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Vol.:(0123456789)
Scientic Reports | (2021) 11:8780 | 
www.nature.com/scientificreports/
Open Access is article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons licence, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
© e Author(s) 2021
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
... Our findings are in line with recent evidence that autonomy boosts curiosity: Participants who watched a video of their choice (from a given set) self-reported higher interest in the topic than those who watched a video without a choice (Schutte & Malouff, 2019), and participants bid larger amounts for lotteries they chose than those they had not chosen ( Jiwa et al., 2021). However, in these recent studies, autonomy was confounded with preference. ...
... This resulted in participants in the choice condition likely choosing their preferred option, and participants in the no-choice condition likely receiving their not-preferred option (with a probability of .66 because there were three options). In the more recent study ( Jiwa et al., 2021), participants always made a choice, but half of the times, their choice was vetoed (resulting in a loss of agency). In agency trials, participants always received their chosen (presumably preferred) option, whereas in no-agency trials, they always received an unchosen (presumably not preferred) option. ...
Article
Full-text available
In our connected era, we spend significant time and effort satisfying our curiosity. Often, we choose which information we seek, but sometimes the selection is made for us. We hypothesized that humans exhibit enhanced curiosity in the context of choice. We designed a task in which healthy participants saw two lotteries on each trial. On some trials, participants chose which lottery to play. On other trials, the lottery was selected for them. Participants then indicated their curiosity about the outcome of the to-be-played lottery via self-report ratings (Experiment 1, N = 34) or willingness-to-wait decisions (Experiment 2, N = 34). We found that participants exhibited higher curiosity ratings and greater willingness to wait for the outcome of lotteries they had chosen than for lotteries that had been selected for them (controlling for initial preference). This demonstrates that choice boosts curiosity, which may have implications for boosting learning, memory, and motivation.
... There has been some work on query formation in Library and Information Science (LIS) [70,96], much conceptual in nature. There is some empirical work asking participants to generate queries in response to a stated information need (e.g., [57]), the work is limited to artificial information needs, an approach that results in less user curiosity about the results and therefore likely changed behavior [54]. A handful of test collections were created that examine query variations [9,23], but that research has focused on the impact of the variation on search results, rather than a query's origin. ...
... Some of this work comes from the library science domain, and is based on asking searchers what query they would generate given an information need scenario (this research is summarized in [103]). We already know that people are less invested in artificially constructed information than they are in their own information needs [54], so these studies, while interesting, offer limited insight into the true query formulation experience. We can supplement this understanding by examining query logs, understanding query elicitation, and looking at query reformulation. ...
Conference Paper
Full-text available
Where do queries-the words searchers type into a search box-come from? The Information Retrieval community understands the performance of queries and search engines extensively, and has recently begun to examine the impact of query variation, showing that different queries for the same information need produce different results. In an information environment where bad actors try to nudge searchers toward misinformation, this is worrisome. The source of query variation-searcher characteristics, contextual or linguistic prompts, cognitive biases, or even the influence of external parties-while studied in a piecemeal fashion by other research communities has not been studied by ours. In this paper we draw on a variety of literatures (including information seeking, psychology , and misinformation), and report some small experiments to describe what is known about where queries come from, and demonstrate a clear literature gap around the source of query variations in IR. We chart a way forward for IR to research, document and understand this important question, with a view to creating search engines that provide more consistent, accurate and relevant search results regardless of the searcher's framing of the query.
Article
Full-text available
Motivation is characterized by a willingness to overcome both cognitive and physical effort costs. Impairments in motivation are common in striatal disorders, such as Huntington’s disease (HD), but whether these impairments are isolated to particular domains of behavior is controversial. We ask whether HD differentially affects the willingness of individuals to overcome cognitive versus physical effort. We tested 20 individuals with pre-manifest HD and compared their behavior to 20 controls. Across separate trials, participants made choices about how much cognitive or physical effort they were willing to invest for reward. Our key results were that individuals with pre-manifest HD were less willing than controls to invest cognitive effort but were no different in their overall preference for physical effort. These results cannot be explained by group differences in neuropsychological or psychiatric profiles. This dissociation of cognitive- and physical-effort-based decisions provides important evidence for separable, domain-specific mechanisms of motivation.
Article
Full-text available
For more than 60 years, it has been known that people report higher (lower) subjective values for items after having selected (rejected) them during a choice task. This phenomenon is coined “choice-induced preference change” or CIPC, and its established interpretation is that of “cognitive dissonance” theory. In brief, if people feel uneasy about their choice, they later convince themselves, albeit not always consciously, that the chosen (rejected) item was actually better (worse) than they had originally estimated. While this might make sense from an intuitive psychological standpoint, it is challenging from a theoretical evolutionary perspective. This is because such a cognitive mechanism might yield irrational biases, whose adaptive fitness would be unclear. In this work, we consider an alternative possibility, namely that CIPC is -at least partially- due to the refinement of option value representations that occurs while people are pondering about choice options. For example, contemplating competing possibilities during a choice may highlight aspects of the alternative options that were not considered before. In the context of difficult decisions, this would enable people to reassess option values until they reach a satisfactory level of confidence. This makes CIPC the epiphenomenal outcome of a cognitive process that is instrumental to the decision. Critically, our hypothesis implies novel predictions about how observed CIPC should relate to two specific meta-cognitive processes, namely: choice confidence and subjective certainty regarding pre-choice value judgments. We test these predictions in a behavioral experiment where participants rate the subjective value of food items both before and after choosing between equally valued items; we augment this traditional design with both reports of choice confidence and subjective certainty about value judgments. The results confirm our predictions and provide evidence that many quantitative features of CIPC (in particular: its relationship with metacognitive judgments) may be explained without ever invoking post-choice cognitive dissonance reduction explanation. We then discuss the relevance of our work in the context of the existing debate regarding the putative cognitive mechanisms underlying CIPC.
Article
Full-text available
Immense amounts of information are now accessible to people, including information that bears on their past, present and future. An important research challenge is to determine how people decide to seek or avoid information. Here we propose a framework of information-seeking that aims to integrate the diverse motives that drive information-seeking and its avoidance. Our framework rests on the idea that information can alter people’s action, affect and cognition in both positive and negative ways. The suggestion is that people assess these influences and integrate them into a calculation of the value of information that leads to information-seeking or avoidance. The theory offers a framework for characterizing and quantifying individual differences in information-seeking, which we hypothesize may also be diagnostic of mental health. We consider biases that can lead to both insufficient and excessive information-seeking. We also discuss how the framework can help government agencies to assess the welfare effects of mandatory information disclosure. Sharot and Sunstein propose a framework of information-seeking, whereby individuals decide to seek or avoid information based on combined estimates of the potential impact of information on their action, affect and cognition.
Article
Full-text available
Curiosity—our desire to know—is a fundamental drive in human behaviour, but its mechanisms are poorly understood. A classical question concerns the curiosity motives. What drives individuals to become curious about some but not other sources of information?¹ Here we show that curiosity about probabilistic events depends on multiple aspects of the distribution of these events. Participants (n = 257) performed a task in which they could demand advance information about only one of two randomly selected monetary prizes that contributed to their income. Individuals differed markedly in the extent to which they requested information as a function of the ex ante uncertainty or ex ante value of an individual prize. This heterogeneity was not captured by theoretical models describing curiosity as a desire to learn about the total rewards of a situation2,3. Instead, it could be explained by an extended model that allowed for attribute-specific anticipatory utility—the savouring of individual components of the eventual reward—and postulates that this utility increased nonlinearly with the certainty of receiving the reward. Parameter values fitting individual choices were consistent for information about gains or losses, suggesting that attribute-specific anticipatory utility captures fundamental heterogeneity in the determinants of curiosity.
Article
Full-text available
This study examined the effect of satisfaction of the basic psychological need for autonomy on curiosity. One hundred and fifty-four participants first completed measures of autonomy-need satisfaction and curiosity. Participants were then randomly assigned to either a condition that supported autonomy of choice or a condition not supporting autonomy of choice. The autonomy-choice intervention provided participants with choice of topic for a video they could watch, while those in the no-autonomy of choice condition did not have choice. All participants then rated their curiosity regarding the topic of the video. Results showed that participants whose need for autonomy was more satisfied had higher levels of curiosity. Participants randomly assigned to the autonomy of choice condition providing choice of topic showed greater curiosity regarding the topic than participants who did not have a choice of topic. Autonomy of choice was most beneficial in stimulating a high level of curiosity about the topic for participants who had low general autonomy need satisfaction. The results of the study support the importance of self-determination in fostering the emotion of curiosity.
Article
Full-text available
Hard decisions between equally valued alternatives can result in preference changes, meaning that subsequent valuations for chosen items increase and decrease for rejected items. Previous research suggests that this phenomenon is a consequence of cognitive dissonance reduction after the decision, induced by the mismatch between initial preferences and decision outcomes. In contrast, this functional magnetic resonance imaging and eye-tracking study with male and female human participants found that preferences are already updated online during the process of decision-making. Preference changes were predicted from activity in left dorsolateral prefrontal cortex and precuneus while making hard decisions. Fixation durations during this phase predicted both choice outcomes and subsequent preference changes. These preference adjustments became behaviorally relevant only for choices that were remembered and were in turn associated with hippocampus activity. Our results suggest that preferences evolve dynamically as decisions arise, potentially as a mechanism to prevent stalemate situations in underdetermined decision scenarios.SIGNIFICANCE STATEMENT Most theories of decision-making assume that we always choose the best option available, based on a set of stable preferences. However, what happens for hard decisions when the available options are preferred equally? We show that in such stalemate situations, decision-makers adjust their preferences dynamically during the process of decision-making, and these preference adjustments are predicted by a left prefrontal-parietal network. We also show that eye movements during decision-making are predictive of the magnitude of the upcoming value change. Our results suggest that preferences are dynamic, adjusted every time a hard decision is made, prompting a re-evaluation of existing frameworks of decision-making.
Article
Full-text available
Significance Humans desire to know what the future holds. Yet, at times they decide to remain ignorant (e.g., reject medical screenings). These decisions have important societal implications in domains ranging from health to finance. We show how the opportunity to gain information is valued and explain why knowledge is not always preferred. Specifically, the mesolimbic reward circuitry selectively treats the opportunity to gain knowledge about favorable, but not unfavorable, outcomes as a reward to be approached. This coding predicts biased information seeking: Participants choose knowledge about future desirable outcomes more than about undesirable ones, vice versa for ignorance, and are willing to pay for both. This work demonstrates a role for valence in how the human brain values knowledge.
Article
Aversion to uncertainty about the future has been proposed as a transdiagnostic trait underlying psychiatric diagnoses including obsessive-compulsive disorder and generalized anxiety. This association might explain the frequency of pathological information-seeking behaviors such as compulsive checking and reassurance-seeking in these disorders. Here we tested the behavioral predictions of this model using a noninstrumental information-seeking task that measured preferences for unusable information about future outcomes in different payout domains (gain, loss, and mixed gain/loss). We administered this task, along with a targeted battery of self-report questionnaires, to a general-population sample of 146 adult participants. Using computational cognitive modeling of choices to test competing theories of information valuation, we found evidence for a model in which preferences for costless and costly information about future outcomes were independent, and in which information preference was modulated by both outcome mean and outcome variance. Critically, we also found positive associations between a model parameter controlling preference for costly information and individual differences in latent traits of both anxiety and obsessive-compulsion. These associations were invariant across different payout domains, providing evidence that individuals high in obsessive-compulsive and anxious traits show a generalized increase in willingness-to-pay for unusable information about uncertain future outcomes, even though this behavior reduces their expected future reward. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Article
Significance It is more important than ever to seek information adaptively. While it is optimal to acquire information based solely on its instrumental benefit, humans also often acquire useless information because of psychological motives, such as curiosity and pleasure of anticipation. Here we show that instrumental and noninstrumental motives are multiplexed in subjective value of information (SVOI) signals in human brains. Subjects’ information seeking in an economic decision-making task was captured by a model of SVOI, which reflects not only information’s instrumental benefit but also utility of anticipation it provides. SVOI was represented in traditional value regions, sharing a common code with more basic reward value. This demonstrates that valuation system combines multiple motives to drive information-seeking behavior.
Book
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.