Using Primary Reinforcement to Enhance Translatability

Using Primary Reinforcement to Enhance Translatability
of a Human Affect and Decision-Making
Judgment Bias Task

Vikki Neville1

, Peter Dayan2,3*, Iain D. Gilchrist1*,

Elizabeth S. Paul1*, and Michael Mendl1*

Abstracto

■ Good translatability of behavioral measures of affect (emo-
ción) between human and nonhuman animals is core to compar-
ative studies. The judgment bias ( JB) tarea, which measures
“optimistic” and “pessimistic” decision-making under ambiguity
as indicators of positive and negative affective valence, ha sido
used in both human and nonhuman animals. Sin embargo, one key
disparity between human and nonhuman studies is that the for-
mer typically use secondary reinforcers (p.ej., dinero) mientras que el
latter typically use primary reinforcers (p.ej., alimento). To address this
deficiency and shed further light on JB as a measure of affect, nosotros
developed a novel version of a JB task for humans using primary
reinforcers. Data on decision-making and reported affective state
during the JB task were analyzed using computational modeling.
En general, participants grasped the task well, and as anticipated,

their reported affective valence correlated with trial-by-trial varia-
tion in offered volume of juice. Además, previous findings from
monetary versions of the task were replicated: More positive pre-
diction errors were associated with more positive affective va-
lence, a higher lapse rate was associated with lower affective
arousal, and affective arousal decreased as a function of number
of trials completed. There was no evidence that more positive va-
lence was associated with greater “optimism,” but instead, allá
was evidence that affective valence influenced the participants’
decision stochasticity, whereas affective arousal tended to influ-
ence their propensity for errors. This novel version of the JB task
provides a useful tool for investigation of the links between pri-
mary reward and punisher experience, afectar, and decision-
haciendo, especially from a comparative perspective. ■

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INTRODUCCIÓN

An important goal in cognitive neuroscience, psychophar-
macology, and affective science is the development of
translational tasks that can be used to assess affective
(emotional) estados. Por ejemplo, the development of novel
treatments for affective disorders in humans depends on
the use of animal models of affect (Rupniak, 2003).
Because we cannot simply ask animals to report their af-
fective state, proxy indicators such as tests of anhedonia
(Van der Harst & Spruijt, 2007) or cognitive bias (Mendl,
Burman, parker, & Pablo, 2009) are often designed to sim-
ulate and assess behavioral characteristics observed in
humans experiencing affective states such as depression
or anxiety (American Psychiatric Association, 2013;
williams, Mathews, & MacLeod, 1996; Wright & Bower,
1992; MacLeod, Mathews, & Tata, 1986). The judgment
bias task has been demonstrated to provide a measure of
affective valence (positivity or negativity) in a range of non-
human animals with relatively “optimistic” (risk-seeking)
decision-making being associated with environmental

1University of Bristol, 2Max Planck Institute for Biological
Cibernética, 3University of Tübingen
*These authors contributed equally.

© 2021 Instituto de Tecnología de Massachusetts

or pharmacological manipulations designed to induce
positive affect and relatively “pessimistic” (risk-averse)
decision-making being associated with environmental or
pharmacological manipulations designed to induce more
negative affect (Lagisz et al., 2020; Neville, Nakagawa, et al.,
2020). Since its conception in 2004 by Harding, Pablo,
and Mendl (2004), several studies have used judgment
bias in nonhuman animals as a translational tool to inves-
tigate human affective disorders and pharmacological
treatments for such disorders (Hales, robinson, &
Houghton, 2016; anderson, Munafò, & robinson, 2013;
Papciak, Popik, Fuchs, & Rygula, 2013; Enkel et al., 2010).
Conducting judgment bias studies with humans, OMS
can report how they feel, may help to provide a better
insight into how performance in judgment bias tasks
reflects affective state. It might also help to elucidate the
putative adaptive function of affect-modulated decision-
haciendo, específicamente, leading to an understanding of
precisely how and why rewarding experiences might lead
to positive affect and then to “optimistic” decision-making,
y viceversa. Sin embargo, human judgment bias studies to
date have painted a mixed picture. Whereas some studies
have found that “pessimism” correlates with subjective
reports of more negative affect (Positive and Negative
Affect Schedule [Paul et al., 2010], State–Trait Anxiety

Revista de neurociencia cognitiva 33:12, páginas. 2523–2535
https://doi.org/10.1162/jocn_a_01776

Inventario [Aylward, Hales, robinson, & robinson, 2020;
anderson, Hardcastle, Munafò, & robinson, 2012], Arroyo
Depression Inventory [Daniel-Watanabe, McLaughlin,
Gormley, & robinson, in press], Visual Analog Scale for
ansiedad [Anderson et al., 2012]), other studies have found
no relationship between reported affect and judgment
inclinación (affect grid and Positive and Negative Affect Schedule
[Iigaya et al., 2016], State–Trait Anxiety Inventory [Daniel-
Watanabe et al., in press]) or even that “optimistic” decision-
making is associated with more negative reported affect
(affect grid [Neville, Dayán, Gilchrist, Pablo, & Mendl, 2021]).
There are several differences between the human and
nonhuman animal judgment bias studies that may have
led to these inconsistencies; humans are required to
learn the task in a shorter period (es decir., within an hour-
long session as opposed to over several days for most
nonhuman animals), humans do not live under the highly
controlled conditions typical of laboratory animals, y
there may be social factors influencing human decision-
haciendo (p.ej., wanting to perform well to satisfy the ex-
perimenter). Sin embargo, one difference that might be of
particular significance is that judgment bias studies in
nonhuman animals are typically conducted using primary
reinforcers such as food or electric shocks, whereas all
judgment bias testing in humans has used secondary re-
inforcers such as monetary gain or loss.

Many studies have identified differences between the
neural processing of primary and second reinforcers
(Sescousse, Caldú, Segura, & Dreher, 2013; delgado,
Jou, & Phelps, 2011; Arroyo, Locke, Savine, Jimura, &
Más valiente, 2010; Grimm & Ver, 2000). Por ejemplo, Tiene
been demonstrated that primary rewards are more
strongly represented in evolutionary older brain regions,
such as the anterior insula, whereas secondary rewards
are more strongly represented in evolutionary newer
regiones del cerebro, such as the anterior OFC (Sescousse
et al., 2013; Delgado et al., 2011). Given these differ-
ences, it is important to develop tasks for humans that
utilize primary reinforcers and hence more closely resem-
ble animal tasks and to investigate whether they yield
similar findings to the typical secondary reinforcer hu-
man task. The use of primary reinforcers in this way is
particularly pertinent to our understanding of judgment
bias from a functional and evolutionary perspective, given
that affect is hypothesized both to reflect ongoing and
prior experience of rewards and punishers and also to
mediate the way this experience guides decision-making
(Bach & Dayán, 2017; marshall, Trimmer, houston,
& McNamara, 2013; Nettle & Bateson, 2012; Mendl,
Burman, & Pablo, 2010). Además, primary reinforcers
may tap into fundamental affective processes and pathol-
ogies that underlie affective disorders more reliably than
secondary reinforcers, given their more direct relevance
to our ability to survive and reproduce. Como consecuencia, a
better understand judgment bias as a measure of affect,
we need a task for humans in which we administer primary
reinforcers and in which we can obtain a fine-scale picture

of sequential effects that might be influencing decision-
making and affective state.

Para tal fin, we aimed to develop a translation of the au-
tomated rat judgment bias task (Neville, Rey, et al., 2020;
Jones et al., 2018) for humans that uses primary rein-
forcers: apple juice and cold salty tea (Pauli et al., 2015).
Using this novel variant of the judgment bias task along-
side computational modeling, we additionally aimed to
elucidate the extent to which latent processes underlying
decision-making might relate to subjective experiences of
afectar, premio, and punishment within the task. We hy-
pothesized that more positive reported affect would be
associated with model parameters characterizing biases
toward the “optimistic” response and that, consistente con
previous research (Neville et al., 2021; Rutledge, Skandali,
Dayán, & Dolan, 2014), the absolute favorability (es decir., aver-
age earning rate) of within-task experience would inform
decision-making and the relative favorability (es decir., premio
prediction error) of within-task experience would inform
reported affective valence.

MÉTODOS

Participantes

Thirty-three people from the Bristol Veterinary School
community participated in the study. All participants
provided written informed consent, and the study was
approved by the Faculty of Science Research Ethics
Committee at the University of Bristol. This sample size
was based on a previous human judgment study (39 par-
ticipants, across two conditions [Neville et al., 2021]) y un
previous study using primary reinforcers as part of a con-
ditioning paradigm (29 Participantes [Pauli et al., 2015]).

The inclusion criteria were that the participant enjoyed
apple juice; was not allergic to apple juice, salt, or black
tea; was not hypertensive or had any medical condition
that meant that he or she must limit his or her salt intake;
and was over the age of 18 años. Participants were asked
to abstain from drinking anything (except water) or eating
in the hour before the study. To cover their time and
expenses, participants were paid £7 for the hour-long
session. To encourage full engagement with the study,
participants were informed that the top three ranking
participants in terms of accuracy would receive an addi-
tional £7 bonus.

Apparatus

The task was conducted on a laptop (Dell Latitude), cual
was connected to two syringe pumps (SPLG100, Mundo
Precision Instruments) that were set to pump liquid at a
tasa de 2 mL per minute. Sterilized food-grade PVC tubing
(outer diameter: 11 mm, inner diameter: 8 mm) eran
attached to syringes (Becton Dickinson; 50-mL Plastipak)
that were driven by the syringe pumps. This tubing was
connected via a tube connector to smaller sterilized

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Revista de neurociencia cognitiva

Volumen 33, Número 12

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food-grade PVC tubing (outer diameter: 6 mm, inner
diameter: 4 mm) and held in place in front of the partici-
pant using a retort clamp and stand, the height of which
could be adjusted by the participant at the start of the ex-
perimento. The participants placed the end of these tubes
in their mouth. Individuals made responses on a keyboard
connected to the laptop. The code for the task was written
in MATLAB (MathWorks, Cª) using the PsychToolBox
extensions (Kleiner et al., 2007; Brainard, 1997).

Procedimiento

The methodology was adapted from that of the human
monetary judgment bias task (Neville et al., 2021), cual
itself was a translation of a rodent judgment bias task
(Jones et al., 2018). On each trial of the task, Participantes
were instructed to press and hold the enter key before
being presented with a fixation cross for 1000 mseg
followed by a random dot kinematogram (RDK) dis-
played for 2000 mseg, which across trials varied in direc-
tion of motion (leftward or rightward) and coherence
(proportion of dots moving in a coherent direction:
0.01, 0.02, 0.16, o 0.32). Participants had two options
when the RDK was presented: to release the key before
the end of the 2-sec RDK presentation (“leave”) or con-
tinue to hold the key for 2 segundo (“stay”). The outcome as-
sociated with either response depended on the stage of
training and the direction of the RDK, with one direction
being favorable and requiring a “stay” response to gain
reward and the other being unfavorable and requiring a
“leave” response to avoid punishment (either leftward or
rightward, counterbalanced across participants).

Participants were provided with written instructions
about the rules of the task and were then provided with
practice trials that were composed of two blocks of 48
ensayos. The aim of the first practice block was to introduce
the participants to the task and train them on the correct
responses to the RDK. In this first practice block, la palabra
“correct” was shown on screen for correct responses (es decir.,
those where “stay” was executed when the RDK direction
was favorable, and “leave” was executed when the RDK
direction was unfavorable), and “incorrect” was shown
on screen for incorrect responses. The direction of motion
of the RDK was very easy to detect (coherence = 0.32) en
all trials, and the direction of motion was leftward on 50%
of trials and rightward on the remaining 50%.

The aim of the second practice block was to acquaint
participants with the delivery and taste of the apple juice
(Apple Juice from Concentrate, Morrisons) and salty tea.
Salty tea was prepared each morning as per Pauli et al.
(2015) with two black tea bags (Bettys & Taylors of
Harrogate; Yorkshire Tea) y 29 g of salt per liter of
boiling water, which was chilled before data collection.
We opted to use salty tea as opposed to electric shocks
as we considered this to be a milder punisher (hence less
of an ethical concern) and so that the modality of the
punisher was the same as the reward (es decir., both gustatory).

En tono rimbombante, liquid reinforcers such as juice and salty tea
have successfully been used in fMRI studies (Pauli et al.,
2015; Metereau & Dreher, 2013; kim, Shimojo, &
O’Doherty, 2011), hence their use in our task paves the
way for future investigations of the neural underpinnings
of judgment bias in humans. In this block, the direction of
the RDK was easy to detect (coherence = 0.16) en 50% de
ensayos, of which half were leftward and half were rightward,
and moderately difficult to detect (coherence = 0.02) en
the remaining 50%, of which half were leftward and half
were rightward. The volume of juice received for “stay”
responses when the direction of the RDK was favorable
era 0.457 mL, and likewise, the volume of salty tea
received for “stay” responses when the RDK was unfavor-
able was 0.457 mL. While juice was delivered and also for
3000 msec after delivery, the words “Juice delivered” were
displayed on screen, whereas during salty tea delivery and
para 3000 msec after this, the words “Salty tea delivered”
were displayed on screen. The potential volume of juice
on each trial (es decir., “0.457 mL”) was shown above an
orange-colored bar with a height proportional to the
potential juice volume, similarly the volume of salty tea
(es decir., “0.457 mL”) was displayed below a brown bar posi-
tioned directly below the orange-colored bar with a height
proportional to the potential tea volume. These bars were
shown before the instruction to press and hold the enter
key. The participant received nothing for making the
“leave” response, and the words “Nothing delivered” were
displayed on screen for 3000 mseg. Across all blocks, el
directions and coherence levels of the RDKs were ran-
domized across trials before the start of the study so that
the order of these was identical for all participants.

The test session (ver figura 1) comprised 90 trials on
which the RDK moved leftward and 90 trials on which it
moved rightward. For each direction, 30 trials had coher-
ence levels of low (0.01), moderate (0.02), and high
(0.16). RDKs with low and moderate coherence levels
are the ambiguous probe cues, whereas those with a high
coherence level are the reference cues. Por eso, each pos-
sible stimulus was shown on 16.7% of trials. The potential
volume of juice fluctuated across trials according to a
noisy sine wave with a mean volume of 0.457 mL and a
desviación estándar de 0.216 mL, ranging from a minimum
de 0.021 mL to a maximum of 0.781 mL. As a result of
ethical concerns about the effect of large quantities of
salty tea, the potential volume of salty tea remained at
0.457 mL throughout testing. As in the second practice
block, the potential volume of juice and salty tea was dis-
played on screen both as text and graphically using two
colored bars with heights proportional to the volume of
juice and salty tea. Before the first trial and every subse-
quent 10 ensayos, participants were asked to report how
they were feeling using a computerized affect grid
(Killgore, 1998). To complete the affect grid, Participantes
had to move a cross that was initially central in the grid to
the location that best described their current affective
state using the arrow keys on a keyboard. Horizontal

Neville et al.

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Cifra 1. Structure of the human primary reward and punisher judgment bias test session: (1) Participants are shown the potential outcomes of the
“stay” response ( juice volume: orange bar; tea volume: brown bar) and then must press “enter”; (2) participants are instructed to press and hold the
enter key; (3) participants are shown a fixation cross for 1000 mseg; (4) participants are presented with an RDK for 2000 mseg, during which they
must either continue holding the enter key (“stay”) or release the enter key (“go”); (5) participants receive the reward or punishment and are shown
the outcome of their action (which is also determined by the true direction of the RDK); y (6) after at least 3000 mseg, either the next trial starts or
the participant is asked to complete an affect grid (after every 10 ensayos).

movements represented changes in affective valence,
with movements to the right reporting a more positively
valenced affect. Vertical movements represented arousal,
with upward movement reporting higher levels of arousal.

Análisis de los datos

Two participants were excluded from data analysis because
of poor performance at the reference cues, which we
defined as not making the correct response significantly
above chance across both reference cues according to a

one-tailed binomial test (Participant 12, pag = .18; Participant
29, pag = .99).

We conducted both a model-dependent and model-
agnostic analysis of the data. The aim of the model-
dependent analysis was to investigate the latent processes
involved in decision-making and how these might be
modulated by different aspects of reward and punisher
experiencia. Judgment bias RT data (“stay”: 2 segundo; “leave”:
<2 sec) were fitted to the partially observable Markov decision process (POMDP) model described in full by Neville et al. (2021). Briefly, we consider that participants transition through a 2-D state space which they 2526 Journal of Cognitive Neuroscience Volume 33, Number 12 accumulate evidence about true direction the presented stimulus as informed by their observations (Dimension 1) across discretized duration trial (Dimension 2). The probability on given trial an individual will opt for safe “leave” response, and the speed with they do so, depend their transitions and value occu- pying each state. In model, movement space and values are determined number parame- ters including those characterizing psychometric function, namely, σ, reflects ability the participant discriminate between stimuli, λref and λamb, lapse rates reference and ambiguous respectively; characterizing the hazard function (probability “timeout,” i.e., time elapsed> 2 segundo, given “timeout” has not already occurred)
to account for the possibility of making the “stay” response
by default because of inaccurate timekeeping (’ and ζ);
and an inverse temperature parameter (B) to reflect deci-
sion stochasticity. Además, we included a set of param-
eters that allowed for biases toward or away from the
“optimistic” response. This set of parameters was com-
posed of a baseline bias parameter (βδ
0) capturing overall
tendencies for risk-seeking or risk aversion, así como el
overall dislike for the salty tea and enjoyment of the apple
juice, and parameters that encompassed the potential
effect of the average earning rate (βδ
(cid:1)R ), most recent
outcome (βδ
wPE ), y
squared weighted prediction error (βδ
wPE2 ) on bias, también
as a parameter that allowed for decision-making to vary as
a function of the number of trials completed (βn) a
account for fatigue. The average earning rate reflects the
learnt value of the test session from previous juice and
salty tea intake and updates according to a Rescorla–
Wagner learning model, with learning rate α(cid:1)R (following
Neville et al., 2021; Guitarte-Masip, Beierholm, Dolan,
Duzel, & Dayán, 2011), whereas the weighted reward
prediction error is the difference between the model-
predicted outcome and the actual outcome across trials
weighted such that the influence of past prediction errors
attenuates over trials, with forgetting factor γwPE (following
Neville et al., 2021; Rutledge et al., 2014).

oh ), weighted prediction error (βδ

As the task used food rewards, we included a parameter
(κ) that captured the potential effect of satiation as juice
intake increased. Específicamente, the subjective worth (Rn)
depended on the offered juice volume (in milliliters) en
trial n via an exponent that varied as a function of total
juice intake,

PAG

n−1
i¼0 Ri:

!

Xn−1

1−κ

Ri

i¼0

R0
norte

¼ R
norte

An exponent was chosen to reflect the nonlinearity of util-
ity functions and, more specifically, to capture the typical
concavity of utility functions; increases in reward gain lead

to diminishing marginal increases in the subjective value
of those gains (Hsee & Rottenstreich, 2004; kahneman
& Tverski, 1979). As the offered volume of juice is always
lower than 1, a negative value of κ would represent
adherence with the law of diminishing marginal utility,
whereas a value of greater than zero would lead to the
opposite (es decir., a steep increase in the subjective value of
the juice with additional consumption).

Models were fitted to the RT data using maximum like-
lihood, with multiple starting values (including values
found to provide the best fit for the core model as starting
valores) because of nonconvexity. Parameters that charac-
terized decision-making in the absence of biases (es decir., B,
λamb, λref σ, ', and ζ) were included in all models to ac-
count for their potential influence on behavior. Nosotros entonces
added parameters in a stepwise manner, first assessing
whether the parameter that characterized constant biases
in decision-making improved model parsimony and then
those that characterized within-task variation in decision-
haciendo. We then assessed whether the addition of single
parameters to the best-fitting model would further improve
the model fit. Models were compared using Bayesian infor-
mation criterion (BIC) valores, and we additionally com-
pared the final set of models using the Akaike information
criterion (AIC). A stepwise model-fitting procedure was
employed as fitting all possible models was not feasible
because of the large number of possible models and the
computational intensiveness of model fitting. Model fitting
was carried out using the computational facilities of the
Advanced Computing Research Centre at the University
of Bristol.

The parameter estimates from the most parsimonious
model were analyzed using permutation tests to assess
whether they varied significantly from zero, where this
was meaningful (es decir., for βδ

0 and κ).
The aim of the model-agnostic analysis was to investigate
the relationship, primero, between primary reward and
punisher experience and reported affect and, segundo,
between the parameters characterizing decision-making
and reported affect. These analyses were conducted in R
(R Core Team, 2015) using the nlme package (Pinheiro,
Bates, DebRoy, Sarkar, & R Core Team, 2017). Likelihood
ratio tests were used to assess whether the difference in
model deviance was significant after removal of a parame-
ter from a model.

The reported valence (affect grid x coordinate) and re-
ported arousal (affect grid y coordinate) were analyzed
using general linear mixed models (GLMMs) en el cual
both the intercept and slope were allowed to vary among
Participantes, with the predictor variables for which a ran-
dom slope was included determined using BIC compari-
son (es decir., comparing a model with the variable included in
the random effects structure to the model without the
variable included in the random effects structure). El
predictor variables were those encompassing reward
and punisher experience, included those derived from
the best-fitting POMDP model: the most recent volume

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of offered juice, weighted reward prediction error (wPE),
squared weighted reward prediction error (wPE2), el
previous outcome O, total juice consumed, the number
of trials completed, and the average earning rate (cid:1)R.
Because of correlations between wPE, wPE2, and O, y
also between total juice consumed, number of trials com-
pleted, y (cid:1)R, these predictor variables were not included
in the same GLMM but instead were included separately,
and each model was then compared using their BIC
valores. The GLMMs that provided the best fit were
analyzed further.

Además, the parameter estimates from the POMDP
model were analyzed using a general linear model with
the mean reported arousal and mean reported valence
as fixed effects, except ζ and ’, in which the mean timeout
probability that was jointly determined by these two pa-
rameters was instead used for a more intuitive interpre-
tation of the results, as in Neville et al. (2021). The values
of B were log-transformed because of the presence of ex-
treme outliers exerting undue influence on the model;
the GLMM was also run after removal of these outliers.

RESULTADOS

Judgment Bias

The most parsimonious model of judgment bias RT, ac-
cording to the BIC values, included the following core pa-
rameters: B, λamb, λref σ, ', and ζ, which characterized
decision-making in the absence of biases, in addition to
βδ
0, characterizing baseline biases, and κ, characterizing
the rate of satiation (ver tabla 1). This model fit the ob-
served data well (ver figura 2). The AIC values indicated
that inclusion of βδ
wPE outperformed the BIC best
modelo. Sin embargo, permutation tests revealed that the es-
timates of these parameters did not differ significantly
from zero (βδ
(cid:1)R : mean = −0.13, SE = 0.11, pag = .28;
βδ
wPE: mean = 0.005, SE = 0.01, pag = .72). Por eso, allá
was no strong evidence for the inclusion of these param-
eters, and we selected the BIC best model as our final
model for further analysis.

(cid:1)R or βδ

Decision-Making and Reported Affect

Visual inspection of the parameter estimates revealed
several potential outliers. These were formally defined
as estimates that were either above the third quartile or
below the first quartile by more than 1.5 times the inter-
quartile range (es decir., below Q1–1.5 × IQR or above Q3 +
1.5 × IQR). All analyses were conducted both before and
after the removal of these outliers. The presented results
are from the analyses including the outliers, excepto
where the removal of outliers changed the results quali-
tatively, in which case both results are presented.

The parameter characterizing satiation was significantly
lower than zero (κ: mean = −5.84, SE = 4.23, pag < .001), reflecting the fact that participants valued juice less with Table 1. Comparison of POMDP Models Model wPE σ, λamb, λref, ζ, ’, B σ, λamb, λref, ζ, ’, B, βδ 0 σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’’’’’’’’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’’’’’’’’, B, βδ σ, λamb, λref, ζ, ’, B, βδ σ, λamb, λref, ζ, ’, B, βδ 0 σ, λamb, λref, ζ, ’, B, βδ 0 0, βδ (cid:1)R 0, βδ (cid:1)R, α(cid:1)R 0, βδ 0, βδ 0, βδ 0, βδ 0, βδ 0, βn 0, κ 0, κ, βδ (cid:1)R 0, κ, βδ 0, κ, βδ ; κ, βδ O ; κ, βn O wPE wPE2 wPE, γwPE wPE2 wPE2 , γwPE AIC BIC 15024.49 15656.70 13652.29 14389.86 13600.18 14443.12 13560.34 14508.65 13646.15 14489.09 13704.29 14652.60 13662.85 14505.80 13590.04 14538.35 13676.61 14519.55 13528.49 14371.43 12641.70 13484.64 12639.33 13587.64 12600.02 13548.33 12991.77 13940.08 12700.81 13649.12 12700.61 13648.92 Values in bold highlight the minimum value for each comparison method. increasing juice consumption. Overall, the subjective value of the juice depreciated across the test session and, for much of the test session, remained less appetitive than the salty tea was aversive (Figure 3). This decrease in the subjective value of the juice exerted a strong influ- ence on behavior; participants were more risk-averse than would be expected if there was no effect of satiation (Figure 4). The effect of satiation was also reflected in the observed decrease in RTs and proportion of “stay” responses made as the number of trials increased (Figure 5). The estimates of κ were not associated with either reported valence (beta weight = −5.72, SE = 4.64, likelihood ratio test [LRT] = 2.03, p = .16) or arousal (beta weight = 0.98, SE = 4.64, LRT = 0.075, p = .79). The bias parameter was significantly greater than zero (βδ 0: mean = 0.05, SE < 0.01, p < .001), reflecting the par- ticipants’ overall bias toward persisting with the “stay” response for a longer duration on a trial than would otherwise be anticipated (Figure 4), but this parameter was not predicted by reported affective valence (beta weight < 0.01, SE < 0.01, LRT < 0.01, p = .95) or arousal (beta weight < 0.01, SE < 0.01, LRT = 0.55, p = .46). Thus, although the value of the juice depreciated considerably over trials (Figure 3), participants occasionally continued to risk making the “stay” response, which was driven by this fixed bias. There was no evidence that the extent of this bias, or the speed of satiation, was influenced by affective state. 2528 Journal of Cognitive Neuroscience Volume 33, Number 12 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 1 2 2 5 2 3 1 9 7 0 8 5 3 / / j o c n _ a _ 0 1 7 7 6 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 1 2 2 5 2 3 1 9 7 0 8 5 3 / / j o c n _ a _ 0 1 7 7 6 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 2. Observed versus generated RT data. The mean discretized RT and mean proportion of stay responses for each stimulus level for both the model-generated and observed judgment bias data. Error bars represent 1 SE. Participants who reported lower arousal tended to make more errors to the ambiguous stimulus; the associ- ation between the lapse rate and reported arousal was marginally nonsignificant (λamb: beta weight = −0.02, SE = 0.01, LRT = 3.77, p = .052). The reference lapse rate did not depend on reported arousal (λref: beta weight < 0.01, SE < 0.01, LRT = 2.04, p = .15). Neither λamb (beta weight < 0.01, SE = 0.01, LRT < 0.01, p = .93) nor λref (beta weight < 0.01, SE < 0.01, LRT = 2.13, p = .15) depended on reported valence. The slope parameter, σ, did not significantly depend on reported affective valence (beta weight = 0.04, SE = 0.04, LRT = 1.10, p = .30) or arousal (beta weight = 0.03, SE = 0.04, LRT = 0.78, p = .38). Likewise, the mean timeout probability (determined by ζ and ’) was not significantly associated with reported affective valence (beta weight < 0.01, SE = 0.03, LRT < 0.01, p = .95) or arousal (beta weight = 0.01, SE = 0.03, LRT = 0.41, p = .52). Figure 3. The value of juice across trials; the black line reflects the offered value, and the gray line reflects the mean satiation-dependent value across participants as determined by the POMDP model. The dashed line shows the threatened volume of salty tea. The shaded error bars represent 1 SE. Neville et al. 2529 Figure 4. Observed versus generated RT data where there is full satiation or is no satiation, and the generated RT data where there is no bias. The mean discretized RT for each stimulus level for both the model-generated (with κ = 0, βδ 0 = 0, and cumulative juice intake equal to the maximum juice intake for the participant) and observed judgment bias data. Error bars represent 1 SE. Participants who reported more positive valence had significantly higher inverse temperature parameter values (B: beta weight = 1.05, SE = 0.48, LRT = 4.92, p = .027; Figure 6), and there was no significant relationship between affective arousal and B (beta weight = 0.22, SE = 0.48, LRT = 0.23, p = .63). However, after outlier removal, no significant effect of valence (beta weight = −0.21, SE = 0.30, LRT = 0.52, p = .47) or arousal (beta weight = −0.46, SE = 0.28, LRT = 2.97, p = .085) on inverse temperature was observed (Figure 6). The median value of the inverse temperature parameters was 336.4297 with an interquartile range of 1224.997, and any value over 3172.95 was deemed to be an outlier. The six outliers iden- tified, whose removal altered the results qualitatively, had values of 4099.67, 5309.31, 82248.43, 15654.76, 121346.67, and 2779806.37 (Figure 6). Reported Affect and Reward and Punisher Experience The best-fitting GLMM of reported valence included the potential outcome as solely a fixed effect (ΔBIC = 12.27 vs. inclusion as a random linear effect), number of trials completed with a slope coefficient that varied among participants (ΔBIC = 26.91 vs. solely a fixed effect; as opposed to the average earning rate − ΔBIC = 26.75, and total juice consumed − ΔBIC = 1.10), and weighted prediction error as solely a fixed effect (ΔBIC = 14.98 vs. Figure 5. Observed versus generated RT and decision data. The mean discretized RT and mean proportion of stay responses for each stimulus level for both the model-generated and observed judgment bias data split by trials in the first and second half of the test session. Error bars represent 1 SE. 2530 Journal of Cognitive Neuroscience Volume 33, Number 12 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 1 2 2 5 2 3 1 9 7 0 8 5 3 / / j o c n _ a _ 0 1 7 7 6 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Figure 6. The relationship between the log-transformed inverse temperature parameter (with unit mL−1) and reported affective valence with regression lines for the data including (solid line) and excluding (dashed line) outliers. inclusion as a random linear effect; as opposed to the squared weighted prediction error − ΔBIC = 54.25, or previous outcome ΔBIC = 38.14). Reported valence was positively correlated with both the prediction error (beta weight = 40.70, SE = 5.34, LRT = 52.14, p < .001) and potential outcome (beta weight = 14.30, SE = 4.24, LRT = 11.33, p < .001). The random slope coefficients for trial did not differ significantly from zero according to a permutation test (mean = −0.03, SE = 0.12, p = .81). The best-fitting GLMM of reported arousal included potential outcome as solely a fixed effect (ΔBIC = 15.34 vs. inclusion as a random linear effect), number of trials completed with a slope coefficient that varied among participants (ΔBIC = 71.70 vs. solely a fixed effect; as opposed to the average earning rate − ΔBIC = 83.80, or total juice consumed − ΔBIC = 0.95), and squared prediction error as solely a fixed effect (ΔBIC = 18.37 vs. inclusion as a random linear effect; as opposed to the weighted prediction error − ΔBIC = 1.11, or pre- vious outcome − ΔBIC = 1.12). A higher potential out- come tended to be associated with greater reported arousal (beta weight = 7.06, SE = 4.66, LRT = 2.89, p = .09). The squared weight prediction error had no significant effect of affective arousal (beta weight = 4.96, SE = 4.66, LRT = 1.14, p = .26). The random slope coefficients for trial were overall significantly lower than zero (mean = −0.52, SE = 0.17, p = .004), suggesting a decrease in reported arousal as the test session progressed. DISCUSSION In this study, we developed a human judgment bias task that closely mirrored nonhuman animal versions of the task by using primary, rather than secondary, reinforcers. We did this to increase the translatability of the task and to allow more effective use of results from the human task to shed light on judgment bias as a measure of affect in nonhuman animals. To achieve this, we modified a hu- man judgment bias task, which was itself originally de- signed as a translation of a rodent judgment bias task, by replacing monetary gain with apple juice and mone- tary loss with salty tea. We then sought to investigate the relationship between prior experience, affect, and decision-making using computational modeling. The task itself was largely successful, with more than 90% of participants performing better than chance in their responses to the reference cues to which they had been trained. Furthermore, the data produced were highly amenable to computational modeling. Our initial analyses demonstrated that the favorability of the potential outcome of participants’ responses mod- ulated self-reported affective valence, with a greater vol- ume of offered juice leading to more positive affective valence. There was also a tendency for a greater volume of offered juice to lead to greater reported affective arousal. This supports the definition of “emotion” (i.e., short-term affect) as a state elicited by (anticipated) rewards or punishers (Mendl & Paul, 2020; Rolls, 2013). It also indicates that participants were sensitive to the fluctuating volume of potential apple juice and were, indeed, perceiving larger volumes of apple juice as more rewarding. This demonstrates that the manipulation of reward experience worked well, having induced the anticipated shifts in affective states. The finding that positive affective valence was related to recently experienc- ing rewards that were greater than anticipated (positive reward prediction errors) aligns with the results of a num- ber of other studies (Neville et al., 2021; Otto & Eichstaedt, 2018; Rutledge et al., 2014). Our results provide evidence for a complex relation- ship between affect and decision-making that could only Neville et al. 2531 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 1 2 2 5 2 3 1 9 7 0 8 5 3 / / j o c n _ a _ 0 1 7 7 6 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 be teased apart through use of computational modeling. Contrary to our expectations, there was no evidence in this study that an “optimistic” bias was directly associated with more positive reported affect. This conflicts with some (but not all) previous findings from judgment bias studies in humans and with the meta-analytic findings in animal studies that relatively positive affective states are associated with more “optimistic” decision-making (Lagisz et al., 2020; Neville, Nakagawa, et al., 2020). Importantly, the meta-analyses identified a small-to- moderate effect size with high heterogeneity, using manipulations including those designed to induce substantial shifts in affective valence (e.g., enriched vs. barren housing; administration of pharmacological substances; restraint stress). It is also possible that the primary reinforcers used in these animal studies may have been far more salient to their nonhuman subjects than the juice/salty tea was for the humans in this study (e.g., animals were food restricted; animals were maintained on a nonvaried diet). Given this, it is perhaps not entirely unsurprising that no significant relationship was found between the parameter estimates encompassing “optimism” and affective valence in our nonclinical popula- tion of human participants. These findings, first, raise the question of whether judgment bias can be sufficiently sensitive to detect more subtle variation in baseline affect and, second, highlights that contextual differences in how humans and animals experience the tasks may present significant hurdles to the development of truly translational and directly comparable tests, even given the explicit attempt. We did find that, when looking at the data as a whole, the inverse temperature parameter was associated with reported valence; lower decision stochasticity was associ- ated with more positive reported affect. This result is consistent with findings that depression, a clinically neg- atively valenced affective state, has been associated with more stochastic decision-making (Harlé, Guo, Zhang, Paulus, & Yu, 2017; Huys, Pizzagalli, Bogdan, & Dayan, 2013). However, this result appeared to be driven by a subset of participants whose data revealed large outlying estimates of the inverse temperature parameter and who tended to report affective valence at the more positive end of the scale. The cause of the outlying values is unclear; speculatively, it could reflect a trait characterized by heightened sensitivity to rewards and punishers and overall more positive affect. It could also reflect that the model fits particularly well to these participants (given that the inverse temperature parameter can also capture model misfit), although the reasons for this are not apparent. Nonetheless, the results of our study imply a relationship between affective valence and the cogni- tive processes underlying judgment bias. The potential link between decision stochasticity and affect requires further investigation. We also identified a weak relationship between affec- tive arousal and the cognitive processes underlying judgment bias. In particular, a higher ambiguous stimulus lapse rate tended to be associated with lower arousal, which corroborates results from the monetary version of the task (Neville et al., 2021) and likely reflects that higher arousal may lead to higher engagement with the task and result in better performance. It would therefore be useful to assess whether lapse rates, extracted using computational modeling, could provide a measure of arousal in judgment bias tasks for nonhuman animals. Our results also highlighted the influence of reward and punisher experience on both affect and decision- making. First, the total juice consumed by the participant was the largest experiential contributor to their decision- making. The value of the juice decreased as a function of increasing juice intake, which is consistent with partici- pants becoming satiated. In accordance with the findings of the monetary version of the task (Neville et al., 2021), we also found decreasing arousal as more trials had been completed, which may indicate that participants found the task to be tiring. Greater consideration should thus be given to the potential for satiation and potential fa- tigue (and individual differences that lead to variation in how rapidly an individual becomes sated) when con- ducting nonhuman animal judgment bias tasks. The degree of satiation indicated in our human partic- ipants was arguably extreme; the absolute subjective value (i.e., subjective magnitude) of the juice remained largely below the value of the salty tea after approximately 20 trials. Yet, despite this, and even after accounting for errors that were characterized by a lapse rate, participants con- tinued to make the “optimistic” response more often than would be anticipated; they exhibited an overall “optimis- tic” bias. The precise nature of this bias is unclear. One pos- sibility is that this bias, particularly at the favorable and near-favorable cue, reflects the intrinsic reward associated with making accurate (correct) responses. Similarly, it may reflect that the feedback provided by the “stay” response was itself rewarding, given that the “leave” response pro- vides no feedback about the correct action. This would be in line with studies that have shown that being correct is in itself rewarding (Satterthwaite et al., 2012; Han, Huettel, Raposo, Adcock, & Dobbins, 2010). An alternative is that the “gamble” of the “stay” may have elicited a reward- ing frisson of excitement, particularly given the repetitive and likely dull nature of the task. Studies have shown that risky decision-making can induce excitement and that this increases under boredom (Kılıç, van Tilburg, & Igou, 2020; Binde, 2013; Mercer & Eastwood, 2010). This further high- lights issues in developing truly translational versions of the judgment bias task; nonhuman animals may not experi- ence an intrinsic reward associated with being correct, and judgment bias tasks for nonhuman animals have been suggested to be enriching for them (Krakenberg et al., 2021; Mallien et al., 2016). The extent to which recent outcomes were better or worse than anticipated was found to determine self-reported affect. This is in agreement with previous studies 2532 Journal of Cognitive Neuroscience Volume 33, Number 12 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 1 2 2 5 2 3 1 9 7 0 8 5 3 / / j o c n _ a _ 0 1 7 7 6 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 demonstrating that prediction error is a key determinant of subjectively experienced affective valence in humans (Neville et al., 2021; Otto & Eichstaedt, 2018; Rutledge et al., 2014), at least in relation to short-term rewarding experiences. As outlined above, several findings were consistent between this task and the monetary version from which it was adapted: A higher lapse rate is associated with lower affective arousal, more positive prediction errors are associated with more positive reported affective valence, and reported affective arousal decreases as a func- tion of number of trials completed. However, there were also a couple of inconsistencies. First, we found no evidence for a relationship between time estimation and affective valence, and second, we found no effect of the average earning rate or previous outcome on judgment bias (Neville et al., 2021). It is unclear whether this might stem from differences in the processing of primary and secondary rewards, or other factors such as satiation having an overriding effect on behavior. This therefore warrants further examination. In summary, we have developed a novel version of the judgment bias task for humans that uses primary as opposed to secondary reinforcers and, as a result, is more comparable to versions of the task designed for nonhuman animals. We identified a relationship between decision stochasticity and affective valence. Specifically, we generally observed higher decision stochasticity in participants who reported more negatively valenced affect, although only when a cluster of outlying partici- pants were included. We also found that lower reported affective arousal tended to be associated with a greater propensity for errors. The decision-making processes underlying judgment bias on this task are hence linked to both reported affective valence and arousal. In addi- tion, we confirmed some, but not all, previous findings that had been observed in a monetary version of the task, such as the positive association between prediction errors and reported valence. We conclude that our novel version of the judgment bias task provides a means to investigate the relationship between affect and decision- making in greater depth and in a manner that is more comparable to animal versions of the task. Reprint requests should be sent to Vikki Neville, Bristol Veterinary School, University of Bristol, Langford BS40 5DU, United Kingdom, or via e-mail: vikki.neville@bristol.ac.uk. Author Contributions Vikki Neville: Conceptualization; Data curation; Formal analy- sis; Investigation; Methodology; Writing—Original draft; Writing—Review & editing. Peter Dayan: Conceptualization; Formal analysis; Methodology; Supervision; Writing— Original draft; Writing—Review & editing. Iain D. Gilchrist: Conceptualization; Methodology; Supervision; Writing— Review & editing. Elizabeth S. Paul: Conceptualization; Methodology; Supervision; Writing—Review & editing. Michael Mendl: Conceptualization; Methodology; Supervision; Writing—Original draft; Writing—Review & editing. Funding Information Vikki Neville, Biotechnology and Biological Sciences R e s e a r c h C o u n c i l ( h t t p s : / / d x . d o i . o r g / 1 0 . 1 3 0 3 9 /501100000268), grant number: BB/ M009122/1. Peter Dayan, Alexander von Humboldt-Stiftung (https://dx.doi .org/10.13039/100005156). Peter Dayan, Max-Planck- Gesellschaft (https://dx.doi.org/10.13039/501100004189). All authors, Biotechnology and Biological Sciences Research Council (https://dx.doi.org/10.13039/501100000268), grant number: BB/T002654/1. Diversity in Citation Practices A retrospective analysis of the citations in every article published in this journal from 2010 to 2020 has revealed a persistent pattern of gender imbalance: Although the pro- portions of authorship teams (categorized by estimated gender identification of first author/last author) publishing in the Journal of Cognitive Neuroscience ( JoCN) during this period were M(an)/M = .408, W(oman)/M = .335, M/ W = .108, and W/ W = .149, the comparable proportions for the articles that these authorship teams cited were M/M = .579, W/M = .243, M/ W = .102, and W/ W = .076 (Fulvio et al., JoCN, 33:1, pp. 3–7). Consequently, JoCN encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article’s gender citation balance. 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Using Primary Reinforcement to Enhance Translatability image
Using Primary Reinforcement to Enhance Translatability image
Using Primary Reinforcement to Enhance Translatability image
Using Primary Reinforcement to Enhance Translatability image
Using Primary Reinforcement to Enhance Translatability image
Using Primary Reinforcement to Enhance Translatability image

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