Effect of prior beliefs and cognitive deficits on learning in first-episode schizophrenia patients

Abstract

Introduction
It is known that cognitive deficits are a core feature of schizophrenia and that in the general population, prior beliefs significantly influence learning and reasoning processes. However, the interaction of prior beliefs with cognitive deficits and their impact on performance in schizophrenia patients is still poorly understood. This study investigates the role of beliefs and cognitive variables (CVs) like working memory, associative learning, and processing speed on learning processes in individuals with schizophrenia. We hypothesize that beliefs will influence the ability to learn correct predictions and that first-episode schizophrenia patients (FEP) will show impaired learning due to cognitive deficits.

Methods
We used a predictive-learning task to examine how FEP (n = 23) and matched controls (n = 23) adjusted their decisional criteria concerning physical properties during the learning process when predicting the sinking behavior of two transparent containers filled with aluminum discs when placed in water.

Results
On accuracy, initial differences by group, trial type, and interaction effects of these variables disappeared when CVs were controlled. The differences by conditions, associated with differential beliefs about why the objects sink slower or faster, were seen in patients and controls, despite controlling the CVs' effect.

Conclusions
Differences between groups were mainly explained by CVs, proving that they play an important role than what is assumed in this type of task. However, beliefs about physical events were not affected by CVs, and beliefs affect in the same way the decisional criteria of the control or FEP patients' groups.

Full text

Schizophrenia Research: Cognition 38 (2024) 100318
2215-0013/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Short Communication
Effect of prior beliefs and cognitive decits on learning in rst-episode
schizophrenia patients
Daniel Nú˜
nez
a
,
b
, Javiera Rodríguez-Delgado
a
, Ram´
on D. Castillo
a
,
*
, Jos´
e Yupanqui
c
,
Heidi Kloos
d
a
Centro de Investigaci´
on en Ciencias Cognitivas, Facultad de Psicología, Universidad de Talca, Chile
b
Millennium Nucleus to Improve the Mental Health of Adolescents and Youths, Imhay, Chile
c
Servicio de Psiquiatría, Hospital de Curic´
o, Chile
d
Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, OH, USA
ARTICLE INFO
Keywords:
Cognitive decits
Beliefs
Predictive learning
First-episode schizophrenia
ABSTRACT
Introduction: It is known that cognitive decits are a core feature of schizophrenia and that in the general
population, prior beliefs signicantly inuence learning and reasoning processes. However, the interaction of
prior beliefs with cognitive decits and their impact on performance in schizophrenia patients is still poorly
understood. This study investigates the role of beliefs and cognitive variables (CVs) like working memory,
associative learning, and processing speed on learning processes in individuals with schizophrenia. We hy-
pothesize that beliefs will inuence the ability to learn correct predictions and that rst-episode schizophrenia
patients (FEP) will show impaired learning due to cognitive decits.
Methods: We used a predictive-learning task to examine how FEP (n =23) and matched controls (n =23)
adjusted their decisional criteria concerning physical properties during the learning process when predicting the
sinking behavior of two transparent containers lled with aluminum discs when placed in water.
Results: On accuracy, initial differences by group, trial type, and interaction effects of these variables disappeared
when CVs were controlled. The differences by conditions, associated with differential beliefs about why the
objects sink slower or faster, were seen in patients and controls, despite controlling the CVs' effect.
Conclusions: Differences between groups were mainly explained by CVs, proving that they play an important role
than what is assumed in this type of task. However, beliefs about physical events were not affected by CVs, and
beliefs affect in the same way the decisional criteria of the control or FEP patients' groups.
1. Introduction
Beliefs are crucial to predict the future and guide our decisions
(Castillo et al., 2015; Valton et al., 2019). Schizophrenia patients present
decits in updating beliefs based on new evidence and changing be-
haviors in response to negative feedback (Adams et al., 2018; Evans
et al., 2015; Frith and Friston, 2013; Serrano-Guerrero et al., 2020).
These impairments could lead to inaccurate inferences (Grifn and
Fletcher, 2017), biased internal models about the environment (Valton
et al., 2019), and have been linked to positive symptoms (Horga et al.,
2014; Schmack et al., 2013, 2015; Kaplan et al., 2016; Teufel et al.,
2015). However, some scholars propose that the evidence on how
schizophrenia patients update their beliefs is inconclusive (Firestone
and Scholl, 2016; Teufel and Nanay, 2017; Sterzer et al., 2018).
Predictions based on physical object properties are affected by be-
liefs, as shown in studies using the sinking objects paradigm (Kloos,
2007), which reveal that beliefs differentially affect predictions, even
when object sinking conditions are the same (Castillo et al., 2017). This
performance pattern is driven by prior knowledge and beliefs, but
cognitive factors like working memory, processing speed, and associa-
tive learning also play a role (Bruny´
e and Taylor, 2008; Copeland and
Radvansky, 2004; Kail et al., 2016; Klauer et al., 2000; Tamez et al.,
2008).
Concerning cognitive functioning, some studies have found a worse
patient's performance in syllogistic reasoning causal and probabilistic
learning. In comparison, others have not seen differences and even
better patient performance, suggesting that general intelligence and
cognitive functions could be potential mechanisms that explain such
* Corresponding author at: Faculty of Psychology, Avenida Lircay s/n, Talca, Chile.
E-mail address: racastillo@utalca.cl (R.D. Castillo).
Contents lists available at ScienceDirect
Schizophrenia Research: Cognition
journal homepage: www.elsevier.com/locate/scog
https://doi.org/10.1016/j.scog.2024.100318
Received 15 November 2023; Received in revised form 4 June 2024; Accepted 4 June 2024

Schizophrenia Research: Cognition 38 (2024) 100318
2
contradictory ndings (Cardella and Gangemi, 2015). Studies with
psychiatric patients have found that processing speed could predict uid
reasoning, but only when working memory was considered (Kim and
Park, 2018). Similarly, Randers et al. (2020) found that individuals at
ultra-high risk for psychosis often show impaired processing speed,
which likely contributes to their overall cognitive difculties. To explore
these cognitive aspects further, we focused on a predictive task
encompassing learning, reasoning, and belief-tracking activities. These
activities are closely related to cognitive functions like working mem-
ory, associative learning, and processing speed, which are strongly
connected to reasoning abilities. Given the cognitive impairments found
in schizophrenia (Zanelli et al., 2019) and its impact on learning and
reasoning (Cardella and Gangemi, 2015; Stuke et al., 2018), we tested
the effects of CVs on performance by using the sinking-object paradigm
in FEP and matched controls.
2. Method
2.1. Selection and description of participants
We included 23 FEP and 23 matched controls (Table 1). We excluded
control participants exhibiting neurological, psychiatric disorders, or
rst-degree relatives suffering from schizophrenia spectrum disorders.
Participants were recruited at three Chilean hospitals between 2016 and
2017. All participants provided written informed consent, following the
protocol approved by the Universidad de Talca Ethics Committee (IRB,
2016–2019, #1161503).
2.2. Materials and procedure
We used a sinking-object task to analyze participants' predictions and
learning patterns (Castillo et al., 2015). Participants were asked to
predict the behavior of transparent containers lled with aluminum
discs when placed in water. Objects were pictures of transparent glass
jars of different sizes (large, medium, and small) that could hold various
aluminum discs. Five trial types were constituted by 12 unique jar-disc
combinations with different sizes and weights (Fig. 1). A full description
of this procedure can be obtained from Castillo et al. (2017).
The experiment encompassed three stages. The rst and last stages
(pre-test and post-test) were identical, each consisting of 60 trials: Par-
ticipants had to predict which of two objects would sink faster (or
slower) depending on the experimental condition. The middle stage
(feedback training) asked the participant to predict the sinking behavior
of the jars (60 trials randomly repeated twice), but participants received
feedback. After each prediction, they were shown an image of a water
container in which jars were dropped (Fig. 2B).
2.3. Measures
Cognitive variables (CVs) were evaluated by subscales of the
Wechsler Intelligence Test (Wechsler, 2012): processing speed (PS;
Symbol search), working memory (WM; Digit span) and Associative
learning (AL; Letter number sequencing). We assessed psychotic symp-
toms by the Positive and Negative Syndrome Scale (PANSS, Kay and
Opler, 1987).
2.4. Statistics
We split the 240-trial experimental session into four segments: pre-
test (PET: Trial 1–60), training (T1: Trial 61–120, T2: Trial 121–160),
and post-test (POT: Trial 161–180). We performed separate 5-by-2-by-2
ANOVAs for each segment, considering trial types, group (control vs.
patients), and conditions (sink-faster vs. sink-slower) as factors
(Table 2). Subsequently, we used an ANCOVA to control for the effect of
CV and assess the stability of main and interaction effects before and
after this control (Table 3). If these effects are still consistent, the CV
impact is negligible. Lastly, we conducted a correlation analysis between
CV and clinical variables (symptoms, age of illness onset, illness dura-
tion, and medication).
3. Results
3.1. Effect of CVs on performance
ANOVA showed main and interaction effects, with the control group
consistently outperforming the FEP group in all experimental
conditions.
Across the experiment, the Small-Light trial type consistently dis-
played lower accuracy compared to other trial types, regardless of group
or experimental condition.
Table 1
Demographic and clinical characteristics of patient and control groups — Mean
(Standard Deviation).
Patients Controls
N 23 23 p
Gender (m =male; f =female) 13 m, 10 f 13 m, 10 f
Age (years) 19.83 (6.69)
19.57
(6.43) 0.89
Educational level N
≤12 years 19 18 >0.05
>13 years 4 5
Average educational level 10.61 (2.59)
11.09
(2.65) 0.57
Duration of illness
a
9.55 (8.99)
PANSS positive 15.48 (5.72)
PANSS negative 19.04 (7.86)
PANSS general
37.22
(14.40)
Processing speed (WAIS, Symbol search)
24.08
(16.93)
33.45
(6.57) 0.020
Associative learning (WAIS, Letter-
number sequencing)
45.96
(18.30)
71.00
(12.51) 0.001
Attention, working memory (WAIS, Digit
span) 18.34 (5.76) 21.13
(3.48) 0.057
Antipsychotic medication
Chlorpromazine equivalent (mg)
420.22
(674.48)
Atypical antipsychotics (%) 23 (100)
Typical antipsychotics (%) 3 (13.04)
Antidepressants (%) 12 (52.1)
Anticonvulsants 6 (23.08)
a
Number of months between the rst admission and the experiment.
Fig. 1. Example pairs of objects, one for each different type of pair. The underlined object signies the object that would sink faster in the pair. A: Small. B: Heavy. C:
Big-Heavy. D: Small-Heavy. E: Small-Light trial types.
D. Nú˜
nez et al.

Schizophrenia Research: Cognition 38 (2024) 100318
3
Accuracy was consistently lower in the slow-sinking condition than
in the fast-sinking condition, regardless of participant group or trial
type. A signicant trial type-by-condition interaction effect showed
higher accuracy in the Small-Light trial type under the slow-sinking
condition. During T1, there was a group-by-trial type interaction ef-
fect, showing reduced accuracy for FEP across all trial types and reduced
accuracy within the Small-Light trial type for the control group. In POT,
an interaction effect between group and condition appeared. The control
group showed no notable differences between conditions, while FEP had
more correct responses in the fast-sinking condition.
An intricate trial type-by-group-by-condition interaction effect
showed the control group performing better across all trial types in the
fast-sinking condition and FEP excelling only in the Small-Light trial
type under the slow-sinking condition.
Upon controlling for covariates (CVs), the signicant differences
among trial types across all experimental phases became non-
signicant. Likewise, the interaction effects involving group and trial
type, as well as group and condition during T1 and POT, respectively,
lost their statistical signicance.
Of the observed between-group differences across the four experi-
mental phases, only the distinction found in POT remained statistically
signicant. Similarly, among the interactions between trial type and
condition across all four experimental phases, only those in PET and
POT sustained their signicance. Initially seen in PET, the group-by-trial
type-condition interaction maintained its signicance even after con-
trolling for covariates. Remarkably, the differences related to experi-
mental conditions persisted independently of covariates.
3.2. Correlations between clinical variables and CVs
We saw signicant negative links between symptoms, associative
learning, and working memory. Specically, associative learning is
strongly associated with most negative symptoms, some general symp-
toms, and one positive symptom. Working memory was correlated with
specic negative symptoms and one positive symptom (see Supple-
mentary information Table 4).
However, our study did not reveal any correlations between symp-
toms and other clinical factors. Additionally, no associations were found
between symptoms, illness duration, or medication dosage. It's worth
noting that although medication dosage was associated with processing
speed, it did not exhibit signicant links with symptoms.
4. Discussion
We examined rst-episode schizophrenia patients and their matched
controls in a reasoning-learning task where they predicted the sinking
speed of objects based on prior beliefs about their physical properties.
Fig. 2. Schematic representation of prediction trials in Experiments. A: Example trial during pre- or post-test. B: Example trial during feedback training.
Table 2
Performance (% of correct responses), patients and controls.
Group Controls Patients
Fast Slow Fast Slow
Pretest
(PET)
Small (S) 0.81 (0.08) 0.66 (0.11) 0.59 (0.09) 0.58 (0.10)
Heavy
(H) 0.94 (0.04) 0.73 (0.12) 0.93 (0.05) 0.39 (0.13)
Big-
Heavy
(BH) 0.93 (0.04) 0.71 (0.11) 0.91 (0.07) 0.43 (0.13)
Small-
Heavy
(SH) 0.94 (0.05) 0.74 (0.10) 0.92 (0.04) 0.48 (0.09)
Small-
Light
(SL) 0.43 (0.01) 0.41 (0.13) 0.31 (0.09) 0.67 (0.12)
Training
1
(T1)
Small (S) 0.94 (0.03) 0.77 (0.12) 0.66 (0.09) 0.48 (0.08)
Heavy
(H) 0.97 (0.03) 0.70 (0.12) 0.92 (0.05) 0.67 (0.13)
Big-
Heavy
(BH) 0.93 (0.04) 0.70 (0.10) 0.93 (0.04) 0.66 (0.14)
Small-
Heavy
(SH) 0.99 (0.01) 0.81 (0.12) 0.90 (0.07) 0.72 (0.11)
Small-
Light
(SL) 0.64 (0.08) 0.60 (0.08) 0.31 (0.09) 0.41 (0.12)
Training
2
(T2)
Small (S) 0.92 (0.05) 0.80 (0.13) 0.79 (0.06) 0.45 (0.11)
Heavy
(H) 0.97 (0.02) 0.77 (0.13) 0.89 (0.06) 0.64 (0.14)
Big-
Heavy
(BH) 0.92 (0.03) 0.69 (0.10) 0.90 (0.04) 0.63 (0.14)
Small-
Heavy
(SH) 0.99 (0.01) 0.81 (0.12) 0.89 (0.06) 0.65 (0.14)
Small-
Light
(SL) 0.63 (0.08) 0.75 (0.08) 0.45 (0.10) 0.43 (0.14)
Posttest
(POT)
Small (S) 0.95 (0.03) 0.90 (0.09) 0.76 (0.07) 0.35 (0.10)
Heavy
(H) 0.98 ((0.02) 0.87 (0.09) 0.92 (0.06) 0.46 (0.16)
Big-
Heavy
(BH) 0.92 (0.03) 0.83 (0.09) 0.90 (0.04) 0.48 (0.15)
Small-
Heavy
(SH) 0.97 (0.03) 0.89 (0.10) 0.92 (0.05) 0.48 (0.13)
Small-
Light
(SL) 0.71 (0.08) 0.76 (0.06) 0.40 (0.10) 0.54 (0.13)
D. Nú˜
nez et al.

Schizophrenia Research: Cognition 38 (2024) 100318
4
After accounting for CVs effects, we found that both patients and con-
trols performed similarly, and their beliefs about sinking objects were
independent of CVs. Furthermore, we observed better performance in
the faster sinking condition and an interaction between the condition
and trial type. This indicates that, regardless of group membership,
different beliefs can be activated depending on instructions for pre-
dicting object sinking speed, even when the stimuli and task remain the
same. Additionally, the interaction effect revealed that participants
performed better in the sinking slower condition when working with the
Small-Light trial type, as previously reported in healthy undergraduate
students (Castillo et al., 2015, 2017).
Our ndings suggest that the availability of cognitive resources may
explain the lower patient performance, as found in adult schizophrenia
patients. Collins et al. (2014) linked impaired performance in a rein-
forcement learning task to working memory, while Culbreth et al.
(2017) attributed decits in a decision-making task to IQ levels and
working memory. Cardella and Gangemi's (2015) review indicated that
differences in reasoning tasks between patients and controls could be
accounted for by IQ and cognitive abilities. Similarly, Zhu et al. (2021)
found reduced cognitive exibility in schizophrenia and depressive pa-
tients aged 18–65, with differences disappearing after controlling for IQ
scores. However, caution should be exercised when comparing these
ndings to ours, considering differences in tasks and participant age
ranges.
We observed that in patients, cognitive variables (CVs) were linked
to general, positive, and negative symptoms. Therefore, the symptoms of
FEP could contribute to decits in cognitive variables and explain their
lower performance compared to the control group. This assumption is
based on our study not including symptom measurements in the control
group.
This study has limitations. Firstly, the small sample size prevents us
from drawing denitive conclusions. Secondly, we did not investigate
other CVs, such as executive functioning, inhibitory control, monitoring,
and perceptual inference, potentially associated with our task.
Our results indicated that performance in the sinking-object task and
the patient-control differences were attributable to cognitive func-
tioning variables. Beliefs about sinking objects remained unaffected by
cognitive variables. More research is needed to dissect the specic im-
pacts of cognitive functioning variables in various stages of our pre-
dictive task and to determine the extent to which beliefs remain
independent of attentional and perceptual processes.
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scog.2024.100318.
Financial disclosure
This work was funded by ANID – Millennium Science Initiative
Program – NCS2021_081, Fondecyt de Iniciaci´
on # 11140099 and
Proyecto FONDEQUIP EQM190153. Additionally, this work was funded
by la Universidad de Talca through the Programa de Investigaci´
on
Asociativa (PIA) en Ciencias Cognitivas (RU-158-2019).
CRediT authorship contribution statement
Daniel Nú˜
nez: Conceptualization, Funding acquisition, Methodol-
ogy, Project administration, Supervision, Validation, Visualization,
Writing – original draft, Writing – review & editing. Javiera Rodríguez-
Delgado: Investigation, Methodology, Resources, Validation, Visuali-
zation, Writing – original draft, Writing – review & editing. Ram´
on D.
Castillo: Conceptualization, Formal analysis, Funding acquisition,
Investigation, Methodology, Project administration, Software, Supervi-
sion, Validation, Visualization, Writing – original draft, Writing – review
& editing. Jos´
e Yupanqui: Investigation, Methodology, Project
administration, Supervision, Validation, Visualization. Heidi Kloos:
Conceptualization, Formal analysis, Investigation, Methodology, Vali-
dation, Visualization, Writing – original draft, Writing – review &
editing.
Declaration of competing interest
The authors declare the following nancial interests/personal re-
lationships which may be considered as potential competing interests:
Ramon Castillo reports nancial support was provided by University of
Talca Faculty of Psychology. Ramon D. Castillo reports a relationship
with National Agency for Research and Development that includes:
funding grants. If there are other authors, they declare that they have no
known competing nancial interests or personal relationships that could
have appeared to inuence the work reported in this paper.
Table 3
Main and interaction effects.
Effects Group
F(1,41)
Trial Type
F(4,164)
Condition
F(1,41)
Group * Trial
Type
F(4,164)
Group * Condition
F(1,41)
Trial Type *
Condition
F(4,164)
Group * Trial Type *
Condition
F(4,164)
ANOVA
PET 4.79; p ¼.03;
η
2
¼0.11
9.17; p < .001;
η
2
¼0.18
14.54; p ¼.00;
η
2
¼0.26 1.29; p =.27 0.386; p =.54 7.12; p ¼.00;
η
2
¼0.15
3.13; p ¼.02;
η
2
¼
0.07
T1 5.57; p ¼.02;
η
2
¼0.12
9.17; p < .001;
η
2
¼0.19
8.05; p ¼.01;
η
2
¼0.16
2.86; p ¼.03;
η
2
¼0.07 0.038; p =.85 2.55; p ¼.04;
η
2
¼0.06 0.22; p =.93
T2 5.59; p ¼.02;
η
2
¼0.12
7.27; p ¼.010;
η
2
¼0.15
5.59; p ¼.02;
η
2
¼0.12 1.37; p =.25 0.60; p =.44 2.72; p ¼.03;
η
2
¼0.06 0.24; p =.92
POT 18.69; p ¼.00;
η
2
¼0.31
10.01; p ¼.00;
η
2
¼0.20
10.01; p ¼.00;
η
2
¼0.20 1.89; p =.11 4.75; p ¼.04;
η
2
¼0.10
5.10; p ¼.01;
η
2
¼0.11 0.99; p =.42
Effects Group
F(1,37)
Trial Type
F(4,148)
Condition
F(1,37)
Group * Trial
Type
F(4,148)
Group *
Condition
F(1,37)
Trial Type *
Condition
F(4,148)
Group * Trial Type *
Condition
F(4,148)
ANCOVA
PET 1.52; p =.23 0.06; p =
.99
16.00; p ¼.00;
η
2
¼
0.30 0.60; p =.66 0.08; p =.79 5.84; p ¼.00;
η
2
¼
0.14 2.48; p ¼.05;
η
2
¼0.06
T1 0.99; p =.33 0.57; p =
.69
6.09; p ¼.02;
η
2
¼
0.14 1.37; p =.25 0.37; p =.55 2.29; p =.06 0.24; p =.92
T2 1.08; p =.31 0.34; p =
.85
5.45; p ¼.03;
η
2
¼
0.13 1.15; p =.34 0.16; p =.69 2.32; p =.06 0.18; p =.95
POT 4.52; p ¼.04;
η
2
¼
0.11
0.25; p =
.91
7.15; p ¼.01;
η
2
¼
0.16 1.16; p =.33 2.90; p =.10 4.76; p ¼.01;
η
2
¼
0.11 1.88; p =.12
D. Nú˜
nez et al.

Schizophrenia Research: Cognition 38 (2024) 100318
5
Data availability
The data that support the ndings of this study are available on
request from the corresponding author, RDC. The data are not publicly
available due to information that could compromise the privacy of
research participants.
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