Examinando por Autor "Matute, Helena"

Mostrando 1 - 9 de 9
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    The combined effect of patient classification systems and availability of resources can bias the judgments of treatment effectiveness
    (Nature Research, 2025-05-07) Viñas Gómez, Aranzazu; Blanco Bregón, Fernando; Matute, Helena
    Patient classification systems (PCS) support clinical decision-making but may rely on incorrect, outdated, or insufficient data. Doctors can sometimes override errors using their experience. However, certain factors such as scarcity of resources could lead to reliance on incorrect PCS recommendations, with consequences for patients. We conducted two experiments where participants interacted with a PCS that incorrectly classified fictitious patients as more or less sensitive to a treatment. Participants had the opportunity to administer the treatment on a series of patients, and use the feedback to learn that the PCS was wrong and all patients were equally sensitive. This was tested in contexts of abundant and scarce resources. Additionally, the treatment was effective in Experiment 1, but ineffective in Experiment 2. Results indicate that people generally trust the PCS recommendation, to some extent neglecting the information they collect during the task. This can lead to uneven resource allocation, especially in scarcity conditions, and incorrect perceptions of effectiveness, which in Experiment 2 implies believing that an ineffective treatment works. We preregistered the experiments, and all data and materials are public.
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    A debiasing intervention to reduce the causality bias in undergraduates: the role of a bias induction phase
    (Springer, 2023-01-14) Martínez Pereña, Naroa; Rodríguez Ferreiro, Javier; Barberia Fernández, Itxaso ; Matute, Helena
    The causality bias, or causal illusion, occurs when people believe that there is a causal relationship between events that are actually uncorrelated. This bias is associated with many problems in everyday life, including pseudoscience, stereotypes, prejudices, and ideological extremism. Some evidence-based educational interventions have been developed to reduce causal illusions. To the best of our knowledge, these interventions have included a bias induction phase prior to the training phase, but the role of this bias induction phase has not yet been investigated. The aim of the present research was to examine it. Participants were randomly assigned to one of three groups (induction + training, training, and control, as a function of the phases they received before assessment). We evaluated their causal illusion using a standard contingency judgment task. In a null contingency scenario, the causal illusion was reduced in the training and induction-training groups as compared to the control group, suggesting that the intervention was effective regardless of whether or not the induction phase was included. In addition, in a positive contingency scenario, the induction + training group generated lower causal judgments than the control group, indicating that sometimes the induction phase may produce an increase in general skepticism. The raw data of this experiment are available at the Open Science Framework at https://osf.io/k9nes/.
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    Forenpsy: un banco estandarizado de testimonios ficticios de testigos para la investigación en psicología experimental y judicial
    (Colegio Oficial de la Psicología de Madrid, 2025-02) Álvarez, Mario; Martínez Pereña, Naroa; Agudo Díaz, Ujué; Matute, Helena
    Para realizar experimentos controlados y replicables simulando juicios hemos creado ForenPsy, el primer banco de testimonios estandarizado y abierto en español. ForenPsy incluye nueve historias (tres de cada tipo de delito: homicidio, amenazas y allanamiento) con 14 testimonios cada una (siete de inocencia y siete de culpabilidad), lo que hace un total de 126. Trescientos participantes respondieron dos preguntas sobre cada testimonio: una sobre si el testimonio indicaba inocencia o culpabilidad, que muestra que el índice de acuerdo con el valor esperado fue .85, y otra sobre el grado de culpabilidad que atribuían a cada testimonio, que fue significativamente inferior para los testimonios de inocencia que para los de culpabilidad, lo que indica que los estímulos funcionaron adecuadamente. ForenPsy, así como las normas de los testimonios, está disponible en OSF y puede utilizarse y mejorarse de manera colaborativa para realizar experimentos replicables simulando contextos judiciales.
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    The impact of AI errors in a human-in-the-loop process
    (Springer Science and Business Media Deutschland GmbH, 2024-01-07) Agudo Díaz, Ujué; Liberal, Karlos G.; Arrese, Miren; Matute, Helena
    Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human–computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered
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    Indefensión aprendida y conducta supersticiosa: posibles efectos de la falta de control sobre efectos ambientales
    (Universidad de Deusto, 1989-07-21) Matute, Helena; Nicolás y Martínez, Luis de; Facultad de Filosofía y Ciencias de la Educación
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    A large-scale study and six-month follow-up of an intervention to reduce causal illusions in high school students
    (Royal Society Publishing, 2024-08) Martínez Pereña, Naroa; Matute, Helena; Blanco Bregón, Fernando; Barberia Fernández, Itxaso
    Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes and other unjustified beliefs. Thus, it seems important to develop educational interventions to reduce them. To our knowledge, the only debiasing intervention designed to be used at schools was developed by Barberia et al. (Barberia et al. 2013 PLoS One 8, e71303 (doi:10.1371/journal.pone.0071303)), focusing on base rates, control conditions and confounding variables. Their assessment used an active causal illusion task where participants could manipulate the candidate cause. The intervention reduced causal illusions in adolescents but was only tested in a small experimental project. The present research evaluated it in a large-scale project through a collaboration with the Spanish Foundation for Science and Technology (FECYT), and was conducted in schools to make it ecologically valid. It included a pilot study (n = 287), a large-scale implementation (n = 1668; 40 schools) and a six-month follow-up (n = 353). Results showed medium-to-large and long-lasting effects on the reduction of causal illusions. To our knowledge, this is the first research showing the efficacy and long-term effects of a debiasing intervention against causal illusions that can be used on a large scale through the educational system.
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    Machine learning systems as mentors in human learning: a user study on machine bias transmission in medical training
    (Academic Press, 2025-04) Vicente Holgado, Lucía; Matute, Helena; Fregosi, Caterina; Cabitza, Federico
    While accurate AI systems can enhance human performance, exerting both an augmentation and good mentoring effect, imperfect systems may act as poor mentors, transmitting biases and systematic errors to users. However, there is still limited research on the potential for AI to transmit biases to humans, an effect that could be even more pronounced for less experienced users, such as novices or trainees, making decisions supported by AI-based systems. To investigate the bias transmission effect and the potential of AI to serve as a mentor, we involved eighty-six medical students, dividing them into an AI-assisted group and a control group. We tasked them with classifying simulated tissue samples for a fictitious disease. In the first phase of the task, the AI group received diagnostic advice from a simulated AI system that made systematic errors for a specific type of case, while being accurate for all other types. The control group did not receive any assistance. In the second phase, participants in both groups classified new tissue samples, including ambiguous cases, without any support to test the residual impact of AI bias. The results showed that the AI-assisted group exhibited a higher error rate when classifying cases where the AI provided systematically erroneous advice, both in the AI-assisted and the subsequent unassisted phase, suggesting the persistence of AI-induced bias. Our study emphasizes the need for careful implementation and continuous evaluation of AI systems in education and training to mitigate potential negative impacts on trainee learning outcomes.
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    Recent advances in the study of the illusion of causality: theory, methods, and practical implications
    (Universidad de Valencia = Universitat de València, Departamento de Metodología de las Ciencias del Comportamiento, 2023-12-17) Moreno Fernández, María Manuela; Blanco Bregón, Fernando ; Matute, Helena
    Learning causal relations provides the knowledge that allows us to make accurate predictions. Some of these predictions may have a high value for survival, and some of them provide us with a body of knowledge that maximize context adaptation. This is why researchers have tried to understand how people make causal inferences and learn about the causal structures in their environment. In this article, we will outline some of the most recent advances in the understanding of causal learning, and specifically of the biases that often appear in decision-making.
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    The tendency to stop collecting information is linked to illusions of causality
    (Nature Research, 2021-02-16) Moreno Fernández, María Manuela ; Blanco Bregón, Fernando; Matute, Helena
    Previous research proposed that cognitive biases contribute to produce and maintain the symptoms exhibited by deluded patients. Specifically, the tendency to jump to conclusions (i.e., to stop collecting evidence soon before making a decision) has been claimed to contribute to delusion formation. Additionally, deluded patients show an abnormal understanding of cause-effect relationships, often leading to causal illusions (i.e., the belief that two events are causally connected, when they are not). Both types of bias appear in psychotic disorders, but also in healthy individuals. In two studies, we test the hypothesis that the two biases (jumping to conclusions and causal illusions) appear in the general population and correlate with each other. The rationale is based on current theories of associative learning that explain causal illusions as the result of a learning bias that tends to wear off as additional information is incorporated. We propose that participants with higher tendency to jump to conclusions will stop collecting information sooner in a causal learning study than those participants with lower tendency to jump to conclusions, which means that the former will not reach the learning asymptote, leading to biased judgments. The studies provide evidence in favour that the two biases are correlated but suggest that the proposed mechanism is not responsible for this association.