DeustoTeka Examinando por Autor "Matute, Helena"

Examinando por Autor "Matute, Helena"

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Assessing emotion and sensitivity of AI artwork
(Frontiers Media S.A., 2022-04-05) Agudo Díaz, Ujué; Arrese, Miren; Liberal, Karlos G. ; Matute, Helena
Artificial Intelligence (AI) is currently present in areas that were, until recently, reserved for humans, such as, for instance, art. However, to the best of our knowledge, there is not much empirical evidence on how people perceive the skills of AI in these domains. In Experiment 1, participants were exposed to AI-generated audiovisual artwork and were asked to evaluate it. We told half of the participants that the artist was a human and we confessed to the other half that it was an AI. Although all of them were exposed to the same artwork, the results showed that people attributed lower sensitivity, lower ability to evoke their emotions, and lower quality to the artwork when they thought the artist was AI as compared to when they believed the artist was human. Experiment 2 reproduced these results and extended them to a slightly different setting, a different piece of (exclusively auditory) artwork, and added some additional measures. The results show that the evaluation of art seems to be modulated, at least in part, by prior stereotypes and biases about the creative skills of AI. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/3r7xg/. Experiment 2 was preregistered at AsPredicted: https://aspredicted.org/fh2u2.pdf.
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Patient classification systems (PCS) support clinical decision-making but may rely on incorrect, outdated, or insufficient data.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.
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/.
Expensive seems better: the price of a non-effective drug modulates its perceived efficacy
(Springer Science and Business Media Deutschland GmbH, 2023-01-26) Díaz Lago, Marcos; Blanco Bregón, Fernando ; Matute, Helena
Previous studies have shown that the price of a given product impacts the perceived quality of such product. This finding was also observed in medical contexts, showing that expensive drugs increase the placebo effect compared to inexpensive ones. However, addressing a drug's efficacy requires making causal inferences between the drug and the healing. These inferences rely on the contingency between these two events, a factor that is difficult to control in the placebo research. The present study aimed to test whether the price of a given drug modulates its perceived efficacy using a proper (though fictitious) non-effective drug, so that not only the objective contingency, but also the probability of the cause and the probability of the effect could be adequately controlled for. We expected higher efficacy judgements for the expensive non-effective drug than for the inexpensive one. To test this hypothesis, 60 volunteers participated in a contingency learning task that was programmed so that 72% of the patients healed regardless of whether they took the drug. Approximately one-half of the participants were told that the drug was expensive, whereas the other half were told that it was inexpensive. As expected, the efficacy judgements of participants who saw the expensive drug were significantly higher than those who saw the inexpensive one. Overall, our results showed that the price of a non-effective drug modulates its perceived efficacy, an effect that seems to be mediated by the estimated number of doses administered. This result parallels findings in the placebo literature but using a laboratory methodology that allows stronger control of the variables, suggesting that the illusory overestimation produced by the more expensive treatments might be on the basis of the greater efficacy of the more expensive placebos.
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.
Humans inherit artificial intelligence biases
(Nature Research, 2023-10-03) Vicente Holgado, Lucía; Matute, Helena
Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.
I want to believe: prior beliefs influence judgments about the effectiveness of both alternative and scientific medicine
(Society for Judgment and Decision making, 2023-03-24) Vicente Holgado, Lucía; Blanco Bregón, Fernando; Matute, Helena
Previous research suggests that people may develop stronger causal illusions when the existence of a causal relationship is consistent with their prior beliefs. In the present study, we hypothesized that prior pseudoscientific beliefs will influence judgments about the effectiveness of both alternative medicine and scientific medicine. Participants (N = 98) were exposed to an adaptation of the standard causal illusion task in which they had to judge whether two fictitious treatments, one described as conventional medicine and the other as alternative medicine, could heal the crises caused by two different syndromes. Since both treatments were completely ineffective, those believing that any of the two medicines worked were exhibiting a causal illusion. Participants also responded to the Pseudoscience Endorsement Scale (PES) and some questions about trust in alternative therapies that were taken from the Survey on the Social Perception of Science and Technology conducted by FECYT. The results replicated the causal illusion effect and extended them by revealing an interaction between the prior pseudoscientific beliefs and the scientific/pseudoscientific status of the fictitious treatment. Individuals reporting stronger pseudoscientific beliefs were more vulnerable to the illusion in both scenarios, whereas participants with low adherence to pseudoscientific beliefs seemed to be more resistant to the illusion in the alternative medicine scenario.
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
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
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.
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.
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.
Scarcity affects cognitive biases: the case of the illusion of causality
(Elsevier B.V., 2023-09) Viñas Gómez, Aranzazu; Blanco Bregón, Fernando ; Matute, Helena
Previous research indicates that economic scarcity affects people's judgments, decisions, and cognition in a variety of contexts, and with various consequences. We hypothesized that scarcity could sometimes reduce cognitive biases. Specifically, it could reduce the causal illusion, a cognitive bias that is at the heart of superstitions and irrational thoughts, and consists of believing that two events are causally connected when they are not. In three experiments, participants played the role of doctors deciding whether to administer a drug to a series of patients. The drug was ineffective, because the percentage of patients recovering was identical regardless of whether they took the drug. We manipulated the budget available to buy the drugs, tough all participants had enough for all their patients. Even so, participants in the scarce group reduced the use of the drug and showed a lower causal illusion than participants in the wealthy group. Experiments 2 and 3 added a phase in which the budget changed. Participants who transitioned from scarcity to wealth exhibited a reduced use of resources and a lower causal illusion, whereas participants transitioning from wealth to scarcity were unaffected by their previous history.
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.
When did it happen?: verbal information about causal relations affects time estimation
(Academic Press Inc., 2023-08) Pérez Cubillas, Carmelo; Matute, Helena
Usually, the closer two events occur, the more likely people infer a causal relationship between them. Recent studies have shown that this relationship between time and causality is bidirectional. Participants also tend to judge events closer in time if they assume that they are causally related. We present six experiments showing causal binding, but unlike other experiments, participants do not emit any motor action, and no physical feedback is given. Rather, all stimuli and causal information are provided verbally. After reading a list of events, participants were asked to estimate the time elapsed between two of them. Those participants who were informed that there was a causal relationship between the two events estimated them as occurring closer to each other. These results support causality- and heuristic-based explanations of temporal binding, as opposed to other explanations such as sensory integration or intentionality of action.

Examinando por Autor "Matute, Helena"

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