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Research shows that stimuli in the environment can trigger behavior via the activation of goal representations. This process can be tested in the Pavlovian-to-Instrumental Transfer (PIT) paradigm, where stimuli can only affect behavior through the activation of the representation of its desired outcome (i.e., the PIT effect). Previous research has demonstrated that the PIT effect is stronger when the goal is more desirable. While this research only looked at actions that have single outcomes (e.g., obtaining a snack to satisfy appetite), in the present paper, we reason that actions that are instrumental in obtaining outcomes that are desirable in multiple ways (e.g., obtaining a snack to satisfy one’s appetite, giving it to a friend, trading it for money) should produce stronger PIT effects. In two experiments, participants learned to perform left and right key presses to earn a snack, either framed as having a single function or multiple functions. Participants also learned to associate the two differently framed snacks with two cues. In a PIT test, they were required to press the keys as fast as possible upon exposure to the cues (i.e., the PIT effect). We found that cues associated with the multi-functional snack facilitated the actions that earned those snacks before, while cues associated with the single-functional snack did not facilitate such actions. We discuss these findings in the context of research on free choice and personal autonomy and how people appreciate the multi-functional nature of their goal-directed behavior in the environment.

Previous research suggests that cues can motivate goal-directed behavior directly. According to the framework of human unconscious goal pursuit, exposure to goal-relevant cues yields two distinct behavioral effects: action initiation and subsequent action persistence. However, the evidence for such a full motivational control effect in human goal-directed behavior is meager. The present study builds on the Pavlovian-to-instrumental transfer (PIT) approach to test the cue-based motivational control. We employed a tapping task that considers the speed of decreasing the distance between an outcome and oneself as an index of motivation of behavior. Thus, we could separate action initiation and persistence in one single test task. Participants first underwent instrumental and Pavlovian training. They learned to press two keys to earn 20 or 5 cents (high- or low-value outcomes) and to associate the two outcomes with two specific cues. Next, they had to press one of the two keys multiple times to bring a Pavlovian cue to the front of their computer screen. Results showed that participants responded faster with the high-value key to the high- versus low-value cue, indicating value-sensitive action initiation effects. However, this effect did not translate into action persistence, as the response time steeply declined over time. These results point to the importance of differentiating between action initiation and persistence of action in cue-based goal-directed behavior as modeled by PIT.

Algorithms support many processes in modern society. Research using trust games frequently reports that people are less inclined to cooperate when believed to play against an algorithm. Trust is, however, malleable by contextual factors and social presence can increase the willingness to collaborate. We investigated whether situating cooperation with an algorithm in the presence of another person increases cooperative trust. Three groups of participants played a trust game against a pre-programmed algorithm in an online webhosted experiment. The first group was told they played against another person who was present online. The second group was told they played against an algorithm. The third group was told they played against an algorithm while another person was present online. More cooperative responses were observed in the first group compared to the second group. A difference in cooperation that replicates previous findings. In addition, cooperative trust dropped more over the course of the trust game when participants interacted with an algorithm in the absence another person compared to the other two groups. This latter finding suggests that social presence can mitigate distrust in interacting with an algorithm. We discuss the cognitive mechanisms that can mediate this effect.

Artificial intelligence increasingly plays a crucial role in daily life. At the same time, artificial intelligence is often met with reluctance and distrust. Previous research demonstrated that faces that are visibly artificial are considered to be less trustworthy and remembered less accurately compared to natural faces. Current technology, however, enables the generation of artificial faces that are indistinguishable from natural faces. In five experiments (total N = 867), we tested whether natural faces that are merely labelled to be artificial are also trusted less. A meta-analysis of all five experiments suggested that natural faces merely labeled as being artificial were judged to be less trustworthy. This bias did not depend on the degree of trustworthiness and attractiveness of the faces (Experiments 1-3). It was not modulated by changing raters' attitude towards artificial intelligence (Experiments 2-3) or by information communicated by the faces (Experiment 4). We also did not observe differences in recall performance between faces labelled as artificial or natural (Experiment 3). When participants only judged one type of face (i.e., either labelled as artificial or natural), the difference in trustworthiness judgments was eliminated (Experiment 5) suggesting that the contrast between the natural and artificial categories in the same task promoted the labelling effect. We conclude that faces that are merely labelled to be artificial are trusted less in situations that also include faces labelled to be real. We propose that understanding and changing social evaluations towards artificial intelligence goes beyond eliminating physical differences between artificial and natural entities.

A large amount of literature demonstrates that social behaviour can be triggered by environmental cues. A long-standing debate involves the question of whether such stimuli trigger behaviour directly (i.e. habits) or whether these effects mediate goals. As studies on automatic goal pursuit typically use real-world cues that are already associated with the behaviour and potentially the goal, it is impossible to make strong claims about the nature of the effects. In the present paper, we use a paradigm inspired by the Pavlovian-to-instrumental transfer (PIT) literature to examine how the environment can trigger goal-directed behaviour. Building on the essence of pro-self and pro-social motives in humans, two experiments explored the PIT effect when the outcomes were framed in terms of self- versus other-interest. Participants performed actions to earn money for themselves or a charity. Each outcome was linked to a different cue. The results showed that a cue predictive of self-interest outcomes facilitated responses instrumental in gaining the outcome, while such specific PIT effect for other-interest outcomes only emerged when participants were free to donate the money. We briefly discuss these findings reflecting on whether the PIT effect in our paradigm is indeed sensitive to the value of social goals.

While there are abundant reasons that might lead us to form wrong first impressions, further interaction (sampling) opportunities should allow us to attenuate such initial biases. Sometimes, however, theses biases persist despite repeated sampling opportunities, such as in superstitions or stereotypes. In two studies (Ns = 100), we investigate this phenomenon.We demonstrate that in a task in which participants could repeatedly choose between two options to gain rewards, erroneous initial impressions about yielded outcomes can lead to persisting biases toward a clearly inferior option. We argue that a premature focus on reward pursuit (exploitation) rather than exploration is the cause of these biases, which persist despite plenty of opportunities and a presumed motivation to overcome them. By focusing on a supposedly best option, participants never give themselves the chance to sufficiently try out alternatives and thereby overcome their initial biases. We conclude that going for the money is not always the best strategy.

Personal autonomy is central to people's experiences of agency and abilities to actively take part in society. To address the challenges of supporting autonomy, we propose a functional model of autonomy, according to which the experience of agency is a function of the opportunity to determine what to do, when to act and how to act in goal-pursuit. We tested the model in three experiments where the three goal-pursuit components could be constrained by another person or an artificial intelligence (AI) agent. Results showed that removing any of the three components from one's own decisions reduced experienced agency (Study 1a and 1b) and lowered motivation to pursue goals in organisational contexts (Study 2). In comparison to the strong and robust main effects, interactions between the components and the effects of the source of restriction (human vs. AI) were negligible. Implications for personal autonomy, algorithmic decision-making and behaviour change interventions are discussed.

Mensen kunnen ziek worden van bacteriën, parasieten of virussen in voedsel. Dat kan ook door chemische stoffen in voedsel. Om nieuwe risico’s voor de voedselveiligheid zo vroeg mogelijk in beeld te brengen, is in 2020 het Signaleringsoverleg Voedselveiligheid opgericht. Als nieuwe risico’s sneller in beeld zijn, kunnen sneller maatregelen worden genomen om de gezondheid van mensen te beschermen. Het Signaleringsoverleg heeft twee onderdelen: een overleg over microbiologische risico’s (het SO-VM) en een overleg over chemische risico’s (het SO-VC). In beide onderdelen zitten experts op het gebied van voedselveiligheid van verschillende instituten en het bedrijfsleven. Deze experts verzamelen signalen die zij met elkaar bespreken en betekenis geven. In 2022 zijn beide onderdelen elk vier keer bij elkaar gekomen. In totaal zijn 65 signalen ingebracht en uitgewerkt. Als er naar aanleiding van een signaal meer onderzoek of maatregelen nodig zijn, meldt het Signaleringsoverleg het signaal aan de NVWA of aan het Coördinerend Overleg. In dit overleg zijn de ministeries van Volksgezondheid, Welzijn en Sport (VWS) en Landbouw, Natuur en Voedselkwaliteit (LNV) en de Nederlandse Voedsel- en Warenautoriteit (NVWA) vertegenwoordigd. In 2022 heeft het SO-VM geen signalen gemeld. Het SO-VC heeft 14 signalen gemeld aan het Coördinerend Overleg en 2 signalen aan de NVWA.

Previous research indicates that the experience of agency over one's actions and movements is influenced by movement predictability as well as movement distance (Hon, Seow, & Pereira, 2018). Addressing previous limitations, we present a compelling test of the relation between movement distance and movement agency. Participants in two studies moved targets predictably or unpredictably, and for short, medium, or long distances. Following prior research, distractor cues moved in the opposite direction of the targets. Results showed that movement agency scores were higher for predictable compared to unpredictable movements. Results also consistently showed that when movements were predictable, longer distances by either the target or the distractor cues increased agency relative to shorter distances. Our findings replicate and extend previous findings showing that stimulus movement distances influence judgments of movement agency.

The original BOID [5] is a cognitive architecture that unifies Belief, Obligation, Intention and Desire rules to calculate which actions should an agent undertake next. In the current paper, we adapt the original BOID with an aim to model autonomous agency. The new BOID* architecture is able to capture anticipation that we believe to be one of the hallmarks of autonomous agency. We focus on developing algorithms for anticipatory reasoning through a new BOID* goal deliberation component. The key method that BOID* introduces is abductive reasoning as a way to represent motivational attitudes, such as desires and obligations. As a result of deliberation via abduction, BOID* specifies intention revision procedures that connect motivational and informational attitudes. The BOID* is a part of the project to build autonomous AI models that make explicit the reasoning behind adopting future goals, prioritizing selected goals and forming intentions.

Research on the nature and origins of human motivation has addressed the role of rewards in learning and behaviour. Gamification finds its raison d'être in being able to leverage motivational theories, to foster motivation in users through the use of game elements. One of the main criticisms moved to the use of gamification for learning purposes is related to the one-size-fits-all approach that tends to characterize many gamified applications. In this paper we explore the possibilities that can arise from the convergence of Self-Determination Theory principles and machine learning, to improve the efficacy of gamification reward systems.

Artificial intelligence plays a crucial role on our daily lives. At the same time, artificial intelligence is often met with reluctance and distrust. Previous research demonstrated that faces that are visibly artificial are considered to be less trustworthy and remembered less accurately compared to natural faces. Current technology, however, enables the generation of artificial faces that are indistinguishable from natural faces. Accordingly, we tested whether natural faces that are merely labelled to be artificial are also trusted less. In three experiments (N = 399), we observed that natural faces merely labeled as being artificial were judged to be less trustworthy. This bias was robust and did not depend on the degree of trustworthiness and attractiveness of the faces, nor could it be modulated by changing raters’ attitude towards artificial intelligence. At the same time, we did not observe differences in recall performance. We conclude that understanding and changing social evaluations towards artificial intelligence goes beyond eliminating physical differences between artificial and natural entities.

This study investigates whether there exist differences in facial emotion detection accuracy in patients suffering from Vestibular Schwannoma (VS) due to their facial paresis. Forty-four VS patients, half of them with, and half of them without a facial paresis, had to classify pictures of facial expressions as being emotional or non-emotional. The visual information of images was systematically manipulated by adding different levels of visual noise. The study had a mixed design with emotional expression (happy vs. angry) and visual noise level (10% to 80%) as repeated measures and facial paresis (present vs. absent) and degree of facial dysfunction as between subjects’ factors. Emotion detection accuracy declined when visual information declined, an effect that was stronger for anger than for happy expressions. Overall, emotion detection accuracy for happy and angry faces did not differ between VS patients with or without a facial paresis, although exploratory analyses suggest that the ability to recognize emotions in angry facial expressions was slightly more impaired in patients with facial paresis. The findings are discussed in the context of the effects of facial paresis on emotion detection, and the role of facial mimicry, in particular, as an important mechanism for facial emotion processing and understanding.

Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

Item does not contain fulltext, This study investigates whether there exist differences in facial emotion detection accuracy in patients suffering from Vestibular Schwannoma (VS) due to their facial paresis. Forty-four VS patients, half of them with, and half of them without a facial paresis, had to classify pictures of facial expressions as being emotional or non-emotional. The visual information of images was systematically manipulated by adding different levels of visual noise. The study had a mixed design with emotional expression (happy vs. angry) and visual noise level (10% to 80%) as repeated measures and facial paresis (present vs. absent) and degree of facial dysfunction as between subjects' factors. Emotion detection accuracy declined when visual information declined, an effect that was stronger for anger than for happy expressions. Overall, emotion detection accuracy for happy and angry faces did not differ between VS patients with or without a facial paresis, although exploratory analyses suggest that the ability to recognize emotions in angry facial expressions was slightly more impaired in patients with facial paresis. The findings are discussed in the context of the effects of facial paresis on emotion detection, and the role of facial mimicry, in particular, as an important mechanism for facial emotion processing and understanding.

Contains fulltext : 289199.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 234953.pdf (Publisher’s version ) (Open Access), This study investigates whether there exist differences in facial emotion detection accuracy in patients suffering from Vestibular Schwannoma (VS) due to their facial paresis. Forty-four VS patients, half of them with, and half of them without a facial paresis, had to classify pictures of facial expressions as being emotional or non-emotional. The visual information of images was systematically manipulated by adding different levels of visual noise. The study had a mixed design with emotional expression (happy vs. angry) and visual noise level (10% to 80%) as repeated measures and facial paresis (present vs. absent) and degree of facial dysfunction as between subjects' factors. Emotion detection accuracy declined when visual information declined, an effect that was stronger for anger than for happy expressions. Overall, emotion detection accuracy for happy and angry faces did not differ between VS patients with or without a facial paresis, although exploratory analyses suggest that the ability to recognize emotions in angry facial expressions was slightly more impaired in patients with facial paresis. The findings are discussed in the context of the effects of facial paresis on emotion detection, and the role of facial mimicry, in particular, as an important mechanism for facial emotion processing and understanding.