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Understanding public perception of technology is crucial to aligning research, development, and governance of technology. This article introduces micro scenarios as an integrative method to evaluate mental models and social acceptance across numerous technologies and concepts using a few single-item scales within a single comprehensive survey. This approach contrasts with traditional methods that focus on detailed assessments of as few as one scenario. The data can be interpreted in two ways: Perspective (1): Average evaluations of each participant can be seen as individual differences, providing reflexive measurements across technologies or topics. This helps in understanding how perceptions of technology relate to other personality factors. Perspective (2): Average evaluations of each technology or topic can be interpreted as technology attributions. This makes it possible to position technologies on visuo-spatial maps to simplify identification of critical issues, conduct comparative rankings based on selected criteria, and to analyze the interplay between different attributions. This dual approach enables the modeling of acceptance-relevant factors that shape public opinion. It offers a framework for researchers, technology developers, and policymakers to identify pivotal factors for acceptance at both the individual and technology levels. I illustrate this methodology with examples from my research, provide practical guidelines, and include R code to enable others to conduct similar studies. This paper aims to bridge the gap between technological advancement and societal perception, offering a tool for more informed decision-making in technology development and policy-making. [ABSTRACT FROM AUTHOR]

Understanding public perception of technology is crucial to aligning research, development, and governance of technology. This article introduces micro scenarios as an integrative method to evaluate mental models and social acceptance across numerous technologies and concepts using a few single-item scales within a single comprehensive survey. This approach contrasts with traditional methods that focus on detailed assessments of as few as one scenario. The data can be interpreted in two ways: Perspective (1): Average evaluations of each participant can be seen as individual differences, providing reflexive measurements across technologies or topics. This helps in understanding how perceptions of technology relate to other personality factors. Perspective (2): Average evaluations of each technology or topic can be interpreted as technology attributions. This makes it possible to position technologies on visuo-spatial maps to simplify identification of critical issues, conduct comparative rankings based on selected criteria, and to analyze the interplay between different attributions. This dual approach enables the modeling of acceptance-relevant factors that shape public opinion. It offers a framework for researchers, technology developers, and policymakers to identify pivotal factors for acceptance at both the individual and technology levels. I illustrate this methodology with examples from my research, provide practical guidelines, and include R code to enable others to conduct similar studies. This paper aims to bridge the gap between technological advancement and societal perception, offering a tool for more informed decision-making in technology development and policy-making.

Agent-based modeling (ABM) has been employed to understand and capture the complexity of the coupled human-nature processes in water resource systems. One of the challenges is to model human decisions in the coupled human and natural systems. Hydraulic fracturing water uses were distributed through a depot-based water allocation system in the Bakken region of western North Dakota, United States. In this study, a spatially explicit ABM was developed to simulate this unique water allocation system. In the ABM, institution theory was used to model the State’s regulatory policies and procedures, while evolutionary programming was employed to allow the water-depot owners (or agents) to select appropriate strategies when applying for water permits. Cognitive maps simulated the water-depot agents’ ability and willingness to compete for more water sales. All agents had their influence boundaries that restricted their competitive behavior toward their neighbors, but not for non-neighboring agents. The decision-making process was constructed and parameterized with both quantitative and qualitative information. The ABM was calibrated against real-world water-use data, and the calibration results showed that the spatial ABM performed well in simulating the total number of water depots as well as the locations and water uses of water depots at the county level. By linking institution theory, evolutionary programming, and cognitive maps, our study exhibited a new exploration of modeling the highly complex dynamics of the decision-making process involved in coupled human-nature water resource systems.

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations. [ABSTRACT FROM AUTHOR]

The hippocampal formation (HF) is a structure critical to navigation and many forms of memory. In mammals, the firing of place cells is widely regarded as the fundamental unit of HF information processing. Supporting homology between the avian and mammalian HF, context-specific patterns of Egr1 have been reported in birds that are comparable to those produced by place cell firing in mammals. Recent electrophysiological data, however, suggest that many avian species lack place cells, potentially undermining the correspondence between Egr1 and place cell-related firing in the avian brain. To clarify this, the current study examines Egr1 expression in Japanese quail under conditions known to elicit only weakly spatially modulated firing patterns and report robust context-dependent Egr1 expression. These data confirm that context-dependent expression of Egr1 is not dependent on precise place fields and provide insight into how these birds are able to perform complex spatial tasks despite lacking mammalian-like place cells. [ABSTRACT FROM AUTHOR]

The hippocampal formation (HF) is a structure critical to navigation and many forms of memory. In mammals, the firing of place cells is widely regarded as the fundamental unit of HF information processing. Supporting homology between the avian and mammalian HF, context-specific patterns of Egr1 have been reported in birds that are comparable to those produced by place cell firing in mammals. Recent electrophysiological data, however, suggest that many avian species lack place cells, potentially undermining the correspondence between Egr1 and place cell-related firing in the avian brain. To clarify this, the current study examines Egr1 expression in Japanese quail under conditions known to elicit only weakly spatially modulated firing patterns and report robust context-dependent Egr1 expression. These data confirm that context-dependent expression of Egr1 is not dependent on precise place fields and provide insight into how these birds are able to perform complex spatial tasks despite lacking mammalian-like place cells.

The functional role of the entorhinal-hippocampal system has been a long withstanding mystery. One key theory that has become most popular is that the entorhinal-hippocampal system represents space to facilitate navigation in one's surroundings. In this Perspective article, I introduce a novel idea that undermines the inherent uniqueness of spatial information in favor of time driving entorhinal-hippocampal activity. Specifically, by spatializing events that occur in succession (i.e., across time), the entorhinal-hippocampal system is critical for all types of cognitive representations. I back up this argument with empirical evidence that hints at a role for the entorhinal-hippocampal system in non-spatial representation, and computational models of the logarithmic compression of time in the brain. [ABSTRACT FROM AUTHOR]

The functional role of the entorhinal-hippocampal system has been a long withstanding mystery. One key theory that has become most popular is that the entorhinal-hippocampal system represents space to facilitate navigation in one’s surroundings. In this Perspective article, I introduce a novel idea that undermines the inherent uniqueness of spatial information in favor of time driving entorhinal-hippocampal activity. Specifically, by spatializing events that occur in succession (i.e., across time), the entorhinal-hippocampal system is critical for all types of cognitive representations. I back up this argument with empirical evidence that hints at a role for the entorhinal-hippocampal system in non-spatial representation, and computational models of the logarithmic compression of time in the brain.

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool's application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool's convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool's construct validity by relating mental model complexity to participants' education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups. [ABSTRACT FROM AUTHOR]

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool’s application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool’s convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool’s construct validity by relating mental model complexity to participants’ education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.

The potential for prescribed fire to address fuel management and forest restoration goals has received considerable attention. However, many wildfire risk mitigation practitioners and researchers consider prescribed fire to be an underutilized tool for forest and fire management. Prescribed fire can affect a broad range of values (e.g., air quality, wildlife habitat, timber, protection of homes) and these effects, which we term valued outcomes, may result from complex dynamics operating within fire-prone social-ecological systems. Increasing the effective use of prescribed fire requires a better understanding of how these dynamics are perceived by stakeholders, whose support is crucial for forest and fire management initiatives that affect diverse groups of people. We evaluated perceptions of the effects of prescribed fire on valued outcomes using data from 111 cognitive maps elicited from stakeholders in the wildfire-prone Eastern Cascades Ecoregion of central Oregon. As representations of relationships among biological, physical, social, political, and other factors that structure individuals' understanding of a system, cognitive maps are ideal for analyzing perceptions of dynamics in complex social-ecological systems. We found that prescribed fire was perceived to positively affect valued outcomes in individuals' cognitive maps. However, when we aggregated individuals' cognitive maps to evaluate perceptions of prescribed fire at varying stakeholder group sizes, we found that perception of desirable effects declined with group size. Additionally, representatives of fire response and non-governmental organizations tended to perceive prescribed fire more favorably, while private citizens and representatives of private businesses emphasized adverse effects. Finally, we measured how the perceptions of the effects of prescribed fire varied across 15 distinct valued outcomes and found that air quality, aesthetic values, and wildlife habitat were perceived to be most negatively affected by prescribed fire, while cultural and historical values, protection of flora, water quality, and firefighter safety were perceived to be most positively affected. Taken together, our results help to explain the challenge of scaling up the use of prescribed fire and highlight the need for policy processes that account for stakeholders' views of the multiple—and potentially opposing—effects of prescribed fire on different valued outcomes.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot. [ABSTRACT FROM AUTHOR]

Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (Q-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.

Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (Q -learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature. [ABSTRACT FROM AUTHOR]

We have recently shown that vision is important to improve spatial auditory cognition. In this study we investigate whether touch is as effective as vision to create a cognitive map of a soundscape. In particular we tested whether the creation of a mental representation of a room, obtained through tactile exploration of a 3D model, can influence the perception of a complex auditory task in sighted people. We tested two groups of blindfolded sighted people – one experimental and one control group – in an auditory space bisection task. In the first group the bisection task was performed three times: specifically, the participants explored with their hands the 3D tactile model of the room and were led along the perimeter of the room between the first and the second execution of the space bisection. Then, they were allowed to remove the blindfold for a few minutes and look at the room between the second and third execution of the space bisection. Instead, the control group repeated for two consecutive times the space bisection task without performing any environmental exploration in between. Considering the first execution as a baseline, we found an improvement in the precision after the tactile exploration of the 3D model. Interestingly, no additional gain was obtained when room observation followed the tactile exploration, suggesting that no additional gain was obtained by vision cues after spatial tactile cues were internalized. No improvement was found between the first and the second execution of the space bisection without environmental exploration in the control group, suggesting that the improvement was not due to task learning. Our results show that tactile information modulates the precision of an ongoing space auditory task as well as visual information. This suggests that cognitive maps elicited by touch may participate in cross-modal calibration and supra-modal representations of space that increase implicit knowledge about sound propagation.

We have recently shown that vision is important to improve spatial auditory cognition. In this study, we investigate whether touch is as effective as vision to create a cognitive map of a soundscape. In particular, we tested whether the creation of a mental representation of a room, obtained through tactile exploration of a 3D model, can influence the perception of a complex auditory task in sighted people. We tested two groups of blindfolded sighted people – one experimental and one control group – in an auditory space bisection task. In the first group, the bisection task was performed three times: specifically, the participants explored with their hands the 3D tactile model of the room and were led along the perimeter of the room between the first and the second execution of the space bisection. Then, they were allowed to remove the blindfold for a few minutes and look at the room between the second and third execution of the space bisection. Instead, the control group repeated for two consecutive times the space bisection task without performing any environmental exploration in between. Considering the first execution as a baseline, we found an improvement in the precision after the tactile exploration of the 3D model. Interestingly, no additional gain was obtained when room observation followed the tactile exploration, suggesting that no additional gain was obtained by vision cues after spatial tactile cues were internalized. No improvement was found between the first and the second execution of the space bisection without environmental exploration in the control group, suggesting that the improvement was not due to task learning. Our results show that tactile information modulates the precision of an ongoing space auditory task as well as visual information. This suggests that cognitive maps elicited by touch may participate in cross-modal calibration and supra-modal representations of space that increase implicit knowledge about sound propagation. [ABSTRACT FROM AUTHOR]

Although music psychology has a long tradition of expert-novice comparisons, experimental training studies are rare. Studies on the learning progress of trained novices in hearing harmonic relationships are still largely lacking. This paper presents a simple training concept using the example of tone/triad similarity ratings, demonstrating the gradual progress of non-musicians compared to musical experts: In a feedback-based rapid learning paradigm, participants had to decide for single tones and chords whether paired sounds matched each other well. Before and after the training sessions, they provided similarity judgments for a complete set of sound pairs. From these similarity matrices, individual relational sound maps, aiming to map the mental representations, were calculated by means of non-metric multidimensional scaling (NMDS), which were compared to an expert model through procrustean transformation. Approximately half of the novices showed substantial learning success, with some participants even reaching the level of professional musicians. Results speak for a fundamental ability to quickly train an understanding of harmony, show inter-individual differences in learning success, and demonstrate the suitability of the scaling method used for music psychological research. Results are discussed in the context of the giftedness debate.