David Guez

Researchers employ science fiction and fantasy in public engagement, advocacy, and education as significant sources of insights to identify public interests, inspire public policy, and influence future science. These uses of science fiction as a source that is expected to reflect public interests are undermined if the examples employed by researchers are interpreted differently by the intended audience or beneficiaries of research. We surveyed the public to identify their definitions and discovered a categorization based on clearly defined features. These align with some academic theories but differ from postmodern approaches as the analysis suggests science fiction can be defined categorically. The empirical survey data are consistent and demonstrate an unmistakable distinction between popular definitions of science fiction and fantasy. Our theoretical analysis implies some definitions may be confused by evaluating secondary “fuzzy” characteristics as if they were fundamental features of the genre. We suggest Wittgenstein’s family resemblances, between subjects associated with the genre at any specific time, should be interpreted as an ephemeral grouping validated by correlation with enduring core features, rather than definitive. On the basis of the common themes identified from the survey responses and a critique of existing genre models, we suggest the classical concept of techne may best describe the empirical essence of science fiction. Researchers intending to employ science fiction for applications that have an influence in the public realm may wish to consider this when designing their research.

Data from an audience survey on the characteristics of the science fiction and fantasy genres was compared to existing approaches to genre classification to build an alternative genre classification for science fiction based on popular understanding of the genre.

Inference by exclusion is the ability to select a given option by excluding the others. When designed appropriately, tests of this ability can reveal choices that cannot be explained by associative processes. Over the past decade, exclusion reasoning has been explored in several non-human taxonomic groups, including birds, mainly in Corvids and Parrots. To increase our understanding of the taxonomic distribution of exclusion reasoning and, therefore, its evolution, we investigated exclusion performances in red-tailed black cockatoos (Calyptorhynchus banksii), an Australian relative of the Goffin cockatoo (Cacatua goffini), using a food-finding task. Cockatoos were required to find a food item hidden in 1 of the 2 experimenter's hands. Following training sessions in which they reliably selected the closed baited hand they had just been shown open, each individual was tested on 4 different conditions. Critical to demonstrating exclusion reasoning was the condition in which they were shown the empty hand and then offered a choice of both closed hands. The performance of all birds was above chance on all experimental conditions but not on an olfactory and/or cuing control condition. The results suggest that the birds might be able to infer by exclusion, although an explanation based on rule learning cannot be excluded. This first experiment in red-tailed black cockatoo highlights the potential of this species as a model to study avian cognition and paves the pathway for future investigations.

Establishment in urbanized environments is associated with changes in physiology, behaviour, and problem-solving. We compared the speed of learning in urban and rural female common mynas, Acridotheres tristis, using a standard visual discrimination task followed by a reversal learning phase. We also examined how quickly each bird progressed through different stages of learning, including sampling and acquisition within both initial and reversal learning, and persistence following reversal. Based on their reliance on very different food resources, we expected urban mynas to learn and reversal learn more quickly but to sample new contingencies for proportionately longer before learning them. When quantified from first presentation to criterion achievement, urban mynas took more 20-trial blocks to learn the initial discrimination, as well as the reversed contingency, than rural mynas. More detailed analyses at the level of stage revealed that this was because urban mynas explored the novel cue-outcome contingencies for longer, and despite transitioning faster through subsequent acquisition, remained overall slower than rural females. Our findings draw attention to fine adjustments in learning strategies in response to urbanization and caution against interpreting the speed to learn a task as a reflection of cognitive ability.

The possibility that variation in the propensity to forage innovatively is attributable to variation in cognition is a matter of debate. Motor flexibility and persistence offer alternative viewpoints. The present study used a computational model to evaluate the relative contribution of these mechanisms to the innovation process. We modeled the effects of low and high motor flexibility on problem-solving performance, which provided a baseline against which to examine how performance changed when combined with operant learning or persistence. We titrated our models through a wide range of parameter values in order to explore where in the outcome space biologically meaningful effect sizes are likely to be detected. The baseline model accurately reproduced an enhancement of performance when relative frequencies of motor expression were balanced (high motor flexibility) rather than skewed (low motor flexibility). Operant learning enhanced performance, but only when agents persisted until they solved and only when motor flexibility was low. In scenarios where agents gave up even if they had not solved, persistence in response to occurrence of secondary cues improved problem solving in both motor flexible and motor inflexible individuals. In scenarios, where the benefits of persistence and learning were compared directly, the benefits of persisting were typically equal, if not greater than those of learning. Given the high metabolic cost of neural tissue, our simulations predict that selection for enhanced problem solving should select for processes that increase persistence (e.g., personality changes) rather than learning.

Sociality is associated with a variety of costs and benefits, one of which can be to increase the likelihood of individuals solving novel problems. Several hypotheses explaining why groups show higher innovative problem-solving efficiencies than individuals alone have been proposed including the sharing of antipredator vigilance and the pool-of-competence effect, whereby larger groups containing a more diverse range of individuals are more likely to contain individuals with the skills necessary to solve the particular problem at hand. Interference between group members may cause groups to have lower problem solving abilities, however. Using a simulation approach, we model the shape of the relationship between group-level problem-solving probability and group size across a range of facilitation and inhibition scenarios, various population distributions of problem solving, and a task requiring 1 action or 2 actions to be solved. Simulations showed that both sharing of antipredator vigilance and the addition of competent individuals to an existing group lead to positive relationships between group-level problem solving and group size that reach 100% solving probability, whereas interference effects generate group-solving probabilities that rise to a maximum and decrease again, generating a group size for which problem solving is maximized. In contrast, both inhibition and facilitation scenarios generate identical patterns of individual efficiencies. Our results have important implications for our ability to understand the mechanisms that underpin group-size effects on problem solving in nonhumans.