A METHOD FOR CHOOSING A STRATEGY FOR THE BEHAVIOR OF A CELLULAR AUTOMATON WHEN SOLVING THE PROBLEM OF FINDING TARGETS BY A GROUP OF MOVING OBJECTS

Currently, a large number of different mathematical models and methods aimed at solving problems of multidimensional optimization and modeling of complex behavioral systems have been developed. One of the areas of search for solutions is the search for solutions in conditions of incomplete information and the need to take into account changing external factors. Often such problems are solved by the method of complete search. In some conditions, the method of complete search can be significantly improved through the implementation and use of behavioral models of natural formations. Examples of such formations can be group behavior of insects, birds, fish, various flocks, etc. The idea of copying group activity of a shoal of fishes at the decision of problems of joint activity on extraction of food is used in work. The reasoning based on the simulation of the behavior of such a natural object allowed to justify the choice as a mathematical model - cellular automata. The paper examines the key features of such a model. Modeling of his work is carried out, strategies of behavior of group of mobile objects at search of the purposes are developed, key characteristics are investigated and the method of adaptive choice of strategy and change of rules of behavior taking into account features of the solved problem is developed. The search strategy is implemented in the work, which takes into account the need to solve the optimization problem on two parameters. The obtained results testify to the high descriptive possibility of such an approach, the possibility of finding the optimal strategy for the behavior of the cellular automaton and the formalization of the process of selecting the parameters of its operation. A further improvement of this approach can be the implementation of simulation to study the properties of the developed model, the formation of the optimal set of rules and parameters of the machine for the whole set of tasks.

Entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Understanding children’s preference for park features that encourage physical activity: an adaptive choice based conjoint analysis

Background Parks are a key setting for physical activity for children. However, little is known about which park features children prefer and which features are most likely to encourage them to be active in parks. This study examined the relative importance of park features among children for influencing their choice of park for engaging in park-based physical activity. Methods Children (n = 252; 8-12 years, 42% male) attending three primary schools in Melbourne, Australia completed a survey at school. They were required to complete a series of Adaptive Choice-Based Conjoint analysis tasks, with responses used to identify the part-worth utilities and relative importance scores of selected park features using Hierarchical Bayes analyses within Sawtooth Software. Results For the overall sample and both boys and girls, the most important driver of choice for a park that would encourage them to be active was presence of a flying fox (overall conjoint analysis relative importance score: 15.8%; 95%CI = 14.5, 17.1), followed by a playground (13.5%; 95%CI = 11.9, 15.2). For the overall sample, trees for climbing had the third highest importance score (10.2%; 95%CI = 8.9, 11.6); however, swings had 3rd highest importance for girls (11.1, 95%CI = 9.3, 12.9) and an obstacle course/parkour area had the 3rd highest importance score for boys (10.7, 95%CI = 9.0, 12.4). For features with two levels, part-worth utility scores showed that the presence of a feature was always preferred over the absence of a feature. For features with multiple levels, long flying foxes, large adventure playgrounds, lots of trees for climbing, large round swings, large climbing equipment, and large grassy open space were the preferred levels. Conclusion To ensure parks appeal as a setting that encourages children to engage in physical activity, park planners and local authorities and organisations involved in park design should prioritise the inclusion of a long flying fox, large adventure playgrounds, lots of trees for climbing, large round swings and obstacle courses/parkour areas.

A neuronal ensemble encoding adaptive choice during sensory conflict in Drosophila

Feeding decisions are fundamental to survival, and decision making is often disrupted in disease. Here, we show that neural activity in a small population of neurons projecting to the fan-shaped body higher-order central brain region of Drosophila represents food choice during sensory conflict. We found that food deprived flies made tradeoffs between appetitive and aversive values of food. We identified an upstream neuropeptidergic and dopaminergic network that relays internal state and other decision-relevant information to a specific subset of fan-shaped body neurons. These neurons were strongly inhibited by the taste of the rejected food choice, suggesting that they encode behavioral food choice. Our findings reveal that fan-shaped body taste responses to food choices are determined not only by taste quality, but also by previous experience (including choice outcome) and hunger state, which are integrated in the fan-shaped body to encode the decision before relay to downstream motor circuits for behavioral implementation.

Generalization of attentional control strategies across distinct tasks

Does attentional control strategy generalize across different visual search tasks? Previous research has failed to observe significant correlations in strategy metrics between different visual search tasks (Clarke et al., 2020), suggesting that strategy is not unitary, or determined by a single trait variable. Here we question just how heterogeneous (non-unitary) strategies are, hypothesizing a similarity gradient account, which holds that strategy does generalize to some degree, specifically across tasks with similar attentional components. To test this account, we employed the Adaptive Choice Visual Search (ACVS; Irons & Leber, 2018a), a visual search paradigm designed to directly measure attentional control strategy. In two studies, we had participants complete the ACVS and a modified, but similar, task with one altered attentional component (specifically, the requirement to use feature-based attention and enumeration, respectively). We found positive correlations in strategy optimality between tasks that do vs. do not involve feature-based attention (r = .38, p = .0068) and across tasks that do vs. do not require enumeration (r = .33, p = .018). Thus, attentional control strategies did generalize across sufficiently similar tasks, although the strength of the correlations was weaker than the within-task test-retest reliability of strategy measure. These results support the similarity gradient account.

Canon Fodder and Conscripted Genres

The Hogarth Shakespeare novels bring into focus several features emerging in the encounter between Shakespeare and fiction writing. Hogarth’s ostensibly ‘new’ version of serial Shakespearean publication intersects in provocative ways with both historical adaptations, like Mary Cowden Clarke’s Girlhood of Shakespeare’s Heroines, and with current, less high-profile Shakespearean novels. In the context of current serial adaptations, the Hogarth novels foreground Shakespeare as a principle of collectivity, a gesture towards coherence in works whose larger alliances reside in genre or authorship. Hogarth’s Shakespearean frame also draws attention to new adaptive choices which expand but perhaps dilute Shakespeare as a useful collective canon. As a result, the series both contributes to and emphasises Shakespeare’s participation in the three zones of cultural capital: our individual and collective artistic investment in series, culturally provoked shifts in adaptive choice, and evolving genres that increasingly test former lines between literary and genre fiction.

Cingulate-motor circuits update rule representations for sequential choice decisions

Anterior cingulate cortex mediates the flexible updating of an animal's choice responses upon rule changes in the environment. However, how anterior cingulate cortex entrains motor cortex to reorganize rule representations and generate required motor outputs remains unclear. Here, we demonstrate that chemogenetic silencing of the projection terminals of cingulate cortical neurons in secondary motor cortex disrupted sequential choice performance in trials immediately following rule switches, suggesting that these inputs are necessary to update rule representations for choice decisions stored in the motor cortex. Indeed, the silencing of cingulate cortex decreased rule selectivity of secondary motor cortical neurons. Furthermore, optogenetic silencing of cingulate cortical neurons that was temporally targeted to error trials immediately after rule switches exacerbated errors in following trials. These results suggest that cingulate cortex monitors behavioral errors and update rule representations in motor cortex, revealing a critical role for cingulate-motor circuits in adaptive choice behaviors.

Novel entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching. To address this, we developed novel metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct a more accurate model of choice. Together, our novel entropy-based metrics provide a powerful, model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.