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Adults are known to have superior working memory to children, but whether this improvement is driven primarily by differences in storage capacity or attentional control is debated. In particular, the understanding of how capacity and control influence the development of working memory is hampered by the fact that most theorizing about the effect of variation in either on behavior has been verbal. To address this, we extended a computational model of working memory to clearly separate the contributions of capacity and control, fitting the model to a recent developmental study. We find that the combined influence of capacity and control on working memory may be more complicated than previously appreciated. In particular, the general pattern of qualitative differences between children and adults could be produced by increasing either capacity or control alone. These results point to a need for additional experimental paradigms to clearly parse the differential impact of working memory components.

Humans collaborate to improve productivity and collective outcomes, but people do not always exert maximal effort towards accomplishing collaborative goals. Instead, individuals often expend less effort in groups, a phenomenon known as social loafing that is traditionally viewed as detrimental to productivity. However, theories from distributed computer systems suggest that social loafing might be a rational response to the diminishing returns expected from division of labor when group size increases. Here, we examine how considerations of task efficiency affect the perceived acceptability of withholding effort during a collaborative task. We conducted experiments varying workload and group size across scenarios in which all group members except for one are actively contributing to a common goal. We then compare participant judgments to a model inspired by latency speed-up in distributed systems. We find that people are systematically influenced by task efficiency, in addition to social norms, when judging social loafing.

For cognition to evolve, environmental cues must be sufficiently reliable to allow an animal to regularly make correct decisions. For example, the sound of rustling must covary with prey for attempting to detect prey to be possible. However, the reliability of information is not solely a property of the environment, but also results from the sophistication of an animal’s sensory systems. Here, we extend signal detection theory to examine how selection acts on sensory systems, alongside an animal’s policy for using information. Our model finds cognition can still be favored if sensory systems are poor, by compensating with greater bias to avoid costly errors. Yet, investing in reliable information and bias are not always alternatives, and can coevolve in order to be doubly protected from important errors. Sophisticated sensory systems evolve when the environment is complex, metabolic costs are low, and both false alarms and missed detections are moderately costly.

Researchers have studied Stewart-Gough platforms, also known as Gough-Stewart platforms or hexapod platforms extensively for their inherent fine control characteristics. Their studies led to the potential deployment opportunities of Stewart-Gough platforms in many critical applications such as medical field, engineering machines, space research, electronic chip manufacturing, automobile manufacturing, etc. Some of these applications need micro- and nano-level movement control in 3D space for the motions to be precise, complicated, and repeatable; a Stewart-Gough platform fulfills these challenges smartly. For this, the platform must be more accurate than the specified application accuracy level and thus proper calibration for a parallel robot is crucial. Forward kinematics-based calibration for these hexapod machines becomes unnecessarily complex and inverse kinematics complete this task with much ease. To experiment with different calibration techniques, various calibration approaches were implemented by using external instruments, constraining one or more motions of the system, and using extra sensors for auto or self-calibration. This survey paid attention to those key methodologies, their outcome, and important details related to inverse kinematic-based parallel robot calibrations. It was observed during this study that the researchers focused on improving the accuracy of the platform position and orientation considering the errors contributed by one source or multiple sources. The error sources considered are mainly kinematic and structural, in some cases, environmental factors also are reviewed; however, those calibrations are done under no-load conditions. This study aims to review the present state of the art in this field and highlight the processes and errors considered for the calibration of Stewart-Gough platforms. [ABSTRACT FROM AUTHOR]

Species richness is a metric of biodiversity that represents the number of species present in a community. Traditional fisheries assessments that rely on capture of organisms often underestimate true species richness. Environmental DNA (eDNA) metabarcoding is an alternative tool that infers species richness by collecting and sequencing DNA present in the ecosystem. Our objective was to determine how spatial distribution of samples and 'bioinformatic stringency' affected eDNA -metabarcoding estimates of species richness compared with capture-based estimates in a 2.2 ha reservoir. When bioinformatic criteria required species to be detected only in a single sample, eDNA metabarcoding detected all species captured with traditional methods plus an additional 11 noncaptured species. However, when we required species to be detected with multiple markers and in multiple samples, eDNA metabarcoding detected only seven of the captured species. Our analysis of the spatial patterns of species detection indicated that eDNA was distributed relatively homogeneously throughout the reservoir, except near the inflowing stream. We suggest that interpretation of eDNA metabarcoding data must consider the potential effects of water body type, spatial resolution, and bioinformatic stringency. La richesse specifique est une mesure de la biodiversite qui represente le nombre d'especes presentes dans une communaute. Les evaluations traditionnelles des ressources halieutiques qui reposent sur la capture d'organismes sousestiment souvent la richesse specifique reelle. Les meta-codes a barres d'ADN environnemental) (ADNe) constituent un autre outil qui permet d'inferer la richesse specifique en recueillant et en sequencant l'ADN present dans l'ecosysteme. Notre objectif consistait a determiner comment la repartition spatiale des echantillons et la ' rigueur bioinformatique ' influent sur les estimations de la richesse specifique reposant sur les meta-codes a barres d'ADNe par rapport aux estimations reposant sur la capture, dans un reservoir de 2,2 ha. Quand les criteres bioinformatiques exigeaient la detection d'une espece dans un seul echantillon, la methode des meta-codes a barres d'ADNe a detecte toutes les especes capturees par les methodes traditionnelles en plus de 11 autres especes non capturees. Toutefois, quand il fallait que les especes soient detectees sur la base de plus d'un marqueur et dans plus d'un echantillon, les meta-codes a barres d'ADNe n'ont detecte que sept des especes capturees. Notre analyse de la repartition spatiale de la detection d'especes indique que l'ADNe etait reparti de maniere assez uniforme dans tout le reservoir, sauf pres de l'embouchure du cours d'eau qui l'alimente. Nous proposons que l'interpretation des donnees obtenues par la methode des meta-codes a barres d'ADNe doit tenir compte des effets potentiels du type de plan d'eau, de la resolution spatiale et de la rigueur bioinformatique. [Traduit par la Redaction], Introduction Species richness is a biodiversity metric used in community ecology to describe the number of species in a given area at a given time, and has strong underpinnings in [...]

Interceptive eavesdropping on the alarm calls of heterospecifics provides crucial information about predators. Previous research suggests predator discrimination, call relevance, reliability, and reception explain when eavesdropping will evolve. However, there has been no quantitative analysis to scrutinize these principles, or how they interact. We develop a mathematical framework that formalizes the study of the key principles thought to select for eavesdropping. Interceptive eavesdropping appears to be greatly affected by the threat faced by caller and eavesdropper, as well as presence of informational noise affecting the detection of calls and predators. Accordingly, our model uses signal detection theory to examine when selection will favor alarm calling by a sender species and fleeing by an eavesdropping receiver species. We find eavesdropping is most strongly selected when (1) the receiver faces substantial threats, (2) species are ecologically similar, (3) senders often correctly discriminate threats, (4) receivers often correctly perceive calls, and (5) the receiver's personal discrimination of threats is poor. Furthermore, we find (6) that very high predation levels can select against eavesdropping because prey cannot continuously flee and must conserve energy. Reliability of heterospecific calls for identifying threats is thought to be important in selecting for eavesdropping. Consequently, we formally define reliability, showing its connection to specificity and sensitivity, clarifying how these quantities can be measured. We find that high call relevance, due to similar vulnerability to predators between species, strongly favors eavesdropping. This is because senders trade‐off false alarms and missed predator detections in a way that is also favorable for the eavesdropper, by producing less of the costlier error. Unexpectedly, highly relevant calls increase the total number of combined errors and so have lower reliability. Expectedly, when noise greatly affects personally gathered cues to threats, but not heterospecific calls or detection of predators, eavesdropping is favored. [ABSTRACT FROM AUTHOR]