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.

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.

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.