Collective learning and optimal consensus decisions in social animal groups.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.This research was supported by a National Science Foundation Graduate Research Fellowship and National Science Foundation Doctoral Dissertation Improvement Grant 1210029 to ABK, a National Sciences and Engineering Research Council of Canada Fellowship to NM, and National Science Foundation Award PHY-0848755 and EAGER Grant IOS-1251585, Office of Naval Research Award N00014-09-1-1074, Army Research Office Grant W911NG-11-1-0385, and Human Frontiers Science Program Award RGP0065/2012 to IDC

Context variability promotes generalization in reading aloud: Insight from a neural network simulation

This is the final version. Available from the Cognitive Science Society via the link in this recordHow do neural network models of quasiregular domains learn to represent knowledge that varies in its consistency with the domain, and generalize this knowledge appropriately? Recent work focusing on spelling-to-sound correspondences in English proposes that a graded “warping” mechanism determines the extent to which the pronunciation of a newly learned word should generalize to its orthographic neighbors. We explored the micro-structure of this proposal by training a network to pronounce new made-up words that were consistent with the dominant pronunciation (regulars), were comprised of a completely unfamiliar pronunciation (exceptions), or were consistent with a subordinate pronunciation in English (ambiguous). Crucially, by training the same spelling-to-sound mapping with either one or multiple items, we tested whether variation in adjacent, within-item context made a given pronunciation more able to generalize. This is exactly what we found. Context variability, therefore, appears to act as a modulator of the warping in quasiregular domains.Economic and Social Research Council (ESRC)NSERCCF

California Current seascape influences juvenile salmon foraging ecology at multiple scales

Juvenile salmon Oncorhynchus spp. experience variable mortality rates during their first few months in the ocean, and high growth during this period is critical to minimize size selective predation. Examining links between the physical environment and foraging ecology is important to understand mechanisms that drive growth. These mechanisms are complex and include interactions among the physical environment, forage availability, bioenergetics, and salmon foraging behavior. Our objectives were to explore how seascape features (biological and physical) influence juvenile Chinook salmon O. tshawytscha foraging at annual and feedingevent scales in the California Current Ecosystem. We demonstrate that forage abundance was the most influential determinant of mean salmon stomach fullness at the annual scale, while at the feeding-event scale, fullness increased with greater cumulative upwelling during the 10 d prior and at closer distances to thermal fronts. Upwelling promotes nutrient enrichment and productivity, while fronts concentrate organisms, likely resulting in available prey to salmon and increased stomach fullness. Salmon were also more likely to consume krill when there was high prior upwelling,andswitchedtonon-krillinvertebrates(i.e.amphipods,decapods,copepods)inweaker upwelling conditions. As salmon size increased from 72−250 mm, salmon were more likely to consume fish, equal amounts of krill, and fewer non-krill invertebrates. Broad seascape processes determined overall prey availability and fullness in a given year, while fine- and meso-scale processes influenced local accessibility of prey to individual salmon. Therefore, processes occurring at multiple scales will influence how marine organisms respond to changing environment