Publications by authors named "C Gratton"

113 Publications

Historical decrease in agricultural landscape diversity is associated with shifts in bumble bee species occurrence.

Ecol Lett 2021 Jun 18. Epub 2021 Jun 18.

Department of Entomology, University of Wisconsin-Madison, Madison, WI, USA.

Agricultural intensification is a key suspect among putative drivers of recent insect declines, but an explicit link between historical change in agricultural land cover and insect occurrence is lacking. Determining whether agriculture impacts beneficial insects (e.g. pollinators), is crucial to enhancing agricultural sustainability. Here, we combine large spatiotemporal sets of historical bumble bee and agricultural records to show that increasing cropland extent and decreasing crop richness were associated with declines in over 50% of bumble bee species in the agriculturally intensive Midwest, USA. Critically, we found that high crop diversity was associated with a higher occurrence of many species pre-1950 even in agriculturally dominated areas, but that current agricultural landscapes are devoid of high crop diversity. Our findings suggest that insect conservation and agricultural production may be compatible, with increasing on-farm and landscape-level crop diversity predicted to have positive effects on bumble bees.
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http://dx.doi.org/10.1111/ele.13786DOI Listing
June 2021

Reasoning strategies determine the effect of disconfirmation on belief in false claims.

Mem Cognit 2021 May 28. Epub 2021 May 28.

Département de Psychologie, Université du Québec à Montréal, C.P. 8888, Succ A, Monntréal, Québec, H3C 3P8, Canada.

The dual-strategy model of reasoning proposes that people tend to use one of two reasoning strategies: either a statistical or a counterexample strategy, with the latter being more sensitive to potential counterexamples to a given conclusion. Previous studies have examined the effects of reasoning strategy in a variety of contexts. In the present study, we looked at the effects of gist repetition and disconfirmation on belief in an unknown claim. This is particularly interesting since there is no single normative analysis of this situation. We examine the hypotheses that (a) increasing gist repetition will result in higher levels of belief with both counterexample and statistical reasoners, and (b) that counterexample reasoners will have lower belief levels following a single disconfirming instance than will statistical reasoners. In a large-scale online study, over 2,000 adult participants received a False Claim procedure along with a Strategy Diagnostic. Results are consistent with the hypotheses. This provides strong evidence that the dual-strategy model captures a clear difference in information processing that is not captured by any normative/non-normative distinction.
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http://dx.doi.org/10.3758/s13421-021-01190-1DOI Listing
May 2021

Probabilistic mapping of human functional brain networks identifies regions of high group consensus.

Neuroimage 2021 May 15;237:118164. Epub 2021 May 15.

Department of Psychology, Northwestern University, USA; Department of Neurology, Northwestern University, USA; Interdepartmental Neuroscience Program, Northwestern University, USA.

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118164DOI Listing
May 2021

The Value of Hyperalignment to Unpack Neural Heterogeneity in the Precision Psychiatry Movement.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Apr 28. Epub 2021 Apr 28.

Department of Psychology, Northwestern University, Evanston, Illinois; Institute for Policy Research, Northwestern University, Evanston, Illinois.

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http://dx.doi.org/10.1016/j.bpsc.2021.02.006DOI Listing
April 2021

Assessing the potential for deep learning and computer vision to identify bumble bee species from images.

Sci Rep 2021 Apr 7;11(1):7580. Epub 2021 Apr 7.

Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA.

Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.
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http://dx.doi.org/10.1038/s41598-021-87210-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027374PMC
April 2021