| Records |
| Author |
Chaigneau, S.E.; Marchant, N.; Canessa, E.; Aldunate, N. |
| Title |
A mathematical model of semantic access in lexical and semantic decisions |
Type |
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| Year |
2024 |
Publication |
Language and Cognition |
Abbreviated Journal |
Lang. Cogn. |
| Volume |
Early Access |
Issue |
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Pages |
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| Keywords |
lexical decision task; mathematical modeling; property listing; semantic access; semantic decision task |
| Abstract |
In this work, we use a mathematical model of the property listing task dynamics and test its ability to predict processing time in semantic and lexical decision tasks. The study aims at exploring the temporal dynamics of semantic access in these tasks and showing that the mathematical model captures essential aspects of semantic access, beyond the original task for which it was developed. In two studies using the semantic and lexical decision tasks, we used the mathematical model's coefficients to predict reaction times. Results showed that the model was able to predict processing time in both tasks, accounting for an independent portion of the total variance, relative to variance predicted by traditional psycholinguistic variables (i.e., frequency, familiarity, concreteness imageability). Overall, this study provides evidence of the mathematical model's validity and generality, and offers insights regarding the characterization of concrete and abstract words. |
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| ISSN |
1866-9808 |
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| Notes |
WOS:001200317200001 |
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UAI @ alexi.delcanto @ |
Serial |
1964 |
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| Author |
Gutiérrez-Jara, J.P.; Vogt-Geisse, K.; Correa, M.C.G.; Vilches-Ponce, K.; Pérez, L.M.; Chowell, G. |
| Title |
Alert Results Modeling the Impact of Agricultural Mitigation Measures on the Spread of Sharka Disease in Sweet Cherry Orchards 26 of 41 Modeling the Impact of Agricultural Mitigation Measures on the Spread of Sharka Disease in Sweet Cherry Orchards |
Type |
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| Year |
2023 |
Publication |
Plants-Basel |
Abbreviated Journal |
Plants-Basel |
| Volume |
12 |
Issue |
19 |
Pages |
3442 |
| Keywords |
plum pox virus; aphids; mathematical modeling; agricultural management |
| Abstract |
Sharka is a disease affecting stone fruit trees. It is caused by the Plum pox virus (PPV), with Myzus persicae being one of the most efficient aphid species in transmitting it within and among Prunus orchards. Other agricultural management strategies are also responsible for the spread of disease among trees, such as grafting and pruning. We present a mathematical model of impulsive differential equations to represent the dynamics of Sharka disease in the tree and vector population. We consider three transmission routes: grafting, pruning, and through aphid vectors. Grafting, pruning, and vector control occur as pulses at specific instants. Within the model, human risk perception towards disease influences these agricultural management strategies. Model results show that grafting with infected biological material has a significant impact on the spread of the disease. In addition, detecting infectious symptomatic and asymptomatic trees in the short term is critical to reduce disease spread. Furthermore, vector control to prevent aphid movement between trees is crucial for disease mitigation, as well as implementing awareness campaigns for Sharka disease in agricultural communities that provide a long-term impact on responsible pruning, grafting, and vector control. |
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2223-7747 |
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| Notes |
WOS:001083391000001 |
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UAI @ alexi.delcanto @ |
Serial |
1896 |
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| Author |
Marchant, N.; Canessa, E.; Chaigneau, S.E. |
| Title |
An adaptive linear filter model of procedural category learning |
Type |
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| Year |
2022 |
Publication |
Cognitive Processing |
Abbreviated Journal |
Cogn. Process. |
| Volume |
23 |
Issue |
3 |
Pages |
393-405 |
| Keywords |
Category learning; Procedural categorization; Adaptive filter; Mathematical modeling |
| Abstract |
We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought. |
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1612-4782 |
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| Notes |
WOS:000791062800001 |
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UAI @ alexi.delcanto @ |
Serial |
1567 |
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