Vários são os fatores que influenciam as interações entre plantas e visitantes florais, como variações abióticas, bióticas e a forma e intensidade de amostragem. Através de análises de redes, alguns padrões têm sido reconhecidos nessas comunidades interativas, como a estrutura aninhada, a baixa conectância da rede, o maior grau de conectividade de plantas em relação aos visitantes florais e a forma de distribuição desses graus. Neste trabalho, procurou-se avaliar a estabilidade das métricas das redes de interação entre plantas e visitantes florais com relação às variações de esforço amostral, ao aumento da área amostrada, e influência de fitofisionomias do cerrado (campo cerrado e campo sujo). Também foi avaliada se a forma de obtenção de dados para construção das matrizes de interação (se a partir de observação de campo C, a partir de análises palinológicas P, ou junção de ambas as formas - CP) altera essas métricas. Para isso, foram analisadas matrizes de interação entre plantas e visitantes obtidas em 6 parcelas de tamanhos diferentes (100, 400, 900 m2), sendo três em cada fitofisionomia, na Estação Ecológica de Itirapina, Itirapina, SP. Os resultados indicam que, embora as métricas riqueza de espécies de plantas, de visitantes Apoidea, riqueza de interações observadas e possíveis, sejam sensíveis ao tamanho da área amostrada e ao esforço amostral, a generalização das redes (medida por conectância), das espécies de plantas e visitantes (medidas pela média de interações em cada rede) e os graus de aninhamento (NODF) são bastante robustos. A fitofisionomia não influenciou em nenhuma dessas métricas. A forma de obtenção dos dados, por sua vez, indica que há um forte viés com relação à forma de obtenção dos dados no número de espécies de plantas (C<[P=CP] ), número de interações possíveis ([C=P]<CP); interações observadas ([C=P]< CP ); conectância ([C=CP] < P ); grau médio de interações das espécies de plantas (P<[C=CP]); dos visitantes ([P=CP] < C); e grau de aninhamento medido por NODF ([C=CP] < P). Não houve influência da forma de obtenção dos dados na centralização das redes. Estes resultados indicam que embora algumas dessas métricas sejam robustas com relação ao esforço amostral e tamanho da área amostrada, há um forte viés ligado à forma de obtenção dos dados para a construção das matrizes de dados, que pode ser balanceado com a adição de informações palinológicas obtidas através dos visitantes coletados, pois elas ajudam a identificar interações efetuadas por eles antes da sua captura. / Several factors can influence plant-visitors interactions, such as abiotic and biotic changes, and type and intensity of sampling. Through network analysis, some patterns have been recognized in these interactive communities, as nested structure, low connectance network, the greater degree of connectivity of plants in relation to flower visitors and the distribution of these degrees. In this study, we sought to evaluate the stability of the networks topology metrics with respect to variations in sampling effort, increase of the sampling area, and the influence of cerrado phytophysiognomies (campo cerrado and campo sujo). We also assessed if the way of obtaining data for constructing the interaction matrices (up from field observations - C, from pollen analysis - P, or joining both ways - CP) alters these metrics. For this we analysed matrices of interaction between plants and visitors obtained in six plots of different sizes (100, 400, 900 m2), three in each vegetation type at the Ecological Station Itirapina Itirapina, SP. The results indicate that while the metrics of plant species richness, Apoidea visitors, richness of observed and possible interactions, are sensitive to the size of the sampled area and sampling effort, networks generalization (measured by connectance), plant and visitors species generalization (as measured by the average of interactions in each network) and nestedness degree (NODF) are quite robust. Phytophysiognomy did not influence in any of these metrics. The way data were obtained, in turn, indicates that there is a strong bias with respect to the number of plant species (C <[P = CP]), number of possible interactions ([C = P ] < CP), number of observed interactions ([C = P] <CP), connectance ([C = CP] < P), average degree of plant species (P <[C = CP]) and visitors ([ P = CP] <C) interactions and nestedness degree, measured by NODF ([C = CP] < P). There was no influence of the means of acquiring data in network centralization. These results indicate that although some of these metrics are robust with respect to sampling effort and size of sampled area, there is a strong bias linked to how data were obtained for the construction of the data matrices that can be balanced with the addition of pollen information obtained from the visitors collected, because they help identify interactions made by them before their capture.

When large areas of forest are modelled, spatial detail can create excessively large databases and adversely affect the processing time. Spatial generalization can be an efficient means of aggregating polygons into blocks in strategic forest planning models. In this study, a sensitivity analysis on spatial generalization was conducted to examine the trade-off between accuracy and spatial resolution to meet the objectives of strategic planning. Five scenarios were designed by generalizing forest cover polygons into the uniform hexagon block sizes of 5, 10, 20, 50 and 100 ha. To quantitatively assess accuracy, deviations caused by spatial generalization were calculated by criteria for hexagon scenarios relative to the base case. Criteria include model inputs (area of natural disturbance type and ungulate winter range) and outputs (harvest volume, growing stock and seral stage distribution). In general, deviations in all criteria increased with the block size. Spatial resolution was also evaluated by the database size and simulation runtime. A negative relationship was observed between spatial resolution and the block size. The trade-off analysis between accuracy and spatial resolution indicated that using the smallest block size of 5 ha creates more detail than necessary. Although scenarios with the block sizes of 50 and 100 ha reduced spatial resolution significantly, the maximum deviations relative to the base case were as high as 14% and 17% in growing stock, 12% and 12% in seral stage distribution, and 6% and 21% in ungulate winter range, respectively. For this study, the preferred block size is in the range of 10-20 ha, however, in general, the preferred block size will vary depending on the importance of each criterion used in the trade-off analysis. / Forestry, Faculty of / Graduate

This study describes the reliability reporting practices in empirical studies using eight adolescent alcohol screening tools and characterizes and explores variability in internal consistency estimates across samples. Of 119 observed administrations of these instruments, 40 (34%) reported usable reliability information. The Personal Experience Screening QuestionnaireProblem Severity scale generated average reliability estimates exceeding 0.90 (95% CI=0.90-0.96) and the Adolescent Alcohol Involvement Scale generated average score reliability estimates below 0.80 (95% CI=0.67-0.85). Average reliability estimates of the remaining instruments were distributed between these extremes. Sample characteristics were identified as potentially important predictors of variability in the reliability estimates of all the instruments and all instruments under evaluation generated more reliable scores in clinical settings (M=0.89) as opposed to nonclinical settings (M=0.82; r effect size (38)=0.29, p.10). Clinicians facing instrument selection decisions can use these data to guide their choices and researchers evaluating the performance of these instruments can use these data to inform their future studies.

Meta-analytic methods provide a framework around which an inquiry into MAST and SMAST score reliability was completed. Of the 470 measurement opportunities observed between 1971 and 2005, 62 (13.2%) were coupled with accurate reliability information. Weighted reliability estimates centered on.80 suggesting that the MAST and SMAST generally produce scores of similar and adequate reliability for most research purposes. However, the variability of internal consistency estimates shows that at times these tools will not produce reliable scores, particularly among female and nonclinical respondents. Multiple regression equations provide practical guidelines to improve reliability estimates for the future use of these instruments.

A large-scale intervention, designed to increase healthier fast-food consumption, was evaluated at a national fast-food chain. Participants included fast-food consumers at three separate restaurant locations in southwestern Virginia. Each restaurant received three phases, consisting of fourteen days each. Two of the restaurants were exposed to two conditions, A (Baseline) and B (Intervention), while the other restaurant served as a control. Restaurant 1 received the following phases, with each phase lasting two weeks: A--B--A. Restaurant 2 received A--A--B, and Restaurant 3 received A--A--A. Research assistants distributed discount coupons on a new healthy sandwich to consumers during Condition B in Restaurants 2 & 3. This sandwich was available in a healthy combo including salad and water, and a regular combo including soda and fries. At all 3 locations, research assistants collected receipts showing all total menu item sales every day during the six-week intervention. Analyses of variance revealed consumers purchased the healthy sandwich significantly more during the incentive conditions, and also purchased the regular combo more frequently than the healthier combo during the intervention condition. Implications for the social validity of using incentives to motivate nutrition-related behaviors are discussed. / Master of Science

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To make decisions, plan, and act appropriately in a complex world, the brain formulates internal models of how the world works. As we change our goals, shift to novel environments, or as the world simply evolves around us, these models must flexibly adapt. Impairments in the formation of such flexible models are present in numerous disorders such as schizophrenia, Parkinson’s, and Alzheimer’s Disease and plague even the most sophisticated artificial agents. Therefore, understanding how the brain structures efficient internal models is of critical importance. Previous findings indicate that the information extracted from past experiences is organized and encoded into relational structured knowledge by the prefrontal cortex (PFC) and the medial temporal lobe (MTL). This process requires balancing two complementary computations in response to overlap. The first is to generalize the commonalities shared across overlapping experiences. The second is to separate overlapping experiences that fundamentally differed along a critical dimension, such as their outcomes, required actions, or time. How the brain balances these functions when faced with overlap remains poorly understood. In this thesis, I proposed that the analysis of the geometry of neural representational spaces offers valuable insights into the brain’s solution to optimally disambiguate and generalize overlapping experiences. I tested this proposal through the analysis of three functional magnetic resonance imaging (fMRI) experiments leveraging data-driven multivariate techniques to probe the dimensionality, structure, and content of these spaces. The first experiment (chapter 2) explored how PFC subregions respond to partially overlapping spatial environments during goal-directed virtual navigation. Based on previous research conducted in our lab that showed prefrontal activity in response to spatial overlap, we analyzed the dimensionality, structure, and content of prefrontal representations while participants learned a virtual navigation task. These analyses demonstrated compressed and highly orthogonalized codes early in learning that shifted over time towards more integrated and schematic codes. Critically, both prospective and retrospective information was bound to the representations of overlapping routes, with greater weight given to prospective information early in learning to help separate overlap. Based on these results, I advanced the idea that PFC subregions tune the geometry of their representations based on task demands and argued that prefrontal attention acts as a filter to promote both the separation and generalization of overlap. Building on the first experiment, a second study (chapter 3) focused on the re-analysis of a high-resolution fMRI study centered on the MTL that examined how MTL subregions handle prospective spatial overlap when planning routes in the same task. Based on previous research in our lab, we knew that the parahippocampal cortex (PHC) and hippocampal subfields CA1 and CA3/DG likely play a role in disambiguating overlapping routes during planning. We probed the geometry of their representations using the same methods used in experiment 1. The results demonstrated a segregation of roles between compressed schematic codes in PHC and expanded orthogonalized codes in CA3/DG that formed over the course of learning in response to overlap. Importantly, the degree of pattern separation observed in CA3/DG depended on the amount of initial overlap. These findings lead to the conclusion that generalization and separation are balanced in the MTL by distributing these functions to different subregions. Furthermore, the results suggest that MTL integration is supported by compression, whereas its separation is achieved via expansion. The third experiment (chapter 4) further examined how PFC and MTL regions balance generalization and separation processes when faced with abstract overlap between context-dependent rules. An analysis of the geometry of representational spaces in a context-dependent rule learning task found that successful rule learning was characterized by maintaining a balance between high and low dimensional spaces over learning. This equilibrium likely enabled the formation of relational knowledge representations that captured the latent structure of the task rules. Importantly, the only level of abstraction observed was one that perfectly matched the maximal amount of abstraction necessary to perform the task, and this structure only appeared later in learning in the hippocampus relative to extra-hippocampal regions. These results suggest that the brain employs an efficient and flexible coding scheme to respond to task demands. The results also suggest an important interplay between prefrontal and hippocampal codes over the course of learning. These three experiments demonstrate the promise that representational geometries offer in understanding the computations of the brain. Specifically, the results show that the flexible equilibrium between generalization and separation is accompanied by the fine-tuning of the dimensionality, structure, and content of representational spaces across a distributed network of MTL and PFC subregions. In the conclusion chapter, I discuss how these insights fit into existing frameworks regarding efficient and distributed codes.

Previous research suggests learning of children with autism often fails to successfully generalize across changes in settings and stimuli. Much of this research has assessed generalization by first teaching a behavior in one context and then measuring the transfer of the behavior to extra-treatment stimuli and settings. The present study measured generalization of learned behavior by systematically varying the tone of an auditory stimulus present during training to obtain generalization gradients. Generalization gradients are graphical representations of the strength of a response produced by stimuli that vary from the training stimulus along some stimulus dimension. By obtaining generalization gradients, this research may offer a more precise means of characterizing the extent of generalization and the basic processes underlying it. The study also went beyond previous research with children with autism by examining the effects of two different training procedures upon the resulting generalization gradients. Participants were first taught to discriminate between the presence and absence of a specific stimulus, and later, to discriminate between two stimuli varied along the same dimension. Gradients were measured following both trainings. In the first training procedure, three children with autism were taught to engage in a simple communicative request in the presence of a specific tone and to withhold the request when there was no tone. The researchers then measured the extent to which these children continued to engage in the request as the tone was changed in frequency. They graphed the resulting data in the form of a generalization gradient. Although the shape of resulting generalization gradients differed between participants, all three participants in the present study showed a pattern of responding consistent with generalization. Gradients for two of three participants were orderly and decremental. In the second training procedure participants were taught to discriminate between two tones of different frequencies. Generalization gradients were again obtained. Predictable changes in the shape of gradients, consistent with basic research on generalization gradients, were noted for two of three participants. Results are discussed with regard to stimulus control, the behavioral processes of reinforcement and extinction, and what has been called the "peak shift" effect.

Parent’s ability to implement a skill like time out is considered to have generality when it lasts over time, it appears in environments in which it was not initially trained, and it is seen in use for child behaviors it was not initially trained for. A recent study suggested that parents have difficulty implementing time out in the home when they are supposed to (Jensen et al., 2016). The present study aimed to determine if there were difficulties in stimulus or setting generalization. Six parent participants completed pre and post training in-home naturalistic video observations, as well as a generalization probe role-play test following training. Results showed that parent’s accuracy and parent’s ability to implement when they should were two different skills. Parents scored high for accuracy of steps correct in both the post training in-home observations and the generalization probe role-plays. However, parent’s correct attempts out of the possible opportunities were only moderate in the post training generalization probes and poor in both the pre and post training in-home observations. How to implement time out was the only skill directly taught to parents and not when to implement time out. The authors suggest that knowing when to implement time out may be a generalization deficit and a recognition of opportunities in the home deficit. This version of time out training may benefit from additional training to foster generalization. Future research may benefit from systematically analyzing whether parents can recognize opportunities for time out in the home through other parent in-home videos and if they can, more emphasis on generalization of a skill to the parent’s home would be necessary in training.