Dataset Information

Recurrent, robust and scalable patterns underlie human approach and avoidance.

ABSTRACT:

Background

Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory.

Methodology/principal findings

Three sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both.

Conclusions/significance

These law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).

SUBMITTER: Kim BW

PROVIDER: S-EPMC2879576 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Recurrent, robust and scalable patterns underlie human approach and avoidance.

Kim Byoung Woo BW Kennedy David N DN Lehár Joseph J Lee Myung Joo MJ Blood Anne J AJ Lee Sang S Perlis Roy H RH Smoller Jordan W JW Morris Robert R Fava Maurizio M Breiter Hans C HC

PloS one 20100526 5

<h4>Background</h4>Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion fu ...[more]

PMID: 20532247

Similar Datasets

Project description:BackgroundDynamical models are instrumental for exploring the way information required to generate robust developmental patterns arises from complex interactions among genetic and non-genetic factors. We address this fundamental issue of developmental biology studying the leaf and root epidermis of Arabidopsis. We propose an experimentally-grounded model of gene regulatory networks (GRNs) that are coupled by protein diffusion and comprise a meta-GRN implemented on cellularised domains.ResultsSteady states of the meta-GRN model correspond to gene expression profiles typical of hair and non-hair epidermal cells. The simulations also render spatial patterns that match the cellular arrangements observed in root and leaf epidermis. As in actual plants, such patterns are robust in the face of diverse perturbations. We validated the model by checking that it also reproduced the patterns of reported mutants. The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.ConclusionThe spatial meta-GRN model integrates available experimental data and contributes to further understanding of the Arabidopsis epidermal system. It also provides a systems biology framework to explore the interplay among sub-networks of a GRN, cell-to-cell communication, cell shape and domain traits, which could help understanding of general aspects of patterning processes. For instance, our model suggests that the information needed for cell fate determination emerges from dynamic processes that depend upon molecular components inside and outside differentiating cells, suggesting that the classical distinction of lineage versus positional cell differentiation may be instrumental but rather artificial. It also suggests that interlinkage of nonlinear and redundant sub-networks in larger networks is important for pattern robustness. Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible. A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

Project description:Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infections causing significant morbidity and mortality among children and the elderly; two RSV vaccines and a monoclonal antibody have recently been approved. Thus, there is an increasing need for a detailed and continuous genomic surveillance of RSV circulating in resource-rich and resource-limited settings worldwide. However, robust, cost-effective methods for whole genome sequencing of RSV from clinical samples that are amenable to high-throughput are still scarce. We developed Next-RSV-SEQ, an experimental and computational pipeline to generate whole genome sequences of historic and current RSV genotypes by in-solution hybridization capture-based next generation sequencing. We optimized this workflow by automating library preparation and pooling libraries prior to enrichment in order to reduce hands-on time and cost, thereby augmenting scalability. Next-RSV-SEQ yielded near-complete to complete genome sequences for 98% of specimens with Cp values ≤31, at median on-target reads >93%, and mean coverage depths between ~1,000 and >5,000, depending on viral load. Whole genomes were successfully recovered from samples with viral loads as low as 230 copies per microliter RNA. We demonstrate that the method can be expanded to other respiratory viruses like parainfluenza virus and human metapneumovirus. Next-RSV-SEQ produces high-quality RSV genomes directly from culture isolates and, more importantly, clinical specimens of all genotypes in circulation. It is cost-efficient, scalable, and can be extended to other respiratory viruses, thereby opening new perspectives for a future effective and broad genomic surveillance of respiratory viruses.ImportanceRespiratory syncytial virus (RSV) is a leading cause of severe acute respiratory tract infections in children and the elderly, and its prevention has become an increasing priority. Recently, vaccines and a long-acting monoclonal antibody to protect effectively against severe disease have been approved for the first time. Hence, there is an urgent need for genomic surveillance of RSV at the global scale to monitor virus evolution, especially with an eye toward immune evasion. However, robust, cost-effective methods for RSV whole genome sequencing that are suitable for high-throughput of clinical samples are currently scarce. Therefore, we have developed Next-RSV-SEQ, an experimental and computational pipeline that produces reliably high-quality RSV genomes directly from clinical specimens and isolates.

Dataset Information

Recurrent, robust and scalable patterns underlie human approach and avoidance.

Background

Methodology/principal findings

Conclusions/significance

Publications

Recurrent, robust and scalable patterns underlie human approach and avoidance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets