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Pigeons' demand and preference for specific and generalized tokens was examined in a token economy. Pigeons could produce and exchange different colored tokens for food, for water, or for food or water. Token production was measured across three phases, which examined: (1) across-session price increases (typical demand curve method); (2) within-session price increases (progressive-ratio, PR, schedule); and (3) concurrent pairwise choices between the token types. Exponential demand curves were fitted to the response data and accounted for over 90% total variance. Demand curve parameter values, Pmax , Omax and α showed that demand was ordered in the following way: food tokens, generalized tokens, water tokens, both in Phase 1 and in Phase 3. This suggests that the preferences were predictable on the basis of elasticity and response output from the demand analysis. Pmax and Omax values failed to consistently predict breakpoints and peak response rates in the PR schedules in Phase 2, however, suggesting limits on a unitary conception of reinforcer efficacy. The patterns of generalized token production and exchange in Phase 3 suggest that the generalized tokens served as substitutes for the specific food and water tokens. Taken together, the present findings demonstrate the utility of behavioral economic concepts in the analysis of generalized reinforcement.
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http://dx.doi.org/10.1002/jeab.181 | DOI Listing |
Nat Comput Sci
September 2025
PGI-15, Forschungszentrum Jülich, Jülich, Germany.
Transformer networks, driven by self-attention, are central to large language models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. However, graphics processing unit (GPU)-stored projections must be loaded into static random-access memory for each new generation step, causing latency and energy bottlenecks.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Psychological and Brain Sciences, Boston University, Boston, United States.
Background: Lesbian, gay, bisexual, transgender, queer/questioning, intersex, asexual (LGBTQIA+) researchers and participants frequently encounter hostility in virtual environments, particularly on social media platforms where public commentary on research advertisements can foster stigmatization. Despite a growing body of work on researcher virtual hostility, little empirical research has examined the actual content and emotional tone of public responses to LGBTQIA+-focused research recruitment.
Objective: This study aimed to analyze the thematic patterns and sentiment of social media comments directed at LGBTQIA+ research recruitment advertisements, in order to better understand how virtual stigma is communicated and how it may impact both researchers and potential participants.
Digit Discov
August 2025
Institute for Complex Molecular Systems (ICMS), Eindhoven AI Systems Institute (EAISI), Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven The Netherlands
Data augmentation can alleviate the limitations of small molecular datasets for generative deep learning by 'artificially inflating' the number of instances available for training. SMILES enumeration - wherein multiple valid SMILES strings are used to represent the same molecules - has become particularly beneficial to improve the quality of molecule design. Herein, we investigated whether rethinking SMILES augmentation techniques could further enhance the quality of design.
View Article and Find Full Text PDFEur J Pharmacol
September 2025
Cardiovascular Center of Excellence, Louisiana State University Health Sciences Center, New Orleans, Louisiana, 70112, USA; Department of Pharmacology & Experimental Therapeutics, New Orleans, LA, 70112 USA; Southeast Louisiana Veterans Health Care System, New Orleans, LA 70119, USA. Electronic addr
The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format.
View Article and Find Full Text PDFMed Phys
August 2025
Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
Background: Accurate retinal vessel segmentation from Optical Coherence Tomography Angiography (OCTA) images is vital in ophthalmic medicine, particularly for the early diagnosis and monitoring of diseases, such as diabetic retinopathy and hypertensive retinopathy. The retinal vascular system exhibits complex characteristics, including branching, crossing, and continuity, which are crucial for precise segmentation and subsequent medical analysis. However, traditional pixel-wise vessel segmentation methods focus on learning how to effectively divide each pixel into different categories, relying mainly on local features, such as intensity and texture, and often neglecting the intrinsic structural properties of vessels.
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