Cognitive effort modulates connectivity between dorsal anterior cingulate cortex and task-relevant cortical areas

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas

The unfolding action model of initiation times, movement times, and movement paths

Converging evidence has led to a consensus in favor of computational models of behavior implementing continuous information flow and parallel processing between cognitive processing stages. Yet, such models still typically implement a discrete step between the last cognitive stage and motor implementation. This discrete step is implemented as a fixed decision bound that activation in the last cognitive stage needs to cross before action can be initiated. Such an implementation is questionable as it cannot account for two important features of behavior. First, it does not allow to select an action while withholding it until the moment is appropriate for executing it. Second, it cannot account for recent evidence that cognition is not confined prior to movement initiation, but consistently leaks into movement. To address these two features, we propose a novel neurocomputational model of cognitionaction interactions, namely the unfolding action model (UAM). Crucially, the model implements adaptive information flow between the last cognitive processing stage and motor implementation. We show that the UAM addresses the two abovementioned features. Empirically, the UAM accounts for traditional response time data, including positively skewed initiation time distribution, functionally fixed decision bounds and speed-accuracy trade-offs in button-press experimental designs. Moreover, it accounts for movement times, movement paths, and how they are influenced by cognitive-experimental manipulations. This move should close the current gap between abstract decision-making models and behavior observed in natural habitats.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Continuous track paths reveal additive evidence integration in multistep decision making

info:eu-repo/semantics/publishe

Large Language Models are biased to overestimate profoundness

Recent advancements in natural language processing by large language models (LLMs), such as GPT-4, have been suggested to approach Artificial General Intelligence. And yet, it is still under dispute whether LLMs possess similar reasoning abilities to humans. This study evaluates GPT-4 and various other LLMs in judging the profoundness of mundane, motivational, and pseudo-profound statements. We found a significant statement-to-statement correlation between the LLMs and humans, irrespective of the type of statements and the prompting technique used. However, LLMs systematically overestimate the profoundness of nonsensical statements, with the exception of Tk-instruct, which uniquely underestimates the profoundness of statements. Only few-shot learning prompts, as opposed to chain-of-thought prompting, draw LLMs ratings closer to humans. Furthermore, this work provides insights into the potential biases induced by Reinforcement Learning from Human Feedback (RLHF), inducing an increase in the bias to overestimate the profoundness of statements.Comment: 5 pages, 3 figure

Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness

Artificial neural networks typically struggle in generalizing to out-of-context examples. One reason for this limitation is caused by having datasets that incorporate only partial information regarding the potential correlational structure of the world. In this work, we propose TIDA (Targeted Image-editing Data Augmentation), a targeted data augmentation method focused on improving models' human-like abilities (e.g., gender recognition) by filling the correlational structure gap using a text-to-image generative model. More specifically, TIDA identifies specific skills in captions describing images (e.g., the presence of a specific gender in the image), changes the caption (e.g., "woman" to "man"), and then uses a text-to-image model to edit the image in order to match the novel caption (e.g., uniquely changing a woman to a man while maintaining the context identical). Based on the Flickr30K benchmark, we show that, compared with the original data set, a TIDA-enhanced dataset related to gender, color, and counting abilities induces better performance in several image captioning metrics. Furthermore, on top of relying on the classical BLEU metric, we conduct a fine-grained analysis of the improvements of our models against the baseline in different ways. We compared text-to-image generative models and found different behaviors of the image captioning models in terms of encoding visual encoding and textual decoding

Determination of sun protection factor and antioxidant properties of six Chilean Altiplano plants

In the present study, we investigated sun protection factor and antioxidant properties of Parastrephia lepidophyla Cabr., Fabiana squamata Phil., Ephedra chilensis K.Presl., Lampaya medicinalis Phil., Baccharis tola Phil., and Azorella compacta Phil. The ethanol extracts were tested regarding their in vitro free radical scavenger ability and sun protection factor (SPF). Due to its antioxidant and photoprotective properties, B. tola is a promising candidate for use in cosmetic formulations. To evaluate the regenerative capacity of the B. tola extract, the planarian regeneration assay (Dugesia tigrina) was performed. Identification of phenolic compounds in B. tola, was performed by HPLC-ESI-MS/MS. Based on freeze-dried extracts of B. tola, a facial cream and a biphasic lotion with repairing tip action were formulated. These two formulations were evaluated by additional assays including organoleptic tests, measurement of pH, centrifugation and patch test to check a potential hypersensitivity (skin irritation) which can be induced by the products as well as a sensory survey. Stability studies, carried out over 12 months, prove that formulations were stable over time. It can be concluded that both products are innovative and shown solar protection, antioxidant and regenerative properties

Comparative effect of N-substituted dehydroamino acids and alpha-tocopherol on rat liver lipid peroxidation activities

Free radical damage has been associated with a growing number of diseases and conditions, such as autoimmune diseases, neurodegenerative disorders and multiple types of cancer. Some dehydroamino acids and corresponding peptides can function as radical scavengers. In this study the in vitro effects on rat liver lipid peroxidation levels of fourteen N-substituted dehydroamino acid derivatives and alpha-tocopherol were investigated. alpha-Tocopherol is a powerful antioxidant that is beneficial in the treatment of many free radical related diseases. The results indicated that all the compounds showed very good inhibitory effect on the lipid peroxidation compound with alpha-tocopherol at 1 mM concentrations and the inhibition rate was in the range of 70-79 % with the exception of compound 5. At 0.1 mM concentrations compounds 1, 2 and 9 were found more active than alpha-tocopherol. The results confirmed that molecules such as dehydroamino acids which have reactive double bonds can act as a guard in vitro against oxidants.- (undefined

Precision-Cut Liver Slices of Salmo salar as a tool to investigate the oxidative impact of CYP1A-mediated PCB 126 and 3-methylcholanthrene metabolism

a b s t r a c t Fish isolated cell systems have long been used to predict in vivo toxicity of man-made chemicals. In present study, we tested the suitability of Precision-Cut Liver Slices (PCLS) as an alternative to these models that allows the evaluation of a global tissue response to toxicants, to investigate oxidative stress response to cytochrome P450 1A (CYP1A) induction in fish liver. PCLS of Salmo salar were exposed for 21 h to increasing doses of 3-methylcholanthrene (3-MC) and Polychlorobiphenyl 126 (PCB 126). 3-MC (25 lM) strongly induced CYP1A transcription. In dose-response analysis (25-100 lM), EROD activity was strongly increased at intermediate 3-MC concentrations. We found the counter-intuitive decline of EROD at the highest 3-MC doses to result from reversible competition with ethoxyresorufin. No increases of H 2 O 2 production, antioxidant enzymes activities or oxidative damage to lipids were found with 3-MC treatments. PCLS subjected to PCB 126 (2-200 nM) showed increased contamination levels and a parallel increased CYP1A mRNA synthesis and EROD activity. H 2 O 2 production tended to increase but no oxidative damage to lipids was found. As antioxidant enzymes activities declined at the highest PCB 126 dose, it is suggested that longer incubation periods could be required to generate oxidative stress in PCLS