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6
Date Added: Jan 28, 2022
Date Added: Jan 28, 2022
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain’s cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry.
3
Date Added: Jan 7, 2022
Common sense (and stock market dynamics) would suggest they do. However, the literature is not consistent on this point: the studies centering around COVID-19 have found very heterogeneous results, and previous scattered studies were, likewise, at odds with each other.Given the recent studies, I personally lean against this hypothesis, even if a paper of mine (in press) found more evidence for it.
Neutral
2 researchers are split
10
Date Added: Jan 27, 2022
Date Added: Jan 27, 2022
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
4
Date Added: Jan 28, 2022
Date Added: Jan 28, 2022
The open-ended and internally driven nature of curiosity makes characterizing the information seeking that accompanies it a daunting endeavour. We use a historico-philosophical taxonomy of information seeking coupled with a knowledge network building framework to capture styles of information-seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes represent distinct concepts and edges represent the similarity between concepts. We quantify the tightness of knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying information-seeking. Deprivation curiosity (the tendency to seek information that eliminates knowledge gaps) is associated with the creation of relatively tight networks and a relatively greater tendency to return to previously visited concepts. With this framework in hand, future research can readily quantify the information seeking associated with curiosity.
Paper
4
Date Added: Jan 28, 2022
Date Added: Jan 28, 2022
Evaluating one’s own performance on a task, typically known as ‘self-assessment’, is perceived as a fundamental skill, but people appear poorly calibrated to their abilities. Studies seem to show poorer calibration for low performers than for high performers, which could indicate worse metacognitive ability among low performers relative to others (the Dunning–Kruger effect). By developing a rational model of self-assessment, we show that such an effect could be produced by two psychological mechanisms, in either isolation or conjunction: influence of prior beliefs about ability or a relation between performance and skill at determining correctness on each problem. To disentangle these explanations, we conducted a large-scale replication of a seminal paper with approximately 4,000 participants in each of two studies. Comparing the predictions of two variants of our rational model provides support for low performers being less able to estimate whether they are correct in the domains of grammar and logical reasoning.
3
Date Added: Jan 2, 2022
Date Added: Jan 2, 2022
While the widely studied allocentric spatial representation holds a special status in neuroscience research, its exact nature and neural underpinnings continue to be the topic of debate, particularly in humans. Here, based on a review of human behavioral research, we argue that allocentric representations do not provide the kind of map-like, metric representation one might expect based on past theoretical work. Instead, we suggest that almost all tasks used in past studies involve a combination of egocentric and allocentric representation, complicating both the investigation of the cognitive basis of an allocentric representation and the task of identifying a brain region specifically dedicated to it. Indeed, as we discuss in detail, past studies suggest numerous brain regions important to allocentric spatial memory in addition to the hippocampus, including parahippocampal, retrosplenial, and prefrontal cortices. We thus argue that although allocentric computations will often require the hippocampus, particularly those involving extracting details across temporally specific routes, the hippocampus is not necessary for all allocentric computations. We instead suggest that a non-aggregate network process involving multiple interacting brain areas, including hippocampus and extra-hippocampal areas such as parahippocampal, retrosplenial, prefrontal, and parietal cortices, better characterizes the neural basis of spatial representation during navigation. According to this model, an allocentric representation does not emerge from the computations of a single brain region (i.e., hippocampus) nor is it readily decomposable into additive computations performed by separate brain regions. Instead, an allocentric representation emerges from computations partially shared across numerous interacting brain regions. We discuss our non-aggregate network model in light of existing data and provide several key predictions for future experiments.
Paper
98
Date Added: Jan 18, 2022
Date Added: Jan 18, 2022
Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.
5
Date Added: Jan 6, 2022
Date Added: Jan 6, 2022
Mobile Brain-Body Imaging (MoBI) technology was deployed to record multi-modal data from 209 participants to examine the brain’s response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6–8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E), or given no explanation (Guided-NE). The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada) headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL) control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP) during the guided tour. In this study, we report data related to participants’ demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG) in a select subgroup of 18–30 year-old subjects (Nc = 25) that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15–25 Hz) in the prefrontal electrodes (AF7 and AF8) during appreciation of subjects’ favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP). No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art appreciation is independent of the artists’ intent or original interpretation and related to the individual message that viewers themselves provide to each piece.
Paper
2
Date Added: Jan 22, 2022
Date Added: Jan 22, 2022
Background Diverse studies have investigated the relationship between diet and depression. In fact some cross-sectional studies suggested that a healthy diet reduced the risk for depression. The main objective of this study was to assess the relationship of consumption of different food groups with depression. The food groups were selected based on their content of substances that were precursors to neurotransmitters (tryptophan or inositol) or their effect on oxidative stress. Methods This observational retrospective study compared the diets of individuals who were with depressive symptoms (Beck Depression Inventory Questionnaire [BDI] ≥ 10; 53 women, 23 men, age 38+/− 11) and with no depressive levels (BDI < 10; 33 women, 23 men, age 41+/− 13). Dietary data were collected from a questionnaire that asked about consumption of legumes, nuts, whole-grain foods, fruits and vegetables, chocolate, and sweet foods and refined sugars. Results Depressed individuals consumed significantly lower amounts of legumes, fruits, and vegetables, but higher amounts of sweets and refined sugars (p < 0.05 for all comparisons). After statistical adjustment for age and sex, the consumption of no legumes (adjusted odds ratio [aOR] = 2.60, 95% confidence interval [CI] = 1.19–5.67), low consumption of fruits and vegetables (aOR = 2.69, 95% CI = 1.18–6.13), and high consumption of sweet foods and refined sugars (aOR = 1.91, 95% CI = 1.23–2.99) were significantly associated with depression. The two groups had no significant differences in the consumption of chocolate. Discussion The results indicate significant relationships of the consumption of certain foods with depression, although the study design precludes any conclusions regarding causality. Further studies are necessary to determine the causal relationships of the consumption of specific foods with depression, and of depression with the consumption of specific foods. Conclusion In spite of the limitations, we find that individuals without depression consumed more legumes, fruits, and vegetables, but fewer sweets and pastries than those with depression.
Paper
2
Date Added: Jan 28, 2022
Date Added: Jan 28, 2022
People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N = 1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting that emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviours challenge standard decision-making models that have focused solely on reward.
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