Response to Fittipaldi etal. (2024)
Trends Cogn Sci. 2024 Apr 5:S1364-6613(24)00074-3. doi: 10.1016/j.tics.2024.03.008. Online ahead of print.NO ABSTRACTPMID:38582655 | DOI:10.1016/j.tics.2024.03.008 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - April 6, 2024 Category: Neuroscience Authors: Nicholas J Fendinger Pia Dietze Eric D Knowles Source Type: research
Brain states as wave-like motifs
Trends Cogn Sci. 2024 Apr 5:S1364-6613(24)00057-3. doi: 10.1016/j.tics.2024.03.004. Online ahead of print.ABSTRACTThere is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space...
Source: Trends Cogn Sci - April 6, 2024 Category: Neuroscience Authors: Maya Foster Dustin Scheinost Source Type: research
Response to Fittipaldi etal. (2024)
Trends Cogn Sci. 2024 Apr 5:S1364-6613(24)00074-3. doi: 10.1016/j.tics.2024.03.008. Online ahead of print.NO ABSTRACTPMID:38582655 | DOI:10.1016/j.tics.2024.03.008 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - April 6, 2024 Category: Neuroscience Authors: Nicholas J Fendinger Pia Dietze Eric D Knowles Source Type: research
The computational foundations of dynamic coding in working memory
Trends Cogn Sci. 2024 Apr 4:S1364-6613(24)00053-6. doi: 10.1016/j.tics.2024.02.011. Online ahead of print.ABSTRACTWorking memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, an...
Source: Trends Cogn Sci - April 5, 2024 Category: Neuroscience Authors: Jake P Stroud John Duncan M áté Lengyel Source Type: research
The computational foundations of dynamic coding in working memory
Trends Cogn Sci. 2024 Apr 4:S1364-6613(24)00053-6. doi: 10.1016/j.tics.2024.02.011. Online ahead of print.ABSTRACTWorking memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, an...
Source: Trends Cogn Sci - April 5, 2024 Category: Neuroscience Authors: Jake P Stroud John Duncan M áté Lengyel Source Type: research
When liars are considered honest
This article introduces a theoretical model of truth and honesty from a psychological perspective. We examine its application in political discourse and discuss empirical findings distinguishing between conceptions of honesty and their influence on public perception, misinformation dissemination, and the integrity of democracy.PMID:38575465 | DOI:10.1016/j.tics.2024.03.005 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - April 4, 2024 Category: Neuroscience Authors: Stephan Lewandowsky David Garcia Almog Simchon Fabio Carrella Source Type: research
When liars are considered honest
This article introduces a theoretical model of truth and honesty from a psychological perspective. We examine its application in political discourse and discuss empirical findings distinguishing between conceptions of honesty and their influence on public perception, misinformation dissemination, and the integrity of democracy.PMID:38575465 | DOI:10.1016/j.tics.2024.03.005 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - April 4, 2024 Category: Neuroscience Authors: Stephan Lewandowsky David Garcia Almog Simchon Fabio Carrella Source Type: research
Arousal and performance: revisiting the famous inverted-U-shaped curve
Trends Cogn Sci. 2024 Apr 2:S1364-6613(24)00078-0. doi: 10.1016/j.tics.2024.03.011. Online ahead of print.ABSTRACTArousal level is thought to be a key determinant of variability in cognitive performance. In a recent study, Beerendonk, Mejías et al. show that peak performance in decision-making tasks is reached at moderate levels of arousal. They also propose a neurobiologically informed computational model that can explain the inverted-U-shaped relationship.PMID:38570252 | DOI:10.1016/j.tics.2024.03.011 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - April 3, 2024 Category: Neuroscience Authors: Sander Nieuwenhuis Source Type: research
Computational role of structure in neural activity and connectivity
Trends Cogn Sci. 2024 Mar 28:S1364-6613(24)00056-1. doi: 10.1016/j.tics.2024.03.003. Online ahead of print.ABSTRACTOne major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by se...
Source: Trends Cogn Sci - March 29, 2024 Category: Neuroscience Authors: Srdjan Ostojic Stefano Fusi Source Type: research
Computational role of structure in neural activity and connectivity
Trends Cogn Sci. 2024 Mar 28:S1364-6613(24)00056-1. doi: 10.1016/j.tics.2024.03.003. Online ahead of print.ABSTRACTOne major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by se...
Source: Trends Cogn Sci - March 29, 2024 Category: Neuroscience Authors: Srdjan Ostojic Stefano Fusi Source Type: research
Computational role of structure in neural activity and connectivity
Trends Cogn Sci. 2024 Mar 28:S1364-6613(24)00056-1. doi: 10.1016/j.tics.2024.03.003. Online ahead of print.ABSTRACTOne major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by se...
Source: Trends Cogn Sci - March 29, 2024 Category: Neuroscience Authors: Srdjan Ostojic Stefano Fusi Source Type: research
Computational role of structure in neural activity and connectivity
Trends Cogn Sci. 2024 Mar 28:S1364-6613(24)00056-1. doi: 10.1016/j.tics.2024.03.003. Online ahead of print.ABSTRACTOne major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by se...
Source: Trends Cogn Sci - March 29, 2024 Category: Neuroscience Authors: Srdjan Ostojic Stefano Fusi Source Type: research
Aphantasia and hyperphantasia: exploring imagery vividness extremes
Trends Cogn Sci. 2024 Mar 9:S1364-6613(24)00034-2. doi: 10.1016/j.tics.2024.02.007. Online ahead of print.ABSTRACTThe vividness of imagery varies between individuals. However, the existence of people in whom conscious, wakeful imagery is markedly reduced, or absent entirely, was neglected by psychology until the recent coinage of 'aphantasia' to describe this phenomenon. 'Hyperphantasia' denotes the converse - imagery whose vividness rivals perceptual experience. Around 1% and 3% of the population experience extreme aphantasia and hyperphantasia, respectively. Aphantasia runs in families, often affects imagery across sever...
Source: Trends Cogn Sci - March 28, 2024 Category: Neuroscience Authors: Adam Zeman Source Type: research
Aphantasia and hyperphantasia: exploring imagery vividness extremes
Trends Cogn Sci. 2024 Mar 9:S1364-6613(24)00034-2. doi: 10.1016/j.tics.2024.02.007. Online ahead of print.ABSTRACTThe vividness of imagery varies between individuals. However, the existence of people in whom conscious, wakeful imagery is markedly reduced, or absent entirely, was neglected by psychology until the recent coinage of 'aphantasia' to describe this phenomenon. 'Hyperphantasia' denotes the converse - imagery whose vividness rivals perceptual experience. Around 1% and 3% of the population experience extreme aphantasia and hyperphantasia, respectively. Aphantasia runs in families, often affects imagery across sever...
Source: Trends Cogn Sci - March 28, 2024 Category: Neuroscience Authors: Adam Zeman Source Type: research
Infants and markers: reply to Taylor and Bremner
Trends Cogn Sci. 2024 Mar 7:S1364-6613(24)00052-4. doi: 10.1016/j.tics.2024.02.010. Online ahead of print.NO ABSTRACTPMID:38521637 | DOI:10.1016/j.tics.2024.02.010 (Source: Trends Cogn Sci)
Source: Trends Cogn Sci - March 23, 2024 Category: Neuroscience Authors: Tim Bayne Joel Frohlich Rhodri Cusack Julia Moser Lorina Naci Source Type: research
