scholarly journals Cognition Without Neural Representation: Dynamics of a Complex System

2022 ◽  
Vol 12 ◽  
Author(s):  
Inês Hipólito
Keyword(s):  
Complex System ◽  
Dynamical Systems ◽  
Systems Theory ◽  
Cognitive Neuroscience ◽  
Computational Models ◽  
Neural Representation ◽  
Information Representation ◽  
Causal Modelling ◽  
Enactive Cognitive Science ◽  
The Brain

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

scholarly journals What does the interactive brain hypothesis mean for social neuroscience? A dialogue

2016 ◽  
Vol 371 (1693) ◽  
pp. 20150379 ◽  
Author(s):  
Hanne De Jaegher ◽  
Ezequiel Di Paolo ◽  
Ralph Adolphs
Keyword(s):  
Social Cognition ◽  
Dynamical Systems ◽  
Social Environment ◽  
Cognitive Neuroscience ◽  
Social Neuroscience ◽  
Phenomenological Philosophy ◽  
The Social ◽  
New Directions ◽  
The Brain ◽  
And Robotics

A recent framework inspired by phenomenological philosophy, dynamical systems theory, embodied cognition and robotics has proposed the interactive brain hypothesis (IBH). Whereas mainstream social neuroscience views social cognition as arising solely from events in the brain, the IBH argues that social cognition requires, in addition, causal relations between the brain and the social environment. We discuss, in turn, the foundational claims for the IBH in its strongest form; classical views of cognition that can be raised against the IBH; a defence of the IBH in the light of these arguments; and a response to this. Our goal is to initiate a dialogue between cognitive neuroscience and enactive views of social cognition. We conclude by suggesting some new directions and emphases that social neuroscience might take.

scholarly journals Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lin Gan ◽  
Mu Zhang ◽  
Jiajia Jiang ◽  
Fajie Duan
Keyword(s):  
Complex System ◽  
Cognitive Neuroscience ◽  
Auditory Stimulation ◽  
External Information ◽  
Eeg Signals ◽  
Baseline Drift ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
The Brain

People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual-auditory stimulation on electroencephalogram (EEG) of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated.

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

Concepts and Compositionality: In Search of the Brain's Language of Thought

2020 ◽  
Vol 71 (1) ◽  
pp. 273-303 ◽  
Author(s):  
Steven M. Frankland ◽  
Joshua D. Greene
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Neuroscience ◽  
Default Mode Network ◽  
Computational Models ◽  
Language Of Thought ◽  
Default Mode ◽  
Episodic Simulation ◽  
High Level ◽  
The Brain ◽  
Language Of Thought Hypothesis

Imagine Genghis Khan, Aretha Franklin, and the Cleveland Cavaliers performing an opera on Maui. This silly sentence makes a serious point: As humans, we can flexibly generate and comprehend an unbounded number of complex ideas. Little is known, however, about how our brains accomplish this. Here we assemble clues from disparate areas of cognitive neuroscience, integrating recent research on language, memory, episodic simulation, and computational models of high-level cognition. Our review is framed by Fodor's classic language of thought hypothesis, according to which our minds employ an amodal, language-like system for combining and recombining simple concepts to form more complex thoughts. Here, we highlight emerging work on combinatorial processes in the brain and consider this work's relation to the language of thought. We review evidence for distinct, but complementary, contributions of map-like representations in subregions of the default mode network and sentence-like representations of conceptual relations in regions of the temporal and prefrontal cortex.

scholarly journals Neural differentiation of incorrectly predicted memories

2016 ◽  
Author(s):  
Ghootae Kim ◽  
Kenneth A. Norman ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Neural Differentiation ◽  
Computational Models ◽  
Activity Patterns ◽  
Neural Representation ◽  
Memory Representation ◽  
Neural Connections ◽  
Sequence Of Events ◽  
Network Simulations ◽  
The Brain ◽  
Target Memory

AbstractWhen an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here we explore what happens when such previously mispredicted items are later re-encountered. According to prior neural network simulations, this sequence of events - misprediction and subsequent restudy - should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context, and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item‘s neural representation away from the neural representation of the previous context. We tested this hypothesis using fMRI, by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared to items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Thus, the consequences of prediction error go beyond memory weakening: If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening.SignificanceCompetition between overlapping memories leads to weakening of non-target memories over time, making it easier to access target memories. However, a non-target memory in one context might become a target memory in another context. How do such memories get re-strengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.

THE DYNAMICS OF EMBODIED COGNITION

2010 ◽  
Vol 20 (04) ◽  
pp. 943-972 ◽  
Author(s):  
SCOTT HOTTON ◽  
JEFF YOSHIMI
Keyword(s):  
Neural Networks ◽  
Dynamical Systems ◽  
Embodied Cognition ◽  
Systems Theory ◽  
Open System ◽  
The Body ◽  
Closed Systems ◽  
The Brain ◽  
Embodied Agent

Historically cognition was understood as the result of processes occurring solely in the brain. Recently, however, cognitive scientists and philosophers studying "embodied" or "situated" cognition have begun emphasizing the role of the body and environment in which brains are situated, i.e. they view the brain as an "open system". However, these theorists frequently rely on dynamical systems which are traditionally viewed as closed systems. We address this tension by extending the framework of dynamical systems theory. We show how structures which appear in the state space of an embodied agent differ from those that appear in closed systems, and we show how these structures can be used to model representational processes in embodied agents. We focus on neural networks as models of embodied cognition.

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