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Cognitive neurosciences have made significant progress in learning about brain activity in situat... more Cognitive neurosciences have made significant progress in learning about brain activity in situated cognition, thanks to adopting stimuli that simulate immersion in naturalistic conditions instead of isolated artificial stimuli. In particular, the use of films in neuroscientific experiments, a paradigm often referred to as neurocinematics, has contributed to this success. The use of cinematic stimuli, however, has also revealed a fundamental shortcoming of neuroimaging studies: The lack of conceptual and methodological means to handle the viewers' experience of narrative events in their temporally extended contexts in the scale of full cinematic narrative, not to mention life itself. In order to give a conceptual structure to the issue of temporal contexts, we depart from the neurophenomenological approach to time consciousness by neurobiologist Francisco Varela, which in turn builds on Husserl's phenomenology of time. More specifically, we will discuss the experience of narrative tension, determined by backward-looking conceptualizing retention, and forward-looking anticipatory protention. Further, this conceptual structure is built into a preliminary mathematical model, simulating the dynamics of decaying and refreshing memory traces that aggregates a retentive perspective for each moment of nowness, which in turn may trigger anticipations for coming events, in terms of Varela and Husserl, protentions. The present tentative mathematical model is constructed using simple placeholder functions, with the intention that they would eventually be replaced by models based on empirical observations on the psychological capabilities that support narrative sensemaking. The final goal is a model that successfully simulates the way how the memory system maintains narrative tension beyond the transient nowness window, and thereby allows mappings to observed brain activity with a rich temporal system of narrative contexts.
One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to int... more One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed
brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies
have shown less inter-subject synchronization across viewers of random video footage than story-driven films,
new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between
our fMRI data collected during viewing of a deliberately non-narrative silent film ‘At Land’ by Maya Deren
(1944) and its annotated content, we combined the method of elastic-net regularization with the modeldriven
linear regression and the well-established data-driven independent component analysis (ICA) and
inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI)
time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of
film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear
regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared
against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Nonparametric
permutation testing scheme was applied to evaluate the statistical significance of regression. We
found statistically significant correlation between the annotationmodel and 9 ICs out of 40 ICs. Regression analysis
was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression
analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal
lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression
since it detected a larger number of significant ICs and ROIs. Along with the ISC rankingmethods, our regression
analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated
cinematic features. The novelty of our method is – in comparison to the hypothesis-driven manual preselection
and observation of some individual regressors biased by choice – in applying data-driven approach to
all content features simultaneously. We found especially the combination of regularized regression and ICA
useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and
correlated features.
© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Cognitive neurosciences have made significant progress in learning about brain activity in situat... more Cognitive neurosciences have made significant progress in learning about brain activity in situated cognition, thanks to adopting stimuli that simulate immersion in naturalistic conditions instead of isolated artificial stimuli. In particular, the use of films in neuroscientific experiments, a paradigm often referred to as neurocinematics, has contributed to this success. The use of cinematic stimuli, however, has also revealed a fundamental shortcoming of neuroimaging studies: The lack of conceptual and methodological means to handle the viewers' experience of narrative events in their temporally extended contexts in the scale of full cinematic narrative, not to mention life itself. In order to give a conceptual structure to the issue of temporal contexts, we depart from the neurophenomenological approach to time consciousness by neurobiologist Francisco Varela, which in turn builds on Husserl's phenomenology of time. More specifically, we will discuss the experience of narrative tension, determined by backward-looking conceptualizing retention, and forward-looking anticipatory protention. Further, this conceptual structure is built into a preliminary mathematical model, simulating the dynamics of decaying and refreshing memory traces that aggregates a retentive perspective for each moment of nowness, which in turn may trigger anticipations for coming events, in terms of Varela and Husserl, protentions. The present tentative mathematical model is constructed using simple placeholder functions, with the intention that they would eventually be replaced by models based on empirical observations on the psychological capabilities that support narrative sensemaking. The final goal is a model that successfully simulates the way how the memory system maintains narrative tension beyond the transient nowness window, and thereby allows mappings to observed brain activity with a rich temporal system of narrative contexts.
One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to int... more One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed
brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies
have shown less inter-subject synchronization across viewers of random video footage than story-driven films,
new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between
our fMRI data collected during viewing of a deliberately non-narrative silent film ‘At Land’ by Maya Deren
(1944) and its annotated content, we combined the method of elastic-net regularization with the modeldriven
linear regression and the well-established data-driven independent component analysis (ICA) and
inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI)
time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of
film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear
regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared
against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Nonparametric
permutation testing scheme was applied to evaluate the statistical significance of regression. We
found statistically significant correlation between the annotationmodel and 9 ICs out of 40 ICs. Regression analysis
was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression
analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal
lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression
since it detected a larger number of significant ICs and ROIs. Along with the ISC rankingmethods, our regression
analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated
cinematic features. The novelty of our method is – in comparison to the hypothesis-driven manual preselection
and observation of some individual regressors biased by choice – in applying data-driven approach to
all content features simultaneously. We found especially the combination of regularized regression and ICA
useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and
correlated features.
© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).