The modal view in the cognitive and neural sciences holds that consciousness is necessary for abstract, symbolic, and rule-following computations. Hence, semantic processing of multiple-word expressions, and performing of abstract mathematical computations, are widely believed to require consciousness. We report a series of experiments in which we show that multiple-word verbal expressions can be processed outside conscious awareness and that multistep, effortful arithmetic equations can be solved unconsciously. All experiments used Continuous Flash Suppression to render stimuli invisible for relatively long durations (up to 2,000 ms). Where appropriate, unawareness was verified using both objective and subjective measures. The results show that novel word combinations, in the form of expressions that contain semantic violations, become conscious before expressions that do not contain semantic violations, that the more negative a verbal expression is, the more quickly it becomes conscious, and that subliminal arithmetic equations prime their results. These findings call for a significant update of our view of conscious and unconscious processes.nonconscious processes | automaticity | CFS T he scientific investigation of consciousness and the human unconscious is an ongoing interdisciplinary effort that is central to our understanding of the human mind. The goal is simple: to map the functions performed by nonconscious processes and the functions that are performed consciously, and to understand how these two sets of functions are implemented in the brain. The modal view in cognitive sciences associates consciousness with capabilities that are uniquely (or largely) human. Two prime examples of capabilities of this kind, which are cataloged among the greatest achievements of human culture, are complex language and abstract mathematics. It is not surprising then that the modal view holds that the semantic processing of multiple-word expressions and performing of abstract mathematical computations require consciousness (1-4). In more general terms, sequential rule-following manipulations of abstract symbols are thought to lie outside the capabilities of the human unconscious.This view has received extensive empirical support. Although numerous studies have documented processing of subliminally presented single units of meaning (e.g., a word or a number) (5-8) as well as unconscious retrieval of simple arithmetic facts (9-11), previous research has generally failed to document unconscious performance of functions that require multiple (and sequenced) rule-based operations on more than one abstract unit (12)(13)(14).[Recently, work by Ric and Muller (10) has shown that simple addition (adding two numbers with a sum that is not greater than six) can occur nonconsciously. Although addition of this sort does not require more than one operation, we find these data very encouraging in terms of the challenge that we propose here.]The present study challenges this modal view of consciousness and the unconscious. Specifically...
Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
Numerous emotion researchers have asked their study participants to attend to the distinct feelings of arousal and valence, and self-report and physiological data have supported the independence of the two. We examined whether this dissociation reflects introspection about distinct emotional qualia or the way in which valence is measured. With either valence (Experiment 1) or arousal (Experiment 2) as the primary focus, when valence was measured using a bipolar scale (ranging from negative to positive), it was largely dissociable from arousal. By contrast, when two separate unipolar scales of pleasant and unpleasant valence were used, their sum was equivalent to feelings of arousal and its autonomic correlates. The association (or dissociation) of valence and arousal was related to the estimation (or nonestimation) of mixed-valence experiences, which suggests that the distinction between valence and arousal may reflect less the nature of emotional experience and more how it is measured. These findings further encourage use of unipolar valence scales in psychological measurement.
Exposure models are needed to evaluate the chronic health effects of ambient ultrafine particles (<0.1 μm) (UFPs). We developed a land use regression model for ambient UFPs in Toronto, Canada using mobile monitoring data collected during summer/winter 2010-2011. In total, 405 road segments were included in the analysis. The final model explained 67% of the spatial variation in mean UFPs and included terms for the logarithm of distances to highways, major roads, the central business district, Pearson airport, and bus routes as well as variables for the number of on-street trees, parks, open space, and the length of bus routes within a 100 m buffer. There was no systematic difference between measured and predicted values when the model was evaluated in an external dataset, although the R(2) value decreased (R(2) = 50%). This model will be used to evaluate the chronic health effects of UFPs using population-based cohorts in the Toronto area.
The bipolar valence-arousal model of conscious experience of emotions is prominent in emotion research. In this work, we examine the validity of this model in the context of feelings elicited by visual stimuli. In particular, we examine whether arousal has a unique contribution over bivariate valence (separate measures for pleasure and displeasure) in explaining physiological arousal (electrodermal activity, EDA) and self-reported feelings at the level of item-specific responses across and within individuals. Our results suggest that self-reports of arousal have neither an advantage in predicting EDA nor make a unique contribution when valence is present in the model. Acceptance of the null hypothesis was confirmed with the use of the Bayesian information criterion. Arousal also showed no advantage over valence in predicting global feelings, but demonstrated a small unique component (1.5% to 4% of variance explained). These results have practical implications for both experimental design in the study of emotions and the underlying bases of their conscious experience.
Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure.
Departing from rule-based linguistic models, advances in deep learning resulted in a new type of autoregressive deep language models (DLMs). These models are trained using a self-supervised next word prediction task. We provide empirical evidence for the connection between autoregressive DLMs and the human language faculty using spoken narrative and electrocorticographic recordings. Behaviorally, we demonstrate that humans have a remarkable capacity for word prediction in natural contexts, and that, given a sufficient context window, DLMs can attain human-level prediction performance. Leveraging on DLM embeddings we demonstrate that the brain constantly and spontaneously predicts the identity of the next word in natural speech, hundreds of milliseconds before they are perceived. Finally, we demonstrate that contextual embeddings derived from autoregressive DLMs capture neural representations of the unique, context-specific meaning of words in the narrative. Our findings suggest that DLMs provides a novel biologically feasible computational framework for studying the neural basis of language.
Abstract. In studies that use subliminal presentations, participants may become aware of stimuli that are intended to remain subliminal. A common solution to this problem is to analyze the results of the group of participants for whom the stimuli remained subliminal. A recent article ( Shanks, 2017 ) argued that this method leads to a regression to the mean artifact, which may account for many of the observed effects. However, conceptual and statistical characteristics of the original publication lead to overestimation of the influence of the artifact. Using simulations, we demonstrate that this overestimation leads to the mistaken conclusion that regression to the mean accounts for nonconscious effects. We conclude by briefly outlining a new description of the influence of the artifact and how it should be statistically addressed.
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