Perception and Cognition Are Largely Independent, but Still Affect Each Other in Systematic Ways: Arguments from Evolution and the Consciousness-Attention Dissociation

The main thesis of this paper is that two prevailing theories about cognitive penetration are too extreme, namely, the view that cognitive penetration is pervasive and the view that there is a sharp and fundamental distinction between cognition and perception, which precludes any type of cognitive penetration. These opposite views have clear merits and empirical support. To eliminate this puzzling situation, we present an alternative theoretical approach that incorporates the merits of these views into a broader and more nuanced explanatory framework. A key argument we present in favor of this framework concerns the evolution of intentionality and perceptual capacities. An implication of this argument is that cases of cognitive penetration must have evolved more recently and that this is compatible with the cognitive impenetrability of early perceptual stages of processing information. A theoretical approach that explains why this should be the case is the consciousness and attention dissociation framework. The paper discusses why concepts, particularly issues concerning concept acquisition, play an important role in the interaction between perception and cognition

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness

On the evolution of conscious attention

This paper aims to clarify the relationship between consciousness and attention through theoretical considerations about evolution. Specifically, we will argue that the empirical findings on attention and the basic considerations concerning the evolution of the different forms of attention demonstrate that consciousness and attention must be dissociated regardless of which definition of these terms one uses. To the best of our knowledge, no extant view on the relationship between consciousness and attention has this advantage. Because of this characteristic, this paper presents a principled and neutral way to settle debates concerning the relationship between consciousness and attention, without falling into disputes about the meaning of these terms. A decisive conclusion of this approach is that extreme views on the relationship between consciousness and attention must be rejected, including identity and full dissociation views. There is an overlap between the two within conscious attention, but developing a full understanding of this mechanism requires further empirical investigations

The Role of Information in Consciousness

This article comprehensively examines how information processing relates to attention and consciousness. We argue that no current theoretical framework investigating consciousness has a satisfactory and holistic account of their informational relationship. Our key theoretical contribution is showing how the dissociation between consciousness and attention must be understood in informational terms in order to make the debate scientifically sound. No current theories clarify the difference between attention and consciousness in terms of information. We conclude with two proposals to advance the debate. First, neurobiological homeostatic processes need to be more explicitly associated with conscious information processing, since information processed through attention is algorithmic, rather than being homeostatic. Second, to understand subjectivity in informational terms, we must define information uniqueness in consciousness (e.g., irreproducible information, biologically encrypted information). These approaches could help cognitive scientists better understand conflicting accounts of the neural correlates of consciousness and work toward a more unified theoretical framework

Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study

Parkinson’s disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test. Minimum Redundancy Maximum Relevance (mRMR) and sequential feature selection with both forward (SFS) and backward (SBS) propagation directions were used in this study. The number of selected features was narrowed down from 201 to 4–15 features by applying SFS and mRMR methods. From the methods compared in this study, the highest accuracy for individual steps was achieved with SFS (7 features) and Naive Bayes classifier (accuracy 75.3%), and the second highest accuracy with SFS (4 features) and k Nearest neighbours (accuracy 75.1%). Leave-one-subject-out cross-validation was used in the analysis. For the overall classification of each subject, which was based on the majority vote of the classified steps, k Nearest Neighbors provided the most accurate result with an accuracy of 84.5% and an error rate of 15.5%. This study shows the differences in feature selection methods and classifiers and provides generalizations for optimizing methodologies for smartphone-based monitoring of PD patients. The results are promising for further developing the analysis system for longer measurements carried out in free-living conditions.Peer reviewe

Management and outcome of pregnancies in women with red cell isoimmunization: a 15-year observational study from a tertiary care university hospital

Isoimmunization; Newborn hemolytic disease; Intrauterine transfusionIsoinmunización; Enfermedad hemolítica del recién nacido; Transfusión intrauterinaIsoimmunització; Malaltia hemolítica del nounat; Transfusió intrauterinaBackground: The aims of this study were to determine the prevalence of the different anti-erythrocytic alloantibodies, to describe pregnancy outcomes according to a low-risk and high-risk classification for fetal anemia and to determine the factors that influence adverse perinatal outcomes. Methods: This retrospective observational study included women referred to our center following the identification of maternal anti-erythrocytic alloantibodies between 2002 and 2017. Pregnancies were classified as high risk for fetal anemia in cases with clinically significant antibodies, no fetal-maternal compatibility and titers ≥1:16 or any titration in cases of Kell system incompatibility. In high-risk pregnancies, maternal antibody titration and the fetal middle cerebral artery peak systolic velocity (MCA-PSV) were monitored. Low-risk pregnancies underwent routine pregnancy follow-up. Results: Maternal antibodies were found in 337 pregnancies, and 259 (76.9%) of these antibodies were clinically significant. The most frequent antibodies were anti-D (53%) and anti-K (19%). One hundred forty-three pregnancies were classified as low risk for fetal anemia, 65 (25%) cases were classified as no fetal-maternal incompatibility, 78 had clinically nonsignificant antibodies, 4 (2.8%) resulted in first-trimester pregnancy loss, and 139 (97.2%) resulted in livebirths. Of the 194 high-risk pregnancies, 38 had titers 1.5 MoM, resulting in 3 intrauterine deaths, 6 terminations and 48 livebirths. Ninety-two intrauterine transfusions were performed in 45 fetuses (87% anti-D). Adverse outcomes were related to a MCA-PSV > 1.5 MoM (p < 0.001), hydrops (p < 0.001) and early gestational age at first transfusion (p = 0.029) Conclusion: Anti-D remains the most common antibody in fetuses requiring intrauterine transfusion. A low or high-risk classification for fetal anemia based on the type of antibody, paternal phenotype and fetal antigen allows follow-up of the pregnancy accordingly, with good perinatal outcomes in the low-risk group. In the high-risk group, adverse perinatal outcomes are related to high MCA-PSV, hydrops and early gestational age at first transfusion

Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study

Parkinson's disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test. Minimum Redundancy Maximum Relevance (mRMR) and sequential feature selection with both forward (SFS) and backward (SBS) propagation directions were used in this study. The number of selected features was narrowed down from 201 to 4-15 features by applying SFS and mRMR methods. From the methods compared in this study, the highest accuracy for individual steps was achieved with SFS (7 features) and Naive Bayes classifier (accuracy 75.3%), and the second highest accuracy with SFS (4 features) and k Nearest neighbours (accuracy 75.1%). Leave-one-subject-out cross-validation was used in the analysis. For the overall classification of each subject, which was based on the majority vote of the classified steps, k Nearest Neighbors provided the most accurate result with an accuracy of 84.5% and an error rate of 15.5%. This study shows the differences in feature selection methods and classifiers and provides generalizations for optimizing methodologies for smartphone-based monitoring of PD patients. The results are promising for further developing the analysis system for longer measurements carried out in free-living conditions

Motion and position shifts induced by the double-drift stimulus are unaffected by attentional load.

The double-drift stimulus produces a strong shift in apparent motion direction that generates large errors of perceived position. In this study, we tested the effect of attentional load on the perceptual estimates of motion direction and position for double-drift stimuli. In each trial, four objects appeared, one in each quadrant of a large screen, and they moved upward or downward on an angled trajectory. The target object whose direction or position was to be judged was either cued with a small arrow prior to object motion (low attentional load condition) or cued after the objects stopped moving and disappeared (high attentional load condition). In Experiment 1, these objects appeared 10° from the central fixation, and participants reported the perceived direction of the target's trajectory after the stimulus disappeared by adjusting the direction of an arrow at the center of the response screen. In Experiment 2, the four double-drift objects could appear between 6 ° and 14° from the central fixation, and participants reported the location of the target object after its disappearance by moving the position of a small circle on the response screen. The errors in direction and position judgments showed little effect of the attentional manipulation-similar errors were seen in both experiments whether or not the participant knew which double-drift object would be tested. This suggests that orienting endogenous attention (i.e., by only attending to one object in the precued trials) does not interact with the strength of the motion or position shifts for the double-drift stimulus