Optic Flow Drives Human Visuo-Locomotor Adaptation

SummaryTwo strategies can guide walking to a stationary goal: (1) the optic-flow strategy, in which one aligns the direction of locomotion or “heading” specified by optic flow with the visual goal [1, 2]; and (2) the egocentric-direction strategy, in which one aligns the locomotor axis with the perceived egocentric direction of the goal [3, 4] and in which error results in optical target drift [5]. Optic flow appears to dominate steering control in richly structured visual environments [2, 6–8], whereas the egocentric- direction strategy prevails in visually sparse environments [2, 3, 9]. Here we determine whether optic flow also drives visuo-locomotor adaptation in visually structured environments. Participants adapted to walking with the virtual-heading direction displaced 10° to the right of the actual walking direction and were then tested with a normally aligned heading. Two environments, one visually structured and one visually sparse, were crossed in adaptation and test phases. Adaptation of the walking path was more rapid and complete in the structured environment; the negative aftereffect on path deviation was twice that in the sparse environment, indicating that optic flow contributes over and above target drift alone. Optic flow thus plays a central role in both online control of walking and adaptation of the visuo-locomotor mapping

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study:a machine learning approach

Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF &gt;= 65) and poor (GAF &lt; 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.</p

Visual requirements for reading: The importance of a large field of view in reading with a magnifier

It is assumed that too low values for optimal field of view in magnifier reading were obtained in the past by applying the &apos;Drifting Text &apos; Technique [1,3,4] in which the subjects had no control of the movements of the magnified image. On the basis of the view that reading involves alternating sequences of locating and recognizing textual information [9], it is argued that part of the magnified image is required for the movement control of the visual display. Higher values are predicted than the 1-6 characters proposed in the model of Whittaker and Lovie-Kitchin [1]. 14 Male and female subjects with a macular degeneration ranging in age from 20 to 82 years of age participated in an experiment to determine the optimal field of view in CCTV-magnifier reading. Large effects of width and height are found on reading rate and the data suggest that the optimal values are even higher than the maximum value of 12 characters that could be technically realized in the present experiment. Large age effects are found in both reading rate and smoothness of control of the platform. The data on the movements of the platform and the eyes are discussed. It is concluded that the elderly subject applied another strategy to move the platform than the other subjects. In all subjects large variations are observed in the velocity of transportation of the platform. It is assumed that these variations signal the flexibility of the motor control process that is required to adapt the reading process as a whole to fluctuations in the comprehension process

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach

Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5–67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient’s and clinicians' needs in prognostication