Institute of Psychology

This article discusses how the analysis of interactions between action and awareness allows us to better understand the mechanisms of visual awareness. We argue that action is one of several factors that influence visual awareness and we provide a number of examples. We also discuss the possible mechanisms that underlie these influences on both the cognitive and the neural levels. We propose that action affects visual awareness for the following reasons: (1) it serves as additional information in the process of evidence accumulation; (2) it restricts the number of alternatives in the decisional process; (3) it enables error detection and performance monitoring; and (4) it triggers attentional mechanisms that modify stimulus perception. We also discuss the possible neuronal mechanisms of the aforementioned effects, including feedback-dependent prefrontal cortex modulation of the activity of visual areas, error-based modulation, interhemispheric inhibition of motor cortices, and attentional modulation of visual cortex activity triggered by motor processing.

The article presents a research study on recognizing therapy progress among children with autism spectrum disorder. The progress is recognized on the basis of behavioural data gathered via five specially designed tablet games. Over 180 distinct parameters are calculated on the basis of raw data delivered via the game flow and tablet sensors-i.e. touch screen, accelerometer and gyroscope. The results obtained confirm the possibility of recognizing progress in particular areas of development. The recognition accuracy exceeds 80%. Moreover, the study identifies a subset of parameters which appear to be better predictors of therapy progress than others. The proposed method-consisting of data recording, parameter calculation formulas and prediction models-might be implemented in a tool to support both therapists and parents of autistic children. Such a tool might be used to monitor the course of the therapy, modify it and report its results. Autism is a complex developmental disorder that influences the ability to communicate and learn. Autism is nowadays a growing challenge, as the number of children diagnosed with autism is rising worldwide 1. The disorder exhibits a spectrum of symptoms that might range from mild to severe in a particular case, varying from skill to skill and from child to child. This makes diagnosis and therapy progress evaluation difficult and at the same time crucial for the effectiveness of the therapy. Early identification and proper therapy translates into a greater chance for preventing a person with autism from social exclusion. Therefore, any idea that might improve therapeutic practice is worth investigating. This paper presents one such idea, which incorporates the analysis of behavioural characteristics observed in autistic children while they play specially designed tablet games. This interdisciplinary study combines computer science and special pedagogy, by applying computer technologies and machine learning methods in the process of therapy for autistic children. Computer technologies may support both the diagnosis of autism and related therapy, although most solutions so far refer to the process of therapy. There are numerous applications designed for individuals with autism. These applications focus on particular issues by teaching specific skills-e.g. expressing needs, learning certain behaviours 2 , improving verbal communication, answering questions, interacting with other people in typical situations 3 , recognizing and expressing emotions 4-6. These solutions take advantage of the fact, that children with autism are usually enthusiastic about tasks supported by computer technology, which offers a predictable framework without causing stress 2. Another group of tools is designed for therapists. One of the most commonly used solutions are computer versions of standardized questionnaires that evaluate an individual's state 7. Another popular way of supporting the therapy is to provide the experts with video recordings of the children's behaviour. However, there are also some more sophisticated solutions, which may be used not only in therapy but also in the diagnostic process. Some of them are based on eye tracking and they reveal that people with autism rarely focus their attention on the area around the eyes when compared to other parts of the face. This idea can be implemented in the early diagnosis of ASD 8,9. Others apply camera-based motion capture and automatic analysis of recordings presenting individuals performing tasks that are commonly evaluated by specialists 10. Another interesting idea is based on the analysis of individual hand movements while indicating objects on a touch screen 11. Moreover, machine learning algorithms have been applied to predict the state of a person on the basis of information taken from commonly used diagnostic questionnaires 12,13. These are usually experimental methods used in laboratories, often tested on small groups. In many cases, the use of these technologies requires specialized equipment, which constitutes their major

Introduction Recent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3–5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom. Methods and analysis This is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is n...

A meta-analysis was carried out to demonstrate the existence of publication bias in research on the relationship between measures of fluid intelligence and working memory. Reanalysis of data collected in Ackerman, Beier, & Boyle, 2005 was conducted. A heterogeneous distribution of correlation coefficients in the absence of asymmetry in the distribution of coefficients was observed. According to the author of the analysis, there are no arguments for the presence of publication bias in this particular set of results drawn from research on intelligence and working memory.

A meta-analysis was carried out to demonstrate the existence of publication bias in research on the relationship between measures of fluid intelligence and working memory. Reanalysis of data collected in Ackerman, Beier, & Boyle, 2005 was conducted. A heterogeneous distribution of correlation coefficients in the absence of asymmetry in the distribution of coefficients was observed. According to the author of the analysis, there are no arguments for the presence of publication bias in this particular set of results drawn from research on intelligence and working memory.

Consciousness can be measured in various ways, but different measures often yield different conclusions about the extent to which awareness relates to performance. Here, we compare five different subjective measures of awareness in the context of an artificial grammar learning task. Participants (N=217) expressed their subjective awareness of rules using one of five different scales: confidence ratings (CRs), post-decision wagering (PDW), feeling of warmth (FOW), rule awareness (RAS), and continuous scale (SDS). All scales were equally sensitive to conscious knowledge. PDW, however, was affected by risk aversion, and both RAS and SDS applied different minimal criteria for rule awareness. CR seems to capture the largest range of consciousness, but failed to indicate unconscious knowledge with the guessing criterion. We close by discussing the theoretical implications of scale sensitivity and propose that CR's unique features enable (in conjunction with RAS and FOW) a finer assessment of subjective states of awareness.Copyright © 2012 Elsevier Inc. All rights reserved.

Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and therefore may bias performance on the primary task in different ways. Here, we used a random number generation (RNG) task, which may allow for a closer monitoring of a participant’s engagement in a secondary task than the popular secondary task in sequence learning studies: tone counting (TC). In the first two experiments, we investigated the interference associated with performing RNG concurrently with a serial reaction time (SRT) task. In a third experiment, we compared the effects of RNG and TC. In all three experiments, we directly evaluated participants’ knowledge of the sequence with a subsequent sequence generation task. Sequence learning was consistently observed in all experiments, but was impaired under dual-task conditions. Most importantly, our data suggest that RNG is more demanding and impairs learning to a greater extent than TC. Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation. Our studies indicate that RNG is a promising task to explore the involvement of attention in the SRT task.