The development of causal categorization

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

Quantifying the psychopathic stare: Automated assessment of head motion is related to antisocial traits in forensic interviews

Clinicians have long noted that individuals with high psychopathic traits exhibit unique interpersonal style often observable during forensic interviews. Here we develop an automated approach for quantifying head dynamics during video-recorded naturalistic clinical interviews. We expected head dynamics would be related to psychopathic traits. As predicted, dwell times indicate that those with higher levels of psychopathic traits are characterized by more stationary head positions, focused directly towards the camera/interviewer, than were individuals low in psychopathic traits. These associations were primarily driven by developmental/antisocial features of psychopathy, indicating that those with severe and life-course persistent antisocial behavior exhibit more rigid and focused orienting of their head during interpersonal communication. These results encourage more research into the automated quantification of behavioral manifestations of personality to support clinical observations that psychopaths exhibit unique qualities in non-verbal interpersonal communication.     

Is cognition seasonal?

The idea that our mental life is more sluggish in winter is apparently seductive. This article reviews the evidence for the claim that there is a seasonal cycle in human cognition. Several methodologies have been used with participants such as over-wintering scientists in Antarctica, residents of polar regions, and patients with seasonal psychopathology and there is no clear tendency for cognition to be impaired in winter. Some large-scale public health studies report seasonality of cognition but are inconsistent regarding when in the year there is a peak and trough. Claims that human cognition has an annual cycle should be regarded as speculative and the burden of proof must be borne by anyone inclined to claim that such a cycle exists. An analysis of the influence of some key studies shows that their results are misleadingly cited and that there is resistance to the null conclusion.     

The update of semantic memories in amnestic mild cognitive impairment

It is still controversial to what extent neocortical consolidated memories are susceptible of change by processes of reconsolidation and transformation throughout experience, and whether the medial temporal lobes are necessary for this update of semantic consolidated memories, as they are for episodic remembering. We hypothesize that patients with amnestic mild cognitive impairment (aMCI) who have deficits in episodic memory may also have difficulties in updating information on added new features of objects. Sixteen participants with aMCI and 20 healthy control participants performed a semantic word-to-picture task, in which they were asked to identify as belonging to a given semantic category NEW objects, that have incorporated novel features, as well as OLD items, semantically and visually SIMILAR items and UNRELATED items. Patients with aMCI made a greater percentage of errors than healthy controls. Participants globally made greater percentages of errors in difficult types of items, namely NEW and SIMILAR, as compared to easier ones, OLD and UNRELATED. Importantly, an item by diagnostic group interaction effect was observed, and post hoc analysis showed that patients with aMCI made a higher percentage of errors than controls in NEW items only. In conclusion, patients with aMCI had a particular difficulty in identifying the NEW items of the word-to-picture task as compared to the control participants, supporting the concept of a flexible and dynamic conceptual knowledge system, involving the update of semantic memories and the integration of new attributes in a constant transformation process, which is impaired in these patients.     

Valence,form, and content of self-talk predict sport type and level of performance

In this paper, we aimed to test whether we could predict sport type (badminton or running) and marathon proficiency from the valence, form, and content of the athletes’ self-reported inner speech. Additionally, we wanted to assess the difference between self-talk during high intensity and low intensity exercise. The present study corroborated existing research – we were able to predict both sport type in Study 1 and intensity level as well as marathon proficiency in Study 2 from questionnaire data using machine learning models. In Study 1, we found that badminton players engage more in worry and anxiety-control while runners are more prone to task disengagement. Interestingly, it seemed in Study 2 that the more participants engaged in condensed, positive, and repetitive self-talk when not pushing themselves, the slower their fastest marathons and half marathons were. We discuss potential explanations for these findings and make suggestions for future research.     

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

GoalTo apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.MethodsCognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.ConclusionApproaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.     

No evidence for infants’ contingency detection as a precursor of toddlers’ self-recognition

The aim of the present study was to assess the relationship between infants’ contingency preference and detection in the first year of life and toddlers’ mirror and video self-recognition in the second and third year of life in a longitudinal study (N = 113). Six- and 9-month-olds’ preference for a noncontingent over a perfect contingent view (contingency preference) and their differentiation between the two views – indicated by longer looking times to either view – (contingency detection) were assessed in two contingency tasks. A mirror-face-recognition task and a mirror-leg-recognition task were conducted at 18 months. A video-face-recognition task and a mirror-leg-recognition task were conducted at 26 months. Results revealed no predictive relationships between infants’ contingency preference and detection in the first year of life and their ability to recognize themselves in a mirror or on a video monitor in the second and third year of life. This finding supports the notion that self-recognition emerges independently from the experience of contingencies (Bischof-Köhler, 1991, 2012). Thus, a representation of the self seems to rely on more than a specific developmental pathway leading from contingency preference and detection to self-recognition.     

Additional considerations when measuring adaptive control in conflict tasks

A recent review article by Braem et al. (2019, Trends in Cognitive Sciences) summarises some of the major challenges associated with investigating the dynamics of cognitive control in conflict tasks and recommends steps that researchers can take to minimise these challenges. This commentary highlights two further considerations for researchers studying the dynamics of cognitive control. First, researchers should consider analysing effects stemming from trial n - 2 to identify (a) how the congruency sequence effect emerges over a longer series of trials and (b) whether residual feature-integration effects remain in confound-minimised designs. Second, data from hand-tracking studies indicates that the dynamics observed in button-press measures of response time reflect the functioning of dissociable processes. Consequently, researchers should consider how the various manipulations used to investigate the dynamics of cognitive control might impact different processes underlying performance.