Paying attention to meaning

Several paradigms show that responses to one event compromise responses to a second event for around 500 ms. Such effects are generally attributed to attentional capacity limitations associated with processing information in the first event. In a task in which targets could be distinguished only by their meaning, we varied the semantic relationship between distractors and targets following at different lags. Semantic relatedness alone produced a classic attentional blink. We conclude by discussing how attention theory might best accommodate these new effects

Motor (but not auditory) attention affects syntactic choice

Understanding the determinants of syntactic choice in sentence production is a salient topic in psycholinguistics. Existing evidence suggests that syntactic choice results from an interplay between linguistic and non-linguistic factors, and a speaker’s attention to the elements of a described event represents one such factor. Whereas multimodal accounts of attention suggest a role for different modalities in this process, existing studies examining attention effects in syntactic choice are primarily based on visual cueing paradigms. Hence, it remains unclear whether attentional effects on syntactic choice are limited to the visual modality or are indeed more general. This issue is addressed by the current study. Native English participants viewed and described line drawings of simple transitive events while their attention was directed to the location of the agent or the patient of the depicted event by means of either an auditory (monaural beep) or a motor (unilateral key press) lateral cue. Our results show an effect of cue location, with participants producing more passive-voice descriptions in the patient-cued conditions. Crucially, this cue location effect emerged in the motor-cue but not (or substantially less so) in the auditory-cue condition, as confirmed by a reliable interaction between cue location (agent vs. patient) and cue type (auditory vs. motor). Our data suggest that attentional effects on the speaker’s syntactic choices are modality-specific and limited to the visual and motor, but not the auditory, domain

From “Oh, OK” to “Ah, yes” to “Aha!”: Hyper-systemizing and the rewards of insight\ud

Hyper-systemizers are individuals displaying an unusually strong bias toward systemizing, i.e. toward explaining events and solving problems by appeal to mechanisms that do not involve intentions or agency. Hyper-systemizing in combination with deficit mentalizing ability typically presents clinically as an autistic spectrum disorder; however, the development of hyper-systemizing in combination with normal-range mentalizing ability is not well characterized. Based on a review and synthesis of clinical, observational, experimental, and neurofunctional studies, it is hypothesized that repeated episodes of insightful problem solving by systemizing result in attentional and motivational sensitization toward further systemizing via progressive and chronic deactivation of the default network. This hypothesis is distinguished from alternatives, and its correlational and causal implications are discussed. Predictions of the default-deactivation model accessible to survey-based instruments, standard cognitive measures and neurofunctional methods are outlined, and evidence pertaining to them considered

A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

Comparing the Online Learning Capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for Building Energy Management Systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities