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Cultural Schemas: What They Are, How to Find Them, and What to Do Once You’ve Caught One
Cultural schemas are a central cognitive mechanism through which culture affects action. In this article, we develop a theoretical model of cultural schemas that is better able to support empirical work, including inferential, sensitizing, and operational uses. We propose a multilevel framework centered on a high-level definition of cultural schemas that is sufficiently broad to capture its major sociological applications but still sufficiently narrow to identify a set of cognitive phenomena with key functional properties in common: cultural schemas are socially shared representations deployable in automatic cognition. We use this conception to elaborate the main theoretical properties of cultural schemas, and to provide clear criteria that distinguish them from other cultural or cognitive elements. We then propose a series of concrete tests empirical scholarship can use to determine if these properties apply. We also demonstrate how this approach can identify potentially faulty theoretical inferences present in existing work. Moving to a lower level of analysis, we elaborate how cultural schemas can be algorithmically conceptualized in terms of their building blocks. This leads us to recommend improvements to methods for measuring cultural schemas. We conclude by outlining questions for a broader research program.
Journal Article
I'm afraid, said the leaf
\"Picture book about how each plant and animal has a vital role in the ecosystem\"-- Provided by publisher.
CHILDBOOK
Hierarchical organization of cortical and thalamic connectivity
The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource
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, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network.
Using mouse lines in which subsets of neurons are genetically labelled, the authors provide generalized anatomical rules for connections within and between the cortex and thalamus.
Journal Article
The Watts Connectedness Scale: a new scale for measuring a sense of connectedness to self, others, and world
RationaleA general feeling of disconnection has been associated with mental and emotional suffering. Improvements to a sense of connectedness to self, others and the wider world have been reported by participants in clinical trials of psychedelic therapy. Such accounts have led us to a definition of the psychological construct of ‘connectedness’ as ‘a state of feeling connected to self, others and the wider world’. Existing tools for measuring connectedness have focused on particular aspects of connectedness, such as ‘social connectedness’ or ‘nature connectedness’, which we hypothesise to be different expressions of a common factor of connectedness. Here, we sought to develop a new scale to measure connectedness as a construct with these multiple domains. We hypothesised that (1) our scale would measure three separable subscale factors pertaining to a felt connection to ‘self’, ‘others’ and ‘world’ and (2) improvements in total and subscale WCS scores would correlate with improved mental health outcomes post psychedelic use.ObjectivesTo validate and test the ‘Watts Connectedness Scale’ (WCS).MethodsPsychometric validation of the WCS was carried out using data from three independent studies. Firstly, we pooled data from two prospective observational online survey studies. The WCS was completed before and after a planned psychedelic experience. The total sample of completers from the online surveys was N = 1226. Exploratory and confirmatory factor analysis were performed, and construct and criterion validity were tested. A third dataset was derived from a double-blind randomised controlled trial (RCT) comparing psilocybin-assisted therapy (n = 27) with 6 weeks of daily escitalopram (n = 25) for major depressive disorder (MDD), where the WCS was completed at baseline and at a 6-week primary endpoint.ResultsAs hypothesised, factor analysis of all WCS items revealed three main factors with good internal consistency. WCS showed good construct validity. Significant post-psychedelic increases were observed for total connectedness scores (η2 = 0.339, p < 0.0001), as well as on each of its subscales (p < 0.0001). Acute measures of ‘mystical experience’, ‘emotional breakthrough’, and ‘communitas’ correlated positively with post-psychedelic changes in connectedness (r = 0.42, r = 0.38, r = 0.42, respectively, p < 0.0001). In the RCT, psilocybin therapy was associated with greater increases in WCS scores compared with the escitalopram arm (ηp2 = 0.133, p = 0.009).ConclusionsThe WCS is a new 3-dimensional index of felt connectedness that may sensitively measure therapeutically relevant psychological changes post-psychedelic use. We believe that the operational definition of connectedness captured by the WCS may have broad relevance in mental health research.
Journal Article
From cognitivism to autopoiesis: towards a computational framework for the embodied mind
Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of 'bodyrepresentations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal 'representations' arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.
Journal Article
Toward the third generation artificial intelligence
There have been two competing paradigms in artificial intelligence (AI) development ever since its birth in 1956, i.e., symbolism and connectionism (or sub-symbolism). While symbolism dominated AI research by the end of 1980s, connectionism gained momentum in the 1990s and is gradually displacing symbolism. This paper considers symbolism as the first generation of AI and connectionism as the second generation. However, each of these two paradigms simulates the human mind from only one perspective. AI cannot achieve true human behaviors by relying on only one paradigm. In order to develop novel AI technologies that are safe, reliable, and extensible, it is necessary to establish a new explainable and robust AI theory. To this end, this paper looks toward developing a third generation artificial intelligence by combining the current paradigms.
Journal Article
A semantic matching energy function for learning with multi-relational data
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base (WordNet). In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.
Journal Article
The Science of Learning to Read Words
The author reviews theory and research by Ehri and her colleagues to document how a scientific approach has been applied over the years to conduct controlled studies whose findings reveal how beginners learn to read words in and out of text. Words may be read by decoding letters into blended sounds or by predicting words from context, but the way that contributes most to reading and comprehending text is reading words automatically from memory by sight. The evidence shows that words are read from memory when graphemes are connected to phonemes. This bonds spellings of individual words to their pronunciations along with their meanings in memory. Readers must know grapheme–phoneme relations and have decoding skill to form connections, and must read words in text to associate spellings with meanings. Readers move through four developmental phases as they acquire knowledge about the alphabetic writing system and apply it to read and write words and build their sight vocabularies. Grapheme–phoneme knowledge and phonemic segmentation are key foundational skills that launch development followed subsequently by knowledge of syllabic and morphemic spelling–sound units. Findings show that when spellings attach to pronunciations and meanings in memory, they enhance memory for vocabulary words. This research underscores the importance of systematic phonics instruction that teaches students the knowledge and skills that are essential in acquiring word-reading skill.
Journal Article
Generative linguistics and neural networks at 60: Foundation, friction, and fusion
The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This article traces the development of these two approaches to cognitive science, from their largely autonomous early development in the first thirty years, through their collision in the 1980s around the past-tense debate (Rumelhart & McClelland 1986, Pinker & Prince 1988) and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The article concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning.
Journal Article