Single Circuit in V1 Capable of Switching Contexts during Movement Using an Inhibitory Population as a Switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.

Not Just About Faces in Context: Face–Context Relation Moderates the Impact of Contextual Threat on Facial Trustworthiness

Recent work showed that the attribution of facial trustworthiness can be influenced by the surrounding context in which a face is embedded: contexts that convey threat make faces less trustworthy. In four studies ( N = 388, three preregistered) we tested whether face–context integration is influenced by how faces and contexts are encoded relationally. In Experiments 1a to 1c, face–context integration was stronger when threatening stimuli were attributable to the human action. Faces were judged less trustworthy when shown in threatening contexts that were ascribable (vs. non-ascribable) to the human action. In Experiment 2, we manipulated face–context relations using instructions. When instructions presented facial stimuli as belonging to the “perpetrators” of the threatening contexts, no difference with the control (no-instructions) condition was found in face–context integration. Instead, the effect was reduced when faces were presented as “victims.” We discussed the importance of considering relational reasoning when studying face–context integration.

Beyond the basics: a detailed conceptual framework of integrated STEM

Given the large variation in conceptualizations and enactment of K− 12 integrated STEM, this paper puts forth a detailed conceptual framework for K− 12 integrated STEM education that can be used by researchers, educators, and curriculum developers as a common vision. Our framework builds upon the extant integrated STEM literature to describe seven central characteristics of integrated STEM: (a) centrality of engineering design, (b) driven by authentic problems, (c) context integration, (d) content integration, (e) STEM practices, (f) twenty-first century skills, and (g) informing students about STEM careers. Our integrated STEM framework is intended to provide more specific guidance to educators and support integrated STEM research, which has been impeded by the lack of a deep conceptualization of the characteristics of integrated STEM. The lack of a detailed integrated STEM framework thus far has prevented the field from systematically collecting data in classrooms to understand the nature and quality of integrated STEM instruction; this delays research related to the impact on student outcomes, including academic achievement and affect. With the framework presented here, we lay the groundwork for researchers to explore the impact of specific aspects of integrated STEM or the overall quality of integrated STEM instruction on student outcomes.

Dropping Beans or Spilling Secrets: How Idiomatic Context Bias Affects Prediction

Idioms can have both a literal interpretation and a figurative interpretation (e.g., to “kick the bucket”). Which interpretation should be activated can be disambiguated by a preceding context (e.g., “The old man was sick. He kicked the bucket.”). We investigated whether the idiomatic and literal uses of idioms have different predictive properties when the idiom has been biased toward a literal or figurative sentence interpretation. EEG was recorded as participants performed a lexical decision task on idiom-final words in biased idioms and literal (compositional) sentences. Targets in idioms were identified faster in both figuratively and literally used idioms than in compositional sentences. Time–frequency analysis of a prestimulus interval revealed relatively more alpha–beta power decreases in literally than figuratively used idiomatic sequences and compositional sentences. We argue that lexico-semantic retrieval plays a larger role in literally than figuratively biased idioms, as retrieval of the word meaning is less relevant in the latter and the word form has to be matched to a template. The results are interpreted in terms of context integration and word retrieval and have implications for models of language processing and predictive processing in general.

Exploring the development of context appreciation in coursework that targets problem-solving for sustainable development

Purpose This study aims to explore the role of planned, sudden shifts in lived experiences, in influencing learner capabilities towards improved problem-solving for sustainable development outcomes. The authors responded to employers of engineering and built environment graduates observing limited “real-life” problem-solving skills, beyond using established formulae and methods, in spite of attempts over more than two decades, to train engineers and other built environment disciplines in areas such as whole system design and sustainable design. Design/methodology/approach A grounded theory approach was used to guide the analysis of data collected through ethnographic methods. The process involved reflecting on authors’ efforts to develop context appreciation within a course called “International Engineering Practice”, using two years of collected data (archived course information, including course profile; completed assessment; lecture and field visit evaluations; and focus groups). The study is built on the authors’ working knowledge of Bloom’s Taxonomy and Threshold Learning Theory, and the well-established role of “context appreciation” in complex problem-solving. After the first iteration of the course, the authors looked for additional theoretical support to help explain findings. The Cynefin framework was subsequently used to augment the authors’ appreciation of “context” – beyond physical context to include relational context, and to evaluate students’ competency development across the four domains of “clear”, “complicated”, “complex” and “chaotic”. Findings This study helped the authors to understand that there was increased capacity of the students to distinguish between three important contexts for problem-solving, including an increased awareness about the importance of factual and relevant information, increased acknowledgement of the varying roles of professional practitioners in problem-solving depending on the type of problem and increased appreciation of the importance of interdisciplinary teams in tackling complex and complicated problems. There were several opportunities for such courses to be more effective in preparing students for dealing with “chaotic” situations that are prevalent in addressing the United Nations’ 17 sustainable development goals (UNSDGs). Drawing on the course-based learnings, the authors present a “context integration model” for developing problem-solving knowledge and skills. Research limitations/implications The research findings are important because context appreciation – including both physical context and relational context – is critical to problem-solving for the UNSDGs, including its 169 targets and 232 indicators. The research findings highlight the opportunity for the Cynefin framework to inform holistic curriculum renewal processes, enhancing an educator’s ability to design, implement and evaluate coursework that develops physical and relational context appreciation. Practical implications The study’s findings and context integration model can help educators develop the full range of necessary problem-solving graduate competencies, including for chaotic situations involving high degrees of uncertainty. Looking ahead, acknowledging the significant carbon footprint of global travel, the authors are interested in applying the model to a domestic and/or online format of the same course, to attempt similar learning outcomes. Originality/value Connecting Bloom’s taxonomy deep learning and threshold learning theory critical path learning insights with the Cynefin framework context domains, provides a novel model to evaluate competency development for problem-solving towards improved holistic physical and relational “context appreciation” outcomes.

Global disruption in excitation-inhibition balance can cause localized network dysfunction and Schizophrenia-like context-integration deficits

Poor context integration, the process of incorporating both previous and current information in decision making, is a cognitive symptom of schizophrenia. The maintenance of the contextual information has been shown to be sensitive to changes in excitation-inhibition (EI) balance. Many regions of the brain are sensitive to EI imbalances, however, so it is unknown how systemic manipulations affect the specific regions that are important to context integration. We constructed a multi-structure, biophysically-realistic agent that could perform context-integration as is assessed by the dot pattern expectancy task. The agent included a perceptual network, a memory network, and a decision making system and was capable of successfully performing the dot pattern expectancy task. Systemic manipulation of the agent’s EI balance produced localized dysfunction of the memory structure, which resulted in schizophrenia-like deficits at context integration. When the agent’s pyramidal cells were less excitatory, the agent fixated upon the cue and initiated responding later than the default agent, which were like the deficits one would predict that individuals on the autistic spectrum would make. This modelling suggests that it may be possible to parse between different types of context integration deficits by adding distractors to context integration tasks and by closely examining a participant’s reaction times.

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit.Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatio-temporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.Author SummaryThe brain processes information at all times and much of that information is context-dependent. The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1).We find that relatively few “switching units” — akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population — is sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration, and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures.

Context sensitivity across multiple time scales with a flexible frequency bandwidth

ABSTRACTEveryday auditory streams are complex, including spectro-temporal content that varies at multiple time scales. Using EEG, we investigate the sensitivity of human auditory cortex to the content of past stimulation in unattended sequences of equiprobable tones. In 3 experiments including 82 participants overall, we found that neural responses measured at different latencies after stimulus onset were sensitive to frequency intervals computed over distinct time scales. Importantly, early responses were sensitive to a longer history of stimulation than later responses. To account for these results, we tested a model consisting of neural populations with frequency-specific but broad tuning that undergo adaptation with exponential recovery. We found that the coexistence of neural populations with distinct recovery rates can explain our results. Furthermore, the adaptation bandwidth of these populations depended on spectral context – it was wider when the stimulation sequence had a wider frequency range. Our results provide electrophysiological evidence as well as a possible mechanistic explanation for dynamic and multi-scale context-dependent auditory processing in the human cortex.SIGNIFICANCE STATEMENTIt has become clear that brain processing of sensory stimuli depends on their temporal context, but context can be construed at time scales from the recent millisecond to life-long. How do different contextual time scales affect sensory processing? We show that auditory context is integrated across at least two separate time scales, and that at both of these time scales responses dynamically adapt to a varying frequency stimulation range. Using computational modeling, we develop a rigorous methodology to estimate the time and frequency scales of context integration for separate response components. Our robust results replicated across 3 EEG experiments, and contribute to the understanding of neural mechanisms supporting complex and dynamic context integration.