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Phenomenal contrast arguments (henceforth, PCAs) comprise the most important and widely used strategy for showing that particular mental features contribute to the phenomenal character of experience. Siegel (2006, 2007, 2009, 2010b) famously uses PCAs to argue that visual phenomenal experience represents such “high level properties” as causation, subject-independence, kind properties (being a pine tree, being a word of Russian), and more. Strawson (1994), Siewert (1998), Horgan and Tienson (2002), Chudnoff (2015a,b), and Kriegel (2015) have used versions of the same argument form to argue for the view that cognitive mental states contribute their own, non-sensory phenomenology to our overall experience (the so-called “cognitive phenomenology view). In this paper we’d like to bring out a general obstacle for PCAs that has not been widely discussed, and to offer reasons for being skeptical that it can be overcome in individual cases.

Molyneux addressed his question to Locke in two forms. The question that is most often discussed in the literature is the 1693 version–about whether a newly sighted man could distinguish a globe and a cube when they are presented to his sight alone. But in 1688, he asked whether this man could know which was the globe. While Locke and Molyneux probably thought this an unnecessary add-on, we argue that it changes the question. Locke had no account of how one could know a contingent singular fact by sight or by vision. We argue that we know in these ways by using a process of active sensory exploration that employs the modality in question. A new form of Molyneux’s question emerges from this understanding of perceptual knowledge. While the variety of sensory exploration methods within and across modalities raises considerable difficulties for a positive answer, the original question, like the later version, can ultimately be answered only by empirical investigation.

Reduced cognitive function in major depression (MDD) is often interpreted as a reduced ability to exert cognitive control. Here we used the Effort Foraging Task to test the hypothesis that reduced cognitive function may be due, in part, to decreased willingness to exert control in MDD because of increased cognitive effort "costs". Contrary to our predictions, neither cognitive nor physical effort costs differed with MDD diagnosis (N MDD=52, N Comparisons=27). However, we found distinct patterns of symptom relationships for cognitive and physical effort costs. In MDD, greater anxiety symptoms were selectively associated with lower cognitive, but not physical effort cost (i.e. greater willingness to exert cognitive effort), whereas greater anhedonia and behavioral apathy symptoms were selectively associated with increased physical (but not cognitive) effort costs. These findings support the measurement of both cognitive and physical effort as decision-making function markers that may inform heterogeneity of MDD.

Current models of cognitive control frame its allocation as a process of expected utility maximization. The benefits of a candidate action are weighed against the costs of that control allocation (e.g. opportunity costs). Recent theorizing has found that it is normative to account for the value of learning when determining control allocation. Here, we sought to test whether learning expectations could explain people's initial control allocation in a standard dot-motion perceptual task. We found that subjects' initial skill level and learning rate in a first block were able to predict their initial willingness to accumulate evidence in a second block, interpreted as a greater control allocation for the task. Our findings support the hypothesis that agents consider the learnability of a task when deciding how much cognitive control to allocate to that task.

Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science suggests neural networks do not share this capacity, concluding that human geometric abilities come from discrete symbolic structure in human mental representations. However, progress in artificial intelligence (AI) suggests that neural networks begin to demonstrate more human-like reasoning after scaling up standard architectures in both model size and amount of training data. In this study, we revisit empirical results in cognitive science on geometric visual processing and identify three key biases in geometric visual processing: a sensitivity towards complexity, regularity, and the perception of parts and relations. We test tasks from the literature that probe these biases in humans and find that large pre-trained neural network models used in AI demonstrate more human-like abstract geometric processing.

The human cognitive system exhibits remarkable flexibility and generalization capabilities, partly due to its ability to form low-dimensional, compositional representations of the environment. In contrast, standard neural network architectures often struggle with abstract reasoning tasks, overfitting, and requiring extensive data for training. This paper investigates the impact of the relational bottleneck—a mechanism that focuses processing on relations among inputs—on the learning of factorized representations conducive to compositional coding and the attendant flexibility of processing. We demonstrate that such a bottleneck not only improves generalization and learning efficiency, but also aligns network performance with human-like behavioral biases. Networks trained with the relational bottleneck developed orthogonal representations of feature dimensions latent in the dataset, reflecting the factorized structure thought to underlie human cognitive flexibility. Moreover, the relational network mimics human biases towards regularity without pre-specified symbolic primitives, suggesting that the bottleneck fosters the emergence of abstract representations that confer flexibility akin to symbols.

Perception not only registers property instances, but also connects with and attributes properties to individual entities—so-called sensory individuals, or SIs. But what are SIs? The most-discussed answers are: (i) SIs are ordinary material objects—cohesive, temporally persistent objects extended and bounded in space, and (ii) SIs are locations or regions in spacetime. I will argue for the object view of SIs on the grounds that its rival, the locational view, faces obstacles concerning the relationship between SIs and spacetime: it makes a mystery of perception's representation of SIs as occupying locations in and moving in ordinary spacetime.

Decision theory requires agents to assign probabilities to states of the world and utilities to the possible outcomes of different actions. When agents commit to having the probabilities and/or utilities in a decision problem defined by objective features of the world, they may find themselves unable to decide which actions maximize expected utility. Decision theory has long recognized that work-around strategies are available in special cases; this is where dominance reasoning, minimax, and maximin play a role. Here we describe a different work around, wherein a rational decision about one decision problem can be reached by “interpolating” information from another problem that the agent believes has already been rationally solved.

Significant variations in the way objects appear across different viewing conditions pose a challenge to the view that they have some true, determinate color. This view would seem to require that we break the symmetry between multiple appearances in favor of a single variant. A wide range of philosophical and non-philosophical writers have held that the symmetry can be broken by appealing to daylight viewing conditions---that the appearances of objects in daylight have a stronger, and perhaps unique, claim to reveal their true colors. In this note we argue that, whatever else its merits, this appeal to daylight is not a satisfactory answer to the problem posed by perceptual variation.

Molyneux's Question (MQ) concerns whether a newly sighted man would recognize/distinguish a sphere and a cube by vision, assuming he could previously do this by touch. We argue that MQ splits into questions about (a) shared representations of space in different perceptual systems, and about (b) shared ways of constructing higher dimensional spatiotemporal features from information about lower dimensional ones, most of the technical difficulty centring on (b). So understood, MQ resists any monolithic answer: everything depends on the constraints faced by particular perceptual systems in extracting features of higher dimensionality from those of lower.

The premutation of the fragile X messenger ribonucleoprotein 1 (FMR1) gene is characterized by an expansion of the CGG trinucleotide repeats (55 to 200 CGGs) in the 5 untranslated region and increased levels of FMR1 mRNA. Molecular mechanisms leading to fragile X-premutation-associated conditions (FXPAC) include cotranscriptional R-loop formations, FMR1 mRNA toxicity through both RNA gelation into nuclear foci and sequestration of various CGG-repeat-binding proteins, and the repeat-associated non-AUG (RAN)-initiated translation of potentially toxic proteins. Such molecular mechanisms contribute to subsequent consequences, including mitochondrial dysfunction and neuronal death. Clinically, premutation carriers may exhibit a wide range of symptoms and phenotypes. Any of the problems associated with the premutation can appropriately be called FXPAC. Fragile X-associated tremor/ataxia syndrome (FXTAS), fragile X-associated primary ovarian insufficiency (FXPOI), and fragile X-associated neuropsychiatric disorders (FXAND) can fall under FXPAC. Understanding the molecular and clinical aspects of the premutation of the FMR1 gene is crucial for the accurate diagnosis, genetic counseling, and appropriate management of affected individuals and families. This paper summarizes all the known problems associated with the premutation and documents the presentations and discussions that occurred at the International Premutation Conference, which took place in New Zealand in 2023.

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participant's responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

Spleen tyrosine kinase (SYK) is a previously unidentified therapeutic target that inhibits neutrophil and macrophage activation in coronavirus disease 2019 (COVID-19). Fostamatinib, a SYK inhibitor, was studied in a phase 2 placebo-controlled randomized clinical trial and was associated with improvements in many secondary end points related to efficacy. Here, we used a multiomic approach to evaluate cellular and soluble immune mediator responses of patients enrolled in this trial. We demonstrated that SYK inhibition was associated with reduced neutrophil activation, increased circulation of mature neutrophils (CD10+CD33-), and decreased circulation of low-density granulocytes and polymorphonuclear myeloid-derived suppressor cells (HLA-DR-CD33+CD11b-). SYK inhibition was also associated with normalization of transcriptional activity in circulating monocytes relative to healthy controls, an increase in frequency of circulating nonclassical and HLA-DRhi classical monocyte populations, and restoration of interferon responses. Together, these data suggest that SYK inhibition may mitigate proinflammatory myeloid cellular and soluble mediator responses thought to contribute to immunopathogenesis of severe COVID-19.

Decades of research have established that while people’s performance suffers when they need to quickly switch between tasks, they can reduce these performance costs the more time they have to prepare. Two major theories have attempted to explain how people actively prepare for tasks over time, debating whether these task state transitions are discrete or gradual. We attempted to bring clarity to this debate by developing new statistical methods for single-trial modeling of task state transitions, which we use in a task that combines the strengths of cued and predictable task-switching. We found that participants’ behavior was best explained as a hybrid between discrete and gradual transitions. Over the preparation period, participants discretely transitioned from an unprepared state into a dynamic, increasingly prepared state. These findings provide a new account of cognitive flexibility, paving the way for mechanistic models of task-switching.

The symposium ‘Advances in the Study of Visual and Multisensory Objects’ provides convergent focus of research from a broad range of subject areas. Using both nuanced philosophical analysis and informed empirical work, the symposium offers an interdisciplinary look at different aspects of visual and multisensory objects. It integrates three methodological approaches in a well-balanced way: philosophy of perception, experimental psychology, and cognitive neuroscience. Presenting a crucial part of the forthcoming collection (A. Mroczko-Wąsowicz & R. Grush (Eds.) Sensory Individuals: Unimodal and Multimodal Perspectives. Oxford: OUP), the symposium focuses on specific and closely related questions.

Normative accounts of decision-making predict that people attempt to balance the immediate rewards associated with correct responses against the costs of deliberation. However, humans frequently deliberate longer than normative models say they should. We propose that people try to optimize not only their rate of material rewards, but also their rate of information gain. A computational model that implements this idea successfully mimics human decision makers, reproducing key patterns of behavior not predicted by alternative models. Moreover, simulations reveal a normative basis for our model: An agent that exchanges even a small amount of immediate reward for information will improve its decision-making ability through learning, allowing it to earn more reward in the long run than an agent disinterested in information. Maximizing a combination of reward and information rate is a simple yet effective strategy for solving the speed-accuracy tradeoff that may resolve lingering mysteries about human decision-making.

The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is sufficient to recapitulate the extrapolation abilities of people in this domain. Here, we propose to ad- dress this gap by augmenting the transformer architecture with two simple inductive learning biases, that are directly adapted from recent models of abstract reasoning in cognitive science. The results we report demonstrate that these biases are helpful in the context of large neural network models, as well as shed light on the types of inductive learning biases that may contribute to human abilities in extrapolation.

FMR1 premutation cytosine-guanine-guanine repeat expansion alleles are relatively common mutations in the general population that are associated with a neurodegenerative disease (fragile X-associated tremor/ataxia syndrome), reproductive health problems and potentially a wide range of additional mental and general health conditions that are not yet well-characterised. The International Fragile X Premutation Registry (IFXPR) was developed to facilitate and encourage research to better understand the FMR1 premutation and its impact on human health, to facilitate clinical trial readiness by identifying and characterising diverse cohorts of individuals interested in study participation, and to build community and collaboration among carriers, family members, researchers and clinicians around the world. Here, we describe the development and content of the IFXPR, characterise its first 747 registrants from 32 countries and invite investigators to apply for recruitment support for their project(s). With larger numbers, increased diversity and potentially the future clinical characterisation of registrants, the IFXPR will contribute to a more comprehensive and accurate understanding of the fragile X premutation in human health and support treatment studies.

BackgroundFragile X syndrome (FXS) is associated with dysregulated endocannabinoid signaling and may therefore respond to cannabidiol therapy.DesignCONNECT-FX was a double-blind, randomized phase 3 trial assessing efficacy and safety of ZYN002, transdermal cannabidiol gel, for the treatment of behavioral symptoms in children and adolescents with FXS.MethodsPatients were randomized to 12 weeks of ZYN002 (250 mg or 500 mg daily [weight-based]) or placebo, as add-on to standard of care. The primary endpoint assessed change in social avoidance (SA) measured by the Aberrant Behavior Checklist-Community Edition FXS (ABC-CFXS) SA subscale in a full cohort of patients with a FXS full mutation, regardless of the FMR1 methylation status. Ad hoc analyses assessed efficacy in patients with ≥ 90% and 100% methylation of the promoter region of the FMR1 gene, in whom FMR1 gene silencing is most likely.ResultsA total of 212 patients, mean age 9.7 years, 75% males, were enrolled. A total of 169 (79.7%) patients presented with ≥ 90% methylation of the FMR1 promoter and full mutation of FMR1. Although statistical significance for the primary endpoint was not achieved in the full cohort, significant improvement was demonstrated in patients with ≥ 90% methylation of FMR1 (nominal P = 0.020). This group also achieved statistically significant improvements in Caregiver Global Impression-Change in SA and isolation, irritable and disruptive behaviors, and social interactions (nominal P-values: P = 0.038, P = 0.028, and P = 0.002). Similar results were seen in patients with 100% methylation of FMR1. ZYN002 was safe and well tolerated. All treatment-emergent adverse events (TEAEs) were mild or moderate. The most common treatment-related TEAE was application site pain (ZYN002: 6.4%; placebo: 1.0%).ConclusionsIn CONNECT-FX, ZYN002 was well tolerated in patients with FXS and demonstrated evidence of efficacy with a favorable benefit risk relationship in patients with ≥ 90% methylation of the FMR1 gene, in whom gene silencing is most likely, and the impact of FXS is typically most severe.Trial registrationThe CONNECT-FX trial is registered on Clinicaltrials.gov (NCT03614663).

In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the marginal value theorem; MVT): to leave a patch when the reward rate in the current patch depletes to the average reward rate across patches. Prior studies implicate an important role for the anterior cingulate cortex (ACC) in foraging decisions based on MVT: within single trials, ACC activity increases immediately preceding foraging decisions, and across trials, these dynamics are modulated as the value of staying in the patch depletes to the average reward rate. Here, we test whether these activity patterns reflect dynamic encoding of decision-variables and whether these signals are directly involved in decision-making. We developed a leaky accumulator model based on the MVT that generates estimates of decision variables within and across trials, and tested model predictions against ACC activity recorded from male rats performing a patch foraging task. Model predicted changes in MVT decision variables closely matched rat ACC activity. Next, we pharmacologically inactivated ACC in male rats to test the contribution of these signals to decision-making. ACC inactivation had a profound effect on rats foraging decisions and response times (RTs) yet rats still followed the MVT decision rule. These findings indicate that the ACC encodes foraging-related variables for reasons unrelated to patch-leaving decisions.SIGNIFICANCE STATEMENT The ability to make adaptive patch-foraging decisions, to remain with a depleting resource or search for better alternatives, is critical to animal well-being. Previous studies have found that anterior cingulate cortex (ACC) activity is modulated at different points in the foraging decision process, raising questions about whether the ACC guides ongoing decisions or serves a more general purpose of regulating cognitive control. To investigate the function of the ACC in foraging, the present study developed a dynamic model of behavior and neural activity, and tested model predictions using recordings and inactivation of ACC. Findings revealed that ACC continuously signals decision variables but that these signals are more likely used to monitor and regulate ongoing processes than to guide foraging decisions.

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.

Understanding how people generalize and extrapolate fromlimited amounts of data remains an outstanding challenge. Westudy this question in the domain of scalar function learning,and propose a simple model based on the Principle of Maxi-mum Entropy (Jaynes, 1957). Through computational model-ing, we demonstrate that the theory makes two specific predic-tions about peoples’ extrapolation judgments, that we validatethrough experiments. Moreover, we show that existing Gaus-sian Process models of function learning cannot account forthese effects.

Decision theory requires agents to assign probabilities to states of the world and utilities to the possible outcomes of different actions. When agents commit to having the probabilities and/or utilities in a decision problem defined by objective features of the world, they may find themselves unable to decide which actions maximize expected utility. Decision theory has long recognized that work-around strategies are available in special cases; this is where dominance reasoning, minimax, and maximin play a role. Here we describe a different work around, wherein a rational decision about one decision problem can be reached by “interpolating” information from another problem that the agent believes has already been rationally solved.

The sentence 'The boss fired the employee who is always late' invites the defeasible inference that the speaker is attempting to convey that the lateness caused the firing (cf. 'The boss fired the employee who is from Philadelphia', which does not invite an analogous inference). We argue that such inferences cannot be understood in terms of familiar approaches to extrasemantic enrichment such as implicature, impliciture, explicature, or species of local enrichment already in the literature. Rather, we propose that they arise from more basic cognitive strategies, grounded in processes of coherence establishment, that thinkers use to make sense of the world. Attention to such cases provides a richer and more varied landscape of extrasemantic enrichment than has been appreciated to date.

Molyneux's Question (MQ) concerns whether a newly sighted man would recognize/distinguish a sphere and a cube by vision, assuming he could previously do this by touch. We argue that MQ splits into questions about (a) shared representations of space in different perceptual systems, and about (b) shared ways of constructing higher dimensional spatiotemporal features from information about lower dimensional ones, most of the technical difficulty centring on (b). So understood, MQ resists any monolithic answer: everything depends on the constraints faced by particular perceptual systems in extracting features of higher dimensionality from those of lower.

Significant variations in the way objects appear across different viewing conditions pose a challenge to the view that they have some true, determinate color. This view would seem to require that we break the symmetry between multiple appearances in favor of a single variant. A wide range of philosophical and non-philosophical writers have held that the symmetry can be broken by appealing to daylight viewing conditions---that the appearances of objects in daylight have a stronger, and perhaps unique, claim to reveal their true colors. In this note we argue that, whatever else its merits, this appeal to daylight is not a satisfactory answer to the problem posed by perceptual variation.

How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control learned in one situation to others with shared features, even when demands for control are different. This makes the intriguing prediction that what a person learned in one setting could cause them to misestimate the need for, and potentially overexert, control in another setting, even if this harms their performance. To test this prediction, we had participants perform a novel variant of the Stroop task in which, on each trial, they could choose to either name the color (more control-demanding) or read the word (more automatic). Only one of these tasks was rewarded each trial and could be predicted by one or more stimulus features (the color and/or word). Participants first learned colors and then words that predicted the rewarded task. Then, we tested how these learned feature associations transferred to novel stimuli with some overlapping features. The stimulus-task-reward associations were designed so that for certain combinations of stimuli, transfer of learned feature associations would incorrectly predict that more highly rewarded task would be color-naming, even though the actually rewarded task was word-reading and therefore did not require engaging control. Our results demonstrated that participants overexerted control for these stimuli, providing support for the feature-based learning mechanism described by the LVOC model.

Current models of cognitive control address selection among tasks in terms of a cost-benefit tradeoff. Importantly, they usually assume a fixed level of competence for each candidate task when estimating its value. However, performing a task can improve competence through learning, which should be factored into estimates of future value. Here, we consider an extension of the Expected Value of Control (EVC) theory that includes such estimates. We demonstrate that control allocation is a function of task learnability, and show the use of this model by generating novel predictions in cognitive effort discounting tasks. We argue that the value of learning in control allocation may account for the seemingly paradoxical finding that sometimes participants prefer more difficult (i.e., costly) tasks, and discuss how the model can be leveraged to further our understanding of human decision making and cognitive impairments.

Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinforcement learning agent to select N items from a binary vector when instructed (known as the give-$N$ task). We found that a memory-augmented modular network architecture based on the recently proposed Emergent Symbol Binding Network (ESBN) exhibited an inflection during learning that resembled human development. This model was also capable of systematic extrapolation outside the range of its training set - for example, trained only to select between 1 and 10 items, it could succeed at selecting 11 to 15 items as long as it could make use of an arbitrary count sequence of at least that length. The close parallels to child development and the capacity for extrapolation suggest that our model could shed light on the emergence of systematicity in humans.

Artificial systems currently outperform humans in diverse computational domains, but none has achieved parity in speed and overall versatility of mastering novel tasks. A critical component to human success, in this regard, is the ability to redeploy and redirect data passed between cognitive subsystems (via abstract feature representations) in response to changing task demands. However, analyzing shared representations is difficult in neural systems with distributed nonlinear coding. This work presents a simple but effective approach to this problem. In experiments, the proposed model robustly predicts behavior and performance of multitasking networks on natural language data (MNIST) using common deep network architectures. Consistent with existing theory in cognitive control, representation structure varies in response to (a) environmental pressures for representation sharing, (b) demands for parallel processing capacity, and (c) tolerance for crosstalk. Implications for geometric (dimension, curvature), functional (automaticity, generalizability, modularity), and applied aspects of representation learning are discussed.

We suggest a new class of tests for searching for lepton flavornon-universality (LFNU) using ratio observables and based on correlations amongthe underlying LFNU new physics (NP) effects in several (seemingly independent)di-lepton and single lepton + jet(s) processes. This is demonstrated bystudying the effects generated by LFNU 4-Fermi interactions involving 3rdgeneration quarks. We find that the sensitivity to the scale ($\Lambda$) of theLFNU 4-Fermi operators significantly improves when the correlations among thevarious di-lepton +jets and single-lepton + jets processes are used, reaching$\Lambda \sim {\cal O}(10)$~TeV at the HL-LHC.

We suggest a new class of tests for searching for lepton flavornon-universality (LFNU) using ratio observables and based on correlations amongthe underlying LFNU new physics (NP) effects in several (seemingly independent)di-lepton and single lepton + jet(s) processes. This is demonstrated bystudying the effects generated by LFNU 4-Fermi interactions involving 3rdgeneration quarks. We find that the sensitivity to the scale ($\Lambda$) of theLFNU 4-Fermi operators significantly improves when the correlations among thevarious di-lepton +jets and single-lepton + jets processes are used, reaching$\Lambda \sim {\cal O}(10)$~TeV at the HL-LHC.

We present a new quantum-markovian model of two-alternative forced choice (2AFC) decision-making. We treatthe decision-making process as an accumulation of evidencebetween two competing alternatives, analogous to the drift dif-fusion model (DDM), in which the stimulus acts as a gener-ative process, emitting bits of information that are treated asquantum particles. The particles are acted on by a landscapedetermined by the agent’s experience with the task or stimu-lus, signal strength, and allocated cognitive control. We de-rive closed form expressions for success rates under both theinterrogation and free response paradigms. Under the free re-sponse paradigm, we show that this model reduces to a Markovprocess with closed form response time (RT) distributions thattake the form of inverse gaussians (IGs) with periodic noisecharacteristic to the task set. In the limit of long RT, the RTdistributions become smooth, recovering true IG distributionsanalogous to the standard DDM.

A key aspect of human reasoning is the ability to recognize abstract patterns in sequential data and then use those patternsto make novel inferences. Capturing this capacity for abstract reasoning is a major challenge for neural network modelsof human cognition. We present a recurrent neural network model of abstract sequential reasoning that is augmented witha form of episodic memory. This memory system enables the network to accomplish a form of variable-binding that haslong been considered an important component of abstract reasoning. We evaluate the model using visually grounded,abstract sequential reasoning and pattern completion tasks, including a task based on relations commonly found in RavensProgressive Matrices.

Determinantal Point Processes (DPPs) are probabilisticmodels of repulsion, capturing negative dependenciesbetween states. Here, we show that a DPP inrepresentation-space predicts inferential biases towardmutual exclusivity commonly observed in word learning(mutual exclusivity bias) and reasoning (disjunctivesyllogism) tasks. It does so without requiring explicitrule representations, without supervision, and withoutexplicit knowledge transfer. The DPP attempts tomaximize the total ”volume” spanned by the set ofinferred code-vectors. In a representational system inwhich combinatorial codes are constructed by re-usingcomponents, a DPP will naturally favor the combinationof previously un-used components. We suggest thatthis bias toward the selection of volume-maximizingcombinations may exist to promote the efficient retrievalof individuals from memory. In support of this, we showthe same algorithm implements efficient ”hashing”,minimizing collisions between key/value pairs withoutexpanding the required storage space. We suggestthat the mechanisms that promote efficient memorysearch may also underlie cognitive biases in structuredinference.

There is a dearth of information about cardiovascular problems in fragile X premutation carriers who have 55-200 CGG repeats in fragile X mental retardation 1 (FMR1) gene. The FMR1 expansion in the premutation range leads to toxic RNA gain-of-function resulting in cellular dysregulation. The mechanism of RNA toxicity underlies all of the premutation disorders including fragile X-associated tremor/ataxia syndrome, fragile X-associated primary ovarian insufficiency, and fragile X-associated neuropsychiatric disorder. Cardiovascular problems particularly autonomic dysfunction, hypertension, and cardiac arrhythmias are not uncommon in premutation carriers. Some arterial problems and valvular heart diseases have also been reported. This article reviews cardiovascular problems in premutation carriers and discusses possible contributing mechanisms including RNA toxicity and mild fragile X mental retardation protein deficiency. Further research studies are needed in order to prove a direct association of the cardiovascular problems in fragile X premutation carriers because such knowledge will lead to better preventative treatment.

Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this Eighty Five Percent Rule for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.

Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the similarity in behavior between these two contexts, previous efforts to reconcile these observations in terms of a consistent pattern of time preferences have failed. Here, via extensive behavioral testing and quantitative modeling, we show that rats exhibit similar time preferences in both contexts: they prefer immediate vs. delayed rewards and they are sensitive to opportunity costs of delays to future decisions. Further, a quasi-hyperbolic discounting model, a form of hyperbolic discounting with separate components for short- and long-term rewards, explains individual rats' time preferences across both contexts, providing evidence for a common mechanism for myopic behavior in foraging and intertemporal choice.

We examine new aspects of leptoquark (LQ) phenomenology using effective fieldtheory (EFT). We construct a complete set of leading effective operatorsinvolving SU(2) singlets scalar LQ and the SM fields up to dimension six. Weshow that, while the renormalizable LQ-lepton-quark interaction Lagrangian canaddress the persistent hints for physics beyond the Standard Model in theB-decays $\bar B \to D^{(*)} \tau \bar\nu$, $\bar B \to \bar K \ell^+ \ell^-$and in the measured anomalous magnetic moment of the muon, the LQ higherdimensional effective operators may lead to new interesting effects associatedwith lepton number violation. These include the generation of one-loop sub-eVMajorana neutrino masses, mediation of neutrinoless double-$\beta$ decay andnovel LQ collider signals. For the latter, we focus on 3rd generation LQ($\phi_3$) in a framework with an approximate $Z_3$ generation symmetry, andshow that one class of the dimension five LQ operators may give rise to astriking asymmetric same-charge $\phi_3 \phi_3$ pair-production signal, whichleads to low background same-sign leptons signals at the LHC. For example, with$M_{\phi_3} \sim 1$ TeV and a new physics scale of $\Lambda \sim 5$ TeV, weexpect about $5000$ positively charged $\tau^+ \tau^+$ events via $pp \to\phi_3 \phi_3 \to \tau^+ \tau^+ + 2 \cdot j_b$ ($j_b$=b-jet) at the 13 TeV LHCwith an integrated luminosity of 300 fb$^{-1}$. It is interesting to note that,in the LQ EFT framework, the expected same-sign lepton signals have a ratewhich is several times larger than the QCD LQ-mediated opposite-sign leptonssignals, $gg,q \bar q \to \phi_3 \phi_3^* \to \ell^+ \ell^- +X$. We alsoconsider the same-sign charged lepton signals in the LQ EFT framework at higherenergy hadron colliders such as a 27 TeV HE-LHC and a 100 TeV FCC-hh.

A fundamental question in memory research is how different forms of memory interact. Previous research has shown that people rely on working memory (WM) in short-term recognition tasks; a common view is that episodic memory (EM) only influences performance on these tasks when WM maintenance is disrupted. However, retrieval of memories from EM has been widely observed during brief periods of quiescence, raising the possibility that EM retrievals during maintenance-critically, before a response can be prepared-might affect short-term recognition memory performance even in the absence of distraction. We hypothesized that this influence would be mediated by the lingering presence of reactivated EM content in WM. We obtained support for this hypothesis in three experiments, showing that delay-period EM reactivation introduces incidentally associated information (context) into WM, and that these retrieved associations negatively impact subsequent recognition, leading to substitution errors (Experiment 1) and slowing of accurate responses (Experiment 2). FMRI pattern analysis showed that slowing is mediated by the content of EM reinstatement (Experiment 3). These results expose a previously hidden influence of EM on WM, raising new questions about the adaptive nature of their interaction.