Structured representations in visual working memory

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-195).How much visual information can we hold in mind at once? A large body of research has attempted to quantify the capacity of visual working memory by focusing on how many individual objects or visual features can be actively maintained in memory. This thesis presents a novel theoretical framework for understanding working memory capacity, suggesting that our memory representations are complex and structured even for simple visual displays, and formalizing such structured representations is necessary to understand the architecture and capacity of visual working memory. Chapter 1 reviews previous empirical research on visual working memory capacity, and argues that an understanding of memory capacity requires moving beyond quantifying how many items people can remember and instead focusing on the content of our memory representations. Chapter 2 argues for structured memory representations by demonstrating that we encode a summary of all of the items on a display in addition to information about particular items, and use both item and summary information to complete working memory tasks. Chapter 3 describes a computational model that formalizes the roles of perceptual organization and the encoding of summary statistics in visual working memory, and provides a way to quantify capacity even in the presence of richer, more structured memory representations. This formal framework predicts how well observers will be able to remember individual working memory displays, rather than focusing on average performance across many displays. Chapter 4 uses information theory to examine visual working memory through the framework of compression, and demonstrates that introducing regularities between items allows us to encode more colors in visual working memory. Thus, working memory capacity needs to be understood by taking into account learned knowledge, rather than simply focusing on the number of items to be remembered. Together, this research suggests that visual working memory capacity is best characterized by structured representations where prior knowledge influences how much can be stored and displays are encoded at multiple levels of abstraction.by Timothy F. Brady.Ph.D

Detecting changes in real-world objects: The relationship between visual long-term memory and change blindness

A large body of literature has shown that observers often fail to notice significant changes in visual scenes, even when these changes happen right in front of their eyes. For instance, people often fail to notice if their conversation partner is switched to another person, or if large background objects suddenly disappear.1,2 These 'change blindness' studies have led to the inference that the amount of information we remember about each item in a visual scene may be quite low.1 However, in recent work we have demonstrated that long-term memory is capable of storing a massive number of visual objects with significant detail about each item.3 In the present paper we attempt to reconcile these findings by demonstrating that observers do not experience 'change blindness' with the real world objects used in our previous experiment if they are given sufficient time to encode each item. The results reported here suggest that one of the major causes of change blindness for real-world objects is a lack of encoding time or attention to each object (see also refs. 4 and 5).Psycholog

The Effect for Category Learning on Recognition Memory: A Signal Detection Theory Analysis

Previous studies have shown that category learning affects subsequent recognition memory. However, questions remain as to how category learning affects discriminability during recognition. In this three-stage study, we employed sets of simulated flowers with category- and non-category-inclusion features appearing with equal probabilities. In the learning stage, participants were asked to categorize flowers by identifying the category-inclusion feature. Next, in the studying stage, participants memorized a new set of flowers, a third of which belonged to the learned category. Finally, in the testing stage, participants received a recognition test with old and new flowers, some from the learned category, some from a not-learned category, some from both categories, and some from neither category. We applied hierarchical Bayesian signal detection theory models to recognition performance and found that prior category learning affected both discriminability as well as criterion bias. That is, people that learned the category well, exhibited improved discriminability and a shifted bias toward flowers from the learned relative to the not learned category

Spontaneous Motor Entrainment to Music in Multiple Vocal Mimicking Species

SummaryThe human capacity for music consists of certain core phenomena, including the tendency to entrain, or align movement, to an external auditory pulse [1–3]. This ability, fundamental both for music production and for coordinated dance, has been repeatedly highlighted as uniquely human [4–11]. However, it has recently been hypothesized that entrainment evolved as a by-product of vocal mimicry, generating the strong prediction that only vocal mimicking animals may be able to entrain [12, 13]. Here we provide comparative data demonstrating the existence of two proficient vocal mimicking nonhuman animals (parrots) that entrain to music, spontaneously producing synchronized movements resembling human dance. We also provide an extensive comparative data set from a global video database systematically analyzed for evidence of entrainment in hundreds of species both capable and incapable of vocal mimicry. Despite the higher representation of vocal nonmimics in the database and comparable exposure of mimics and nonmimics to humans and music, only vocal mimics showed evidence of entrainment. We conclude that entrainment is not unique to humans and that the distribution of entrainment across species supports the hypothesis that entrainment evolved as a by-product of selection for vocal mimicry

Modeling visual working memory with the MemToolbox

The MemToolbox is a collection of MATLAB functions for modeling visual working memory. In support of its goal to provide a full suite of data analysis tools, the toolbox includes implementations of popular models of visual working memory, real and simulated data sets, Bayesian and maximum likelihood estimation procedures for fitting models to data, visualizations of data and fit, validation routines, model comparison metrics, and experiment scripts. The MemToolbox is released under the permissive BSD license and is available at http:// memtoolbox.org

The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices.

BACKGROUND: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally

Genetic complexity of diagnostically unresolved Ehlers-Danlos syndrome

Background: The Ehlers-Danlos syndromes (EDS) are heritable disorders of connective tissue (HDCT), reclassified in the 2017 nosology into 13 subtypes. The genetic basis for hypermobile Ehlers-Danlos syndrome (hEDS) remains unknown. Methods: Whole exome sequencing (WES) was undertaken on 174 EDS patients recruited from a national diagnostic service for complex EDS and a specialist clinic for hEDS. Patients had already undergone expert phenotyping, laboratory investigation and gene sequencing, but were without a genetic diagnosis. Filtered WES data were reviewed for genes underlying Mendelian disorders and loci reported in EDS linkage, transcriptome and genome-wide association studies (GWAS). A genetic burden analysis (Minor Allele Frequency (MAF) <0.05) incorporating 248 Avon Longitudinal Study of Parents and Children (ALSPAC) controls sequenced as part of the UK10K study was undertaken using TASER methodology. Results: Heterozygous pathogenic (P) or likely pathogenic (LP) variants were identified in known EDS and Loeys-Dietz (LDS) genes. Multiple variants of uncertain significance where segregation and functional analysis may enable reclassification were found in genes associated with EDS, LDS, heritable thoracic aortic disease (HTAD), Mendelian disorders with EDS symptomatology and syndromes with EDS-like features. Genetic burden analysis revealed a number of novel loci, although none reached the threshold for genome-wide significance. Variants with biological plausibility were found in genes and pathways not currently associated with EDS or HTAD. Conclusions: We demonstrate the clinical utility of large panel-based sequencing and WES for patients with complex EDS in distinguishing rare EDS subtypes, LDS and related syndromes. Although many of the P and LP variants reported in this cohort would be identified with current panel testing, they were not at the time of this study, highlighting the use of extended panels and WES as a clinical tool for complex EDS. Our results are consistent with the complex genetic architecture of EDS and suggest a number of novel hEDS and HTAD candidate genes and pathways

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts