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1. Perceiving ensemble statistics of novel image sets

   https://doi.org/10.3758/s13414-020-02174-0  

   Khayat, Noam; Fusi, Stefano; Hochstein, Shaul (April 2021, Attention, Perception, & Psychophysics)

   null (Ed.)

   Abstract Perception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi ( bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism. 

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2. Theory for Deep Learning Regression Ensembles with Application to Raman Spectroscopy Analysis

   https://doi.org/10.1109/ICMLA52953.2021.00172  

   Li, Wenjing; Paffenroth, Randy C.; Timko, Michael T.; Rando, Matthew P.; Brown, Avery B.; Deskins, N. Aaron (December 2021, 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA))

   Regression ensembles consisting of a collection of base regression models are often used to improve the estimation/prediction performance of a single regression model. It has been shown that the individual accuracy of the base models and the ensemble diversity are the two key factors affecting the performance of an ensemble. In this paper, we derive a theory for regression ensembles that illustrates the subtle trade-off between individual accuracy and ensemble diversity from the perspective of statistical correlations. Then, inspired by our derived theory, we further propose a novel loss function and a training algorithm for deep learning regression ensembles. We then demonstrate the advantage of our training approach over standard regression ensemble methods including random forest and gradient boosting regressors with both benchmark regression problems and chemical sensor problems involving analysis of Raman spectroscopy. Our key contribution is that our loss function and training algorithm is able to manage diversity explicitly in an ensemble, rather than merely allowing diversity to occur by happenstance. 

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3. A neurocognitive model for predicting the fate of individual memories

   Tubridy, S.; Halpern, D.; Davachi, L.; Gureckis, T.M. (July 2018, Proceedings of the 40th Annual Conference of the Cognitive Science Society.)

   One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without ex- plicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later. 

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4. Structure and process-level lexical interactions in memory search: A case study of individuals with cochlear implants and normal hearing.

   Kumar, A.A.; Kang, M.; Kronenberger, W.G.; Jones, M.N.; Pisoni, D. (July 2024, Proceedings of the 46th Annual Meeting of the Cognitive Science Society)

   Searching through memory is mediated by complex interactions between the underlying mental lexicon and the processes that operate on this lexicon. However, these interactions are difficult to study due to the effortless manner in which neurotypical individuals perform cognitive tasks. In this work, we examine these interactions within a sample of prelingually deaf individuals with cochlear implants and normal hearing individuals who were administered the verbal fluency task for the "animals" category. Specifically, we tested how different candidates for underlying mental lexicons and processes account for search behavior within the verbal fluency task across the two groups. The models learned semantic representations from different combinations of textual (word2vec) and speech-based (speech2vec) information. The representations were then combined with process models of memory search based on optimal foraging theory that incorporate different lexical sources for transitions within and between clusters of items produced in the fluency task. Our findings show that semantic, word frequency, and phonological information jointly influence search behavior and highlight the delicate balance of different lexical sources that produces successful search outcomes. 

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5. The perceptual and mnemonic effects of ensemble representation on individual size representation

   https://doi.org/10.3758/s13414-024-02963-x  

   Choi, Yong Min; Golomb, Julie D (October 2024, Attention, Perception, & Psychophysics)

   Abstract Our visual world consists of multiple objects, necessitating the identification of individual objects. Nevertheless, the representation of visual objects often exerts influence on each other. Even when we selectively attend to a subset of visual objects, the representations of surrounding items are encoded and influence the processing of the attended item(s). However, it remains unclear whether the effect of group ensemble representation on individual item representation occurs at the perceptual encoding phase, during the memory maintenance period, or both. Therefore, the current study conducted visual psychophysics experiments to investigate the contributions of perceptual and mnemonic bias on the observed effect of ensemble representation on individual size representation. Across five experiments, we found a consistent pattern of repulsive ensemble bias, such that the size of an individual target circle was consistently reported to be smaller than it actually was when presented alongside other circles with larger mean size, and vice versa. There was a perceptual component to the bias, but mnemonic factors also influenced its magnitude. Specifically, the repulsion bias was strongest with a short retention period (0–50 ms), then reduced within a second to a weaker magnitude that remained stable for a longer retention period (5,000 ms). Such patterns of results persisted when we facilitated the processing of ensemble representation by increasing the set size (Experiment 1B) or post-cueing the target circle so that attention was distributed across all items (Experiment 2B). 

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