Transfer of visual perceptual learning over a task-irrelevant feature through feature-invariant representations: Behavioral experiments and model simulations.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.

Analysis of 15 bile acids in human plasma based on C18 functionalized magnetic organic polymer nanocomposite coupled with liquid chromatography-tandem mass spectrometry.

Because of the "enterohepatic circulation" of bile acid, liver damage can be reflected by monitoring the content of bile acid in the serum of the organism. To monitor the concentration of 15 bile acids in plasma samples, a new technique of PRiME (process, ruggedness, improvement, matrix effect, ease of use) pass-through cleanup procedure combined with high performance liquid chromatography-tandem quadrupole mass spectrometry (HPLC-MS/MS) was developed. The sorbent used in the PRiME pass-through cleanup procedure is a new type of magnetic organic resin composite nano-material modified by C18 (C18-PS-DVB-GMA-Fe3O4), which has high cleanup efficiency of plasma samples. It also shows good performance in the separation and analysis of 15 kinds of bile acids. Under the optimal conditions, the results show higher cleanup efficiency of C18-PS-DVB-GMA-Fe3O4 with recoveries in the range of 82.1-115 %. The limit of quantitative (LOQs) of 15 bile acids were in the range of 0.033 µg/L-0.19 µg/L, and the RSD values of 15 bile acids were in the range of 3.00-11.9 %. Validation results on linearity, specificity, accuracy and precision, as well as on the application to analysis of 15 bile acids in 100 human plasma samples demonstrate the applicability to clinical studies.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling.

The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in Zhao et al. (submitted): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a Bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical Bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., 2010, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest half width of 68.2% credible interval of the learning curves compared to the BIP and HBMv. The parametric HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.

Informational feedback accelerates learning in multi-alternative perceptual judgements of orientation.

Experience or training can substantially improve perceptual performance through perceptual learning, and the extent and rate of these improvements may be affected by feedback. In this paper, we first developed a neural network model based on the integrated reweighting theory (Dosher et al., 2013) to account for perceptual learning and performance in n-alternative identification tasks and the dependence of learning on different forms of feedback. We then report an experiment comparing the effectiveness of response feedback (RF) versus accuracy feedback (AF) or no feedback (NF) (full versus partial versus no supervision) in learning a challenging eight-alternative visual orientation identification (8AFC) task. Although learning sometimes occurred in the absence of feedback (NF), RF had a clear advantage above AF or NF in this task. Using hybrid supervision learning rules, a new n-alternative identification integrated reweighting theory (I-IRT) explained both the differences in learning curves given different feedback and the dynamic changes in identification confusion data. This study shows that training with more informational feedback (RF) is more effective, though not necessary, in these challenging n-alternative tasks, a result that has implications for developing training paradigms in realistic tasks.

Learning spatial frequency identification through reweighted decoding.

Perceptual learning, the improvement of perceptual judgments with practice, occurs in many visual tasks. There are, however, relatively fewer studies examining perceptual learning in spatial frequency judgments. In addition, perceptual learning has generally been studied in two-alternative tasks, occasionally in n-alternative tasks, and infrequently in identification. Recently, perceptual learning was found in an orientation identification task (eight-alternatives) and was well accounted for by a new identification integrated reweighting theory (I-IRT) (Liu et al., submitted). Here, we examined perceptual learning in a similar eight-alternative spatial frequency absolute identification task in two different training protocols, finding learning in the majority but not all observers. We fit the I-IRT to the spatial frequency learning data and discuss possible model explanations for variations in learning.

Vasorin contributes to lung injury via FABP4-mediated inflammation.

BACKGROUND: Lung injury caused by pulmonary inflammation is one of the main manifestations of respiratory diseases. Vasorin (VASN) is a cell-surface glycoprotein encoded by the VASN gene and is expressed in the lungs of developing mouse foetuses. Previous research has revealed that VASN is associated with many diseases. However, its exact function in the lungs and the underlying mechanism remain poorly understood. METHODS AND RESULTS: To investigate the molecular mechanisms involved in lung disease caused by VASN deficiency, a VASN gene knockout (VASN-/-) model was established. The pathological changes in the lungs of VASN-/- mice were similar to those in a lung injury experimental mouse model. We further analysed the transcriptomes of the lungs of VASN-/- mice and wild-type mice. Genes in twenty-four signalling pathways were enriched in the lungs of VASN-/- mice, among which PPAR signalling pathway genes (3 genes, FABP4, Plin1, AdipoQ, were upregulated, while apoA5 was downregulated) were found to be closely related to lung injury. The most significantly changed lung injury-related gene, FABP4, was selected for further verification. The mRNA and protein levels of FABP4 were significantly increased in the lungs of VASN-/- mice, as were the mRNA and protein levels of the inflammatory factors IL-6, TNF-α and IL-1ß. CONCLUSIONS: We believe that these data provide molecular evidence for the regulatory role of VASN in inflammation in the context of lung injury.

Vasorin deficiency leads to cardiac hypertrophy by targeting MYL7 in young mice.

Vasorin (VASN) is an important transmembrane protein associated with development and disease. However, it is not clear whether the death of mice with VASN deficiency (VASN-/- ) is related to cardiac dysfunction. The aim of this research was to ascertain whether VASN induces pathological cardiac hypertrophy by targeting myosin light chain 7 (MYL7). VASN-/- mice were produced by CRISPR/Cas9 technology and inbreeding. PCR amplification, electrophoresis, real-time PCR and Western blotting were used to confirm VASN deficiency. Cardiac hypertrophy was examined by blood tests, histological analysis and real-time PCR, and key downstream factors were identified by RNA sequencing and real-time PCR. Western blotting, immunohistochemistry and electron microscopy analysis were used to confirm the downregulation of MYL7 production and cardiac structural changes. Our results showed that sudden death of VASN-/- mice occurred 21-28 days after birth. The obvious increases in cardiovascular risk, heart weight and myocardial volume and the upregulation of hypertrophy marker gene expression indicated that cardiac hypertrophy may be the cause of death in young VASN-/- mice. Transcriptome analysis revealed that VASN deficiency led to MYL7 downregulation, which induced myocardial structure abnormalities and disorders. Our results revealed a pathological phenomenon in which VASN deficiency may lead to cardiac hypertrophy by downregulating MYL7 production. However, more research is necessary to elucidate the underlying mechanism.

Preparation of an anti-NEK2 monoclonal antibody and its application in liver cancer.

BACKGROUND: Never in mitosis gene-A (NIMA)-related expressed kinase 2 (NEK2) is a serine/threonine protein kinase regulated by the cell cycle. The purpose of this study was to obtain NEK2 protein to prepare an anti-NEK2 monoclonal antibody (mAb) and explore the application of the anti-NEK2 mAb of therapeutic and diagnostic in hepatocellular carcinoma (HCC). RESULTS: The NEK2 gene sequence was cloned from the normal liver cell line HL7702, and the full-length NEK2 gene sequence was cloned into the prokaryotic expression vector pET30a and transformed into Escherichia coli BL21 (DE3) cells. The recombinant fusion protein was obtained under optimized conditions and injected in BALB/c mice to prepare an anti-NEK2 mAb. By screening, we obtained a stable hybridoma cell line named 3A3 that could stably secrete anti-NEK2 mAb. Anti-NEK2 3A3 mAb was purified from ascites fluid. The isotype was IgG1, and the affinity constant (Kaff) was 6.0 × 108 L/mol. Western blot, indirect enzyme-linked immunosorbent assay (iELISA), immunofluorescence and immunocytochemical analyses showed that the mAb could specifically recognize the NEK2 protein. MTT assays showed that the mAb 3A3 could inhibit the proliferation of HCC cells. KEGG pathway analysis showed that NEK2 might affected pathways of the cell cycle. Moreover, NEK2-related genes were mainly enriched in the S and G2 phases and might act as tumor-promoting genes by regulating the S/G2 phase transition of HCC cells. CONCLUSIONS: An anti-NEK2 mAb with high potency, high affinity and high specificity was prepared by prokaryotic expression system in this study and may be used in the establishment of ELISA detection kits and targeted treatment of liver cancer.

Roving: The causes of interference and re-enabled learning in multi-task visual training.

People routinely perform multiple visual judgments in the real world, yet, intermixing tasks or task variants during training can damage or even prevent learning. This paper explores why. We challenged theories of visual perceptual learning focused on plastic retuning of low-level retinotopic cortical representations by placing different task variants in different retinal locations, and tested theories of perceptual learning through reweighting (changes in readout) by varying task similarity. Discriminating different (but equivalent) and similar orientations in separate retinal locations interfered with learning, whereas training either with identical orientations or sufficiently different ones in different locations released rapid learning. This location crosstalk during learning renders it unlikely that the primary substrate of learning is retuning in early retinotopic visual areas; instead, learning likely involves reweighting from location-independent representations to a decision. We developed an Integrated Reweighting Theory (IRT), which has both V1-like location-specific representations and higher level (V4/IT or higher) location-invariant representations, and learns via reweighting the readout to decision, to predict the order of learning rates in different conditions. This model with suitable parameters successfully fit the behavioral data, as well as some microstructure of learning performance in a new trial-by-trial analysis.

A field amplification enhanced paper-based analytical device with a robust chemiluminescence detection module.

A sensitive detection method combined with an effective on-line concentration may improve the analytical performance of a paper-based analytical device (PAD), and its merits of low cost and portability in POCT are fully demonstrated. Here, a sensitive PAD system with chemiluminescence (CL) detection and electrokinetic preconcentration was introduced, and the performance was demonstrated by the detection of hemin. A commercially available low cost and miniaturized optical detection module was used for the CL detection. Firstly, hemin was stacked on a simple paper fluidic channel based on field amplified stacking (FAS), and then a CL reagent (luminol-H2O2) was loaded on the stacked band to initiate the CL reaction. The photons were directly detected using the detection module. With optimization of the background electrolyte (BGE), voltage and CL reagent, a limit of detection (LOD) of 0.58 nM for hemin was obtained with a linear range of 1-1000 nM (R2 = 0.995). With FAS, the signal intensity was about 13-fold enhanced. This PAD also exhibited satisfactory selectivity over possible interfering components at a 104 times higher concentration. The applicability of the PAD was demonstrated by the detection of hemin from iron supplements and human serum samples. With total manual operation, recovery rates of 84.8-115.6% were obtained with an RSD of less than 14.3%. With the introduction of the optical detection model, and together with FAS, both the LOD and dynamic range of this PAD were effectively improved.

Electrokinetic stacking of electrically neutral analytes with paper-based analytical device.

Electrokinetic stacking (ES) is effective for improving sensitivity of paper-based analytical device (PAD) for charged analytes. In this paper, we successfully demonstrated ES of electrically neutral analytes on PAD, and the performance was characterized by smartphone-based colorimetry and fluorescence. Firstly, SDS from cathode reservoir stacked as a micelle band on an open paper fluidic channel by ES, and the target analyte was swept by the micelle. Meanwhile, the probes at the other side were carried by electroosmotic flow (EOF). Eventually, neutral components preloaded on the channel were concentrated as the narrow stacking band. Taking the rhodamine B as a probe, the effects of EOF, background electrolyte concentration and anionic surfactant concentration were investigated. Fluorescence detection of rhodamine B and colorimetric analysis of Sudan III demonstrated the sensitivity enhanced and its potential for the semi-quantitative test. Under the optimized conditions, fluorescence detection limit of 50 nM of rhodamine B was achieved with a linear range of 1.0-10 µM (R2 = 0.99). The colorimetric detection limit for Sudan III was 5.2 µM and the linear range was 5-40 µM (R2= 0.99). Compared with direct analysis without stacking, the signal levels of rhodamine B and Sudan III were increased by 30-fold and 6-fold, respectively. This study showed that with ES, sensitive and rapid PAD detection of electrically neutral analytes could be achieved.

Augmented Hebbian reweighting accounts for accuracy and induced bias in perceptual learning with reverse feedback.

Using an asymmetrical set of vernier stimuli (-15″, -10″, -5″, +10″, +15″) together with reverse feedback on the small subthreshold offset stimulus (-5″) induces response bias in performance (Aberg & Herzog, 2012; Herzog, Eward, Hermens, & Fahle, 2006; Herzog & Fahle, 1999). These conditions are of interest for testing models of perceptual learning because the world does not always present balanced stimulus frequencies or accurate feedback. Here we provide a comprehensive model for the complex set of asymmetric training results using the augmented Hebbian reweighting model (Liu, Dosher, & Lu, 2014; Petrov, Dosher, & Lu, 2005, 2006) and the multilocation integrated reweighting theory (Dosher, Jeter, Liu, & Lu, 2013). The augmented Hebbian learning algorithm incorporates trial-by-trial feedback, when present, as another input to the decision unit and uses the observer's internal response to update the weights otherwise; block feedback alters the weights on bias correction (Liu et al., 2014). Asymmetric training with reversed feedback incorporates biases into the weights between representation and decision. The model correctly predicts the basic induction effect, its dependence on trial-by-trial feedback, and the specificity of bias to stimulus orientation and spatial location, extending the range of augmented Hebbian reweighting accounts of perceptual learning.

Modeling trial by trial and block feedback in perceptual learning.

Feedback has been shown to play a complex role in visual perceptual learning. It is necessary for performance improvement in some conditions while not others. Different forms of feedback, such as trial-by-trial feedback or block feedback, may both facilitate learning, but with different mechanisms. False feedback can abolish learning. We account for all these results with the Augmented Hebbian Reweight Model (AHRM). Specifically, three major factors in the model advance performance improvement: the external trial-by-trial feedback when available, the self-generated output as an internal feedback when no external feedback is available, and the adaptive criterion control based on the block feedback. Through simulating a comprehensive feedback study (Herzog & Fahle, 1997), we show that the model predictions account for the pattern of learning in seven major feedback conditions. The AHRM can fully explain the complex empirical results on the role of feedback in visual perceptual learning.

An integrated reweighting theory of perceptual learning.

Improvements in performance on visual tasks due to practice are often specific to a retinal position or stimulus feature. Many researchers suggest that specific perceptual learning alters selective retinotopic representations in early visual analysis. However, transfer is almost always practically advantageous, and it does occur. If perceptual learning alters location-specific representations, how does it transfer to new locations? An integrated reweighting theory explains transfer over retinal locations by incorporating higher level location-independent representations into a multilevel learning system. Location transfer is mediated through location-independent representations, whereas stimulus feature transfer is determined by stimulus similarity at both location-specific and location-independent levels. Transfer to new locations/positions differs fundamentally from transfer to new stimuli. After substantial initial training on an orientation discrimination task, switches to a new location or position are compared with switches to new orientations in the same position, or switches of both. Position switches led to the highest degree of transfer, whereas orientation switches led to the highest levels of specificity. A computational model of integrated reweighting is developed and tested that incorporates the details of the stimuli and the experiment. Transfer to an identical orientation task in a new position is mediated via more broadly tuned location-invariant representations, whereas changing orientation in the same position invokes interference or independent learning of the new orientations at both levels, reflecting stimulus dissimilarity. Consistent with single-cell recording studies, perceptual learning alters the weighting of both early and midlevel representations of the visual system.

Mixed training at high and low accuracy levels leads to perceptual learning without feedback.

In this study, we investigated whether mixing easy and difficult trials can lead to learning in the difficult conditions. We hypothesized that while feedback is necessary for significant learning in training regimes consisting solely of low training accuracy trials, training mixtures with sufficient proportions of high accuracy training trials would lead to significant learning without feedback. Thirty-six subjects were divided into one experimental group in which trials with high training accuracy were mixed with those with low training accuracy and no feedback, and five control groups in which high and low accuracy training were mixed in the presence of feedback; high and high training accuracy were mixed or low and low training accuracy were mixed with and without feedback trials. Contrast threshold improved significantly in the low accuracy condition in the presence of high training accuracy trials (the high-low mixture group) in the absence of feedback, although no significant learning was found in the low accuracy condition in the group with the low-low mixture without feedback. Moreover, the magnitude of improvement in low accuracy trials without feedback in the high-low training mixture is comparable to that in the high accuracy training without feedback condition and those obtained in the presence of trial-by-trial external feedback. The results are both qualitatively and quantitatively consistent with the predictions of the Augmented Hebbian Re-Weighting model. We conclude that mixed training at high and low accuracy levels can lead to perceptual learning at low training accuracy levels without feedback.

Augmented Hebbian reweighting: interactions between feedback and training accuracy in perceptual learning.

Feedback plays an interesting role in perceptual learning. The complex pattern of empirical results concerning the role of feedback in perceptual learning rules out both a pure supervised mode and a pure unsupervised mode of learning and leads some researchers to the proposal that feedback may change the learning rate through top-down control but does not act as a teaching signal in perceptual learning (M. H. Herzog & M. Fahle, 1998). In this study, we tested the predictions of an augmented Hebbian reweighting model (AHRM) of perceptual learning (A. Petrov, B. A. Dosher, & Z.-L. Lu, 2005), in which feedback influences the effective rate of learning by serving as an additional input and not as a direct teaching signal. We investigated the interactions between feedback and training accuracy in a Gabor orientation identification task over six training days. The accelerated stochastic approximation method was used to track threshold contrasts at particular performance accuracy levels throughout training. Subjects were divided into 4 groups: high training accuracy (85% correct) with and without feedback, and low training accuracy (65%) with and without feedback. Contrast thresholds improved in the high training accuracy condition, independent of the feedback condition. However, thresholds improved in the low training accuracy condition only in the presence of feedback but not in the absence of feedback. The results are both qualitatively and quantitatively consistent with the predictions of the augmented Hebbian learning model and are not consistent with pure supervised error correction or pure Hebbian learning models.

Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting.

Using the external noise plus training paradigm, we have consistently found that two independent mechanisms, stimulus enhancement and external noise exclusion, support perceptual learning in a range of tasks. Here, we show that re-weighting of stable early sensory representations through Hebbian learning (Petrov et al., 2005, 2006) can generate performance patterns that parallel a large range of empirical data: (1) perceptual learning reduced contrast thresholds at all levels of external noise in peripheral orientation identification (Dosher & Lu, 1998, 1999), (2) training with low noise exemplars transferred to performance in high noise, while training with exemplars embedded in high external noise transferred little to performance in low noise (Dosher & Lu, 2005), and (3) pre-training in high external noise only reduced subsequent learning in high external noise, whereas pre-training in zero external noise left very little additional learning in all the external noise conditions (Lu et al., 2006). In the augmented Hebbian re-weighting model (AHRM), perceptual learning strengthens or maintains the connections between the most closely tuned visual channels and a learned categorization structure, while it prunes or reduces inputs from task-irrelevant channels. Reducing the weights on irrelevant channels reduces the contributions of external noise and additive internal noise. Manifestation of stimulus enhancement or external noise exclusion depends on the initial state of internal noise and connection weights in the beginning of a learning task. Both mechanisms reflect re-weighting of stable early sensory representations.