The effects of UPGRADE your performance on employment soft skills of students with intellectual and developmental disabilities: A pilot study of generalization

Previous research has identified UPGRADE Your Performance as a method for teaching employment soft skills to students with disabilities. UPGRADE Your Performance instruction is a multicomponent intervention including self-evaluation, self-graphing, goal setting, and technology-aided instruction. This pilot study investigated the generalized effects of UPGRADE Your Performance on soft skills of secondary students with intellectual and other developmental disabilities participating in an 18-21 transition program located on a university campus. Results indicated that when students improved in two targeted soft skill areas, generalization occurred to three non-targeted soft skill areas. Implications for practice and suggestions for future research are included.

The generalization-across-dimensions model applied to conditional temporal discrimination

Can simple choice conditional-discrimination choice be accounted for by recent quantitative models of combined stimulus and reinforcer control? In Experiment 1, two sets of five blackout durations, one using shorter intervals and one using longer intervals, conditionally signaled which subsequent choice response might provide food. In seven conditions, the distribution of blackout durations across the sets was varied. An updated version of the generalization-across-dimensions model nicely described the way that choice changed across durations. In Experiment 2, just two blackout durations acted as the conditional stimuli and the durations were varied over 10 conditions. The parameters of the model obtained in Experiment 1 failed adequately to predict choice in Experiment 2, but the model again fitted the data nicely. The failure to predict the Experiment 2 data from the Experiment 1 parameters occurred because in Experiment 1 differential control by reinforcer locations progressively decreased with blackout durations, whereas in Experiment 2 this control remained constant. These experiments extend the ability of the model to describe data from procedures based on concurrent schedules in which reinforcer ratios reverse at fixed times to those from conditional-discrimination procedures. Further research is needed to understand why control by reinforcer location differed between the two experiments.

Intolerance of uncertainty affects the behavioral and neural mechanisms of higher generalization

Intolerance of uncertainty (IU) is associated with several anxiety disorders. In this study, we employed rewards and losses as unconditioned positive and negative stimuli, respectively, to explore the effects of an individual's IU level on positive and negative generalizations using magnetic resonance imaging technology. Following instrumental learning, 48 participants (24 high IU; 24 low IU) were invited to complete positive and negative generalization tasks; their behavioral responses and neural activities were recorded by functional magnetic resonance imaging. The behavior results demonstrated that participants with high IUs exhibited higher generalizations to both positive and negative cues as compared with participants having low IUs. Neuroimaging results demonstrated that they exhibited higher activation levels in the right anterior insula and the default mode network (i.e. precuneus and posterior cingulate gyrus), as well as related reward circuits (i.e. caudate and right putamen). Therefore, higher generalization scores and the related abnormal brain activation may be key markers of IU as a vulnerability factor for anxiety disorders.

Children extract a new linguistic rule more quickly than adults

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language more quickly than adults during real-time exposure to input-indicative of true superior language learning abilities-or whether this advantage stems from other factors. To examine this issue, we compared the rate at which children (8-10 years) and adults extracted a novel, hidden linguistic rule, in which novel articles probabilistically predicted the animacy of associated nouns (e.g., "gi lion"). Participants categorized these two-word phrases according to a second, explicitly instructed rule over two sessions, separated by an overnight delay. Both children and adults successfully learned the hidden animacy rule through mere exposure to the phrases, showing slower response times and decreased accuracy to occasional phrases that violated the rule. Critically, sensitivity to the hidden rule emerged much more quickly in children than adults; children showed a processing cost for violation trials from very early on in learning, whereas adults did not show reliable sensitivity to the rule until the second session. Children also showed superior generalization of the hidden animacy rule when asked to classify nonword trials (e.g., "gi badupi") according to the hidden animacy rule. Children and adults showed similar retention of the hidden rule over the delay period. These results provide insight into the nature of the critical period for language, suggesting that children have a true advantage over adults in the rate of implicit language learning. Relative to adults, children more rapidly extract hidden linguistic structures during real-time language exposure. RESEARCH HIGHLIGHTS: Children and adults both succeeded in implicitly learning a novel, uninstructed linguistic rule, based solely on exposure to input. Children learned the novel linguistic rules much more quickly than adults. Children showed better generalization performance than adults when asked to apply the novel rule to nonsense words without semantic content. Results provide insight into the nature of critical period effects in language, indicating that children have an advantage over adults in real-time language learning.

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

Auditory aversive generalization learning prompts threat-specific changes in alpha-band activity

Pairing a neutral stimulus with aversive outcomes prompts neurophysiological and autonomic changes in response to the conditioned stimulus (CS+), compared to cues that signal safety (CS-). One of these changes-selective amplitude reduction of parietal alpha-band oscillations-has been reliably linked to processing of visual CS+. It is, however, unclear to what extent auditory conditioned cues prompt similar changes, how these changes evolve as learning progresses, and how alpha reduction in the auditory domain generalizes to similar stimuli. To address these questions, 55 participants listened to three sine wave tones, with either the highest or lowest pitch (CS+) being associated with a noxious white noise burst. A threat-specific (CS+) reduction in occipital-parietal alpha-band power was observed similar to changes expected for visual stimuli. No evidence for aversive generalization to the tone most similar to the CS+ was observed in terms of alpha-band power changes, aversiveness ratings, or pupil dilation. By-trial analyses found that selective alpha-band changes continued to increase as aversive conditioning continued, beyond when participants reported awareness of the contingencies. The results support a theoretical model in which selective alpha power represents a cross-modal index of continuous aversive learning, accompanied by sustained sensory discrimination of conditioned threat from safety cues.

Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback

We investigate human adults' ability to learn an abstract reasoning task quickly and to generalize outside of the range of training examples. Using a task based on a solution strategy in Sudoku, we provide Sudoku-naive participants with a brief instructional tutorial with explanatory feedback using a narrow range of training examples. We find that most participants who master the task do so within 10 practice trials and generalize well to puzzles outside of the training range. We also find that most of those who master the task can describe a valid solution strategy, and such participants perform better on transfer puzzles than those whose strategy descriptions are vague or incomplete. Interestingly, fewer than half of our human participants were successful in acquiring a valid solution strategy, and this ability was associated with completion of high school algebra and geometry. We consider the implications of these findings for understanding human systematic reasoning, as well as the challenges these findings pose for building computational models that capture all aspects of our findings, and we point toward a role for learning from instructions and explanations to support rapid learning and generalization.

Deriving relations at multiple levels of complexity following minimal instruction: A demonstration

Recommendations for achieving generalized instructional outcomes often overlook the capacity for generative learning for most verbally competent humans. Four children (ages 5-8) participated in this project. In Study 1, we provided decontextualized discrete trial teaching to establish arbitrary relations between colors, pictures of characters, and researcher motor actions. All participants engaged in derivative responding, providing evidence of relational framing. Subsequently, we demonstrated that, with no additional instruction, these derivatives contributed to effective action within a socially valid context (i.e., Candyland gameplay). Study 2 extended the demonstration by teaching frames of opposition. Following teaching, all participants engaged in novel and contextually appropriate responding that entailed the derivation of both coordination and opposition between untrained stimuli. This outcome demonstrates how teaching simple relations can result in learning that manifests at higher levels of complexity (i.e., relational networking), providing some evidence that there can be socially valid benefits to decontextualized discrete trial instruction.

The Pigeon as a Model of Complex Visual Processing and Category Learning

Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon's category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon's visual pathway. Given the pigeon's availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.

A daytime nap with REM sleep is linked to enhanced generalization of emotional stimuli

How memory representations are shaped during and after their encoding is a central question in the study of human memory. Recognition responses to stimuli that are similar to those observed previously can hint at the fidelity of the memories or point to processes of generalization at the expense of precise memory representations. Experimental studies utilizing this approach showed that emotions and sleep both influence these responses. Sleep, and more specifically rapid eye movement sleep, is assumed to facilitate the generalization of emotional memories. We studied mnemonic discrimination by the emotional variant of the Mnemonic Separation Task in participants (N = 113) who spent a daytime nap between learning and testing compared with another group that spent an equivalent time awake between the two sessions. Our findings indicate that the discrimination of similar but previously not seen items from previously seen ones is enhanced in case of negative compared with neutral and positive stimuli. Moreover, whereas the sleep and the wake groups did not differ in memory performance, participants entering rapid eye movement sleep exhibited increased generalization of emotional memories. Our findings indicate that entering into rapid eye movement sleep during a daytime nap shapes emotional memories in a way that enhances recognition at the expense of detailed memory representations.

Discriminative-stimulus effects of cannabidiol oil in Sprague-Dawley rats

Cannabidiol (CBD) is one of the major centrally active phytocannabinoid components of cannabis, and has been approved by the FDA only for the treatment of seizures associated with three rare disorders. It has also been touted as a potential treatment for anxiety in place of more traditional treatments like benzodiazepines. Although there is some evidence of anxiolytic effects of CBD, its suitability as a substitute for benzodiazepines is unknown. This experiment was designed to assess the extent to which CBD shares interoceptive discriminative-stimulus properties with the anxiolytic drug chlordiazepoxide (CDP), a benzodiazepine. In the present experiment, a range of doses (0-1569 mg/kg) of over-the-counter CBD oil was administered (i.g.) in male Sprague-Dawley rats trained to discriminate 5.6 mg/kg CDP from saline. Due to the long time-course effects of CBD, generalization tests were conducted at 90 and 120 min post-CBD administration. The two highest doses of CBD tested (1064 and 1569 mg/kg) were found to partially substitute for 5.6 mg/kg CDP, with mean percent responding on the CDP-associated lever reaching above 20% at time 2 (120 min post-CBD administration), suggesting that high doses of the over-the-counter CBD oils used in this experiment share interoceptive discriminative-stimulus properties to some degree with CDP. These results are novel in comparison to existing research into stimulus effects of CBD, in which substitution for benzodiazepines has not previously been observed.

Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared with adaptation to physical perturbations

We recently showed that subjects can learn motion state-dependent changes to motor output (temporal force patterns) based on explicit visual feedback of the equivalent force field (i.e., without the physical perturbation). Here, we examined the spatiotemporal properties of this learning compared with learning based on physical perturbations. There were two human subject groups and two experimental paradigms. One group (n = 40) experienced physical perturbations (i.e., a velocity-dependent force field, vFF), whereas the second (n = 40) was given explicit visual feedback (EVF) of the force-velocity relationship. In the latter, subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on movement velocity. In the first paradigm (spatial generalization), following vFF or EVF training, generalization of learning was tested by requiring subjects to move to 14 untrained target locations (0° to ±135° around the trained location). In the second paradigm (temporal stability), following training, we examined the decay of learning over eight delay periods (0 to 90 s). Results showed that learning based on EVF did not generalize to untrained directions, whereas the generalization for the vFF was significant for targets ≤ 45° away. In addition, the decay of learning for the EVF group was significantly faster than the FF group (a time constant of 2.72 ± 1.74 s vs. 12.53 ± 11.83 s). Collectively, our results suggest that recalibrating motor output based on explicit motion state information, in contrast to physical disturbances, uses learning mechanisms with limited spatiotemporal properties.NEW & NOTEWORTHY Adjustment of motor output based on limb motion state information can be achieved based on explicit information or from physical perturbations. Here, we investigated the spatiotemporal characteristics of short-term motor learning to determine the properties of the respective learning mechanisms. Our results suggest that adjustments based on physical perturbations are more temporally stable and applied over a greater spatial range than the learning based on explicit visual feedback, suggesting largely separate learning mechanisms.

Evolution at the Origins of Life?

The role of evolutionary theory at the origin of life is an extensively debated topic. The origin and early development of life is usually separated into a prebiotic phase and a protocellular phase, ultimately leading to the Last Universal Common Ancestor. Most likely, the Last Universal Common Ancestor was subject to Darwinian evolution, but the question remains to what extent Darwinian evolution applies to the prebiotic and protocellular phases. In this review, we reflect on the current status of evolutionary theory in origins of life research by bringing together philosophy of science, evolutionary biology, and empirical research in the origins field. We explore the various ways in which evolutionary theory has been extended beyond biology; we look at how these extensions apply to the prebiotic development of (proto)metabolism; and we investigate how the terminology from evolutionary theory is currently being employed in state-of-the-art origins of life research. In doing so, we identify some of the current obstacles to an evolutionary account of the origins of life, as well as open up new avenues of research.

DAMM for the detection and tracking of multiple animals within complex social and environmental settings

Accurate detection and tracking of animals across diverse environments are crucial for behavioral studies in various disciplines, including neuroscience. Recently, machine learning and computer vision techniques have become integral to the neuroscientist's toolkit, enabling high-throughput behavioral studies. Despite advancements in localizing individual animals in simple environments, the task remains challenging in complex conditions due to intra-class visual variability and environmental diversity. These limitations hinder studies in ethologically-relevant conditions, such as when animals are concealed within nests or in obscured environments. Moreover, current tools are laborious and time-consuming to employ, requiring extensive, setup-specific annotation and model training/validation procedures. To address these challenges, we introduce the 'Detect Any Mouse Model' (DAMM), a pretrained object detector for localizing mice in complex environments, capable of robust performance with zero to minimal additional training on new experimental setups. Our approach involves collecting and annotating a diverse dataset that encompasses single and multi-housed mice in various lighting conditions, experimental setups, and occlusion levels. We utilize the Mask R-CNN architecture for instance segmentation and validate DAMM's performance with no additional training data (zero-shot inference) and with few examples for fine-tuning (few-shot inference). DAMM excels in zero-shot inference, detecting mice, and even rats, in entirely unseen scenarios and further improves with minimal additional training. By integrating DAMM with the SORT algorithm, we demonstrate robust tracking, competitively performing with keypoint-estimation-based methods. Finally, to advance and simplify behavioral studies, we made DAMM accessible to the scientific community with a user-friendly Python API, shared model weights, and a Google Colab implementation.

Generalization and discrimination of inhibitory avoidance differentially engage anterior and posterior retrosplenial subregions

Introduction

In a variety of behavioral procedures animals will show selective fear responding in shock-associated contexts, but not in other contexts. However, several factors can lead to generalized fear behavior, where responding is no longer constrained to the conditioning context and will transfer to novel contexts.

Methods

Here, we assessed memory generalization using an inhibitory avoidance paradigm to determine if generalized avoidance behavior engages the retrosplenial cortex (RSC). Male and female Long Evans rats received inhibitory avoidance training prior to testing in the same context or a shifted context in two distinct rooms; one room that had fluorescent lighting (Light) and one that had red LED lighting (Dark).

Results

We found that animals tested in a light context maintained context-specificity; animals tested in the same context as training showed longer latencies to cross and animals tested in the shifted context showed shorter latencies to cross. However, animals tested in the dark generalized their avoidance behavior; animals tested in the same context and animals tested in the shifted context showed similarly-high latencies to cross. We next examined expression of the immediate early gene zif268 and perineuronal nets (PNNs) following testing and found that while activity in the basolateral amygdala corresponded with overall levels of avoidance behaviors, anterior RSC (aRSC) activity corresponded with learned avoidance generally, but posterior RSC (pRSC) activity seemed to correspond with generalized memory. PNN reduction in the RSC was associated with memory formation and retrieval, suggesting a role for PNNs in synaptic plasticity. Further, PNNs did not reduce in the RSC in animals who showed a generalized avoidance behavior, in line with their hypothesized role in memory consolidation.

Discussion

These findings suggest that there is differential engagement of retrosplenial subregions along the rostrocaudal axis to generalization and discrimination.

The effect of masks on infants' ability to fast-map and generalize new words

Young children today are exposed to masks on a regular basis. However, there is limited empirical evidence on how masks may affect word learning. The study explored the effect of masks on infants' abilities to fast-map and generalize new words. Seventy-two Chinese infants (43 males, Mage = 18.26 months) were taught two novel word-object pairs by a speaker with or without a mask. They then heard the words and had to visually identify the correct objects and also generalize words to a different speaker and objects from the same category. Eye-tracking results indicate that infants looked longer at the target regardless of whether a speaker wore a mask. They also looked longer at the speaker's eyes than at the mouth only when words were taught through a mask. Thus, fast-mapping and generalization occur in both masked and not masked conditions as infants can flexibly access different visual cues during word-learning.

The influence of instructions on reversing the generalization of valence, US expectancy, and electrodermal responding in fear conditioning

Pairing a conditional stimulus (CS) with an aversive unconditional stimulus (US) causes negative valence and US expectancy to generalize to stimuli that are perceptually and/or conceptually similar to the CS. Past research has shown that instructing participants that the US is more likely to follow stimuli that are dissimilar to the CS reversed the generalization of US expectancy but left generalized valence unchanged. Here, we examined whether instructions about the relationship between stimuli that are perceptually similar would affect the generalization of valence. A picture of an alien (CS+) was paired with an electric stimulus, while a perceptually different alien stimulus (CS-) was presented alone. After conditioning, valence, US expectancy, and electrodermal responses generalized to different aliens that were perceptually similar (by color and shape) to the CS+ and CS-. Participants were then instructed that aliens perceptually similar to the CS+ belonged to the same group as the CS- and that aliens perceptually similar to the CS- belonged to the same group as the CS+. The instructions caused an elimination (but not a reversal) of generalized expectancy and valence but did not affect generalized electrodermal responses. This suggests that evaluations of generalization stimuli are sensitive to instructions about their relationship to the CS and that dissociations reported in the literature between valence and expectancy after instructions may occur due to the type of instruction used.

Transfer and Retention Effects of a Motor Program in Children With Autism Spectrum Disorders

The current study examined the acquisition, retention, and transfer effects of a motor program. Children with autism spectrum disorder participated in a 9-week program that targeted 13 fundamental motor skills based upon the Test of Gross Motor Development-3. Assessments were conducted before and after the program, as well as at 2-month follow-up. Significant improvements were found on not only the trained fundamental motor skills (acquisition) but also the untrained tasks on balance (transfer). The follow-up tests revealed continuous improvement on the trained locomotor skills (retention), as well as the untrained skills on balance (retention + transfer). These findings highlight the importance of continuous support and long-term participation on motor practices.

Emotional Value of Fear Memory and the Role of the Ventral Hippocampus in Systems Consolidation

Recent studies have explored the circuitry involving the ventral hippocampus (vHPC), the amygdala, and the prefrontal cortex, a pathway mainly activated to store contextual information efficiently. Lesions in the vHPC impair remote memory, but not in the short term. However, how the vHPC is affected by distinct memory strength or its role in systems consolidation has not yet been elucidated. Here, we investigated how distinct training intensities, with strong or weak contextual fear conditioning, affect activation of the dorsal hippocampus (dHPC) and the vHPC. We found that the time course of memory consolidation differs in fear memories of different training intensities in both the dHPC and vHPC. Our results also indicate that memory generalization happens alongside greater activation of the vHPC, and these processes occur faster with stronger fear memories. The vHPC is required for the expression of remote fear memory and may control contextual fear generalization, a view corroborated by the fact that inactivation of the vHPC suppresses generalized fear expression, making memory more precise again. Systems consolidation occurs concomitantly with greater activation of the vHPC, which is accelerated in stronger fear memories. These findings lead us to propose that greater activation of the vHPC could be used as a marker for memory generalization.

How qualitative methods advance the study of causation in psychotherapy research

Objective: The use of qualitative methods for investigating causation has been controversial ever since the "paradigm wars" of the 1980s. Quantitative and experimental researchers have largely dismissed the relevance of qualitative research for causal investigations, while many qualitative researchers have rejected the concept of causation entirely. However, a growing number of scholars, in both research methods and philosophy, have proposed an alternative perspective, one that sees quantitative and qualitative approaches as having complementary strengths and limitations in understanding causation. In this article, we consider this perspective in relation to the study of causality in psychotherapy research. Method: This paper reviews and integrates key descriptions of the mechanisms for identifying causal processes using qualitative research. Results: An overview of how qualitative methods study causation is presented, considering its implications for both identifying causality and for generalizing causal conclusions. Conclusion: The paper holds relevance for establishing outcomes caused by psychotherapy treatments and for developing clinical practice guidance for the field.

Path Planning for Unmanned Surface Vehicles with Strong Generalization Ability Based on Improved Proximal Policy Optimization

To solve the problems of path planning and dynamic obstacle avoidance for an unmanned surface vehicle (USV) in a locally observable non-dynamic ocean environment, a visual perception and decision-making method based on deep reinforcement learning is proposed. This method replaces the full connection layer in the Proximal Policy Optimization (PPO) neural network structure with a convolutional neural network (CNN). In this way, the degree of memorization and forgetting of sample information is controlled. Moreover, this method accumulates reward models faster by preferentially learning samples with high reward values. From the USV-centered radar perception input of the local environment, the output of the action is realized through an end-to-end learning model, and the environment perception and decision are formed as a closed loop. Thus, the proposed algorithm has good adaptability in different marine environments. The simulation results show that, compared with the PPO algorithm, Soft Actor-Critic (SAC) algorithm, and Deep Q Network (DQN) algorithm, the proposed algorithm can accelerate the model convergence speed and improve the path planning performances in partly or fully unknown ocean fields.

The threshold of rule productivity in infants

Most learning theories agree that the productivity of a rule or a pattern relies on regular exemplars being dominant over exceptions; the threshold for productivity is, however, unclear; moreover, gradient productivity levels are assumed for different rules/patterns, regular or irregular. One theory by Yang, the Tolerance Principle (TP), specified a productivity threshold applicable to all rules, calculated by the numbers of total exemplars and exceptions of a rule; furthermore, rules are viewed as quantal, either productive or unproductive, with no gradient levels. We evaluated the threshold and gradience-quantalness questions by investigating infants' generalization. In an implicit learning task, 14-month-olds heard exemplars of an artificial word-order rule and exceptions; their distributions were set closed to the TP-threshold (5.77) on both sides: 11 regular exemplars vs. 5 exceptions in Condition 1 (productiveness predicted), and 10 regular exemplars vs. 6 exceptions in Condition 2 (unproductiveness predicted). These predictions were pitted against those of the statistical majority threshold (50%), a common assumption which would predict generalization in both conditions (68.75, 62.5%). Infants were tested on the trained rule with new exemplars. Results revealed generalization in Condition 1, but not in Condition 2, supporting the TP-threshold, not the statistical majority threshold. Gradience-quantalness was assessed by combined analyses of Conditions 1-2 and previous experiments by Koulaguina and Shi. The training across the conditions contained gradually decreasing regular exemplars (100, 80, 68.75, 62.5, 50%) relative to exceptions. Results of test trials showed evidence for quantalness in infants (productive: 100, 80, 68.75%; unproductive: 62.5, 50%), with no gradient levels of productivity.

Perceptual learning across saccades: Feature but not location specific

Perceptual learning is the ability to enhance perception through practice. The hallmark of perceptual learning is its specificity for the trained location and stimulus features, such as orientation. For example, training in discriminating a grating's orientation improves performance only at the trained location but not in other untrained locations. Perceptual learning has mostly been studied using stimuli presented briefly while observers maintained gaze at one location. However, in everyday life, stimuli are actively explored through eye movements, which results in successive projections of the same stimulus at different retinal locations. Here, we studied perceptual learning of orientation discrimination across saccades. Observers were trained to saccade to a peripheral grating and to discriminate its orientation change that occurred during the saccade. The results showed that training led to transsaccadic perceptual learning (TPL) and performance improvements which did not generalize to an untrained orientation. Remarkably, however, for the trained orientation, we found a complete transfer of TPL to the untrained location in the opposite hemifield suggesting high flexibility of reference frame encoding in TPL. Three control experiments in which participants were trained without saccades did not show such transfer, confirming that the location transfer was contingent upon eye movements. Moreover, performance at the trained location, but not at the untrained location, was also improved in an untrained fixation task. Our results suggest that TPL has both, a location-specific component that occurs before the eye movement and a saccade-related component that involves location generalization.

Research on automatic pilot repetition generation method based on deep reinforcement learning

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.

From features to categories: The development of inductive generalization

Young children can generalize from known to novel, but the underlying mechanism is still debated. Some argue that from an early age generalization is category-based and undergoes little development, while others believe that early generalization is similarity-based, and the use of categories emerges over time. The current research brings new evidence to the debate. In Experiment 1 (N = 118), we presented 3- to 5-year-olds and adults with a category learning task followed by an exemplar generation task. Then, in Experiment 2 (N = 126), we presented the same tasks but provided participants with additional conceptual information about the category members. Our results indicate that early reasoning undergoes dramatic development: whereas young children rely mostly on salient features, adults rely on category information. These results challenge category-based accounts of early generalization while supporting similarity-based accounts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

When Time Shifts the Boundaries: Isolating the Role of Forgetting in Children's Changing Category Representations

In studies of children's categorization, researchers have typically studied how encoding characteristics of exemplars contribute to children's generalization. However, it is unclear whether children's internal cognitive processes alone, independent of new information, may also influence their generalization. Thus, we examined the role that one cognitive process, forgetting, plays in shaping children's category representations by conducting three experiments. In the first two experiments, participants (NExp1=37, Mage=4.02 years; NExp2=32, Mage=4.48 years) saw a novel object labeled by the experimenter and then saw five new objects with between one and five features changed from the learned exemplar. The experimenter asked whether each object was a member of the same category as the exemplar; children saw the five new objects either immediately or after a five-minute delay. Children endorsed category membership at higher rates at immediate test than at delayed test, suggesting that children's category representations became narrower over time. In Experiment 3, we investigated forgetting as a key mechanism underlying the narrowing found in Experiments 1 and 2. We showed participants (NExp3=34, Mage=4.20 years) the same exemplars used in Experiments 1 and 2; then, either immediately or after a five-minute delay, we showed children seven individual object features and asked if each one had been part of the exemplar. Children's accuracy was lower after the delay, showing that they did indeed forget individual features. Taken together, these results show that forgetting plays an important role in changing children's newly-learned categories over time.

Individual Differences in Auditory Training Benefits for Hearing Aid Users

The present study aimed to examine whether individual differences in baseline speech perception could serve as predictors for the effectiveness and generalization of auditory training (AT) to non-trained tasks. Twelve adults, aged 60-75 years with bilateral hearing loss, completed a two-month, home-based, computerized AT program, involving sessions four times per week. Training tasks included the identification of vowel frontal, height, manner of articulation, point of articulation, voicing, and open-set consonant-vowel-consonant (CVC) words. Non-trained speech perception tests were conducted one month before AT, prior to training, after one and two months of training, and during a two-month follow-up. The results showed that one month of AT improved performance in most trained tasks, with generalization observed in the CVC words test and HeBio sentences with speech-shaped noise (SSN). No evidence of spontaneous learning or added benefit from an extra month of training was found. Most importantly, baseline speech perception predicted improvements in both training and post-training generalization tasks. This emphasizes the significance of adopting an individualized approach when determining the potential effectiveness of AT, applicable in both clinical and research contexts.

Distribution-dependent representations in auditory category learning and generalization

A fundamental objective in Auditory Sciences is to understand how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of acquired category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) framework proposes that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, procedural-based learning system, which is optimal for learning categories requiring pre-decisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual novelties and variabilities affect learners' generalization success. Learners can successfully categorize different sets of untrained sounds after only eight blocks of training for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners' generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different computations and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving variabilities to maintain perceptual constancy.

Generalized mutualisms promote range expansion in both plant and ant partners

Mutualism improves organismal fitness, but strong dependence on another species can also limit a species' ability to thrive in a new range if its partner is absent. We assembled a large, global dataset on mutualistic traits and species ranges to investigate how multiple plant-animal and plant-microbe mutualisms affect the spread of legumes and ants to novel ranges. We found that generalized mutualisms increase the likelihood that a species establishes and thrives beyond its native range, whereas specialized mutualisms either do not affect or reduce non-native spread. This pattern held in both legumes and ants, indicating that specificity between mutualistic partners is a key determinant of ecological success in a new habitat. Our global analysis shows that mutualism plays an important, if often overlooked, role in plant and insect invasions.

Behavioral contrast: An exploratory survey of practitioner experiences

Behavioral contrast is defined as a change in reinforcement conditions in one context that causes a change in behavior in the opposite direction in another, unchanged context. Although behavioral contrast has implications for applied behavior analysts, researchers have not examined ramifications or identified common methods of mitigating contrast in applied settings. Therefore, we surveyed Board Certified Behavior Analysts in an exploratory investigation to determine practitioner experiences with behavioral contrast. Participants' responses reflected a variety of themes: contrast resulted in conversations with stakeholders; supporting stakeholders and mitigating factors are important; contrast is due to inconsistencies across settings; and contrast affects stakeholder buy-in, hurts rapport or relationships, and produces negative emotions. Our results suggest that contrast is not an innocuous occurrence in applied settings. We recommend a variety of areas for future research to further predict and control contrast and to identify the extent to which it affects clinical practice.

Reweighting estimators to extend the external validity of clinical trials: methodological considerations

Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population.

Social Concepts Simplify Complex Reinforcement Learning

Humans often generalize rewarding experiences across abstract social roles. Theories of reward learning suggest that people generalize through model-based learning, but such learning is cognitively costly. Why do people seem to generalize across social roles with ease? Humans are social experts who easily recognize social roles that reflect familiar semantic concepts (e.g., "helper" or "teacher"). People may associate these roles with model-free reward (e.g., learning that helpers are rewarding), allowing them to generalize easily (e.g., interacting with novel individuals identified as helpers). In four online experiments with U.S. adults (N = 577), we found evidence that social concepts ease complex learning (people generalize more and at faster speed) and that people attach reward directly to abstract roles (they generalize even when roles are unrelated to task structure). These results demonstrate how familiar concepts allow complex behavior to emerge from simple strategies, highlighting social interaction as a prototype for studying cognitive ease in the face of environmental complexity.

Early mobile app training proficiency predicts how well memory-impaired individuals learn to use digital memory aids in the real world

Functional memory impairment following acquired brain injury can lead to decreased independence. External memory aids such as smartphones can be highly effective compensation tools, but cognitive deficits may create barriers to implementation in daily life. The present study examined predictors of real-world use of mobile calendar applications for memory compensation in an acquired brain injury sample. A retrospective chart review was completed from an outpatient rehabilitation program, extending 15 years into the past, yielding data from 34 eligible participants. All participants demonstrated skill learning of the calendar function in their digital device and subsequently completed the generalization phase of training, which is focused on real-world implementation (measured through prospective memory tasks). The results showed that the length of time required for skill learning of mobile calendars (event entry or responding to alerts) was not predictive of the duration of generalization training. Initial training performance for responding to alerts, but not event entry, was a significant predictor of the duration of generalization training needed to complete the program. A secondary analysis with a subset of the data revealed that individuals with additional executive deficits took significantly longer to complete generalization training compared to those with a more focal memory impairment.

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.

Age-specific walking speed during locomotor adaptation leads to more generalization across contexts

Previous work has shown that compared with young adults, older adults generalize their walking patterns more across environments that impose different motor demands (i.e., split-belt treadmill vs. overground). However, in this previous study, all participants walked at a speed that was more comfortable for older adults than young participants, which leads to the question of whether young adults would generalize more their walking patterns than older adults when exposed to faster speeds that are more comfortable for them. To address this question, we examined the interaction between healthy aging and walking speed on the generalization of a pattern learned on a split-belt treadmill (i.e., legs moving at different speeds) to overground. We hypothesized that walking speed during split-belt walking regulates the generalization of walking patterns in an age-specific manner. To this end, groups of young (<30 y/o) and older (65+ y/o) adults adapted their gait on a split-belt treadmill at either slower or faster walking speeds. We assessed the generalization of movements between the groups by quantifying their aftereffects during overground walking, where larger overground aftereffects represent more generalization, and zero aftereffects represent no generalization. We found an interaction between age and walking speed in the generalization of walking patterns. More specifically, older adults generalized more when adapted at slower speeds, whereas younger adults did so when adapted at faster speeds. These results suggest that comfortable walking speeds lead to more generalization of newly acquired motor patterns beyond the training contexts.

Competition and generalization impede cultural formation in wild jackdaws

Animal cultures have now been demonstrated experimentally in diverse taxa from flies to great apes. However, experiments commonly use tasks with unrestricted access to equal pay-offs and innovations seeded by demonstrators who are trained to exhibit strong preferences. Such conditions may not reflect those typically found in nature. For example, the learned preferences of natural innovators may be weaker, while competition for depleting resources can favour switching between strategies and generalizing from past experience. Here we show that in experiments where wild jackdaws (Corvus monedula) can freely discover depleting supplies of novel foods, generalization has a powerful effect on learning, allowing individuals to exploit multiple new opportunities through both social and individual learning. Further, in contrast to studies with trained demonstrators, individuals that were first to innovate showed weak preferences. As a consequence, many individuals ate all available novel foods, displaying no strong preference and no group-level culture emerged. Individuals followed a 'learn from adults' strategy, but other demographic factors played a minimal role in shaping social transmission. These results demonstrate the importance of generalization in allowing animals to exploit new opportunities and highlight how natural competitive dynamics may impede the formation of culture.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Activity in Barrel Cortex Related to Trace Eyeblink Conditioning

In mammals several memory systems are responsible for learning and storage of associative memory. Even apparently simple behavioral tasks, like pavlovian conditioning, have been suggested to engage, for instance, implicit and explicit memory processes. Here, we used single-whisker tactile trace eyeblink conditioning (TTEBC) to investigate learning and its neuronal bases in the mouse barrel column, the primary neocortical tactile representation of one whisker. Behavioral analysis showed that conditioned responses (CRs) are spatially highly restricted; they generalize from the principal whisker only to its direct neighbors. Within the respective neural representation, the principal column and its direct neighbors, spike activity showed a learning-related spike rate suppression starting during the late phase of conditioning stimulus (CS) presentation that was sustained throughout the stimulus-free trace period (Trace). Trial-by-trial analysis showed that learning-related activity was independent from the generation of eyelid movements within a trial, and set in around the steepest part of the learning curve. Optogenetic silencing of responses and their learning-related changes during CS and Trace epochs blocked CR acquisition but not its recall after learning. Silencing during the Trace alone, which carried major parts of the learning-related changes, had no effect. In summary, we demonstrate specific barrel column spike rate plasticity during TTEBC that can be partially decoupled from the CR, the learned eye closure, a hallmark of implicit learning. Our results, thus, point to a possible role of the barrel column in contributing to other kinds of memory as well.

On Neural Networks Fitting, Compression, and Generalization Behavior via Information-Bottleneck-like Approaches

It is well-known that a neural network learning process-along with its connections to fitting, compression, and generalization-is not yet well understood. In this paper, we propose a novel approach to capturing such neural network dynamics using information-bottleneck-type techniques, involving the replacement of mutual information measures (which are notoriously difficult to estimate in high-dimensional spaces) by other more tractable ones, including (1) the minimum mean-squared error associated with the reconstruction of the network input data from some intermediate network representation and (2) the cross-entropy associated with a certain class label given some network representation. We then conducted an empirical study in order to ascertain how different network models, network learning algorithms, and datasets may affect the learning dynamics. Our experiments show that our proposed approach appears to be more reliable in comparison with classical information bottleneck ones in capturing network dynamics during both the training and testing phases. Our experiments also reveal that the fitting and compression phases exist regardless of the choice of activation function. Additionally, our findings suggest that model architectures, training algorithms, and datasets that lead to better generalization tend to exhibit more pronounced fitting and compression phases.

Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data

Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g., by study site, subject, or experimental batch), leading to spurious associations, poor model fitting, and confounded analyses. While largely unaddressed in deep learning, this problem has been handled in the statistics community through mixed effects models, which separate cluster-invariant fixed effects from cluster-specific random effects. We propose a general-purpose framework for Adversarially-Regularized Mixed Effects Deep learning (ARMED) models through non-intrusive additions to existing neural networks: 1) an adversarial classifier constraining the original model to learn only cluster-invariant features, 2) a random effects subnetwork capturing cluster-specific features, and 3) an approach to apply random effects to clusters unseen during training. We apply ARMED to dense, convolutional, and autoencoder neural networks on 4 datasets including simulated nonlinear data, dementia prognosis and diagnosis, and live-cell image analysis. Compared to prior techniques, ARMED models better distinguish confounded from true associations in simulations and learn more biologically plausible features in clinical applications. They can also quantify inter-cluster variance and visualize cluster effects in data. Finally, ARMED matches or improves performance on data from clusters seen during training (5-28% relative improvement) and generalization to unseen clusters (2-9% relative improvement) versus conventional models.

Generalization of the modulatory effect of social interaction on personal space

Introduction

Personal space (PS) is a safe area around an individual's body that affects spatial distance when socially interacting with others. Previous studies have shown that social interaction may modulate PS. However, these findings are often confounded by the effects of familiarization. Furthermore, whether the potential regulatory effects of social interaction on PS can be generalized from interacting confederates to strangers remains unclear.

Methods

To answer these questions, we enrolled 115 participants in a carefully designed experiment.

Results

We found that prosocial interaction in the form of a cooperative task effectively reduced PS, and this regulatory effect could be generalized from interacting confederates to non-interacting confederates.

Discussion

These findings deepen our understanding of PS regulation and may be aid in the diagnosis and rehabilitation of dysfunctional social behaviors.

Generalization of Costly Pain-Related Avoidance Based on Real-Life Categorical Knowledge

Avoiding activities posing bodily threat is adaptive. However, spreading of avoidance to safe activities may cause functional disability in people with chronic pain. We investigated whether costly pain-related avoidance would generalize from one activity to another on the basis of real-life categorical knowledge in 40 pain-free people (30 female; mean age = 25 years; university students and public of Maastricht, The Netherlands). In a computer task, participants moved a joystick to complete activities from two categories (gardening and cleaning). During activities from the avoidance category, pain could be avoided at the cost of task efficiency by deviating from a short, pain-associated joystick movement. Activities from the safe category were never painful. Subsequently, we tested generalization of avoidance to novel pain-free activities from both categories. Participants generalized avoidance to novel activities from the avoidance category despite the novel activities not being paired with pain and despite avoidance costs, suggesting that costly (pain-related) avoidance generalizes from one activity to another on the basis of category knowledge and can thus be wide reaching, creating detrimental consequences.

Pre-Learning Stress That Is Temporally Removed from Acquisition Impairs Fear Learning

Few studies have examined the time-dependent effects of stress on fear learning. Previously, we found that stress immediately before fear conditioning enhanced fear learning. Here, we aimed to extend these findings by assessing the effects of stress 30 min prior to fear conditioning on fear learning and fear generalization. Two hundred and twenty-one healthy adults underwent stress (socially evaluated cold pressor test) or a control manipulation 30 min before completing differential fear conditioning in a fear-potentiated startle paradigm. One visual stimulus (CS+), but not another (CS-), was associated with an aversive airblast to the throat (US) during acquisition. The next day, participants were tested for their fear responses to the CS+, CS-, and several generalization stimuli. Stress impaired the acquisition of fear on Day 1 but had no significant impact on fear generalization. The stress-induced impairment of fear learning was particularly evident in participants who exhibited a robust cortisol response to the stressor. These findings are consistent with the notion that stress administered 30 min before learning impairs memory formation via corticosteroid-related mechanisms and may help us understand how fear memories are altered in stress-related psychological disorders.

C2RL: Convolutional-Contrastive Learning for Reinforcement Learning Based on Self-Pretraining for Strong Augmentation

Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a self-supervised learning framework with data augmentation in the reinforcement learning architecture can promote generalization to a certain extent. However, excessively large changes in the input images may disturb reinforcement learning. Therefore, we propose a contrastive learning method that can help manage the trade-off relationship between the performance of reinforcement learning and auxiliary tasks against the data augmentation strength. In this framework, strong augmentation does not disturb reinforcement learning and instead maximizes the auxiliary effect for generalization. Results of experiments on the DeepMind Control suite demonstrate that the proposed method effectively uses strong data augmentation and achieves a higher generalization than the existing methods.

Neural knowledge assembly in humans and neural networks

Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible "knowledge assembly" requires few-shot reorganization of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts before exposure to new knowledge that revealed how they were linked. Blood-oxygen-level-dependent (BOLD) signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt online stochastic gradient descent to permit similar rapid knowledge assembly in a neural network model.

On the Generalization of Deep Learning Models in Video Deepfake Detection

The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as "deepfakes", is making it more challenging to differentiate between real and fake content, while various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set. In this study, we carry out an analysis of different deep learning architectures in an attempt to understand which is more capable of better generalizing the concept of deepfake. According to our results, it appears that Convolutional Neural Networks (CNNs) seem to be more capable of storing specific anomalies and thus excel in cases of datasets with a limited number of elements and manipulation methodologies. The Vision Transformer, conversely, is more effective when trained with more varied datasets, achieving more outstanding generalization capabilities than the other methods analysed. Finally, the Swin Transformer appears to be a good alternative for using an attention-based method in a more limited data regime and performs very well in cross-dataset scenarios. All the analysed architectures seem to have a different way to look at deepfakes, but since in a real-world environment the generalization capability is essential, based on the experiments carried out, the attention-based architectures seem to provide superior performances.

Generalization of pain-related avoidance behavior based on de novo categorical knowledge

ABSTRACT

People with chronic pain often fear and avoid movements and activities that were never paired with pain. Safe movements may be avoided if they share some semantic relationship with an actual pain-associated movement. This study investigated whether pain-associated operant responses (movements) can become categorically associated with perceptually dissimilar responses, thus motivating avoidance of new classes of safe movements-a phenomenon known as category-based avoidance generalization. Using a robotic arm, 2 groups were trained to categorize arm movements in different ways. Subsequently, the groups learned through operant conditioning that an arm movement from one of the categories was paired with a high probability of pain, whereas the others were paired with either a medium probability of pain or no pain (acquisition phase). Self-reported pain-related fear and pain expectancy were collected as indices of fear learning. During a final generalization test phase, the movements categorically related to those from the acquisition phase were made available but in the absence of pain. Results showed that the generalization of outcome measures depended on the categorical connections between arm movements, ie, the groups avoided and feared the novel generalization movement categorically related to the pain-associated acquisition movement, depending on how they had previously learned to categorize the movements. This suggests that operant pain-related avoidance can generalize to safe behaviors, which are not perceptually, but categorically, similar to a pain-associated behavior. This form of pain-related avoidance generalization is problematic because category-based relations can be extremely wide reaching and idiosyncratic. Thus, category-based generalization of operant pain-related avoidance merits further investigation.

Transfer of Learned Cognitive Flexibility to Novel Stimuli and Task Sets

Adaptive behavior requires learning about the structure of one's environment to derive optimal action policies, and previous studies have documented transfer of such structural knowledge to bias choices in new environments. Here, we asked whether people could also acquire and transfer more abstract knowledge across different task environments, specifically expectations about cognitive control demands. Over three experiments, participants (Amazon Mechanical Turk workers; N = ~80 adults per group) performed a probabilistic card-sorting task in environments of either a low or high volatility of task rule changes (requiring low or high cognitive flexibility, respectively) before transitioning to a medium-volatility environment. Using reinforcement-learning modeling, we consistently found that previous exposure to high task rule volatilities led to faster adaptation to rule changes in the subsequent transfer phase. These transfers of expectations about cognitive flexibility demands were both task independent (Experiment 2) and stimulus independent (Experiment 3), thus demonstrating the formation and generalization of environmental structure knowledge to guide cognitive control.

A morphine reward generalization mouse model based on conditioned place preference and aversion

BACKGROUND

Conditioned place preference (CPP) is a common behavioral paradigm for studying the association of unconditioned stimulus reward memory with context. Generalization is a flexible memory recall pattern developed on the basis of original memory. Drug-seeking behaviors in substance use disorders (SUDs) exhibit diversity, which we generally attribute to the highly generalized features of SUD memory. However, to date, there are no animal models for SUD generalization studies.

METHODS

We design the generalization box (G-box) and the generalization retrieval process based on the conditioned place preference (CPP) model. In the memory retrieval stage, we replaced the conditioning CPP box (T-box) with a generalization box (G-box) to study drug generalization memory. For appearance, the generalized boxes have different angles and numbers of sides compared to the conditioning boxes. For the visual cues, the shapes of the symbols are different (triangle icons for the hexagonal chamber and dot icons for the round chamber), but the orientation information remains the same. To establish CPP generalization, the mice received morphine on the vertical or horizontal side of a conditioning box (T-box) and saline on the other side. Then, after CPP conditioning, the generalization test was performed in a generalization box (G-box: hexagonal chamber and Gr-box: round chamber) 21 days later.

RESULTS

CPP-conditioned mice still displayed a clear preference for similar visual information in the G-box. CPA-conditioned mice behaved similarly to CPP, with mice consistently avoiding similar visual information in the G-box. We further observed that the generalization results are similar using two generalization boxes (G-box and Gr-box).

CONCLUSION

In this study, we succeeded in creating a simple and effective generalization model for morphine reward. The establishment of this model provides a new tool for generalization studies of SUD and therapy in humans.

Geometric transformation of cognitive maps for generalization across hippocampal-prefrontal circuits

The ability to abstract information to guide decisions during navigation across changing environments is essential for adaptation and requires the integrity of the hippocampal-prefrontal circuitry. The hippocampus encodes navigational information in a cognitive map, but it remains unclear how cognitive maps are transformed across hippocampal-prefrontal circuits to support abstraction and generalization. Here, we simultaneously record hippocampal-prefrontal ensembles as rats generalize navigational rules across distinct environments. We find that, whereas hippocampal representational maps maintain specificity of separate environments, prefrontal maps generalize across environments. Furthermore, while both maps are structured within a neural manifold of population activity, they have distinct representational geometries. Prefrontal geometry enables abstraction of rule-informative variables, a representational format that generalizes to novel conditions of existing variable classes. Hippocampal geometry lacks such abstraction. Together, these findings elucidate how cognitive maps are structured into distinct geometric representations to support abstraction and generalization while maintaining memory specificity.