Rate of forgetting is independent of initial degree of learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Rivera-Lares K
PROVIDER: S-EPMC8735725 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process.
| S-EPMC6104671 | biostudies-literature
Project description:Learning often requires splitting continuous signals into recurring units, such as the discrete words constituting fluent speech; these units then need to be encoded in memory. A prominent candidate mechanism involves statistical learning of co-occurrence statistics like transitional probabilities (TPs), reflecting the idea that items from the same unit (e.g., syllables within a word) predict each other better than items from different units. TP computations are surprisingly flexible and sophisticated. Humans are sensitive to forward and backward TPs, compute TPs between adjacent items and longer-distance items, and even recognize TPs in novel units. We explain these hallmarks of statistical learning with a simple model with tunable, Hebbian excitatory connections and inhibitory interactions controlling the overall activation. With weak forgetting, activations are long-lasting, yielding associations among all items; with strong forgetting, no associations ensue as activations do not outlast stimuli; with intermediate forgetting, the network reproduces the hallmarks above. Forgetting thus is a key determinant of these sophisticated learning abilities. Further, in line with earlier dissociations between statistical learning and memory encoding, our model reproduces the hallmarks of statistical learning in the absence of a memory store in which items could be placed.
| S-EPMC8324515 | biostudies-literature
Project description:Agents living in volatile environments must be able to detect changes in contingencies while refraining to adapt to unexpected events that are caused by noise. In Reinforcement Learning (RL) frameworks, this requires learning rates that adapt to past reliability of the model. The observation that behavioural flexibility in animals tends to decrease following prolonged training in stable environment provides experimental evidence for such adaptive learning rates. However, in classical RL models, learning rate is either fixed or scheduled and can thus not adapt dynamically to environmental changes. Here, we propose a new Bayesian learning model, using variational inference, that achieves adaptive change detection by the use of Stabilized Forgetting, updating its current belief based on a mixture of fixed, initial priors and previous posterior beliefs. The weight given to these two sources is optimized alongside the other parameters, allowing the model to adapt dynamically to changes in environmental volatility and to unexpected observations. This approach is used to implement the "critic" of an actor-critic RL model, while the actor samples the resulting value distributions to choose which action to undertake. We show that our model can emulate different adaptation strategies to contingency changes, depending on its prior assumptions of environmental stability, and that model parameters can be fit to real data with high accuracy. The model also exhibits trade-offs between flexibility and computational costs that mirror those observed in real data. Overall, the proposed method provides a general framework to study learning flexibility and decision making in RL contexts.
| S-EPMC6488101 | biostudies-literature
Project description:Psychological studies in humans and behavioral studies of model organisms suggest that forgetting is a common and biologically regulated process, but the molecular, cellular, and circuit mechanisms underlying forgetting are poorly understood. Here we show that the bidirectional modulation of a small subset of dopamine neurons (DANs) after olfactory learning regulates the rate of forgetting of both punishing (aversive) and rewarding (appetitive) memories. Two of these DANs, MP1 and MV1, exhibit synchronized ongoing activity in the mushroom body neuropil in alive and awake flies before and after learning, as revealed by functional cellular imaging. Furthermore, while the mushroom-body-expressed dDA1 dopamine receptor is essential for the acquisition of memory, we show that the dopamine receptor DAMB, also highly expressed in mushroom body neurons, is required for forgetting. We propose a dual role for dopamine: memory acquisition through dDA1 signaling and forgetting through DAMB signaling in the mushroom body neurons.
| S-EPMC4083655 | biostudies-literature
Project description:Decision-making is assumed to be supported by model-free and model-based systems: the model-free system is based purely on experience, while the model-based system uses a cognitive map of the environment and is more accurate. The recently developed multistep decision-making task and its computational model can dissociate the contributions of the two systems and have been used widely. This study used this task and model to understand our value-based learning process and tested alternative algorithms for the model-free and model-based learning systems. The task used in this study had a deterministic transition structure, and the degree of use of this structure in learning is estimated as the relative contribution of the model-based system to choices. We obtained data from 29 participants and fitted them with various computational models that differ in the model-free and model-based assumptions. The results of model comparison and parameter estimation showed that the participants update the value of action sequences and not each action. Additionally, the model fit was improved substantially by assuming that the learning mechanism includes a forgetting process, where the values of unselected options change to a certain default value over time. We also examined the relationships between the estimated parameters and psychopathology and other traits measured by self-reported questionnaires, and the results suggested that the difference in model assumptions can change the conclusion. In particular, inclusion of the forgetting process in the computational models had a strong impact on estimation of the weighting parameter of the model-free and model-based systems.
| S-EPMC6520826 | biostudies-literature
Project description:Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.
| S-EPMC3386159 | biostudies-other
Project description:One of the reasons why we forget past experiences is because we acquire new memories in the interim. Although the hippocampus is thought to be important for acquiring and retaining memories, there is little evidence linking neural operations during new learning to the forgetting (or remembering) of earlier events. We found that, during the encoding of new memories, responses in the human hippocampus are predictive of the retention of memories for previously experienced, overlapping events. This brain-behavior relationship is evident in neural responses to individual events and in differences across individuals. We found that the hippocampus accomplishes this function by reactivating older memories as new memories are formed; in this case, reactivating neural responses that represented monetary rewards associated with older memories. These data reveal a fundamental mechanism by which the hippocampus tempers the forgetting of older memories as newer memories are acquired.
| S-EPMC2847013 | biostudies-literature
Project description:Memory suppression is investigated with the no-think paradigm, which produces forgetting following repeated practice of not thinking about a memory [Anderson MC, Green C (2001) Nature 410:366-369]. Because the forgotten item is not retrieved even when tested with an independent, semantically related cue, it has been assumed that this forgetting is due to an inhibition process. However, this conclusion is based on a single stage to recall, whereas global memory models, which produce forgetting through a process of interference, include both a sampling and a recovery stage to recall. By assuming that interference exists during recovery, these models can explain cue-independent forgetting. We tested several predictions of this interference explanation of cue-independent forgetting by modifying the think/no-think paradigm. We added a condition where participants quickly pressed enter rather than not thinking. We also manipulated initial memory strength and tested recognition memory. Most importantly, learning to quickly press enter produced as much cue-independent forgetting as no-think instructions. Demonstrating the adequacy of two-stage recall, a simple computational model (SAM-RI) simultaneously captured the original cue, independent cue, and recognition results.
| S-EPMC2747165 | biostudies-other
Project description:Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.
| S-EPMC8479083 | biostudies-literature
Project description:It remains unclear how memory engrams are altered by experience, such as new learning, to cause forgetting. Here, we report that short-term aversive memory in Drosophila is encoded by and retrieved from the mushroom body output neuron MBOn-?2?'1. Pairing an odor with aversive electric shock creates a robust depression in the calcium response of MBOn-?2?'1 and increases avoidance to the paired odor. Electric shock after learning, which activates the cognate dopamine neuron DAn-?2?'1, restores the response properties of MBOn-?2?'1 and causes behavioral forgetting. Conditioning with a second odor restores the responses of MBOn-?2?'1 to a previously learned odor while depressing responses to the newly learned odor, showing that learning and forgetting can occur simultaneously. Moreover, optogenetic activation of DAn-?2?'1 is sufficient for the bidirectional modulation of MBOn-?2?'1 response properties. Thus, a single DAn can drive both learning and forgetting by bidirectionally modulating a cellular memory trace.
| S-EPMC6239218 | biostudies-other