Enhancing learning and retention through the distribution of practice repetitions across multiple sessions.

The acquisition and retention of knowledge is affected by a multitude of factors including amount of practice, elapsed time since practice occurred, and the temporal distribution of practice. The third factor, temporal distribution of practice, is at the heart of research on the spacing effect. This research has consistently shown that separating practice repetitions by a delay slows acquisition but enhances retention. The current study addresses an empirical gap in the spacing effects literature. Namely, how does the allocation of a fixed number of practice repetitions among multiple sessions impact learning and retention? To address this question, we examined participants' acquisition and retention of declarative knowledge given different study schedules in which the number of practice repetitions increased, decreased, or remained constant across multiple acquisition sessions. The primary result was that retention depended strongly on the total number of sessions in which an item appeared, but not on how practice repetitions were distributed among those sessions. This outcome was consistent with predictions from a computational cognitive model of skill acquisition and retention called the Predictive Performance Equation (PPE). The success of the model in accounting for the patterns of performance across a large set of study schedules suggests that it can be used to tame the complexity of the design space and to identify schedules to enhance knowledge acquisition and retention.

The Stability of Influenza Vaccination Behavior Over Time: A Longitudinal Analysis of Individuals Across 8 Years.

BACKGROUND: Seasonal influenza vaccination is an important behavior with significant individual and public health consequences, yet fewer than half of individuals in the USA are vaccinated annually. To promote vaccination adherence, it is important to understand the factors that affect vaccination behavior. PURPOSE: In this research, we focused on one such factor, an individual's vaccination history. We gathered longitudinal data to track and understand the relationship between an individual's vaccination history and their current behaviors. METHODS: U.S. adults completed multiple surveys over an 8 year period, which asked about whether they had received the influenza vaccination during the previous flu season. We analyzed the data to determine the strength of the relationship between vaccination decisions across single-year and multiyear intervals. Additionally, we fitted two mathematical models to the data to determine whether individuals were better characterized as having a stable propensity to vaccinate or a stable propensity to repeat their previous decisions. RESULTS: Individuals exhibited highly consistent behavior across adjacent years, yet, across the complete extent of the longitudinal study, they were far more likely to repeat the earlier decision to vaccinate. Surprisingly, the results of the mathematical model suggest that individuals are better characterized as having a stable propensity to repeat their previous decisions rather than a stable propensity to vaccinate per se. Although most individuals had an extremely strong tendency to repeat the previous decision, some had a far weaker propensity to do so. CONCLUSIONS: This suggests that interventions intended to increase vaccination uptake might be most impactful for those individuals with only a weak tendency to vaccinate or not to vaccinate.

The Effects of Probe Similarity on Retrieval and Comparison Processes in Associative Recognition.

In this study, we investigated the information processing stages underlying associative recognition. We recorded EEG data while participants performed a task that involved deciding whether a probe word triple matched any previously studied triple. We varied the similarity between probes and studied triples. According to a model of associative recognition developed in the Adaptive Control of Thought-Rational cognitive architecture, probe similarity affects the duration of the retrieval stage: Retrieval is fastest when the probe is similar to a studied triple. This effect may be obscured, however, by the duration of the comparison stage, which is fastest when the probe is not similar to the retrieved triple. Owing to the opposing effects of probe similarity on retrieval and comparison, overall RTs provide little information about each stage's duration. As such, we evaluated the model using a novel approach that decomposes the EEG signal into a sequence of latent states and provides information about the durations of the underlying information processing stages. The approach uses a hidden semi-Markov model to identify brief sinusoidal peaks (called bumps) that mark the onsets of distinct cognitive stages. The analysis confirmed that probe type has opposite effects on retrieval and comparison stages.

An attentional-adaptation account of spatial negative priming: evidence from event-related potentials.

Negative priming (NP) refers to a slower response to a target stimulus if it has been previously ignored. To examine theoretical accounts of spatial NP, we recorded behavioral measures and event-related potentials (ERPs) in a target localization task. A target and distractor briefly appeared, and the participant pressed a key corresponding to the target's location. The probability of the distractor appearing in each of four locations varied, whereas the target appeared with equal probabilities in all locations. We found that response times (RTs) were fastest when the prime distractor appeared in its most probable (frequent) location and when the prime target appeared in the location that never contained a distractor. Moreover, NP effects varied as a function of location: They were smallest when targets followed distractors in the frequent distractor location-a finding not predicted by episodic-retrieval or suppression accounts of NP. The ERP results showed that the P2, an ERP component associated with attentional orientation, was smaller in prime displays when the distractor appeared in its frequent location. Moreover, no differences were apparent between negative-prime and control trials in the N2, which is associated with suppression processes, nor in the P3, which is associated with episodic retrieval processes. These results indicate that the spatial NP effect is caused by both short- and long-term adaptation in preferences based on the history of inspecting unsuccessful locations. This article is dedicated to the memory of Edward E. Smith, and we indicate how this study was inspired by his research career.

Modulation of the feedback-related negativity by instruction and experience.

A great deal of research focuses on how humans and animals learn from trial-and-error interactions with the environment. This research has established the viability of reinforcement learning as a model of behavioral adaptation and neural reward valuation. Error-driven learning is inefficient and dangerous, however. Fortunately, humans learn from nonexperiential sources of information as well. In the present study, we focused on one such form of information, instruction. We recorded event-related potentials as participants performed a probabilistic learning task. In one experiment condition, participants received feedback only about whether their responses were rewarded. In the other condition, they also received instruction about reward probabilities before performing the task. We found that instruction eliminated participants' reliance on feedback as evidenced by their immediate asymptotic performance in the instruction condition. In striking contrast, the feedback-related negativity, an event-related potential component thought to reflect neural reward prediction error, continued to adapt with experience in both conditions. These results show that, whereas instruction may immediately control behavior, certain neural responses must be learned from experience.

Electrophysiological responses to feedback during the application of abstract rules.

Much research focuses on how people acquire concrete stimulus-response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individual's representation of a task's states and actions shapes behavior as well as the FRN.

Stages of processing in associative recognition: evidence from behavior, EEG, and classification.

In this study, we investigated the stages of information processing in associative recognition. We recorded EEG data while participants performed an associative recognition task that involved manipulations of word length, associative fan, and probe type, which were hypothesized to affect the perceptual encoding, retrieval, and decision stages of the recognition task, respectively. Analyses of the behavioral and EEG data, supplemented with classification of the EEG data using machine-learning techniques, provided evidence that generally supported the sequence of stages assumed by a computational model developed in the Adaptive Control of Thought-Rational cognitive architecture. However, the results suggested a more complex relationship between memory retrieval and decision-making than assumed by the model. Implications of the results for modeling associative recognition are discussed. The study illustrates how a classifier approach, in combination with focused manipulations, can be used to investigate the timing of processing stages.

Reciprocal relationships among influenza experiences, perceptions, and behavior: Results from a national, longitudinal survey of United States adults.

OBJECTIVE: Our objective was to model the reciprocal relationships of perceived risk of contracting influenza with and without influenza vaccination, vaccination behavior, and reported influenza illness. METHODS: We fit structural equation models to data from a longitudinal survey of adults in the United States collected through the RAND American Life Panel. Data come from fall and spring surveys fielded before and after each of 3 influenza seasons, 2016/2017, 2017/2018, and 2018/2019, for a total of 6 waves. RESULTS: As expected, reported influenza experience was associated with increased perceived influenza risk in subsequent survey waves. Furthermore, perceived risk was associated with subsequent vaccination behavior, such that vaccination was more common for those with higher perceived unvaccinated influenza risk and lower perceived vaccinated influenza risk. Perhaps surprisingly, both elements of perceived risk were also associated with a greater likelihood of subsequent reported influenza illness. This malleability in illness reports may reflect uncertainty, as more respondents reported being sick but being unsure about whether they had influenza than reported certainty that they had influenza. CONCLUSIONS: Interventions that influence perceptions about past experience with influenza, including increased testing and informational campaigns about influenza symptoms, could have unanticipated impacts on perceptions of influenza vaccination and vaccination behavior.

Learning from delayed feedback: neural responses in temporal credit assignment.

When feedback follows a sequence of decisions, relationships between actions and outcomes can be difficult to learn. We used event-related potentials (ERPs) to understand how people overcome this temporal credit assignment problem. Participants performed a sequential decision task that required two decisions on each trial. The first decision led to an intermediate state that was predictive of the trial outcome, and the second decision was followed by positive or negative trial feedback. The feedback-related negativity (fERN), a component thought to reflect reward prediction error, followed negative feedback and negative intermediate states. This suggests that participants evaluated intermediate states in terms of expected future reward, and that these evaluations supported learning of earlier actions within sequences. We examine the predictions of several temporal-difference models to determine whether the behavioral and ERP results reflected a reinforcement-learning process.

The strategic nature of changing your mind.

In two experiments, we studied how people's strategy choices emerge through an initial and then a more considered evaluation of available strategies. The experiments employed a computer-based paradigm where participants solved multiplication problems using mental and calculator solutions. In addition to recording responses and solution times, we gathered data on mouse cursor movements. Participants' motor behavior was revealing; although people rapidly initiated movement to the calculator box or the answer input box, they frequently changed their minds and went to the other box. Movement initiation direction depended on problem difficulty and calculator responsiveness. Ultimate strategy selection also depended on these factors, but was further influenced by movement initiation direction. We conclude that strategy selection is iterative, as revealed by these differences between early cursor movement and eventual strategy implementation. After rapidly initiating movement favoring one strategy, people carefully evaluate the applicability of that strategy in the current context.

Trajectories emerging from discrete versus continuous processing models in phonological competitor tasks: a commentary on Spivey, Grosjean, and Knoblich (2005).

M. J. Spivey, M. Grosjean, and G. Knoblich showed that in a phonological competitor task, participants' mouse cursor movements showed more curvature toward the competitor item when the competitor and target were phonologically similar than when the competitor and target were phonologically dissimilar. Spivey et al. interpreted this result as evidence for continuous cascading of information during the processing of spoken words. Here we show that the results of Spivey et al.need not be ascribed to continuous speech processing. Instead, their results can be ascribed to discrete processing of speech, provided one appeals to an already supported model of motor control that asserts that switching movements from 1 target to another relies on superposition of the 2nd movement onto the 1st. The latter process is a continuous cascade, a fact that indirectly strengthens the plausibility of continuous cascade models. However, the fact that we can simulate the results of Spivey et al.with a continuous motor output model and a discrete perceptual model shows that the implications of Spivey et al.'s experiment are less clear than these authors supposed.

Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides.

Mechanisms underlying the spacing effect in learning: A comparison of three computational models.

The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but enhances retention. The current study tested the suitability of the underlying, explanatory mechanism in three computational models of the spacing effect. The relearning of forgotten material was measured, as the models differ in their predictions of how the initial study conditions should affect relearning. Participants learned Japanese-English paired associates presented in a massed or spaced manner during an acquisition phase. They were tested on the pairs after retention intervals ranging from 1 to 21 days. Corrective feedback was given during retention tests to enable relearning. The results of 2 experiments showed that spacing slowed learning during the acquisition phase, increased retention at the start of tests, and accelerated relearning during tests. Of the 3 models, only 1, the predictive performance equation (PPE), was consistent with the finding of spacing-accelerated relearning. The implications of these results for learning theory and educational practice are discussed. (PsycINFO Database Record

Relationship of P3b single-trial latencies and response times in one, two, and three-stimulus oddball tasks.

The P300 is one of the most widely studied components of the human event-related potential. According to a longstanding view, the P300, and particularly its posterior subcomponent (i.e., the P3b), is driven by stimulus categorization. Whether the P3b relates to tactical processes involved in immediate responding or strategic processes that affect future behavior remains controversial, however. It is difficult to determine whether variability in P3b latencies relates to variability in response times because of limitations in the methods currently available to quantify the latency of the P3b during single trials. In this paper, we report results from the Psychomotor Vigilance Task (PVT), the Hitchcock Radar Task, and a 3-Stimulus Oddball Task. These represent variants of the one-, two-, and three-stimulus oddball paradigms commonly used to study the P3b. The PVT requires simple detection, whereas the Hitchcock Radar Task and the 3-Stimulus Task require detection and categorization. We apply a novel technique that combines hidden semi-Markov models and multi-voxel pattern analysis (HSMM-MVPA) to data from the three experiments. HSMM-MVPA revealed a processing stage in each task corresponding to the P3b. Trial-by-trial variability in the latency of the processing stage correlated with response times in the Hitchcock Radar Task and the 3-Stimulus Task, but not the PVT. These results indicate that the P3b reflects a stimulus categorization process, and that its latency is strongly associated with response times when the stimulus must be categorized before responding. In addition to those theoretical insights, the ability to detect the onset of the P3b and other components on a single-trial basis using HSMM-MVPA opens the door for new uses of mental chronometry in cognitive neuroscience.

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss.

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Association of COVID-19 Vaccination With Influenza Vaccine History and Changes in Influenza Vaccination.

This survey study assesses how COVID-19 vaccination differs across historical influenza vaccination patterns and whether influenza vaccination changed during the COVID-19 pandemic.

Hitting the reset button: An ERP investigation of memory for temporal context.

This study explored how temporal context influences recognition. In an ERP experiment, subjects were asked to judge whether pictures, presented one at a time, had been seen since the previous appearance of a special reset screen. The reset screen separated sequences of successively presented stimuli and signaled a change in temporal context. A "new-repeat" picture was one that had been seen before but was to be called "new" because it had not appeared since the previous reset screen. New-repeat pictures elicited a more negative FN400 component than did "old" pictures even though both had seen before during the experiment. This suggests that familiarity, as indexed by the FN400, is sensitive to temporal context. An earlier frontopolar old/new effect distinguished pictures that were seen for the first time in the experiment from all other pictures. The late positive component (LPC), which is typically greater for old stimuli, was smaller for new-repeat pictures than for pictures seen for the first time in the experiment. Finally, individual differences in task performance were predicted by the differences in amplitude of P3b that was evoked by the onset of the reset screen.

The discovery of processing stages: Extension of Sternberg's method.

We introduce a method for measuring the number and durations of processing stages from the electroencephalographic signal and apply it to the study of associative recognition. Using an extension of past research that combines multivariate pattern analysis with hidden semi-Markov models, the approach identifies on a trial-by-trial basis where brief sinusoidal peaks (called bumps) are added to the ongoing electroencephalographic signal. We propose that these bumps mark the onset of critical cognitive stages in processing. The results of the analysis can be used to guide the development of detailed process models. Applied to the associative recognition task, the hidden semi-Markov models multivariate pattern analysis method indicates that the effects of associative strength and probe type are localized to a memory retrieval stage and a decision stage. This is in line with a previously developed the adaptive control of thought-rational process model, called ACT-R, of the task. As a test of the generalization of our method we also apply it to a data set on the Sternberg working memory task collected by Jacobs, Hwang, Curran, and Kahana (2006). The analysis generalizes robustly, and localizes the typical set size effect in a late comparison/decision stage. In addition to providing information about the number and durations of stages in associative recognition, our analysis sheds light on the event-related potential components implicated in the study of recognition memory. (PsycINFO Database Record

Mechanisms for Robust Cognition.

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.

Navigating complex decision spaces: Problems and paradigms in sequential choice.

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides 2 general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes, cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior but they also provide a useful framework for understanding neural reward valuation and action selection.