Backward Blocking Research Articles

Forward and backward blocking in statistical learning.

Prediction errors have a prominent role in many forms of learning. For example, in reinforcement learning, agents learn by updating the association between states and outcomes as a function of the prediction error elicited by the event. One paradigm often used to study error-driven learning is blocking. In forward blocking, participants are first presented with stimulus A, followed by outcome X (A→X). In the second phase, A and B are presented together, followed by X (AB→X). Here, A→X blocks the formation of B→X, given that X is already fully predicted by A. In backward blocking, the order of phases is reversed. Here, the association between B and X that is formed during the first learning phase of AB→X is weakened when participants learn exclusively A→X in the second phase. The present study asked the question whether forward and backward blocking occur during visual statistical learning, i.e., the incidental learning of the statistical structure of the environment. In a series of studies, using both forward and backward blocking, we observed statistical learning of temporal associations among pairs of images. While we found no forward blocking, we observed backward blocking, thereby suggesting a retrospective revaluation process in statistical learning and supporting a functional similarity between statistical learning and reinforcement learning.

A statistical foundation for derived attention

According to the theory of derived attention, organisms attend to cues with strong associations. Prior work has shown that – combined with a Rescorla–Wagner style learning mechanism – derived attention explains phenomena such as learned predictiveness, inattention to blocked cues, and value-based salience. We introduce a Bayesian derived attention model that explains a wider array of results than previous models and gives further insight into the principle of derived attention. Our approach combines Bayesian linear regression with the assumption that the associations of any cue with various outcomes share the same prior variance, which can be thought of as the inherent importance of that cue. The new model simultaneously estimates cue–outcome associations and prior variance through approximate Bayesian learning. A significant cue will develop large associations, leading the model to estimate a high prior variance and hence develop larger associations from that cue to novel outcomes. This provides a normative, statistical explanation for derived attention. Through simulation, we show that this Bayesian derived attention model not only explains the same phenomena as previous versions, but also retrospective revaluation. It also makes a novel prediction: inattention after backward blocking. We hope that further development of the Bayesian derived attention model will shed light on the complex relationship between uncertainty and predictiveness effects on attention.

Blocking is not 'pure' cue competition: Renewal-like effects in forward and backward blocking indicate contributions by associative cue interference.

Blocking (i.e., reduced responding to cue X following YX-outcome pairings in Phase 2 as a consequence of cue Y having been paired with the outcome in Phase 1) is one of the signature phenomena in Pavlovian conditioning. Its discovery promoted the development of multiple associative models, most of which viewed blocking as an instance of pure cue competition (i.e., a decrease in responding attributable to training two conditioned stimuli in compound). Two experiments are reported in which rats were examined in a fear conditioning paradigm (i.e., lick suppression), and context dependency of retrieval at test was used as an index of associative cue interference (i.e., a decrease in responding to a target cue as a result of training a second cue with the same outcome but without concurrent presentation of the two cues). Specifically, we observed renewal of forward-blocking which parallels renewal of proactive interference, and renewal of backward-blocking which parallels renewal of retroactive interference. Thus, both backward-blocking (Experiment 1, embedded in a sensory preconditioning design) and forward-blocking (Experiment 2, conducted in first-order conditioning) appear to be influenced by retroactive and proactive interference, respectively, as well as cue competition. Consequently, blocking, long regarded as a benchmark example of pure cue competition, is sometimes a hybrid of cue competition and associative interference. Finally, the Discussion considers whether stimulus competition and associative interference are two independent phenomena or products of a single underlying process. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Aging and associative binding in contingency learning

ABSTRACT Cue competition effects are pervasive in young adults’ learning, but evidence for these effects in older adults’ learning is mixed. For example, although older adults show strong forward blocking, they do not show recovery from overshadowing. We examined whether this could be due to problems with associative binding using a rapid, streamed trial contingency learning task to minimize long-term memory retrieval demands. In a forward blocking paradigm , target cues gained less predictive value when the competing companion cues had high predictive value and this forward blocking effect was similar for younger and older adults. In a backward blocking paradigm, target cues lost more predictive value when the competing companion cues had high predictive value, but this backward blocking effect was greater for younger than older adults. These findings, together with evidence that within-compound associations for companion and target cues mediate backward, but not forward cue competition effects, suggest that a decline in associative binding may be responsible for the absence of backward cue competition effects in older adults’ contingency learning .

Ecco: An error correcting comparator theory

Building on the work of Ralph Miller and coworkers (Miller and Matzel, 1988; Denniston et al., 2001; Stout and Miller, 2007), I propose a new formalization of the comparator hypothesis that seeks to overcome some shortcomings of existing formalizations. The new model, dubbed ecco for “Error-Correcting COmparisons,” retains the comparator process and the learning of CS–CS associations based on contingency. ecco assumes, however, that learning of CS–US associations is driven by total error correction, as first introduced by Rescorla and Wagner (1972). I explore ecco's behavior in acquisition, compound conditioning, blocking, backward blocking, and unovershadowing. In these paradigms, ecco appears capable of avoiding the problems of current comparator models, such as the inability to solve some discriminations and some paradoxical effects of stimulus salience. At the same time, ecco exhibits the retrospective revaluation phenomena that are characteristic of comparator theory.

The sometimes competing retrieval and Van Hamme & Wasserman models predict the selective role of within-compound associations in retrospective revaluation

The correlation between blocking and within-compound memory is stronger when compound training occurs before elemental training (i.e., backward blocking) than when the phases are reversed (i.e., forward blocking; Melchers et al., 2004, 2006). This trial order effect is often interpreted as problematic for performance-focused models that assume a critical role for within-compound associations in both retrospective revaluation and traditional cue competition. The present manuscript revisits this issue using a computational modeling approach. The fit of sometimes competing retrieval (SOCR; Stout & Miller, 2007) was compared to the fit of an acquisition-focused model of retrospective revaluation and cue competition. These simulations reveal that SOCR explains this trial order effect in some situations based on its use of local error reduction.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.

Solution of the comparator theory of associative learning.

We derive an analytical solution of the comparator theory of associative learning, as formalized by Stout and Miller (2007). The solution enables us to calculate exactly the predicted responding to stimuli in any experimental design and for any choice of model parameters. We illustrate its utility by calculating the predictions of comparator theory in some paradigmatic designs: acquisition of conditioned responses, compound conditioning, blocking, unovershadowing, and backward blocking. We consider several versions of the theory: first-order comparator theory (close to the original ideas of Miller & Matzel, 1988), second-order comparator theory (Denniston, Savastano, & Miller, 2001), and sometimes-competing retrieval (Stout & Miller, 2007). We show that all versions of comparator theory make a number of surprising predictions, some of which appear hard to reconcile with empirical data. Our solution paves the way for a fuller understanding of the theory and for its empirical evaluation.

Associative repetition priming as a measure of human contingency learning: Evidence of forward and backward blocking.

Associative theories have been widely used to explain human contingency learning. Standard experimental procedures in the field have requested verbal judgments as a measure of the cue-outcome relationships learned. According to these theories, knowledge retrieval is based on spreading activation processes. However, verbal judgments may allow or even promote the engagement of high-order processes that may hinder the interpretation of verbal judgments as the output of automatic retrieval processes like those posited. However, previous studies on human associative memory have shown that priming tests, under the right conditions, can minimize the engagement of high-order processes and serve as a measure of low-level automatic retrieval processes. Thus, a new human contingency learning task that incorporates a recognition priming test was developed and tested here. The results showed that, as predicted by associative theories, repetition priming was found after training. In addition, the results showed that relevant learning phenomena such as forward and backward blocking could also be detected using this test. Finally, training based on instructions did not modulate the priming effect. The relevance of these findings for theories of human contingency learning and priming is discussed.

Collision-Aware Routing and Spectrum Assignment in GMPLS-Enabled Flexible-Bandwidth Optical Network

In a flexible-bandwidth optical network (FBON) with a generalized multiprotocol label switching control plane, the resource reservation protocol with traffic engineering extensions (RSVP-TE) is employed to establish end-to-end bandwidth variable lightpaths. The resource reservation can be blocked due to spectrum contention of different concurrent RSVP instances (called backward blocking), and it is the predominant blocking contribution when traffic load is low and arrival highly dynamic. Also, due to its flexible traffic granularity in FBON, the backward collision would happen more frequently and the collision scenario would be more complicated than that in wavelength-routed networking; thus the need for a collision evaluation and avoiding method. So, a collision-avoiding routing with maximum available spectrum allocation (SA) options and an SA scheme to search the spectrum segment with minimum collision possibility are proposed for backward blocking reduction, as well as an extension to the RSVP-TE. Simulation results show that the proposed algorithm could achieve better blocking performance through significantly reducing the backward reservation collision but with acceptable forward blocking deterioration. At the same time, with the proposed algorithm, different granularity traffic could be served more fairly in FBON.

Computing Path Blocking Probability and Delay in Optical Networks With Retrial

Hybrid optical architectures combining path and packet switching can be good candidates for future optical networks because they exploit the best of both worlds. However, the optimization of some parameters of the hybrid switch by some metrics is vital to maximize the benefit of the hybrid architecture. Blocking rate and reservation delay are two of the most important performance metrics in the path switching layer of the hybrid architecture. In this paper, we propose an analytical method with an improved backward blocking analysis to compute both blocking rates and reservation delays in path switching optical WDM networks with destination-initiated reservation allowing retrial of failed reservation attempts. On a mesh topology, we show that the results of our analytical method and simulations were close to each other, while the analytical method was several orders of magnitude faster than the simulation, which may allow much faster performance analysis, design, and optimization of hybrid networks.

Cuando la contigüidad no es suficiente: bloqueo en relaciones de equivalencia

El fenómeno de bloqueo describe una circunstancia en la cual se impide o se dificulta el la transferencia de funciones entre eventos que se han presentado en una relación de contigüidad espacio-temporal. Desde una perspectiva teórica y empírica, el efecto bloqueo no debería predecirse en la adquisición de clases de estímulos aun cuando algunos de los estímulos de la misma clase se presenten simultáneamente. El presente estudio examina si la historia adicional de entrenamiento A-EI bien sea previa o posterior al entrenamiento con un estímulo muestra compuesto AX, es la variable crítica en la presentación del efecto bloqueo. Los desempeños obtenidos en las pruebas de bloqueo por los participantes de un grupo expuesto al procedimiento de bloqueo, un grupo expuesto al procedimiento de bloqueo invertido, y un grupo control, sugieren que el entrenamiento adicional con un solo elemento del compuesto no impide la inclusión de todos los estímulos dentro de la clase. Los casos en los que se observó el efecto bloqueo se interpretan en términos de tipos de percepciones elementales y configurales.

Cued reacquisition trials during extinction weaken contextual renewal in human predictive learning

Extinction is generally more context specific than acquisition, as illustrated by the renewal effect. While most strategies to counteract renewal focus on decreasing the context specificity of extinction, the present work aimed at increasing the context specificity of acquisition learning. Two experiments examined whether presenting cued reacquisition trials during extinction weaken renewal in human predictive learning. After acquisition in one context (AX) and extinction in another context (B), participants were given tests for renewal in a subset of the acquisition context (A) and the original acquisition context (AX). In Experiment 1, presentation of reacquisition trials during extinction, featured by a contextual cue from acquisition (X), weakened overall recovered responding in Context A but not in Context AX. In Experiment 2, we replicated our findings, but this time reacquisition training weakened renewed responding in Context A but not in Context AX. Most importantly, reacquisition training with a novel contextual cue (Y) did not weaken renewal (in A), suggesting that backward blocking effects increased the contextual control over acquisition performance.

Supplemental Material for Associative Repetition Priming as a Measure of Human Contingency Learning: Evidence of Forward and Backward Blocking

Supplemental Material for Associative Repetition Priming as a Measure of Human Contingency Learning: Evidence of Forward and Backward Blocking

Revisiting the role of within-compound associations in cue-interaction phenomena

Although it is thought that within-compound associations are necessary for the occurrence of both backward blocking and unovershadowing, it is not known whether this variable plays a similar role in mediating the two phenomena. Similarly, the roles of within-compound associations in forward blocking and in reduced overshadowing have not been tested independently. The present experiments evaluated how the strength of within-compound associations affects backward blocking, unovershadowing, forward blocking, and reduced overshadowing. Using an allergy task, the strength of within-compound associations was varied by taking advantage of the participants' prior knowledge of common and uncommon food pairings. Backward blocking and unovershadowing effects were present only when highly memorable compound cues were used. Moreover, the magnitudes of both retrospective revaluation effects were affected by the strength of within-compound associations. Forward blocking and reduced overshadowing effects were independent of within-compound associations. These results have important theoretical implications for causal learning research.

Wavelength Adaptive Assignment Scheme for Optical Transport Network in Intelligent Highway System

In this paper, a wavelength adaptive assignment scheme called CExF (collision-evaluated adaptive fit) has been proposed for Optical Transport Network in Intelligent Highway to solve the date intensive scheduling. CExF assign a limited and more accurate weight, where the weight denotes the concurrent reservation probability, and the destination can select a wavelength with less collision probability by a new adaptive wavelength assignment according to the weight. Simulations show that CExF performs better in backward blocking, and even in forward blocking under medium traffic loads.

A token-based routing mechanism for GMPLS-controlled WDM networks

This paper analyzes destination initiated reservation for Generalized Multi-Protocol Label Switching (GMPLS) based wavelength division multiplexing (WDM) networks and determines, via simulation, probabilities of both forward blocking and backward blocking. During light traffic load, any degradation in performance is due to backward blocking, whereas during heavy traffic load forward blocking dominates. In order to minimize performance degradation due to blocking, this paper proposes a token based routing scheme that is capable of searching for the availability of more than one route from source to destination, meeting the desired quality of service (QoS). Extensive simulation results show that the proposed technique significantly improves the blocking performance and setup delay.

Backward versus Forward Blocking: Evidence for Performance-Based Models of Human Contingency Learning

Two types of theories are usually invoked to account for cue-interaction effects in human-contingency learning, performance-based theories, such as the comparator hypothesis and statistical models, and learning-based theories, such as associative models. Interestingly, the former models predict two important cue-interaction effects, forward and backward blocking, should affect responding in a similar manner, whereas learning-based models predict the effect of forward blocking should be larger than the effect of backward blocking. Previous experiments involved important methodological problems, and results have been contradictory. The present experiment was designed to explore potential asymmetries between forward and backward blocking. Analyses yielded similar effect sizes, thereby favoring the explanation by performance-based models.

Forward and backward blocking in human contingency learning

Forward and backward blocking in human contingency learning

An instance theory of associative learning

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.