Sequential Presentation Protects Working Memory From Catastrophic Interference

Endress, Ansgar D.; Szabó, Szilárd

doi:10.1111/cogs.12828

Cited by 5 publications

(8 citation statements)

References 100 publications

(234 reference statements)

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“…Before presenting our results, it is useful to outline possible psychological interpretations of the forgetting parameter. Similar forgetting parameters are widely used in related models (e.g., Bays, Singh-Curry, Gorgoraptis, Driver, & Husain, 2010;Endress & Szabó, 2020;Gottlieb, 2007;Knops, Piazza, Sengupta, Eger, & Melcher, 2014;Roggeman, Fias, & Verguts, 2010), and seem plausible at least at the single neuron level (e.g., Whitmire & Stanley, 2016). Forgetting functions have also been proposed at the macroscopic, cognitive level (e.g., Wixted & Ebbesen, 1991;Rubin & Wenzel, 1996), though the specific forgetting functions are debated.…”

Section: Resultsmentioning

confidence: 94%

“…While forgetting is time-based in our model, many authors argue that, psychologically speaking, there is no forgetting over time unless there are other stimuli that interfere with the memory items (e.g., Baddeley & Scott, 1971;Berman, Jonides, & Lewis, 2009;Nairne, Whiteman, & Kelley, 1999). Here, we do not attempt to decide between these possibilities; in fact, the model equations in Supplementary Material A make it plausible that our interference parameter might well mimic the role of forgetting (see Endress & Szabó, 2020). Our point simply is that the (time-based or interference-based) mechanisms that lead to forgetting are critical for learning to occur.…”

Section: Resultsmentioning

confidence: 98%

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When forgetting fosters learning: A neural network model for statistical learning

Endress

Johnson

2021

Cognition

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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 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.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 98%

When forgetting fosters learning: A neural network model for statistical learning

Endress

Johnson

2021

Cognition

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“…The network used here is a fairly generic saliency map (e.g., Bays et al., 2010; Endress & Szabó, 2020; Gottlieb, 2007; Roggeman et al., 2010; Sengupta et al., 2014) augmented by a Hebbian learning component. The network comprises units representing populations of neurons encoding syllables (or other items).…”

Section: The Current Studymentioning

confidence: 99%

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress

2024

Developmental Science

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In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words—or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time‐locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word‐final syllables. This is because word‐final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language.Research Highlights Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory‐less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.

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“…Here, I provide computational support for this idea, and show that such electrophysiological results can be explained in a simple, memory-less Hebbian network. The network is a fairly generic saliency map (e.g., Bays, Singh-Curry, Gorgoraptis, Driver, & Husain, 2010;Endress & Szabó, 2020;Gottlieb, 2007;Roggeman, Fias, & Verguts, 2010;Sengupta, Surampudi, & Melcher, 2014) augmented by a Hebbian learning component. The network comprises units representing populations of neurons encoding syllables (or other items).…”

Section: The Current Studymentioning

confidence: 99%

“…While the time step is arbitrary in the absence of external input (see Endress & Szabó, 2020, for a proof), I use the duration of individual units (e.g., syllables, visual symbols etc.) as the time unit in the discretization as associative learning is generally invariant under temporal scaling of the experiment (e.g., Gallistel & Gibbon, 2000;Gallistel, Mark, King, & Latham, 2001).…”

Section: Supplementary Materials a Model Definitionmentioning

confidence: 99%

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress¹

2023

Preprint

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In many domains, learners need to extract recurring units from continuous sequences composed of discrete units. For example, fluent speech is perceived as a continuous signal (at least in unknown languages). Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism tracks how predictive syllables(or other items) are of one another. Syllables within the same word are more predictive of one another than syllables straddling word boundaries. Behaviorally, however, it is controversial whether such statistical learning abilities lead to memories of the underlying units beyond pairwise associations among syllables. The strongest evidence for this possibility comes from evoked electrophysiological responses time-locked to word boundaries (e.g., N400’s) and rhythmic neural activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences, the network activation is rhythmic with a periodicity of the word duration and an activation maximum on word-final syllables. This is because word-final syllables receive more excitatory input from earlier syllables with which they are associated than syllables that occur earlier in words (and are less predictable). Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. N400’s time-locked to word boundaries might thus index the reduced predictability of word-initial syllables rather than word-onsets. Learners might thus need to rely on other cues to learn the words of their native language.

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Sequential Presentation Protects Working Memory From Catastrophic Interference

Cited by 5 publications

References 100 publications

When forgetting fosters learning: A neural network model for statistical learning

When forgetting fosters learning: A neural network model for statistical learning

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

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