Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

doi:10.1111/cogs.12007

Cited by 45 publications

(114 citation statements)

References 89 publications

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“…Models of semantic memory assuming a parallel distributed representation of concepts explain semantic priming on the basis of overlap between mental representations of prime and target, so that processing a prime establishes an activation pattern that is similar to the target pattern. Thus, processing the target is facilitated because transition time from prime to target pattern is fast (Masson, 1995; see also Lerner, Bentin, & Shriki, 2012;McRae, de Sa, & Seidenberg, 1997;Plaut & Booth, 2000;Sharkey, 1990). On first sight, this model seems to be perfectly suited to explain evaluative priming in a semantic priming design.…”

Section: Short-term Increased Accessibility As the Prominent Principlmentioning

confidence: 99%

Priming is not Priming is not Priming

Wentura¹,

Rothermund²

2014

Social Cognition

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We propose a taxonomy according to which priming studies in social cognition research can be classified. Long-term priming is characterized by a long delay between the prime and test situations (several minutes or more) whereas short-term priming studies investigate short-term consequences (occurring within hundreds of milliseconds) of primes on subsequent target processing and response activation. Within short-term priming, we distinguish between response priming and semantic priming designs, and discuss the underlying mechanisms that mediate priming effects in the different designs. On the basis of these distinctions, we discuss the role of short-term priming research for an understanding of long-term priming effects in social cognition.Broadly speaking, priming research in the social cognition literature can be split into two-and-a-half branches by reference to prototypical designs. The most obvious differentiation (into the two dominant branches) can be made between what we will tentatively term long-term versus short-term priming designs. In long-term priming studies, the goal is almost always to activate some broad knowledge structure (a trait category, a stereotype, a goal, a mindset, an emotion). The aftermath of this activation is then tested in an appropriate context. Studies that aim at exploring what Molden (2014, this issue) generally described as social priming fall into the category of long-term priming, that is, studies that are concerned with effects related to standard categories of everyday behavior that are of evident practical or applied interest. What is most important for the remainder of the text is that longterm priming effects are on a time scale ranging from minutes to hours or even days and weeks.

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Section: Short-term Increased Accessibility As the Prominent Principlmentioning

confidence: 99%

Priming is not Priming is not Priming

Wentura¹,

Rothermund²

2014

Social Cognition

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“…If there was indeed no association between the primes and the target, these cases of''unrelated''priming would be incompatible with the fundamental process of propagation of activation involved in semantic priming (see Khalkhali et al 2012). However, we will see that the apparent absence of pair association and pair priming can be accounted for by a classical and simple mechanism reported at the neuronal level and used to describe activation in network models of priming (e.g., Anderson 1976Anderson , 1983aLavigne and Denis 2002;Mongillo et al 2003;Brunel and Lavigne 2009;Lerner et al 2012;Lerner and Shriki 2014;see McRae and Ross 2004;Randall et al 2004;Cree et al 1999;Becker et al 1997;Moss et al 1994;Masson et al 1991;Masson 1995;Plaut 1995;Plaut and Booth 2000). Indeed, in computational models, activation obeys a nonlinear, current-to-frequency transfer function inspired by the one observed in cortical neurons (Ricciardi 1977;Tuckwell 1988;Ermentrout and Kopell 1986).…”

Section: Methodsmentioning

confidence: 98%

“…Computational modeling allows linking behavioral data on semantic priming to biologically inspired properties of cortical networks (Mongillo et al 2003;Brunel and Lavigne 2009;Lavigne et al 2011;Lerner et al 2012;Lerner and Shriki 2014;see Bernacchia et al 2014). Results of the present experiment show that the processing of semantic patterns is content-specific at the level of triplets of words and not only of pairs of words.…”

Section: Cortical Network Modelmentioning

confidence: 99%

Semantic integration by pattern priming: experiment and cortical network model

et al. 2016

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Neural network models describe semantic priming effects by way of mechanisms of activation of neurons coding for words that rely strongly on synaptic efficacies between pairs of neurons. Biologically inspired Hebbian learning defines efficacy values as a function of the activity of pre-and post-synaptic neurons only. It generates only pair associations between words in the semantic network. However, the statistical analysis of large text databases points to the frequent occurrence not only of pairs of words (e.g., ''the way'') but also of patterns of more than two words (e.g., ''by the way''). The learning of these frequent patterns of words is not reducible to associations between pairs of words but must take into account the higher level of coding of three-word patterns. The processing and learning of pattern of words challenges classical Hebbian learning algorithms used in biologically inspired models of priming. The aim of the present study was to test the effects of patterns on the semantic processing of words and to investigate how an inter-synaptic learning algorithm succeeds at reproducing the experimental data. The experiment manipulates the frequency of occurrence of patterns of three words in a multiple-paradigm protocol. Results show for the first time that target words benefit more priming when embedded in a pattern with the two primes than when only associated with each prime in pairs. A biologically inspired inter-synaptic learning algorithm is tested that potentiates synapses as a function of the activation of more than two pre-and postsynaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results.

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“…Recently, we have published a series of computational studies attempting to integrate various results in the semantic priming literature within one, coherent framework (Lerner et al, 2010, 2012a, in press). Basing itself on previous neural-network models of semantic priming, our approach aimed at showing how the addition of several assumptions to the dynamics of a semantic network and the way it is controlled by task-dependent strategies allows fitting a substantial amount of the semantic-priming results in a natural way, and, no less important, demonstrate how various aspects of the relevant cognitive processes interact with each other.…”

Section: Introductionmentioning

confidence: 99%

Internally- and externally-driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation

Lerner

Shriki

2014

Front. Psychol.

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For the last four decades, semantic priming—the facilitation in recognition of a target word when it follows the presentation of a semantically related prime word—has been a central topic in research of human cognitive processing. Studies have drawn a complex picture of findings which demonstrated the sensitivity of this priming effect to a unique combination of variables, including, but not limited to, the type of relatedness between primes and targets, the prime-target Stimulus Onset Asynchrony (SOA), the relatedness proportion (RP) in the stimuli list and the specific task subjects are required to perform. Automatic processes depending on the activation patterns of semantic representations in memory and controlled strategies adapted by individuals when attempting to maximize their recognition performance have both been implicated in contributing to the results. Lately, we have published a new model of semantic priming that addresses the majority of these findings within one conceptual framework. In our model, semantic memory is depicted as an attractor neural network in which stochastic transitions from one stored pattern to another are continually taking place due to synaptic depression mechanisms. We have shown how such transitions, in combination with a reinforcement-learning rule that adjusts their pace, resemble the classic automatic and controlled processes involved in semantic priming and account for a great number of the findings in the literature. Here, we review the core findings of our model and present new simulations that show how similar principles of parameter-adjustments could account for additional data not addressed in our previous studies, such as the relation between expectancy and inhibition in priming, target frequency and target degradation effects. Finally, we describe two human experiments that validate several key predictions of the model.

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Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited

Cited by 45 publications

References 89 publications

Priming is not Priming is not Priming

Priming is not Priming is not Priming

Semantic integration by pattern priming: experiment and cortical network model

Internally- and externally-driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation

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