Spreading activation in an attractor network with latching dynamics: automatic semantic priming revisited

A Simple Computational Model of Semantic Priming in 18-Month-Olds

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by semantic feature overlap, whereas associative priming between words is explained by Hebbian links between semantic representations derived from co-occurrence relations between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap per se need not be learned. We address this paradox in the model by showing that feature overlap itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.

The role of alpha oscillations in free- and goal-directed semantic associations

Alpha oscillations are known to play a central role in several higher-order cognitive functions, especially selective attention, working memory, semantic memory, and creative thinking. Nonetheless, we still know very little about the role of alpha in the generation of more remote semantic associations, which is key to creative and semantic cognition. Furthermore, it remains unclear how these oscillations are shaped by the intention to "be creative," which is the case in most creativity tasks. We aimed to address these gaps in two experiments. In Experiment 1, we compared alpha oscillatory activity (using a method which distinguishes genuine oscillatory activity from transient events) during the generation of free associations which were more vs. less distant from a given concept. In Experiment 2, we replicated these findings and also compared alpha oscillatory activity when people were generating free associations versus associations with the instruction to be creative (i.e. goal-directed). We found that alpha was consistently higher during the generation of more distant semantic associations, in both experiments. This effect was widespread, involving areas in both left and right hemispheres. Importantly, the instruction to be creative seems to increase alpha phase synchronisation from left to right temporal brain areas, suggesting that intention to be creative changed the flux of information in the brain, likely reflecting an increase in top-down control of semantic search processes. We conclude that goal-directed generation of remote associations relies on top-down mechanisms compared to when associations are freely generated.

Prime saliency in semantic priming with 18-month-olds

This study investigated semantic priming in 18-month-old infants using the inter-modal priming technique, focusing on the effects of prime repetition on saliency. Our findings showed that prime repetition led to longer looking times at target referents for related primes compared to unrelated primes, supporting the existence of a structured semantic system in infants as young as 18 months. The results are consistent with both Spreading Activation and Distributed models of semantic priming. Additionally, our findings highlighted the impact of prime-target stimulus onset asynchronies (SOAs) on priming effects, revealing positive, negative, or no priming effects depending on the chosen SOA. A post-hoc explanation of this finding points to negative priming as a possible mechanism. The study also demonstrated the utility of the inter-modal priming task in studying lexical-semantic structure in younger infants with its diverse measures of infant behaviour.

Multistability in neural systems with random cross-connections

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability-defined as the system possessing more than one stable fixed point-in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system.

Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multistability in neural systems with random cross-connections

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems, using a firing-rate model framework, in which clusters of neurons with net self-excitation are represented as units, which interact with each other through random connections. We focus on conditions in which individual units lack sufficient self-excitation to become bistable on their own. Rather, multistability can arise via recurrent input from other units as a network effect for subsets of units, whose net input to each other when active is sufficiently positive to maintain such activity. In terms of the strength of within-unit self-excitation and standard-deviation of random cross-connections, the region of multistability depends on the firing-rate curve of units. Indeed, bistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the firing-rate curve rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which can appear as Zipf's Law when sampled as the proportion of trials within which random initial conditions lead to a particular stable state of the system.

Physical and social trauma: Towards an integrative transdiagnostic perspective on psychological trauma that involves threats to status and belonging

Current theories of psychological trauma assume that posttraumatic symptoms originate from stress reactions caused by extremely adverse life experiences. Since the diagnosis of PTSD is restricted to events that involve threats to the physical or sexual integrity of a person, such as accidents and physical and sexual violence, these theories are not well suited to explain the psychopathological consequences of severe violations of one's social integrity, such as emotional abuse and bullying. However, it is evident that social threats contribute to a broad range of mental disorders and increase symptom severity in patients with posttraumatic stress disorder. The aim of the Physical and Social Trauma (PAST) framework is to extend current memory theories of psychological trauma to incorporate threats to a person's social integrity. Within this perspective, the harmful effects of events that involve social threats result from violations of core social motives such as the need for status and belonging that bring about intense affective reactions, including despair and defeat. Within associative threat structures, these emotions are tied to the stimulus characteristics of the experiences and can be re-activated in social situations. The resulting psychopathology transcends PTSD criteria and other current classifications and suggests a transdiagnostic perspective of psychological trauma. Implications for treatment and further directions for research are discussed.

Tracking the associative boost in infancy

Do words that are both associatively and taxonomically related prime each other in the infant mental lexicon? We explore the impact of these semantic relations in the emerging lexicon. Using the head-turn preference procedure, we show that 18-month-old infants have begun to construct a semantic network of associatively and taxonomically related words, such as dog-cat or apple-cheese. We demonstrate that priming between words is longer-lasting when the relationship is both taxonomic and associative, as opposed to purely taxonomic, reflecting the associative boost reported in the adult priming literature. Our results demonstrate that 18-month-old infants are able to construct a lexical-semantic network based on associative and taxonomic relations between words in the network, and that lexical-semantic links are more robust when they are both associative and taxonomic in character. Furthermore, the manner in which activation is propagated through the emerging lexical-semantic network appears to depend upon the type of semantic relation between words. We argue that 18-month-old infants have a mental lexicon that shares important structural and processing properties with that of the adult system.

Dynamic branching in a neural network model for probabilistic prediction of sequences

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.

Belief traps: Tackling the inertia of harmful beliefs

Beliefs can be highly resilient in the sense that they are not easily abandoned in the face of counterevidence. This has the advantage of guiding consistent behavior and judgments but may also have destructive consequences for individuals, nature, and society. For instance, pathological beliefs can sustain psychiatric disorders, the belief that rhinoceros horn is an aphrodisiac may drive a species extinct, beliefs about gender or race may fuel discrimination, and belief in conspiracy theories can undermine democracy. Here, we present a unifying framework of how self-amplifying feedbacks shape the inertia of beliefs on levels ranging from neuronal networks to social systems. Sustained exposure to counterevidence can destabilize rigid beliefs but requires organized rational override as in cognitive behavioral therapy for pathological beliefs or institutional control of discrimination to reduce racial biases. Black-and-white thinking is a major risk factor for the formation of resilient beliefs associated with psychiatric disorders as well as prejudices and conspiracy thinking. Such dichotomous thinking is characteristic of a lack of cognitive resources, which may be exacerbated by stress. This could help explain why conspiracy thinking and psychiatric disorders tend to peak during crises. A corollary is that addressing social factors such as poverty, social cleavage, and lack of education may be the most effective way to prevent the emergence of rigid beliefs, and thus of problems ranging from psychiatric disorders to prejudices, conspiracy theories, and posttruth politics.

Latching dynamics as a basis for short-term recall

We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.

What makes short-term memory so poor, that over a minute we tend to forget even phone numbers, if we cannot rehearse or record them electronically? In comparison, long-term memory can be amazingly rich and accurate. Was it so difficult to equip our brain with a short-term memory device of reasonable capacity?

We discuss the hypothesis that instead of an ad hoc device, short-term memory relies on long-term representations, and that the short-term recall of multiple items exploits the natural tendency of the cortex to jump from state to state, by only adding imprecisely determined “kicks” that spur cortical dynamics towards the states representing those items. We show that a plausible neural model for such kicks performs similarly to human subjects we have tested, both in conditions when short-term recall is terminated by errors, and when errors are overlooked and subjects are asked to keep trying. The same mechanism can drive serial recall, if combined with equally imprecise kicks encoding item order. Our analysis suggests that a proper short-term memory device may have never evolved in our brain, which had, therefore, to make do with tweaking its superb long-term memory capabilities.

Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them.

Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels

Associative knowledge networks are often explored by using the so-called spreading activation model to find their key items and their rankings. The spreading activation model is based on the idea of diffusion- or random walk -like spreading of activation in the network. Here, we propose a generalisation, which relaxes an assumption of simple Brownian-like random walk (or equally, ordinary diffusion process) and takes into account nonlocal jump processes, typical for superdiffusive processes, by using fractional graph Laplacian. In addition, the model allows a nonlinearity of the diffusion process. These generalizations provide a dynamic equation that is analogous to fractional porous medium diffusion equation in a continuum case. A solution of the generalized equation is obtained in the form of a recently proposed q-generalized matrix transformation, the so-called q-adjacency kernel, which can be adopted as a systemic state describing spreading activation. Based on the systemic state, a new centrality measure called activity centrality is introduced for ranking the importance of items (nodes) in spreading activation. To demonstrate the viability of analysis based on systemic states, we use empirical data from a recently reported case of a university students’ associative knowledge network about the history of science. It is shown that, while a choice of model does not alter rankings of the items with the highest rank, rankings of nodes with lower ranks depend essentially on the diffusion model.

Systemic States of Spreading Activation in Describing Associative Knowledge Networks: From Key Items to Relative Entropy Based Comparisons

Associative knowledge networks are central in many areas of learning and teaching. One key problem in evaluating and exploring such networks is to find out its key items (nodes), sub-structures (connected set of nodes), and how the roles of sub-structures can be compared. In this study, we suggest an approach for analyzing associative networks, so that analysis is based on spreading activation and systemic states that correpond to the state of spreading. The method is based on the construction of diffusion-propagators as generalized systemic states of the network, for an exploration of the connectivity of a network and, subsequently, on generalized Jensen–Shannon–Tsallis relative entropy (based on Tsallis-entropy) in order to compare the states. It is shown that the constructed systemic states provide a robust way to compare roles of sub-networks in spreading activation. The viability of the method is demonstrated by applying it to recently published network representations of students’ associative knowledge regarding the history of science.

Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability

In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions: activation of regular or irregular sequences. The switch between the two types of activation occurs through the modulation of biological parameters, without altering the connectivity matrix. Some of the parameters included in our model are neuronal gain, strength of inhibition, synaptic depression and noise. We investigate how these parameters enable the existence of sequences and influence the type of sequences observed. In particular we show that synaptic depression and noise drive the transitions from one memory item to the next and neuronal gain controls the switching between regular and irregular (random) activation.

Investigating the interaction of direct and indirect relation on memory judgments and retrieval

This study examined the interactive relationship between two measures of association (direct and indirect associations) when predicting relatedness judgments and cued-recall performance. Participants were recruited from Amazon's Mechanical Turk and were given word pairs of varying relatedness to judge for their semantic, thematic, and associative strength. After completing a distractor task, participants then completed a cued-recall task. First, we sought to expand previous work on judgments of associative memory to include semantic- and thematic-based judgments (judgments of relatedness), while also replicating bias and sensitivity findings. Next, we tested for an interaction between direct and indirect association when predicting participant judgments while also expanding upon previous work by examining that interaction when predicting recall. The interaction between direct and indirect association was significant for both judgments and recall. For low indirect association, direct association was the primary predictor of both judgment strength and recall proportions. However, this trend reversed for high indirect association, as higher levels of indirect relation decreased the effectiveness of direct relation as a predictor. Overall, our findings indicate the degree to which the processing of similarity information impacts cognitive processes such as retrieval and item judgments, while also parsing apart the underlying, interactive relationship that exists between the norms used to represent concept information.

spreadr: An R package to simulate spreading activation in a network

The notion of spreading activation is a central theme in the cognitive sciences; however, the tools for implementing spreading activation computationally are not as readily available. This article introduces the spreadr R package, which can implement spreading activation within a specified network structure. The algorithmic method implemented in the spreadr subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (Frontiers in Psychology, 2, 369, 2011), who viewed activation as a fixed cognitive resource that could "spread" among connected nodes in a network. Three sets of simulations were conducted using the package. The first set of simulations successfully reproduced the results reported in Vitevitch et al. (Frontiers in Psychology, 2, 369, 2011), who showed that a simple mechanism of spreading activation could account for the clustering coefficient effect in spoken word recognition. The second set of simulations showed that the same mechanism could be extended to account for higher false alarm rates for low clustering coefficient words in a false memory task. The final set of simulations demonstrated how spreading activation could be applied to a semantic network to account for semantic priming effects. It is hoped that this package will encourage cognitive and language scientists to explicitly consider how the structures of cognitive systems such as the mental lexicon and semantic memory interact with the process of spreading activation.

From Focused Thought to Reveries: A Memory System for a Conscious Robot

We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.

Latching dynamics in neural networks with synaptic depression

Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed.

Semantic integration by pattern priming: experiment and cortical network model

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 post-synaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results.

Logic as Marr's Computational Level: Four Case Studies

We sketch four applications of Marr's levels-of-analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on temporal prepositions can be reanalyzed using formal semantics, addressing a potential confound. The second part of the article demonstrates the predictive power of logical theories drawing on EEG data on processing progressive constructions and on behavioral data on conditional reasoning in people with autism. Logical theories can constrain processing hypotheses all the way down to neurophysiology, and conversely neuroscience data can guide the selection of alternative computational level models of cognition.

Investigating the structure of semantic networks in low and high creative persons

According to Mednick's (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by "flat" (broader associations) instead of "steep" (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations-overlap of similar associative responses ("association clouds"). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's (1962) theory and the feasibility of using network science paradigms to investigate high level cognition.

Integrating the automatic and the controlled: strategies in semantic priming in an attractor network with latching dynamics

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model.

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

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.

Excessive attractor instability accounts for semantic priming in schizophrenia

One of the most pervasive findings in studies of schizophrenics with thought disorders is their peculiar pattern of semantic priming, which presumably reflects abnormal associative processes in the semantic system of these patients. Semantic priming is manifested by faster and more accurate recognition of a word-target when preceded by a semantically related prime, relative to an unrelated prime condition. Compared to control, semantic priming in schizophrenics is characterized by reduced priming effects at long prime-target Stimulus Onset Asynchrony (SOA) and, sometimes, augmented priming at short SOA. In addition, unlike controls, schizophrenics consistently show indirect (mediated) priming (such as from the prime ‘wedding’ to the target ‘finger’, mediated by ‘ring’). In a previous study, we developed a novel attractor neural network model with synaptic adaptation mechanisms that could account for semantic priming patterns in healthy individuals. Here, we examine the consequences of introducing attractor instability to this network, which is hypothesized to arise from dysfunctional synaptic transmission known to occur in schizophrenia. In two simulated experiments, we demonstrate how such instability speeds up the network’s dynamics and, consequently, produces the full spectrum of priming effects previously reported in patients. The model also explains the inconsistency of augmented priming results at short SOAs using directly related pairs relative to the consistency of indirect priming. Further, we discuss how the same mechanism could account for other symptoms of the disease, such as derailment (‘loose associations’) or the commonly seen difficulty of patients in utilizing context. Finally, we show how the model can statistically implement the overly-broad wave of spreading activation previously presumed to characterize thought-disorders in schizophrenia.

Dynamics of the semantic priming shift: behavioral experiments and cortical network model

Multiple semantic priming processes between several related and/or unrelated words are at work during the processing of sequences of words. Multiple priming generates rich dynamics of effects depending on the relationship between the target word and the first and/or second prime previously presented. The experimental literature suggests that during the on-line processing of the primes, the activation can shift from associates to the first prime to associates to the second prime. Though the semantic priming shift is central to the on-line and rapid updating of word meanings in the working memory, its precise dynamics are still poorly understood and it is still a challenge to model how it functions in the cerebral cortex. Four multiple priming experiments are proposed that cross-manipulate delays and association strength between the primes and the target. Results show for the first time that association strength determines complex dynamics of the semantic priming shift, ranging from an absence of a shift to a complete shift. A cortical network model of spike frequency adaptive neuron populations is proposed to account for the non-continuous evolution of the priming shift over time. It allows linking the dynamics of the priming shift assessed at the behavioral level to the non-linear dynamics of the firing rates of neurons populations.