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# Topological Self-Organization and Prediction Learning Support Both Action and Lexical Chains in the Brain

## Details

**Authors** Chersi et al.

**Year** 2014

**Publisher** Topics in Cognitive Science

**Discipline** Neuroscience

[Read it at the publisher](https://doi.org/10.1111/tops.12094) 
10.1111/tops.12094

## In authors' words

### Abstract

A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory‐motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo‐functional organization of the fronto‐parietal network, we present a computational model showing that language processing may have reused or co‐developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self‐organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context‐specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall.

### What they set out to do (purpose)

To explore the relationship between sensory, motor, and langauge centers in the brain.

### What they found (results)

Building relationships causes a change in the brain via the neuronal pathways and a corollary action and lexiconal coding (language systems). Neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Evidence of different pools of neurons being activated by goal-specific motor acts emerged as the result of a process of adaptive specialization of long-term memory circuits for serial cognition. In other words both experiments offer that the relationship drawn among motor and lexical chains are key to understanding.

## Commentary

### In short

Relationships and the systems of data support many critically important processes such as language development, working memory, and sequencing of information. Neural coding of information thereby requires associations and relationships among concepts regardless of the form in which they come, whether it via sensory-motor or lexical (language) inputs.

### In more detail

“A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory‐motor and language systems in the brain” according to Chersi et. al. 2014. Building relationships causes a change in the brain via the neuronal pathways and a corollary action and lexiconal coding (language systems). Examining relationships from a cognitive neuroscience perspective shows that, “neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex.” To test this idea, two

experiments were conducted to explore sensory-motor and lexical chains (sequences of related words seen in written text, both in sentences, passages, or the entirety of written works). The first experiment used a sequence of “goal-activated motor chains” to test the interplay between frequency, competition, and familiarization within these chains. Each chain started with a goal (e.g., eat the food), followed by the motor acts taken to succeed at the goal (e.g., grab, bring to mouth, etc.). The results of this experiment showed “evidence of different pools of neurons being activated by goal-specific motor acts emerged as the result of a process of adaptive specialization of long-term memory circuits for serial cognition.” The second experiment had a similar goal, but used verbs as the starting stimuli because they have a multitude of ways they can be used in terms of tense and mood. In other words both experiments offer that the relationship drawn among motor and lexical chains are key to understanding. Overall, the article makes the point that Relationships and the systems of data support many critically important processes such as language development, working memory, and sequencing of information. Experiment 1 focused on the activation of lexical chains (language) in relation to motor (movement) behavior. Experiment 2 examined how language (lexical chains ) develops through the perceived relationship to other sequences of letters out of context. In other words, Chersi offers that the formation of lexical chains relies heavily on the surrounding context. Further, creating lexical chains without the context of word recognition in surrounding text was not able to be simulated. The results of this experiment showed that neural coding of information thereby requires associations and relationships among concepts regardless of the form in which they come, whether it via sensory-motor or lexical (language) inputs.

**Patterns it shows** R

**How to cite this** Chersi et al. (2014). Topological Self-Organization and Prediction Learning Support Both Action and Lexical Chains in the Brain. Topics in Cognitive Science.