scholarly journals Data-driven computational models reveal perceptual simulation in word processing

2019 ◽  
Author(s):  
Marco Alessandro Petilli ◽  
Fritz Günther ◽  
Alessandra Vergallito ◽  
Marco Ciapparelli ◽  
Marco Marelli
Keyword(s):  
Semantic Priming ◽  
Word Processing ◽  
Computational Models ◽  
Grounded Cognition ◽  
Data Driven ◽  
Continuous Scale ◽  
Behavioural Performance ◽  
Sensorimotor Information ◽  
Conceptual Equivalence ◽  
Prime Target

In their strongest formulation, theories of grounded cognition claim that concepts are made up of sensorimotor information. Following such equivalence, perceptual properties of objects should consistently influence processing, even in purely linguistic tasks, where perceptual information is neither solicited nor required. Previous studies have tested this prediction in semantic priming tasks, but they have not observed perceptual influences on participants’ performances. However, those findings suffer from critical shortcomings, which may have prevented potential visually grounded/perceptual effects from being detected. Here, we investigate this topic by applying an innovative method expected to increase the sensitivity in detecting such perceptual effects. Specifically, we adopt an objective, data-driven, computational approach to independently quantify vision-based and language-based similarities for prime-target pairs on a continuous scale. We test whether these measures predict behavioural performance in a semantic priming mega-study with various experimental settings. Vision-based similarity is found to facilitate performance, but a dissociation between vision-based and language-based effects was also observed. Thus, in line with theories of grounded cognition, perceptual properties can facilitate word processing even in purely linguistic tasks, but the behavioural dissociation at the same time challenges strong claims of sensorimotor and conceptual equivalence.

scholarly journals Data-driven computational models reveal perceptual simulation in word processing

2021 ◽  
Vol 117 ◽  
pp. 104194
Author(s):  
Marco A. Petilli ◽  
Fritz Günther ◽  
Alessandra Vergallito ◽  
Marco Ciapparelli ◽  
Marco Marelli
Keyword(s):  
Word Processing ◽  
Computational Models ◽  
Data Driven ◽  
Perceptual Simulation

Combining the Post-Cue Task and the Perceptual Identification Task to Assess Parallel Activation and Mutual Facilitation of Related Primes and Targets

2018 ◽  
Vol 65 (2) ◽  
pp. 84-97 ◽  
Author(s):  
Demian Scherer ◽  
Dirk Wentura
Keyword(s):  
Semantic Priming ◽  
Stimulus Onset ◽  
The Other ◽  
Perceptual Identification ◽  
Identification Task ◽  
Priming Effects ◽  
Priming Paradigm ◽  
Parallel Activation ◽  
Prime Target

Abstract. Recent theories assume a mutual facilitation in case of semantic overlap for concepts being activated simultaneously. We provide evidence for this claim using a semantic priming paradigm. To test for mutual facilitation of related concepts, a perceptual identification task was employed, presenting prime-target pairs briefly and masked, with an SOA of 0 ms (i.e., prime and target were presented concurrently, one above the other). Participants were instructed to identify the target. In Experiment 1, a cue defining the target was presented at stimulus onset, whereas in Experiment 2 the cue was not presented before the offset of stimuli. Accordingly, in Experiment 2, a post-cue task was merged with the perceptual identification task. We obtained significant semantic priming effects in both experiments. This result is compatible with the view that two concepts can both be activated in parallel and can mutually facilitate each other if they are related.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals The basal ganglia and semantic engagement: Potential insights from semantic priming in individuals with subcortical vascular lesions, Parkinson's disease, and cortical lesions

2003 ◽  
Vol 9 (7) ◽  
pp. 1041-1052 ◽  
Author(s):  
DAVID COPLAND
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Semantic Priming ◽  
Semantic Processing ◽  
Semantic Network ◽  
Vascular Lesions ◽  
Cortical Lesions ◽  
The Impact ◽  
Prime Target

The impact of basal ganglia dysfunction on semantic processing was investigated by comparing the performance of individuals with nonthalamic subcortical (NS) vascular lesions, Parkinson's disease (PD), cortical lesions, and matched controls on a semantic priming task. Unequibiased lexical ambiguity primes were used in auditory prime-target pairs comprising 4 critical conditions; dominant related (e.g., bank–money), subordinate related (e.g., bank–river), dominant unrelated (e.g., foot–money) and subordinate unrelated (e.g., bat–river). Participants made speeded lexical decisions (word/nonword) on targets using a go–no-go response. When a short prime–target interstimulus interval (ISI) of 200 ms was employed, all groups demonstrated priming for dominant and subordinate conditions, indicating nonselective meaning facilitation and intact automatic lexical processing. Differences emerged at the long ISI (1250 ms), where control and cortical lesion participants evidenced selective facilitation of the dominant meaning, whereas NS and PD groups demonstrated a protracted period of nonselective meaning facilitation. This finding suggests a circumscribed deficit in the selective attentional engagement of the semantic network on the basis of meaning frequency, possibly implicating a disturbance of frontal–subcortical systems influencing inhibitory semantic mechanisms. (JINS, 2003, 9, 1041–1052.)

When Perception Trumps Reality

2017 ◽  
Vol 28 (3) ◽  
pp. 346-355 ◽  
Author(s):  
Anders Sand ◽  
Mats E. Nilsson
Keyword(s):  
Semantic Priming ◽  
Cognitive Processes ◽  
Response Times ◽  
Chance Level ◽  
Subliminal Priming ◽  
Prime Stimulus ◽  
Objective Meaning ◽  
And Behavior ◽  
Prime Target ◽  
Made In

Is semantic priming driven by the objective or perceived meaning of the priming stimulus? This question is relevant given that many studies suggest that the objective meaning of invisible stimuli can influence cognitive processes and behavior. In an experiment involving 66 participants, we tested how the perceived meaning of misperceived stimuli influenced response times. Stroop priming (i.e., longer response times for incongruent than for congruent prime-target pairs) was observed in trials in which the prime was correctly identified. However, reversed Stroop priming was observed when the prime stimulus was incorrectly identified. Even in trials in which participants reported no perception of the prime and identified the primes at close to chance level (i.e., trials that meet both subjective and objective definitions of being subliminal), Stroop priming corresponded to perceived congruency, not objective congruency. This result suggests that occasional weak percepts and mispercepts are intermixed with no percepts in conditions traditionally claimed to be subliminal, casting doubt on claims of subliminal priming made in previous reports.

scholarly journals Can computers understand words like humans do? Comparable semantic representation in neural and computer systems

2019 ◽  
Author(s):  
Linmin Zhang ◽  
Lingting Wang ◽  
Jinbiao Yang ◽  
Peng Qian ◽  
Xuefei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Word Processing ◽  
Semantic Processing ◽  
Computational Models ◽  
Time Window ◽  
Early Stage ◽  
Semantic Representation ◽  
Deep Understanding ◽  
Joint Force ◽  
Strong Artificial Intelligence

AbstractSemantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. We investigated comparable representational formats of lexical semantics between these two complex systems with fine temporal resolution neural recordings. We found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.

scholarly journals Data-driven modeling of cholinergic modulation of neural microcircuits: bridging neurons, synapses, and network states

2018 ◽  
Author(s):  
Srikanth Ramaswamy ◽  
Henry Markram
Keyword(s):  
Information Processing ◽  
Computational Models ◽  
Network Activity ◽  
Data Driven ◽  
Global Network ◽  
Cholinergic Modulation ◽  
Anatomy And Physiology ◽  
Synaptic Physiology ◽  
Network States

1AbstractNeuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network states. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the anatomy and physiology of cholinergic modulation of the neocortex. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to predict how ACh affects different types of neurons and synapses, and consequently alters global network states. Preliminary simulations not only corroborate the long-standing notion that ACh desynchronizes network activity, but also reveal a potentially finegrained control over a spectrum of neocortical states. We show that low levels of ACh, such as those during sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as those during wakefulness, govern fast oscillations and network asynchrony. In addition, network states modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the differential regulation of neurons and synapses caused by increasing levels of ACh that enhances cellular excitability by increasing neuronal activity and decreases the efficacy of local synaptic transmission by altering neurotransmitter release probability. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulation in neural information processing.

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