Networks in Cognitive Science

Baronchelli, Andrea; Ferrer-i-Cancho, Ramon; Pastor-Satorras, Romualdo; Chater, Nick; Christiansen, Morten H.

doi:10.1016/j.tics.2013.04.010

Cited by 299 publications

(268 citation statements)

References 148 publications

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“…Unlike spatial models, semantic networks represent semantic memory as a structured network in which concepts (nodes) are connected to semantically similar concepts by edges. 1 With recent computational advances in network science (Albert and Barabási 2002;Watts 2004), there has been a resurgence of interest and use of semantic networks in the study of semantic memory (Baronchelli et al 2013;Falk and Bassett 2017).…”

Section: Introductionmentioning

confidence: 99%

Estimating Semantic Networks of Groups and Individuals from Fluency Data

2018

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One popular and classic theory of how the mind encodes knowledge is an associative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of memory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U-INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologically plausible process model of memory retrieval and one of two known methods that we found to be consistent estimators of this process: if semantic memory retrieval is consistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for estimating psychologically valid semantic networks by comparing people's similarity judgments of edges estimated by each network estimation method. To encourage best practices, we discuss the merits of each network estimation technique, provide a flow chart that assists with choosing an appropriate method, and supply code for others to employ these techniques on their own data.

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Section: Introductionmentioning

confidence: 99%

Estimating Semantic Networks of Groups and Individuals from Fluency Data

2018

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“…In the resulting network, edge frequencies are set to the number of times the given cooccurrence is observed. The resulting networks are typically quite dense and exhibit small-world structure where most word-pairs are only a few edges apart (Baronchelli et al, 2013;Ferror i Cancho and Solé, 2001). To explore the effect of this density, different minimum node-and edgefrequencies were tested (analogous to the wordand co-occurrence frequencies in text).…”

Section: Methodsmentioning

confidence: 99%

Extracting Clusters of Specialist Terms from Unstructured Text

Gerow¹

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Automatically identifying related specialist terms is a difficult and important task required to understand the lexical structure of language. This paper develops a corpus-based method of extracting coherent clusters of satellite terminologyterms on the edge of the lexicon -using co-occurrence networks of unstructured text.Term clusters are identified by extracting communities in the cooccurrence graph, after which the largest is discarded and the remaining words are ranked by centrality within a community. The method is tractable on large corpora, requires no document structure and minimal normalization. The results suggest that the model is able to extract coherent groups of satellite terms in corpora with varying size, content and structure. The findings also confirm that language consists of a densely connected core (observed in dictionaries) and systematic, semantically coherent groups of terms at the edges of the lexicon.

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“…The individual cognitive properties, in this case, are reshaping the effective structure of the communication network in a manner that befits the processing demands of a particular task. This kind of network-centric account is by no means the only way of understanding the mechanistic underpinnings of mandevillian intelligence; it is, however, an account that establishes an important link with work that seeks to explore the relationship between communication network structure and the performance profile of collective cognitive systems (Kearns 2012;Mason et al 2008;Baronchelli et al 2013). …”

Section: Distrust Dogmatism and Dynamic Networkmentioning

confidence: 99%

Mandevillian Intelligence

Smart¹

2018

Oxford Scholarship Online

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success.

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Networks in Cognitive Science

Cited by 299 publications

References 148 publications

Estimating Semantic Networks of Groups and Individuals from Fluency Data

Estimating Semantic Networks of Groups and Individuals from Fluency Data

Extracting Clusters of Specialist Terms from Unstructured Text

Mandevillian Intelligence

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