Editing Semantic memory (section)

===Associative models===
The set of [[Association (psychology)|associations]] among a collection of items in memory is equivalent to the links between nodes in a network, where each node corresponds to a unique item in memory. Indeed, neural networks and semantic networks may be characterized as associative models of cognition. However, associations are often more clearly represented as an ''N''×''N'' matrix, where ''N'' is the number of items in memory; each cell of the matrix corresponds to the strength of the association between the row item and the column item.

Learning of associations is generally believed to be a [[Hebbian]] process, where whenever two items in memory are simultaneously active, the association between them grows stronger, and the more likely either item is to activate the other. See below for specific operationalizations of associative models.

====Search of associative memory====
A standard model of memory that employs association in this manner is the search of associative memory (SAM) model.<ref>{{Cite news | last1=Raaijmakers | first1=J. G. W. | last2=Schiffrin | first2=R. M. | publication-date=1981 |year=1981 |title=Search of associative memory | periodical=Psychological Review |volume=8 |issue=2 |pages=98–134 }}</ref> Though SAM was originally designed to model episodic memory, its mechanisms are sufficient to support some semantic memory representations.<ref>{{Cite journal|last1=Kimball|first1=Daniel R.|last2=Smith|first2=Troy A.|last3=Kahana|first3=Michael J.|date=2007|title=The fSAM model of false recall.|journal=Psychological Review|language=en|volume=114|issue=4|pages=954–993|doi=10.1037/0033-295x.114.4.954|issn=1939-1471|pmc=2839460|pmid=17907869}}</ref> The model contains a short-term store (STS) and long-term store (LTS), where STS is a briefly activated subset of the information in the LTS. The STS has limited capacity and affects the retrieval process by limiting the amount of information that can be sampled and limiting the time the sampled subset is in an active mode. The retrieval process in LTS is cue dependent and probabilistic, meaning that a cue initiates the retrieval process and the selected information from memory is random.  The probability of being sampled is dependent on the strength of association between the cue and the item being retrieved, with stronger associations being sampled before one is chosen. The buffer size is defined as ''r'', and not a fixed number, and as items are rehearsed in the buffer the associative strengths grow linearly as a function of the total time inside the buffer.<ref>{{cite book|last=Raaijmakers|first=J.G.|author2=Shiffrin R.M. |title=SAM: A theory of probabilistic search of associative memory|journal=The Psychology of Learning and Motivation: Advances in Research and Theory|year=1980|volume=14|pages=207–262|doi=10.1016/s0079-7421(08)60162-0|series=Psychology of Learning and Motivation|isbn=9780125433143}}</ref> In SAM, when any two items simultaneously occupy a working memory buffer, the strength of their association is incremented; items that co-occur more often are more strongly associated. Items in SAM are also associated with a specific context, where the strength of that association determined by how long each item is present in a given context. In SAM, memories consist of a set of associations between items in memory and between items and contexts. The presence of a set of items and/or a context is more likely to evoke some subset of the items in memory. The degree to which items evoke one another—either by virtue of their shared context or their co-occurrence—is an indication of the items' [[semantic relatedness]].

In an updated version of SAM, pre-existing semantic associations are accounted for using a semantic [[Matrix (mathematics)|matrix]]. During the experiment, semantic associations remain fixed showing the assumption that semantic associations are not significantly impacted by the episodic experience of one experiment.  The two measures used to measure semantic relatedness in this model are latent semantic analysis (LSA) and word association spaces (WAS).<ref>{{cite journal|last=Sirotin|first=Y.B.|author2=Kahana, d. R |title=Going beyond a single list: Modeling the effects of prior experience on episodic free recall|journal=Psychonomic Bulletin & Review|year=2005|volume=12|issue=5|pages=787–805|doi=10.3758/bf03196773|pmid=16523998|doi-access=free}}</ref> The LSA method states that similarity between words is reflected through their co-occurrence in a local context.<ref>{{cite journal|last=Landauer, T.K|author2=Dumais S.T. |title=Solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge|journal=Psychological Review|volume=104|issue=2 |pages=211–240|doi=10.1037/0033-295x.104.2.211|year=1997 |citeseerx=10.1.1.184.4759 |s2cid=1144461 }}</ref> WAS was developed by analyzing a database of free association norms, and is where "words that have similar associative structures are placed in similar regions of space".<ref>{{cite book|pages=237–249|doi=10.1037/10895-018|chapter=Word Association Spaces for Predicting Semantic Similarity Effects in Episodic Memory|title=Experimental Cognitive Psychology and Its Applications|year=2005|last1=Steyvers|first1=Mark|last2=Shiffrin|first2=Richard M.|last3=Nelson|first3=Douglas L.|isbn=978-1-59147-183-7|chapter-url=http://psiexp.ss.uci.edu/research/papers/SteyversShiffrinNelsonFormatted.pdf|url=http://www.apa.org/pubs/books/4318016.aspx|editor-first1= Alice F. |editor-last1=Healy|citeseerx=10.1.1.66.5334|archive-url=https://web.archive.org/web/20100609210806/http://psiexp.ss.uci.edu/research/papers/SteyversShiffrinNelsonFormatted.pdf|archive-date=2010-06-09}}</ref>

====ACT-R: a production system model====
The adaptive control of thought (ACT)<ref>Anderson, J. R. (1983). ''The Architecture of Cognition''. Cambridge, MA: Harvard University Press.</ref> (and later [[ACT-R]] (Adaptive Control of Thought-Rational)<ref>Anderson, J. R. (1993b). ''Rules of the mind''. Hillsdale, NJ: Erlbaum.</ref>) theory of cognition represents [[declarative memory]] (of which semantic memory is a part) as "chunks", which consist of a label, a set of defined relationships to other chunks (e.g., "this is a _", or "this has a _"), and any number of chunk-specific properties. Chunks can be mapped as a semantic network, given that each node is a chunk with its unique properties, and each link is the chunk's relationship to another chunk. In ACT, a chunk's activation decreases as a function of the time from when the chunk was created, and increases with the number of times the chunk has been retrieved from memory. Chunks can also receive activation from [[Gaussian noise]] and from their similarity to other chunks. For example, if ''chicken'' is used as a retrieval cue, ''canary'' will receive activation by virtue of its similarity to the cue. When retrieving items from memory, ACT looks at the most active chunk in memory; if it is above threshold, it is retrieved; otherwise an "error of omission" has occurred and the item has been forgotten. There is also retrieval latency, which varies inversely with the amount by which the activation of the retrieved chunk exceeds the retrieval threshold. This latency is used to measure the response time of the ACT model and compare it to human performance.<ref>{{cite journal | last1 = Anderson | first1 = J. R. | last2 = Bothell | first2 = D. | last3 = Lebiere | first3 = C. | last4 = Matessa | first4 = M. | year = 1998 | title = An integrated theory of list memory | journal = Journal of Memory and Language | volume = 38 | issue = 4| pages = 341–380 | doi=10.1006/jmla.1997.2553| citeseerx = 10.1.1.132.7920 | s2cid = 14462252 }}</ref>