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Semantic memory

semantic memory, semantic memory examples
Semantic memory is one of the two types of declarative or explicit memory our memory of facts or events that is explicitly stored and retrieved1 Semantic memory refers to general world knowledge that we have accumulated throughout our lives2 This general knowledge facts, ideas, meaning and concepts is intertwined in experience and dependent on culture Semantic memory is distinct from episodic memory, which is our memory of experiences and specific events that occur during our lives, from which we can recreate at any given point3 For instance, semantic memory might contain information about what a cat is, whereas episodic memory might contain a specific memory of petting a particular cat We can learn about new concepts by applying our knowledge learned from things in the past4 The counterpart to declarative or explicit memory is nondeclarative memory or implicit memory5


  • 1 History
  • 2 Empirical evidence
    • 21 Jacoby and Dallas 198114
    • 22 Kelley et al 201415
  • 3 Models
    • 31 Network models
      • 311 Teachable Language Comprehender TLC
      • 312 Semantic networks
    • 32 Feature models
    • 33 Associative models
      • 331 Search of Associative Memory SAM
      • 332 ACT-R: a production system model
    • 34 Statistical models
      • 341 Latent Semantic Analysis LSA
      • 342 Hyperspace Analogue to Language HAL
      • 343 Other statistical models of semantic memory
  • 4 Location of semantic memory in the brain
  • 5 Neural correlates and biological workings
  • 6 Disorders
    • 61 Semantic category specific impairments
    • 62 Modality specific impairments
    • 63 Semantic refractory access and semantic storage disorders
  • 7 Present and future research
  • 8 See also
  • 9 References
  • 10 Further reading
  • 11 External links


The idea of semantic memory was first introduced following a conference in 1972 between Endel Tulving, of the University of Toronto, and W Donaldson on the role of organization in human memory Tulving constructed a proposal to distinguish between episodic memory and what he termed semantic memory6 He was mainly influenced by the ideas of Reiff and Scheers, who in 1959 made the distinction between two primary forms of memory7 One form was entitled "remembrances", the other "memoria" The remembrance concept dealt with memories that contained experiences of an autobiographic index, whereas the memoria concept dealt with those memories that did not reference experiences having an autobiographic index8 Semantic memory reflects our knowledge of the world around us, hence the term 'general knowledge' is often used It holds generic information that is more than likely acquired across various contexts and is used across different situations According to Madigan in his book titled Memory, semantic memory is the sum of all knowledge one has obtained—whether it be vocabulary, understanding of math, or all the facts one knows In his book titled "Episodic and Semantic Memory", Endel Tulving adopted the term "semantic" from linguists to refer to a system of memory for "words and verbal symbols, their meanings and referents, the relations between them, and the rules, formulas, or algorithms for influencing them"9 The use of semantic memory is quite different from that of episodic memory Semantic memory refers to general facts and meanings one shares with others whereas episodic memory refers to unique and concrete personal experiences Tulving's proposal of this distinction between semantic and episodic memory was widely accepted, primarily because it allowed the separate conceptualization of knowledge of the world10 Tulving discusses conceptions of episodic and semantic memory in his book titled Elements of Episodic Memory11 in which he states that several factors differentiate episodic memory and semantic memory differ in ways that include

  1. the characteristics of their operations,
  2. the kind of information they process,
  3. their application to the real world as well as the memory laboratory

Before Tulving's proposal, this area of human memory had been neglected by experimental psychologists Since Tulvings inception of these distinctions, several experimenters have conducted tests to determine the validity of his hypothesized differences between episodic and semantic memory

Recent research has focused on the idea that when people access a word's meaning, sensorimotor information that is used to perceive and act on the concrete object the word suggests is automatically activated In the theory of grounded cognition, the meaning of a particular word is grounded in the sensorimotor systems12 For example, when one thinks of a pear, knowledge of grasping, chewing, sights, sounds, and tastes used to encode episodic experiences of a pear are recalled through sensorimotor simulation A grounded simulation approach refers to context-specific re-activations that integrate the important features of episodic experience into a current depiction Such research has challenged previously utilized amodal views The brain encodes multiple inputs such as words and pictures to integrate and create a larger conceptual idea by using amodal views also known as amodal perceptions Instead of being representations in modality-specific systems, semantic memory representations had previously been viewed as redescriptions of modality-specific states Some accounts of category-specific semantic deficits that are amodal remain even though researchers are beginning to find support for theories in which knowledge is tied to modality-specific brain regions This research defines a clear link between episodic experiences and semantic memory The concept that semantic representations are grounded across modality-specific brain regions can be supported by the fact that episodic and semantic memory appear to function in different yet mutually dependent ways The distinction between semantic and episodic memory has become a part of the broader scientific discourse For example, it has been speculated that semantic memory captures the stable aspects of our personality while episodes of illness may have a more episodic nature13

Empirical evidenceedit

Jacoby and Dallas 198114edit

This study was not created to solely provide evidence for the distinction of semantic and episodic memory stores However, they did use the experimental dissociation method which provides evidence for Tulving's hypothesis

Part one

Subjects were presented with 60 words one at a time and were asked different questions

  • Some questions asked were to cause the subject to pay attention to the visual appearance: Is the word typed in bold letters
  • Some questions caused the participants to pay attention to the sound of the word: Does the word rhyme with ball
  • Some questions caused the subjects to pay attention to the meaning of the word: Does the word refer to a form of communication
  • Half of the questions were "no" answers and the other half "yes"

Part Two

In the second phase of the experiment, 60 "old words" seen in stage one and "20 new words" not shown in stage one were presented to the subjects one at a time

The subjects were given one of two tasks:

  • Perceptual Identification task semantic: The words were flashed on a video-screen for 35ms and the subjects were required to say what the word was
  • Episodic Recognition Task: Subjects were presented with each word and had to decide whether they had seen the word in the previous stage of the experiment
  • The percentages correct in the Semantic task perceptual identification did not change with the encoding conditions of appearance, sound, or meaning
  • The percentages for the episodic task increased from the appearance condition 50, to the sound condition 63, to the meaning condition 86 – The effect was also greater for the "yes" encoding words than the "no" encoding words see stage one

It displays a strong distinction of performance of episodic and semantic tasks, thus supporting Tulving's hypothesis

Kelley et al 201415edit

This experiment demonstrated if serial position functions perceived when the participants reconstructed the order of books or movies differed as a function of whether they "remember" the item in question or just "know" that the particular item occurred in a distinct order One hundred eighty undergraduate students from Lake Forrest College completed the study in groups of approximately 40 students in a classroom setting Each session lasted approximately 15 minutes

Part One

Participants received a list of 7 book titles and 18 film titles The instructions were to provide a familiarity rating for each book or film on a scale of 1–5

  • 1: I have never heard of this book/film
  • 2: I have heard of this book/film, but have no other knowledge of it
  • 3: I have heard of this book/film and have some knowledge of it
  • 4:I have read/seen this book/film once
  • 5: I have read/seen this book/film multiple times
Part Two

Participants received three separate free construction of order tasks They were asked to reconstruct the original order of release for the books/films Each book/film was reordered randomly and paired with a letter from the alphabet


The serial position functions observed, especially the recency effect, did not differ as a function of whether the participant involved had episodic awareness of the learning event When recreating the order of the 7 books, "remember" serial position functions were indistinguishable from "know" serial position functions There was one exception of a significant difference in familiarity ratings when considering just the second position from the end and the final position After analyzing the data, the movies parallel the data from the books: when reconstructing the order of movies, "remember" serial position functions are essentially impartial from "know" serial functions


This concludes that contract to Tulving's 1985b16 original hypothesis, "remember-know" discernments do not accurately reflect the memory supporting performance


The essence of semantic memory is that its contents are not tied to any particular instance of experience, as in episodic memory Instead, what is stored in semantic memory is the "gist" of experience, an abstract structure that applies to a wide variety of experiential objects and delineates categorical and functional relationships between such objects17 Thus, a complete theory of semantic memory must account not only for the representational structure of such "gists", but also for how they can be extracted from experience Numerous models of semantic memory have been proposed; they are summarized below

Network modelsedit

Networks of various sorts play an integral part in many theories of semantic memory Generally speaking, a network is composed of a set of nodes connected by links The nodes may represent concepts, words, perceptual features, or nothing at all The links may be weighted such that some are stronger than others or, equivalently, have a length such that some links take longer to traverse than others All these features of networks have been employed in models of semantic memory, examples of which are found below

Teachable Language Comprehender TLCedit

One of the first examples of a network model of semantic memory is the Teachable Language Comprehender TLC18 In this model, each node is a word, representing a concept like "Bird" With each node is stored a set of properties like "can fly" or "has wings" as well as pointers ie, links to other nodes like "Chicken" A node is directly linked to those nodes of which it is either a subclass or superclass ie, "Bird" would be connected to both "Chicken" and "Animal" Thus, TLC is a hierarchical knowledge representation in that high-level nodes representing large categories are connected directly or indirectly, via the nodes of subclasses to many instances of those categories, whereas nodes representing specific instances are at a lower level, connected only to their superclasses Furthermore, properties are stored at the highest category level to which they apply For example, "is yellow" would be stored with "Canary", "has wings" would be stored with "Bird" one level up, and "can move" would be stored with "Animal" another level up Nodes may also store negations of the properties of their superordinate nodes ie, "NOT-can fly" would be stored with "penguin" This provides an economy of representation in that properties are only stored at the category level at which they become essential, that is, at which point they become critical features see below

Processing in TLC is a form of spreading activation19 That is, when a node becomes active, that activation spreads to other nodes via the links between them In that case, the time to answer the question "Is a chicken a bird" is a function of how far the activation between the nodes for "Chicken" and "Bird" must spread, ie, the number of links between the nodes "Chicken" and "Bird"

The original version of TLC did not put weights on the links between nodes This version performed comparably to humans in many tasks, but failed to predict that people would respond faster to questions regarding more typical category instances than those involving less typical instances20 Collins and Quillian later updated TLC to include weighted connections to account for this effect21 This updated TLC is capable of explaining both the familiarity effect and the typicality effect Its biggest advantage is that it clearly explains priming: you are more likely to retrieve information from memory if related information the "prime" has been presented a short time before There are still a number of memory phenomena for which TLC has no account, including why people are able to respond quickly to obviously false questions like "is a chicken a meteor", when the relevant nodes are very far apart in the network22

Semantic networksedit

TLC is an instance of a more general class of models known as semantic networks In a semantic network, each node is to be interpreted as representing a specific concept, word, or feature That is, each node is a symbol Semantic networks generally do not employ distributed representations for concepts, as may be found in a neural network The defining feature of a semantic network is that its links are almost always directed that is, they only point in one direction, from a base to a target and the links come in many different types, each one standing for a particular relationship that can hold between any two nodes23 Processing in a semantic network often takes the form of spreading activation see above

Semantic networks see the most use in models of discourse and logical comprehension, as well as in Artificial Intelligence24 In these models, the nodes correspond to words or word stems and the links represent syntactic relations between them For an example of a computational implementation of semantic networks in knowledge representation, see Cravo and Martins 199325

Feature modelsedit

Feature models view semantic categories as being composed of relatively unstructured sets of features The semantic feature-comparison model, proposed by Smith, Shoben, and Rips 1974,26 describes memory as being composed of feature lists for different concepts According to this view, the relations between categories would not be directly retrieved, they would be indirectly computed For example, subjects might verify a sentence by comparing the feature sets that represent its subject and predicate concepts Such computational feature-comparison models include the ones proposed by Meyer 1970,27 Rips 1975,28 Smith, et al 197426

Early work in perceptual and conceptual categorization assumed that categories had critical features and that category membership could be determined by logical rules for the combination of features More recent theories have accepted that categories may have an ill-defined or "fuzzy" structure29 and have proposed probabilistic or global similarity models for the verification of category membership30

Associative modelsedit

The "association"—a relationship between two pieces of information—is a fundamental concept in psychology, and associations at various levels of mental representation are essential to models of memory and cognition in general The set of 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 Thus, 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; that is, 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 SAMedit

A standard model of memory that employs association in this manner is the Search of Associative Memory SAM model31 Though SAM was originally designed to model episodic memory, its mechanisms are sufficient to support some semantic memory representations, as well32 The SAM 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 and finally 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 buffer33 In SAM, when any two items simultaneously occupy a working memory buffer, the strength of their association is incremented Thus, 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, then, 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, then, 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 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 the Latent semantic analysis LSA and the Word association spaces WAS34 The LSA method states that similarity between words is reflected through their co-occurrence in a local context35 WAS was developed by analyzing a database of free association norms In WAS, "words that have similar associative structures are placed in similar regions of space"36

ACT-R: a production system modeledit

The ACT Adaptive Control of Thought37 and later ACT-R Adaptive Control of Thought-Rational38 theory of cognition represents declarative memory of which semantic memory is a part with "chunks", which consist of a label, a set of defined relationships to other chunks ie, "this is a _", or "this has a _", and any number of chunk-specific properties Chunks, then, 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 since 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 ie, both are birds, etc 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, ie, the item has been forgotten There is, additionally, a retrieval latency, which varies inversely with the amount by which the activation of the retrieved chunk exceeds the retrieval threshold This latency is used in measuring the response time of the ACT model, to compare it to human performance39

While ACT is a model of cognition in general, and not memory in particular, it nonetheless posits certain features of the structure of memory, as described above In particular, ACT models memory as a set of related symbolic chunks which may be accessed by retrieval cues While the model of memory employed in ACT is similar in some ways to a semantic network, the processing involved is more akin to an associative model

Statistical modelsedit

Some models characterize the acquisition of semantic information as a form of statistical inference from a set of discrete experiences, distributed across a number of "contexts" Though these models differ in specifics, they generally employ an Item × Context matrix where each cell represents the number of times an item in memory has occurred in a given context Semantic information is gleaned by performing a statistical analysis of this matrix

Many of these models bear similarity to the algorithms used in search engines for example, see Griffiths, et al, 200740 and Anderson, 199041, though it is not yet clear whether they really use the same computational mechanisms

Latent Semantic Analysis LSAedit

Perhaps the most popular of these models is Latent Semantic Analysis LSA42 In LSA, a T × D matrix is constructed from a text corpus where T is the number of terms in the corpus and D is the number of documents here "context" is interpreted as "document" and only words—or word phrases—are considered as items in memory Each cell in the matrix is then transformed according to the equation:

M t , d ′ = ln ⁡ 1 + M t , d − ∑ i = 0 D P i | t ln ⁡ P i | t _'= _^Pi|t\ln

where P i | t is the probability that context i is active, given that item t has occurred this is obtained simply by dividing the raw frequency, M t , d _ by the total of the item vector, ∑ i = 0 D M t , i ^\mathbf _ This transformation—applying the logarithm, then dividing by the information entropy of the item over all contexts—provides for greater differentiation between items and effectively weights items by their ability to predict context, and vice versa that is, items that appear across many contexts, like "the" or "and", will be weighted less, reflecting their lack of semantic information A Singular Value Decomposition SVD is then performed on the matrix M ′ ' , which allows the number of dimensions in the matrix to be reduced, thus clustering LSA's semantic representations and providing for indirect association between items For example, "cat" and "dog" may never appear together in the same context, so their close semantic relationship may not be well-captured by LSA's original matrix M However, by performing the SVD and reducing the number of dimensions in the matrix, the context vectors of "cat" and "dog"—which would be very similar—would migrate toward one another and perhaps merge, thus allowing "cat" and "dog" to act as retrieval cues for each other, even though they may never have co-occurred The degree of semantic relatedness of items in memory is given by the cosine of the angle between the items' context vectors ranging from 1 for perfect synonyms to 0 for no relationship Essentially, then, two words are closely semantically related if they appear in similar types of documents

Hyperspace Analogue to Language HALedit

The Hyperspace Analogue to Language HAL model4344 considers context only as the words that immediately surround a given word HAL computes an NxN matrix, where N is the number of words in its lexicon, using a 10-word reading frame that moves incrementally through a corpus of text Like in SAM see above, any time two words are simultaneously in the frame, the association between them is increased, that is, the corresponding cell in the NxN matrix is incremented The bigger the distance between the two words, the smaller the amount by which the association is incremented specifically, Δ = 11 − d , where d is the distance between the two words in the frame As in LSA see above, the semantic similarity between two words is given by the cosine of the angle between their vectors dimension reduction may be performed on this matrix, as well In HAL, then, two words are semantically related if they tend to appear with the same words Note that this may hold true even when the words being compared never actually co-occur ie, "chicken" and "canary"

Other statistical models of semantic memoryedit

The success of LSA and HAL gave birth to a whole field of statistical models of language A more up-to-date list of such models may be found under the topic Measures of semantic relatedness

Location of semantic memory in the brainedit

The cognitive neuroscience of semantic memory is a somewhat controversial issue with two dominant views

On the one hand, many researchers and clinicians believe that semantic memory is stored by the same brain systems involved in episodic memory These include the medial temporal lobes MTL and hippocampal formation In this system, the hippocampal formation "encodes" memories, or makes it possible for memories to form at all, and the cortex stores memories after the initial encoding process is completed

Recently, new evidence has been presented in support of a more precise interpretation of this hypothesis The hippocampal formation includes, among other structures: the hippocampus itself, the entorhinal cortex, and the perirhinal cortex These latter two make up the "parahippocampal cortices" Amnesics with damage to the hippocampus but some spared parahippocampal cortex were able to demonstrate some degree of intact semantic memory despite a total loss of episodic memory This strongly suggests that encoding of information leading to semantic memory does not have its physiological basis in the hippocampus45

Other researchers believe the hippocampus is only involved in episodic memory and spatial cognition This then raises the question where semantic memory may be located Some believe semantic memory lives in temporal neocortex Others believe that semantic knowledge is widely distributed across all brain areas To illustrate this latter view, consider your knowledge of dogs Researchers holding the 'distributed semantic knowledge' view believe that your knowledge of the sound a dog makes exists in your auditory cortex, whilst your ability to recognize and imagine the visual features of a dog resides in your visual cortex Recent evidence supports the idea that the temporal pole bilaterally is the convergence zone for unimodal semantic representations into a multimodal representation These regions are particularly vulnerable to damage in semantic dementia, which is characterised by a global semantic deficit

Neural correlates and biological workingsedit

The hippocampal areas are important to semantic memory's involvement with declarative memory The left inferior prefrontal cortex PFC and the left posterior temporal areas are other areas involved in semantic memory use Temporal lobe damage affecting the lateral and medial cortexes have been related to semantic impairments Damage to different areas of the brain affect semantic memory differently46

Neuroimaging evidence suggests that left hippocampal areas show an increase in activity during semantic memory tasks During semantic retrieval, two regions in the right middle frontal gyrus and the area of the right inferior temporal gyrus similarly show an increase in activity46 Damage to areas involved in semantic memory result in various deficits, depending on the area and type of damage For instance, Lambon Ralph, Lowe, & Rogers 2007 found that category-specific impairments can occur where patients have different knowledge deficits for one semantic category over another, depending on location and type of damage Category-specific impairments might indicate that knowledge may rely differentially upon sensory and motor properties encoded in separate areas Farah and McClelland, 199147

Category-specific impairments can involve cortical regions where living and nonliving things are represented and where feature and conceptual relationships are represented Depending on the damage to the semantic system, one type might be favored over the other In many cases, there is a point where one domain is better than the other ie - representation of living and nonliving things over feature and conceptual relationships or vice versa48

Different diseases and disorders can affect the biological workings of semantic memory A variety of studies have been done in an attempt to determine the effects on varying aspects of semantic memory For example, Lambon, Lowe, & Rogers 2007 studied the different effects semantic dementia and herpes simplex virus encephalitis have on semantic memory They found that semantic dementia has a more generalized semantic impairment Additionally, deficits in semantic memory as a result of herpes simplex virus encephalitis tend to have more category-specific impairments Other disorders that affect semantic memory - such as Alzheimer's disease - has been observed clinically as errors in naming, recognizing, or describing objects Whereas researchers have attributed such impairment to degradation of semantic knowledge Koenig et al 200749

Various neural imaging and research points to semantic memory and episodic memory resulting from distinct areas in the brain Still other research suggests that both semantic memory and episodic memory are part of a singular declarative memory system, yet represent different sectors and parts within the greater whole Different areas within the brain are activated depending on whether semantic or episodic memory is accessed Certain experts are still arguing whether or not the two types of memory are from distinct systems or whether the neural imaging makes it appear that way as a result of the activation of different mental processes during retrieval50


In order to understand semantic memory disorders, one must first understand how these disorders affect memory Semantic memory disorders fractionate into two categories Semantic category specific impairments and modality specific impairments are apparent in disorders of semantic memory Understanding these types of impairments will give insight into how disorders of semantic memory function

Semantic category specific impairmentsedit

Category specific impairments can result in widespread, patchy damage or localized damage Category specific impairments can be broken down into four categories Perceptual and functional features, topographic organization, informativeness and intercorrelations are areas of decreased functioning in disorders of semantic memory Warrington and Shallice, 198451 Alzheimer's disease is a semantic memory disorder that results in errors describing and naming objects, though not necessarily category-specific52 Semantic dementia is another disorder associated with semantic memory Semantic dementia is a language disorder characterized by a deterioration in understanding and recognizing words Impairments include difficulty in generating familiar words, difficulty naming objects and difficulties with visual recognition Research suggests that the temporal lobe might be responsible for category specific impairments of semantic memory disorders In addition to category specific impairments, modality specific impairments are included in disorders of semantic memory Cohen et al 200253

Modality specific impairmentsedit

Semantic memory is also discussed in reference to modality Different components represent information from different sensorimotor channels Modality specific impairments are divided into separate subsystems on the basis of input modality Examples of different input modalities include visual, auditory and tactile input Modality specific impairments are also divided into subsystems based on the type of information Visual vs verbal and perceptual vs functional information are examples of information types54 Modality specificity can account for category specific impairments in semantic memory disorders Damage to visual semantics primarily impairs knowledge of living things, and damage to functional semantics primarily impairs knowledge of nonliving things

Semantic refractory access and semantic storage disordersedit

Semantic memory disorders fall into two groups Semantic refractory access disorders are contrasted with semantic storage disorders according to four factors Temporal factors, response consistency, frequency and semantic relatedness are the four factors used to differentiate between semantic refractory access and semantic storage disorders A key feature of semantic refractory access disorders is temporal distortions Decreases in response time to certain stimuli are noted when compared to natural response times Response consistency is the next factor In access disorders you see inconsistencies in comprehending and responding to stimuli that have been presented many times Temporal factors impact response consistency In storage disorders, you do not see an inconsistent response to specific items like you do in refractory access disorders Stimulus frequency determines performance at all stages of cognition Extreme word frequency effects are common in semantic storage disorders while in semantic refractory access disorders word frequency effects are minimal The comparison of 'close' and 'distant' groups tests semantic relatedness 'Close' groupings have words that are related because they are drawn from the same category For example, a listing of clothing types would be a 'close' grouping 'Distant' groupings contain words with broad categorical differences Non-related words would fall into this group Comparing close and distant groups shows that in access disorders semantic relatedness had a negative effect This is not observed in semantic storage disorders Category specific and modality specific impairments are important components in access and storage disorders of semantic memory55

Present and future researchedit

Semantic memory has had a comeback in interest in the past 15 years, due in part to the development of functional neuroimaging methods such as positron emission tomography PET and functional magnetic resonance imaging fMRI, which have been used to address some of the central questions about our understanding of semantic memory

Positron emission tomography PET and functional magnetic resonance fMRI allow cognitive neuroscientists to explore different hypotheses concerning the neural network organization of semantic memory By using these neuroimaging techniques researchers can observe the brain activity of participants while they perform cognitive tasks These tasks can include, but are not limited to, naming objects, deciding if two stimuli belong in the same object category, or matching pictures to their written or spoken names56

Rather than any one brain region playing a dedicated and privileged role in the representation or retrieval of all sorts of semantic knowledge, semantic memory is a collection of functionally and anatomically distinct systems, where each attribute-specific system is tied to a sensorimotor modality ie vision and even more specifically to a property within that modality ie color Neuroimaging studies also suggest a distinction between semantic processing and sensorimotor processing

A new idea that is still at the early stages of development is that semantic memory, like perception, can be subdivided into types of visual information—color, size, form, and motion Thompson-Schill 200357 found that the left or bilateral ventral temporal cortex appears to be involved in retrieval of knowledge of color and form, the left lateral temporal cortex in knowledge of motion, and the parietal cortex in knowledge of size

Neuroimaging studies suggest a large, distributed network of semantic representations that are organized minimally by attribute, and perhaps additionally by category These networks include "extensive regions of ventral form and color knowledge and lateral motion knowledge temporal cortex, parietal cortex size knowledge, and premotor cortex manipulation knowledge Other areas, such as more anterior regions of temporal cortex, may be involved in the representation of nonperceptual eg verbal conceptual knowledge, perhaps in some categorically-organized fashion"58 It is suggested that within the temperoparietal network, the anterior temporal lobe is relatively more important for semantic processing, and posterior language regions are relatively more important for lexical retrieval

See alsoedit

  • Memory semantics
  • Sparse distributed memory


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Further readingedit

  • John Hart, Michael A Kraut 2007 Neural Basis of Semantic Memory Publisher-Cambridge University Press ISBN 0521848709, 9780521848701
  • Rosale McCarthy 1995 Semantic Knowledge And Semantic Representations: A Special Issue Of Memory Publisher Psychology Press ISBN 0863779360, 9780863779367
  • Frank Krüger 2000 Coding of temporal relations in semantic memory Publisher-Waxmann Verlag ISBN 3893259430, 9783893259434
  • Sandra L Zoccoli 2007 Object Features and Object Recognition: Semantic Memory Abilities During the Normal Aging Process Publisher-ProQuest ISBN 0549321071, 9780549321071
  • Wietske Vonk 1979 Retrieval from semantic memory Publisher Springer-Verlag
  • Sarí Laatu 2003 Semantic memory deficits in Alzheimer's disease, Parkinson's disease and multiple sclerosis: impairments in conscious understanding of concept meanings and visual object recognition Publisher-Turun Yliopisto
  • Laura Eileen Matzen 2008 Semantic and Phonological Influences on Memory, False Memory, and Reminding Publisher-ProQuest ISBN 0549909958, 9780549909958
  • William Damon, Richard M Lerner, Nancy Eisenberg 2006 Handbook of Child Psychology, Social, Emotional, and Personality Development Publisher John Wiley & Sons ISBN 0471272906, 9780471272908
  • Omar, Rohani; Hailstone, Julia C; Warren, Jason D 2012 "Semantic Memory for Music in Dementia" Music Perception 29 5: 467–477 doi:101525/mp2012295467 
  • Vanstone, Ashley D; Sikka, Ritu; Tangness, Leila; Sham, Rosalind; Garcia, Angeles; Cuddy, Lola L 2012 "Episodic and Semantic Memory for Melodies in Alzheimer's Disease" Music Perception 29 5: 501–507 doi:101525/mp2012295501 
  • Smith, Edward E 2000 "Neural Bases of Human Working Memory" Current Directions in Psychological Science 9 2: 45–49 doi:101111/1467-872100058 

External linksedit

  • http://wwwnewscientistcom/articlensid=dn10012&feedId=brain_rss20
  • http://diodoretipggdapl An application of computational semantic memory model Plays 20 questions game on animals domain
  • S-Space Package, an open source Java library that includes several semantic memory implementations, such as PEN and IS for generating Statistical semantics from a text corpus
  • http://wwwsemantikozcom/blog/2008/02/25/hyperspace-analogue-to-language-hal-introduction/ Hyperspace Analogue to Language HAL variation of semantic memory explained in detail

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