Philos Trans R Soc Lond B Biol Sci. 2014 Jan 19; 369(1634): 20120392. Conceptual knowledge reflects our multi-modal ‘semantic database’. As such, it brings meaning to all verbal and non-verbal stimuli, is the foundation for verbal and non-verbal expression and provides the basis for computing appropriate semantic
generalizations. Multiple disciplines (e.g. philosophy, cognitive science, cognitive neuroscience and behavioural neurology) have striven to answer the questions of how concepts are formed, how they are represented in the brain and how they break down differentially in various neurological patient groups. A long-standing and prominent hypothesis is that concepts are distilled from our multi-modal verbal and non-verbal experience such that sensation in one modality (e.g. the smell of an apple)
not only activates the intramodality long-term knowledge, but also reactivates the relevant intermodality information about that item (i.e. all the things you know about and can do with an apple). This multi-modal view of conceptualization fits with contemporary functional neuroimaging studies that observe systematic variation of activation across different modality-specific association regions dependent on the conceptual category or type of information. A second vein of interdisciplinary work
argues, however, that even a smorgasbord of multi-modal features is insufficient to build coherent, generalizable concepts. Instead, an additional process or intermediate representation is required. Recent multidisciplinary work, which combines neuropsychology, neuroscience and computational models, offers evidence that conceptualization follows from a combination of modality-specific sources of information plus a transmodal ‘hub’ representational system that is supported primarily by regions
within the anterior temporal lobe, bilaterally. Keywords: semantic cognition, conceptual knowledge, semantic dementia, anterior temporal lobe, semantic memory Semantic cognition refers to a collection of interactive cognitive mechanisms that support semantically derived behaviours. We use our semantic or conceptual knowledge not only for verbal comprehension but also when we initiate
language production (the purpose of receptive and expressive communication is, after all, the transfer of meaning from the speaker/sender to the listener/receiver). In addition, our considerable database of semantic knowledge is crucial in the non-verbal domain, both receptively (identification of non-verbal stimuli necessitates the transformation of sensation to meaning) and expressively (drawing and other expressive arts are based on the transmission of meaning, whilst effective object use
requires semantic knowledge of each implement). Semantic cognition can be decomposed into three interactive principal components underpinned by separable neural networks: (i) semantic entry/exit, i.e. translation between sensation/motor representations and semantic knowledge; (ii) the long-term representation of concepts/semantic memory; and (iii) ‘semantic control’—mechanisms that interact with our vast quantity of semantic knowledge in order to generate time- and
context-appropriate behaviour [1,2]. Every semantic task (receptive or expressive) requires a variable combination of all three components. Consequently, when any one of them is compromised (after neurological damage or transient brain stimulation),
participants will fail in semantic assessments though the quality of their impairment will vary. This review is focused primarily upon semantic representation—that is the nature of coherent concepts, how they are represented and their neural basis. A brief detour into the nature of semantic entry/exit and control provides important information not only with regard to what each of these principal components of semantic cognition is, but also what semantic representation is not. In
advance, however, it is worth underlining the observation that these three principal components have to be highly interactive in order to support semantic activities. Specifically, variation in efficiency within each system (either because the stimuli/concepts/contexts are inherently challenging or because a component has become compromised) will lead to automatic up- or downregulation of contributions from the other components. For example, the uncertainty that follows from noisy stimuli can be
compensated by upregulating the bidirectional interaction with meaning (i.e. the semantic representations) or context. Likewise, there will be variable involvement of the three components depending on the nature and demands of the task or concept (e.g. for a formal exploration of this issue with respect to concrete and abstract concepts, see Hoffman et al. [3]). Dedicated cognitive and neural machinery is taken up with semantic entry, i.e. reception of sensation and its translation into meaning, and also with semantic exit that is the transformation of meaning into the motor sequences that allow us to express our knowledge to others (e.g. through speech, writing, drawing, etc.). Each sensory–motor domain requires modality-specific computations that are necessary for transformation of sensation and these are supported by different cortical and
subcortical regions and pathways. These sensory-specific processes are observed not only in functional neuroimaging studies but also through the modality-specific disorders exhibited by some neurological patients. Lissauer ([4] Jackson translation) was one of the first researchers to note that within the visual domain, there is a clear separation of patients with damage to the
primary visual machinery (generating ‘apperceptive’ agnosia) versus other patients with deficits in higher-order ‘semantic’ representations (‘associative’ agnosia). Parallel distinctions are found in the other sensory domains with regard both to intact function (as revealed by in vivo neuroimaging) and neuropsychological studies [5]. The crucial distinction between
entry/exit processes and core semantic representation is reflected in the difference between patients with modality-specific agnosia or word deafness and those with damage to core semantic representations. Specifically, the former patients exhibit a modality-specific impairment of entry to meaning—thus, for example, word deaf patients have difficulty understanding heard words but can retrieve full information about the same concepts if they read the word or see a picture of the same item
[6]. By contrast—as discussed in more detail below—other brain regions are ‘transmodal’ and, when damaged, patients exhibit multi-modal deficits reflecting impairment of ‘central’ semantic representations. The triumvirate within semantic cognition is completed with semantic ‘control’—which refers to a collection of executive and working memory-related processes
that manipulate the core semantic representations in order to generate time- and task-appropriate behaviours in both the verbal and non-verbal domains [7,8]. These processes are critical to semantic cognition in all modalities: we store a wealth of information
about the meanings of words/objects but frequently only a subset of this knowledge is required for a task—indeed, other aspects of knowledge can actually be inappropriate and unhelpful. For example, playing a piano requires information about fine movements of the fingers to be retrieved, yet moving a piano across a room requires very different actions [9]. In everyday semantic
activities as well as formal assessments of semantic knowledge, it is often necessary to accentuate subordinate meanings, non-dominant features and to suppress other aspects of the same meaning. This is true both in receptive tasks and in semantically driven production. For example, in speech production, the speaker needs to select the appropriate label (e.g. animal, pet, dog, springer spaniel or ‘Oliver’) in order to convey the correct level of semantic specificity for a given concept (known in
speech production research as the hypernym problem: [10]). Finally, the critical aspects of meaning can also change for the same concept over time, not only in language but also in non-verbal behaviour, e.g. object use. Imagine, for example, the very different uses of the same knife in the task of making a cheese and chutney sandwich: packet opening, bread
cutting, butter spreading, cheese slicing, chutney scooping, etc., all require different, specific aspects of the knife's properties (and ways of holding and manipulating it) to be brought to the fore, one by one, whereas the most commonly listed property of cutting has to be inhibited, more often than not. Indeed, in the case of scooping, the canonical function of the knife has to be disregarded altogether and replaced by a substituted
function in place of another object (spoon). In conclusion, the ability to regulate and shape conceptual information in all expressive and receptive modalities is critical to any adequate account of semantic cognition. In the contemporary literature, the distinction between semantic representation and control was initially highlighted through a series of seminal functional magnetic resonance imaging (fMRI) studies with regard to the role of left inferior prefrontal cortex in
semantic tasks [11–13]. Although there is an ongoing debate about the exact nature of the cognitive processes that are underpinned by this region (selection, inhibition, augmentation, etc.), all studies agree that the region does not store semantic
knowledge per se but rather is important for its task-/context-specific manipulation. More recent studies have observed the same distinction in contrastive neuropsychological studies of patients with degradation of semantic representation (semantic dementia (SD)—the temporal lobe variant of frontotemporal dementia, see below) versus those with dysfunctional semantic control (semantic aphasia, see below:
[1,14,15]). These neuropsychological studies along with parallel transcranial magnetic stimulation investigations
[3,16,17] have highlighted a three-part neural network that seems to underpin the executive elements of semantic control (prefrontal
cortex, posterior middle temporal lobe and the intraparietal sulcus, IPS). Although much of the functional neuroimaging literature has focused upon prefrontal regions, a recent meta-analysis of these fMRI studies found that the same triad of brain regions are implicated in semantic control as those found in patient and repetitive transcranial magnetic stimulation (rTMS) investigations
[18]. While issues of control versus representation are active in the contemporary literature, closely related ideas are much older, stemming back through Luria to Goldstein and Head
[14,19,20]. Indeed, Head's investigations of semantic aphasia highlighted some of the same key findings including the fact that the
patients appeared to retain the core semantic knowledge but were unable to use it appropriately, that these semantic deficits overlapped with a broader range of information-processing (executive) impairments, and that this symptom complex arose from damage to the inferior parietal region. What are conceptual representations and how
does the brain encode them? Perhaps the most dominant hypothesis is that concepts are not stored as unitary representations in a specific brain region but rather reflect the mass action of multiple, modality-specific sources of information, each of which is coded in different cortical regions. While a considerable amount of the contemporary cognitive and cognitive neuroscience literature is still focused on this notion, the key ideas are not new and go back more than a century. Wernicke and
Meynert (see [21]) were interested in how the brain formed and reactivated concepts—a process they referred to as ‘conceptualization’. Meynert and Wernicke's model of conceptualization made the following assumptions: (i) that the building blocks of concepts were modality-specific engrams (stores of information) localized to the cortical areas responsible for the corresponding
sensory, motor or verbal domain; (ii) that these modality-specific engrams, in widespread brain regions, were fully interconnected; and (iii) that this web of connections was the basis of conceptualization—a specific concept being represented by the coactivation of all its associated engrams. For example, when tasting an apple, the taste-specific engram will automatically activate all of the other associated modality-linked engrams, enabling the brain to retrieve other knowledge concerning the
object: its visual form, likely colour, name, presence of seeds, how it is peeled and so on. In this proposal, modality-specific engrams were located in particular brain regions, but conceptualization was not. Indeed, Wernicke–Meynert argued that—unlike forms of agnosia and aphasia—central disorders of conceptualization only occurred as a consequence of global brain damage (dementia), because only such widespread cortical damage would disrupt the engram reactivation process. As will be discussed
below, this assumption turns out to be incorrect as there are indeed transmodal regions in the cortex, which when damaged or stimulated lead to multi-modal yet selective semantic impairment. Direct descendants of these ideas are found in the modern literature though the language to describe them has been updated. In cognitive science, the idea of distributed, experience-dependant formulations of concepts is captured in the hypothesis of ‘embodied cognition’
[22], which can vary in form from weak to strong formulations [23]. Again, the key idea in these theories is that concepts reflect the mass action of multiple information sources that are experienced and encoded in each modality, separately. Considerable
convergent evidence for this approach to conceptualization has come from functional neuroimaging studies [24,25], which have demonstrated that crucial sources of information for different types of concept (e.g. how things move, the sound they make, the way
they look, the way we manipulate them, etc.) are activated in their respective modality-specific association cortices (i.e. human MT, auditory association cortex, ventral occipito-temporal visual association cortex, superior parietal/IPS, etc.) As reviewed in more detail below, the need for and discussion of an additional transmodal representational system is found in multiple literatures, including
philosophy and cognitive science (for careful discussion of related ideas, including ‘grounding by interaction’, see [26]). In addition, contemplation of the nature of different cortical regions and the impairments demonstrated by patients after damage to these areas also provides key insights about the foundation of semantic cognition. Moving beyond secondary
modality-specific association cortices, there are intermediary ‘tertiary’ cortical areas that are not tied to any one particular modality—for which Luria [20] adopted the term ‘transmodal’ cortex. When viewed from the lateral surface, these form a horseshoe running from the IPS through the inferior parietal lobule, the middle temporal gyrus, anterior and inferior temporal
region to various inferior and lateral prefrontal regions (areas that fall into the vascular ‘watershed’ between the middle cerebral, posterior cerebral and anterior communicating arteries). Such transmodal regions provide a neural opportunity not only for multiple sources of information to be merged into a coherent whole but also for other cognitive mechanisms to act on and influence processing in multiple modalities (although it should be noted here that the connectivity and thus exact
functions will vary in graded ways across subregions within this transmodal horseshoe). Indeed, contemporary functional neuroimaging studies of multi-modal semantic activities highlight the very same network of regions [27,28] and, when this network is
damaged, patients demonstrate multi-modal semantic impairment. Geschwind et al. [29], for example, reported a case of ‘isolation of the speech area’ following carbon monoxide poisoning (which tends to affect the vascular watershed regions and in this patient led to damage to most of the transmodal cortical areas) who was still able to repeat, generate
non-propositional phrases and learn new songs but demonstrated significant semantic impairment. Other patient groups may exhibit modality-specific (e.g. verbal) comprehension deficits when lesions are isolated to modality-specific secondary association cortices (e.g. posterior superior temporal gyrus) but multi-modal impairments when the lesions are larger and encroach on the transmodal areas (e.g. posterior middle temporal and inferior parietal regions:
[30,31]). Cognitive functions vary across these transmodal regions. As noted above, functional neuroimaging, neuropsychological and rTMS studies all converge on the notion that prefrontal regions do not represent semantic knowledge per
se but rather are crucial for task- and context-appropriate manipulation of the semantic database. There is less certainty about posterior temporal and inferior parietal contributions to multi-modal semantic processing. There are currently two main hypotheses about the functions of these posterior areas. A subset of patients with semantic aphasia have posterior temporoparietal damage, implicating these areas in semantic control
[1,14] and indeed at least some functional neuroimaging studies and rTMS investigations have shown that posterior middle temporal gyrus (pMTG) and IPS are sensitive to the degree of semantic control required by the tasks
[17,18]—which fits with the considerable evidence for IPS–prefrontal–pMTG interactions in a ‘multi-demand’ control network [32]. This
pattern is also supported by recent comparative studies of chronic Wernicke's aphasia [31,33] which, in addition to a strong modality difference (spoken words < written words < picture comprehension), have found that the patients' multi-modal
semantic impairment has the features of semantic aphasia (control deficits) rather than SD (i.e. representational degradation). A second hypothesis is that some of these posterior areas are, instead, implicated in semantic representation rather than semantic control. Geschwind and co-workers,
[29,34] for example, argued that the angular gyrus (AG) is ideally connected as a transmodal hub to code the meaning of words. In addition, contemporary functional neuroimaging studies have shown that the contrasts of concrete > abstract words, words
> nonwords, etc., generate a significant difference in the AG [27,35,36]. Sophisticated neuropsychological studies of aphasic
naming have recently associated ventral inferior parietal lobe (IPL) lesions with associative semantic errors [37], which could fit with the notion that the AG codes certain types of semantic information- or event-based relationships
[35,38]. It is, of course, entirely possible that both hypotheses are correct such that different aspects of semantic cognition are tightly yet separately packed into the posterior temporal and IPL region. Future studies are required to reveal the nature
and location of functions in this area. Indeed, there are many puzzles that remain. These include the facts that: the exact relationship between semantic control and representation has not been simultaneously mapped across this broader region; unlike other parts of the semantic network, the AG is often deactivated, yet differentially (less so for concrete than abstract, words than nonwords, etc.), which makes interpretation of its underlying function more complex
[39]; and, also the observation that IPL regions are activated by numerous apparently different domains and tasks (including semantics, syntax, episodic memory, theory of mind, attention, phonology, praxis, etc.) and patients with lesions to this area often exhibit deficits in some or all of these domains
[14,40]. Unlike the other regions of transmodal cortex noted above, until relatively recently the potential role of anterior
temporal regions in semantic cognition has received much less attention. This is undoubtedly owing to multiple methodological limitations which amount to an absence of evidence about the anterior temporal lobe (ATL) rather than to evidence of absence. For example, although patients with SD were reported over a century ago [41], it was only modern neuroimaging techniques that
allowed researchers to link the patients' semantic impairment with the underlying ATL damage. In addition, classical models of aphasia were primarily based upon patients with middle cerebral artery stroke which, owing to its vascular supply, is unlikely to damage the ATL region bilaterally, especially in its middle to ventral aspects [42]. Likewise, there is a related
sampling bias within fMRI studies which, owing to various methodological issues (including limited field of view and magnetic inhomogeneities), have not consistently sampled activation in the middle and inferior ATL [43]. The case for the importance of transmodal representation in semantic memory and the role of the ATL in this aspect of semantic cognition has
been heavily shaped by studies of patients with SD. SD, the temporal lobe variant of frontotemporal dementia, is associated with atrophy and hypometabolism of the ATL bilaterally. While the extent of the pathology spreads with progression, the distribution of atrophy is always most pronounced in the polar and ventrolateral aspects of the ATL
[44–46] and is associated with the patients' increasing multi-modal semantic impairment. Owing to the relatively intensive and detailed neuropsychological studies of SD conducted over the past 20 years, we now know a considerable amount about the nature of
the impairment in this patient group and, by extension, the nature of semantic representation and the functions of the ATL [47,48]. Some of the key characteristics of the patients' presentation—its multi-modality, selectivity and
progression—were noted in the first significant modern neuropsychological study by Warrington [49]. She described three patients with progressive brain disease resulting in a range of multi-modal semantic deficits. Other aspects of the patients' cognition, including perceptual abilities and even other forms of memory (everyday episodic memory and short-term memory),
were well preserved. Of course, semantic impairment can be found in a number of other ATL-related neurological conditions, including Alzheimer's disease, herpes simplex virus encephalitis, head injury and neurosurgery. Almost inevitably, however, the semantic impairment in these disorders is: (i) less pervasive than that observed in SD, and (ii) accompanied by other deficits affecting episodic or short-term memory, attention, executive and/or language processing—which makes the interpretation of
impaired task performance harder given that one or more of the deficits beyond the semantic impairment itself may contribute to the observed task dysfunction. By contrast, the selective semantic degradation found in SD provides not only a key feature for differential diagnosis in the clinic [41] but also an unrivalled research opportunity to explore ATL-based contributions to
semantic representation [49]. Detailed neuropsychological investigations have highlighted a plethora of insights about SD but two additional characteristics are important for this review. The first is that the semantic impairment is a graded phenomenon in which concepts and the boundaries between concepts gradually ‘dissolve’ or ‘dim’, rather than dropping out
abruptly [50,51]. The second is that the degradation of concepts is multi-modal in nature, thereby leading to poor performance across all verbal and non-verbal domains, including words, objects, pictures, sounds, smells, and touch, etc., in receptive tasks
and speech, writing, object use, drawing, etc., in expressive activities [48,52–57]. The simple, straightforward conclusion to draw
from these studies of SD is that the ATL (bilaterally) plays a key representational role in conceptual knowledge and, following its transmodal nature, does this for all concepts, irrespective of the modality of input or output. Although the SD neuroanatomical and behavioural results are relatively clear, it is important not to rely solely upon a single source of information and, indeed, for some time the conclusions drawn about semantic memory and the ATL from SD alone were queried
[24]. This is where convergent data from different methods are especially helpful, because the inherent yet non-overlapping weaknesses of each technique are ameliorated by using them conjunctively
[3,58]. In recent years, there has been an accumulation of both functional neuroimaging (positron emission topography (PET), magnetoencephalography (MEG) and more recent distortion-corrected fMRI:
[58,59–61]) and rTMS
[62–65] studies of the left and right ATL regions in neurologically intact participants. These studies have found evidence that replicates and extends the SD patients' key characteristics, including multi-modality, bilaterality and selectivity. Thus, these
studies have found that: (i) the ATL are activated for a range of semantic tasks, irrespective of the modality of input (e.g. words, pictures, sounds, etc.) as well as for concepts of different categories and levels of specificity (although activation and rTMS effects tend to be greater for specific-level concepts
[63,66], they are also present for basic and domain-level distinctions:
[61,65]); (ii) both left and right ATL areas are implicated in semantic processing; and (iii) the same areas do not appear to be involved in equally demanding non-semantic tasks (such as difficult number or novel visual stimulus judgements). The convergent evidence from the studies of SD patients, rTMS and functional neuroimaging can be understood on the basis that the transmodal ATL cortex plays a key and special role in the formation of semantic representations. Using a computational model, Rogers et al.
[48] demonstrated that an interconnected central ‘hub’, which draws together modality-specific information, will behave as a transmodal representational system. The Rogers et al. ‘hub-and-spoke’ model is an extension of the Meynert–Wernicke ‘distributed-only’ framework. Information arising in each specific modality (e.g. the elephant's shape, colour, smell, form of
movement, name, verbal descriptors, etc.) is coded in the corresponding specific cortical sensory or motor or language region. In this sense, the hub-and-spoke network as a whole is neuroanatomically widespread—as proposed originally by Meynert and Wernicke, and captured in modern ‘embodied cognition’ hypotheses. The information from the modality-specific regions, however, is fused together through an additional transmodal representation hub and, as a result, conceptualization
reflects the joint action of the hub and the concept-relevant ‘spokes’ (i.e. the modality-specific sources of information that pertain to the target concept). The Rogers et al. model was trained to take a piece of modality-specific information (e.g. an outline of the elephant's visual form) as input and to reproduce the correct information across the remaining information layers (e.g. its colour or name or various things people might say about it, etc.) by propagating activation through
the intermediate transmodal hub. Rogers et al. were able to demonstrate that simulated damage to these intermediate transmodal units of the trained model reproduced the core features of SD (and, although predating many of the functional neuroimaging and rTMS investigations, is consistent with the finding that the intact ATL regions are involved in normal, multi-modal semantic representation). At the time the Rogers et al.
[48] model was formulated, there was a relative paucity of information about the connectivity of ATL regions although the model assumed widespread connectivity from the transmodal hub to modality-specific association areas. Data from both comparative neurology
[67] and recent human studies (using in vivo distortion-corrected probabilistic tractography: [68]) indicate that this is the case. The ATL region not only receives inputs posteriorly from visual and auditory-associated regions but also
medio-anteriorly from olfactory and limbic-related frontotemporal regions. In addition, the pattern of graded intratemporal lobe connectivity would seem to be a direct foundation for both rostral and lateral convergence of information, from which a graded representational hub (centred on the ventrolateral ATL) could be formed [68]. Before considering in more
detail why coherent concepts might require the addition of a transmodal hub, it might be useful to tackle and clarify five common, interlinked misconceptions/issues which have arisen about the hub-and-spoke hypothesis. (1) Hub-only versus hub-and-spoke: our previous investigations and descriptions of the theory have been summarized and recast by some researchers as a strict contrast between an ATL hub-only representation of semantic knowledge versus the
neuroanatomically distributed accounts associated with embodied cognition. This is, however, a false contrast. As made explicit through computational implementation, the Rogers et al. framework requires a combination of hub-and-spokes to generate multi-modal, coherent concepts. Although previous empirical studies have tended to focus on the importance of specific individual regions for semantic representation (in support of embodied or hub-and-spoke hypotheses), more recent
studies have focused upon the combined roles of transmodal ATL and modality-specific regions in semantic processing [65]. For example, the transient nature of rTMS can be used to investigate and compare different neural regions within the same participants using the same test materials. A study used this approach to test and confirm that both the ATL hub- and
modality-specific spokes do, indeed, contribute simultaneously to semantic representation. Consistent with the general lack of category differences in SD patients [69,70], ATL stimulation slowed healthy participants' ability to use semantic knowledge to
generate the names of animals, manipulable and non-manipulable artefacts to an equal degree. By contrast, stimulation of the IPS region generated a category-specific pattern (slowing the naming of man-made items only) which was driven by a selective interference for manipulable items and is consistent with the hypothesis that this region (spoke) codes praxis information
[65]. (2) Amodal versus transmodal: semantic aficionados may note that the term ‘transmodal’ has been used throughout this paper to describe the nature of the representations coded within the ATL hub. In most of our previous descriptions of the theory, we have tended to use the term ‘amodal’. This adjective was originally selected in order to emphasize
the difference between the ‘spokes’, which are inherently linked to a modality-specific source, and the hub that acts as an intermediary between all modalities rather than belonging to any one in particular. Indeed, as discussed in the next section, there are good reasons to think that coherent concepts require some kind of modality-independent re-representation. For other researchers, however, ‘amodal’ is a loaded term and is associated with symbolic, non-embodied accounts of conceptualization
[22]. As noted above, the hub-and-spoke theory is not an anti-embodied approach but rather emphasizes the need for transmodal distillation of multi-modal verbal and non-verbal experience as the basis for conceptualization. Furthermore, as discussed in more detail below (see ‘Future directions’), there is increasing evidence for a ‘graded’ hub where there is a
softening of the boundaries between modality-specific and transmodal representation. (3) Differences in performance across tasks, modalities and items: the notion of an amodal/transmodal ATL hub implies that its function should be inherently multi-modal in nature and that damage should result in multi-modal impairment. As reviewed above, there is clear convergent evidence for both of these predictions. In addition, some researchers have suggested that the level of
involvement/impairment should be equivalent across different tasks and modalities. If all other things were equal then this would be correct but, as with much else in life, all other things are not equal. As demonstrated across an array of different computational models, performance on any one particular task is influenced not only by the core transmodal representations but also by the nature of the modality-specific resources/representations and, importantly, the nature of the mapping between
them [48,71]. Thus, for example, the same level of damage to the semantic system will tend to generate a larger effect on verbal than picture-based tasks, because there is an arbitrary mapping between words and meaning but a quasi-systematic one from
pictured objects to meaning. As well as the nature of the representational transformations required by the task, the pattern of physical connectivity between regions will also be crucial. Indeed, the impact of a strong left-hemisphere bias in connectivity from ATL to speech production mechanisms was explored in a neuroanatomically constrained model [55] and shown to be
capable of explaining the more severe levels of anomia in patients with greater left than right ATL lesions (e.g. after unilateral resection: [72–74]) or in patients with asymmetrically left > right than right > left ATL damage, in the context of
bilateral disease [55]. The same logic applies to variation of performance across different item types. SD performance is similar for different concepts only as long as the items are matched for key factors. In particular, SD patients exhibit strong effects of concept familiarity/word frequency
[75,76], imageability [77] and typicality
[78]. As long as these factors are controlled then the patients rarely, if ever, demonstrate different levels of performance for different categories of concept
[69,70], other than a preservation of understanding number quantity
[79,80] which is thought to depend on parietal rather than anterior temporal regions. (4) The ATL and specific concepts: various studies have reported patients with poor identification of faces or other specific entities after ATL
lesions [81,82] and SD patients exhibit a graded effect such that performance on specific concepts is always worse than basic and superordinate distinctions
[49,83]. Similarly, ATL rTMS slows down naming of specific concepts and neuroimaging studies have identified ATL activation for the same task
[63,66]. These and other findings have led some researchers to consider the possibility that the ATL regions are important for the representation for specific entities alone. An alternative hypothesis was demonstrated within the Rogers et al.
hub-and-spoke model, which exhibited a graded specificity effect within a single representational system (i.e. specific and general concepts were not represented in separate systems). Instead, at all levels of impairment, the model exhibited a specificity gradient, because specific concepts require precise and fine reactivation of detailed information in order to distinguish one specific-level exemplar from another. By contrast, performance on basic and domain-level concepts is impaired but not
to the same degree, because less semantic precision is required. This suggests that the ATL semantic regions are not dedicated to specific concepts alone but rather the specificity gradient simply reflects the level of semantic precision required by the task. This explanation is consistent with the fact that the effect of ATL rTMS or the deficits in patients with mild ATL-related semantic impairment is more apparent on specific concepts. It also suggests that with sufficient statistical power,
it should be possible to observe engagement of the same areas, in processing of basic or domain-level semantic discriminations, which has been reported more recently in rTMS and distortion-corrected fMRI studies [61,65]. (5) ATL
versus temporal pole: as noted previously, SD patients provided a major source of information about the role of ATL regions in multi-modal semantic processing. Given the progressive and graded nature of the underlying pathology, it is difficult to be sure about the anatomical boundaries of the areas within the ATL that are critical for semantic processing. Consequently, in assessing this region in other patient groups, functional neuroimaging, etc. studies have varied in terms of the
area that different research groups have used as a region of interest: for example, adopting BA38 (temporal polar cortex), y < 0 in Montreal Neurological Institute (MNI) space, the boundary provided by the limen insula, and so on. These anatomical boundary variations are understandable but also will generate important differences and apparent inconsistencies in reported results. From more recent studies, it would appear that focusing on the most anterior, polar regions may lead to
null results, whereas other ATL regions do seem to be heavily implicated in semantic processing. In particular, correlations between semantic dysfunction and glucose hypometabolism in SD point heavily towards a region of the ventral ATL caudal to the temporal pole (centred on y = −26; (even though both exhibit the most atrophy: [84]). Likewise, through the use of
MEG, PET and distortion-corrected fMRI, functional neuroimaging studies are now also implicating a ventrolateral and not temporopolar region as a centre point of the ATL representational hub
[28,58,59,61,85]. Given the angle of the temporal lobe in anterior commissure–posterior commissure aligned MNI space and the technical challenges of using gradient echo planar imaging fMRI in the ventrolateral ATL
[43], regions of interest that focus on temporopolar cortex, y > 0, etc. are very unlikely to sample a key ATL region in semantic studies. (e) Why do we need a transmodal representation for coherent conceptualization?The theoretical approach described here raises the important questions of: (i) what representation is coded or re-coded in the hub, and (ii) why does conceptualization necessitate an additional hub layer? As noted above, the hub-and-spoke model suggests that modality-specific association areas code information arising in each modality and these multiple sources are drawn together into coherent concepts through interactions with an ‘amodal/transmodal’ hub [86]. The computations required to achieve coherent concepts are non-trivial, because the modality-specific semantic features combine in complex, nonlinear ways. The relationship between ingredients and baked goods (e.g. pastries, breads, cakes, etc.) may be a useful analogy. It is always possible to consider each product (concept) and deconstruct it into a list of ingredients (features). Indeed, as per the classical semantic theories and contemporary embodied approaches, it is clear that the ingredients (features) are fundamental to the formation of each product (concept) in that if one of the features is missing (e.g. flour) is impossible to generate the concepts that contain it (e.g. most baked products). In addition to deconstructing the concepts into features, it is also important to consider the construction of concepts from features—given that this is the process that most theories assume underpins the formation of concepts. As with baking, although the presence of the correct ingredients is crucial, a pantry (brain) full of constituents (features) is not sufficient—we need a recipe (hub) as well. Indeed, as all novice bakers have experienced, following a recipe precisely is especially important as slight changes in the combination of ingredients leads to very different outcomes (e.g. a soggy brown lump versus a lemon-drizzle cake). This is because the ingredients (features) have a complex relationship with the product (concept). Indeed, this relationship is multi-dimensional and nonlinear. For example, using a fixed set of eight basic ingredients (flour, water, milk, eggs, butter, sugar, yeast and raising agent), it is possible to generate a cookbook full of baked products and, in some cases, extremely different outcomes start out from an identical ingredients list (e.g. croissants and buttered crumpets contain the very same ingredients). In short, the formation of coherent concepts (good cakes) requires both the modality-specific engrams/features (ingredients) and the transmodal hub representation (recipe). The computational challenges that the formation of coherent concepts poses include the following.
These and many other related challenges have been highlighted before in philosophy [88] and cognitive science [89–91]. Wittgenstein, for example, noted that experience and use play a key role in concepts but that extracting shared meaning is not always possible on the basis of identifying a set of shared features. This included the observation that exemplars can vary in form (e.g. the different types of handles—break, switch, pump, crank—in a train driver's cabin; §12), or that knowledge can be generalized to new exemplars (e.g. knowledge of how to use the king in a set of chess pieces even if the design changes, §31) and that conceptually related items do not necessarily share any particular feature in common and thus cannot be defined in that way (e.g. games; §66). Following Wittgenstein's famous example, consider four category exemplars with the features {A,B,C}, {A,B,D}, {A,C,D} and {B,C,D}. No single feature defines them as a single group but instead they form a group through partial, overlapping features. A potential solution comes in three parts: philosophical–cognitive science, computational and neuroanatomical. Rather than searching endlessly for defining attributes for each and every set of concepts, some philosophers [88, §67] and cognitive scientists have proposed that ‘coherent representations’ might follow if an additional computation or representation is added to our sensory–verbal experience (a full discussion of the various suggested solutions goes beyond the scope of this paper but broad and clear reviews can be found in [89,90]). This idea parallels a similar problem and approach found in computational modelling. In computational terms, this problem cannot be solved by a single layer of feature-coding units (a single layer perceptron), because the representations are not linearly separable. They can, however, be grouped properly if an additional ‘hub’ layer is added because this allows re-representation of the feature input. The ‘hub-and-spoke’ framework allows the formation of modality-invariant multi-dimensional representations that, through the cross-translation of information between modalities, code the higher-order statistical structure that is present in our transmodal experience of each entity. As such, the same core information is activated each time an entity is (re-)encountered even if different aspects occur in separate episodes (challenge 1); verbal and non-verbal features can be linked to different ranges of concept (challenge 2) whether or not the information is systematically or orthogonally related (challenge 3). As well as being able to code the partial similarity structure in specific domains, the modality-invariant representations add greater flexibility in order to deal with concepts that do not follow these surface similarities (challenge 4); and the system provides a mechanism for generalization to new or changing exemplars (challenge 5). How does this modality-invariant representation system work and how does it break down in SD patients? By using an additional layer of representational units, computational models of semantic memory form a multi-dimensional space [48]. Through a gradual training process, each piece of sensory, verbal and motor information becomes associated with subregions of this space. A potentially useful analogy might be different kinds of geographical maps (though these are limited to two dimensions rather than the many found in semantic models). Each type of map (e.g. geological, political, linguistic, agricultural, etc.) codes the same chart/grid system with the presence or absence of each type of feature (e.g. mountainous regions, wheat-growing areas, etc.) that is found in that modality (type of map). This shared representational space (the grid system used in all maps) results in a multi-layered tapestry of information. Any specific location is then associated with (can reproduce) the information mapped to it (the name of the area, its geology, etc.) plus it can generate the likely information, for areas that have never been directly mapped, through interpolation (generalization to new examples). By using many dimensions, it is possible to chart complex nonlinear regions—that is, map relationships between each feature and its associated concepts. In order to map a concept correctly in the high-dimensional, modality-invariant space, it is necessary to have a complex boundary. As this representational hub breaks down, two things are likely to happen: the boundary becomes fuzzy and is simplified, because there are fewer dimensions to code the boundary in. Two recent studies of SD patients investigated the effect that the degradation of the transmodal hub has on this aspect of conceptualization [86,92]. Irrespective of whether items were presented as words or pictures, the concept boundary in SD patients became increasingly dominated by superficial similarities (with reduced dimensions, this is an optimal solution in that classification accuracy can be maximized by aligning the boundary with the shared feature structure). The consequence, however, was that two types of mismapping occurred simultaneously—target exemplars that are superficially different to the average fall outside the changed boundary (undergeneralized) and non-exemplars that happen to share many features in common with the target become swallowed up (overgeneralized). (f) Future directionsBuilding on the important historical foundation provided by Wernicke, Meynert, Head and others, the past 20 years or so has seen considerable multidisciplinary research efforts applied to semantic cognition, including the nature of conceptualization and its neural basis. Inevitably, there are still many important issues and puzzles to be solved in future studies. A full exploration of them is beyond this paper, but four that relate to the nature of conceptual representations are discussed briefly below.
AcknowledgementsThis research was supported by an MRC programme grant (MR/J004146/1). I continue to be grateful to colleagues and students, past and present, for their insightful and encouraging comments that have contributed significantly to the ideas summarized in this paper. In addition, I also thank the many patients and their families for the time and energy they have donated to our studies of semantic cognition. References1. Jefferies E, Lambon Ralph MA. 2006. Semantic impairment in stroke aphasia vs. semantic dementia: a case-series comparison. Brain 129, 2132–2147. ( 10.1093/brain/awl153) [PubMed] [CrossRef] [Google Scholar] 2. Jefferies E. 2013. The neural basis of semantic cognition: converging evidence from neuropsychology, neuroimaging and TMS. Cortex 49, 611–625. ( 10.1016/j.cortex.2012.10.008) [PubMed] [CrossRef] [Google Scholar] 3. Hoffman P, Jefferies E, Lambon Ralph MA. 2010. Ventrolateral prefrontal cortex plays an executive regulation role in comprehension of abstract words: convergent neuropsychological and repetitive TMS evidence. J. Neurosci. 30, 15 450–15 456. ( 10.1523/jneurosci.3783-10.2010) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 4. Lissauer H, Jackson M. 1988. Lissauer H. (1890). Ein Fall von Seelenblindheit nebst einem beitrage zur Theorie derselben. Archiv für Psychiatrie und Nervenkrankheiten, 21, 22–270. English translation by Jackson, M. (1988). Lissauer on agnosia. Cogn. Neuropsychol.5, 157–192 ( 10.1080/02643298808252932) [CrossRef] [Google Scholar] 5. Griffiths TD, Warren JD. 2004. What is an auditory object? Nat. Rev. Neurosci. 5, 887–892. ( 10.1038/nrn1538) [PubMed] [CrossRef] [Google Scholar] 6. Franklin S. 1989. Dissociations in auditory word comprehension: evidence from 9 fluent aphasic patients. Aphasiology 3, 189–207. ( 10.1080/02687038908248991) [CrossRef] [Google Scholar] 7. Corbett F, Jefferies E, Ehsan S, Lambon Ralph MA. 2009. Different impairments of semantic cognition in semantic dementia and semantic aphasia: evidence from the non-verbal domain. Brain 132, 2593–2608. ( 10.1093/brain/awp146) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 8. Noonan KA, Jefferies E, Corbett F, Lambon Ralph MA. 2010. Elucidating the nature of deregulated semantic cognition in semantic aphasia: evidence for the roles of prefrontal and temporo-parietal cortices. J. Cogn. Neurosci. 22, 1597–1613. ( 10.1162/jocn.2009.21289) [PubMed] [CrossRef] [Google Scholar] 9. Saffran EM. 2000. Word retrieval and its disorders. Cogn. Neuropsychol. 16, 777–790. ( 10.1080/026432999380654) [CrossRef] [Google Scholar] 10. Levelt WJM. 1992. Accessing words in speech production: stages, processes and representations. Cognition 42, 1–22. ( 10.1016/0010-0277(92)90038-J) [PubMed] [CrossRef] [Google Scholar] 11. Wagner AD, Paré-Blagoev EJ, Clark J, Poldrack RA. 2001. Recovering meaning: left prefrontal cortex guides controlled semantic retrieval. Neuron 31, 329–338. ( 10.1016/S0896-6273(01)00359-2) [PubMed] [CrossRef] [Google Scholar] 12. Thompson-Schill SL, Desposito M, Aguirre GK, Farah MJ. 1997. Role of left inferior prefrontal cortex in retrieval of semantic knowledge: a reevaluation. Proc. Natl Acad. Sci. USA 94, 14 792–14 797. ( 10.1073/pnas.94.26.14792) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 13. Poldrack RA, Wagner AD, Prull MW, Desmond JE, Glover GH, Gabrieli JDE. 1999. Functional specialization for semantic and phonological processing in the left inferior prefrontal cortex. Neuroimage 10, 15–35. ( 10.1006/nimg.1999.0441) [PubMed] [CrossRef] [Google Scholar] 14. Head H. 1926. Aphasia and kindred disorders of speech. London, UK: Cambridge University Press. [Google Scholar] 15. Jefferies E, Baker SS, Doran M, Lambon Ralph MA. 2007. Refractory effects in stroke aphasia: a consequence of poor semantic control. Neuropsychologia 45, 1065–1079. ( 10.1016/j.neuropsychologia.2006.09.009) [PubMed] [CrossRef] [Google Scholar] 16. Whitney C, Kirk M, O'Sullivan J, Lambon Ralph MA, Jefferies E. 2011. The neural organization of semantic control: TMS evidence for a distributed network in left inferior frontal and posterior middle temporal gyrus. Cereb. Cortex 21, 1066–1075. ( 10.1093/cercor/bhq180) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 17. Whitney C, Kirk M, O'Sullivan J, Lambon Ralph MA, Jefferies E. 2012. Executive semantic processing is underpinned by a large-scale neural network: revealing the contribution of left prefrontal, posterior temporal, and parietal cortex to controlled retrieval and selection using TMS. J. Cogn. Neurosci. 24, 133–147. ( 10.1162/jocn_a_00123) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 18. Noonan K, Jefferies E, Visser M, Lambon Ralph MA. 2013. Going beyond inferior prefrontal involvement in semantic control: evidence for the additional contribution of dorsal angular gyrus and posterior middle temporal cortex. J. Cogn. Neurosci. 25, 1824–1850. [PubMed] [Google Scholar] 19. Goldstein K. 1936. The problem of the meaning of words based upon observation of aphasic patients. J. Psychol. 2, 301–316. ( 10.1080/00223980.1936.9917462) [CrossRef] [Google Scholar] 20. Luria AR. 1976. The working brain: an introduction to neuropsychology. London, UK: Penguin. [Google Scholar] 21. Eggert GH. 1977. Wernicke‘s works on aphasia: a source-book and review. The Hague, The Netherlands: Mouton. [Google Scholar] 22. Barsalou LW, Kyle Simmons W, Barbey AK, Wilson CD. 2003. Grounding conceptual knowledge in modality-specific systems. Trends Cogn. Sci. 7, 84–91. ( 10.1016/S1364-6613(02)00029-3) [PubMed] [CrossRef] [Google Scholar] 23. Meteyard L, Cuadrado SR, Bahrami B, Vigliocco G. 2012. Coming of age: a review of embodiment and the neuroscience of semantics. Cortex 48, 788–804. ( 10.1016/j.cortex.2010.11.002) [PubMed] [CrossRef] [Google Scholar] 24. Martin A. 2007. The representation of object concepts in the brain. Annu. Rev. Psychol. 58, 25–45. ( 10.1146/annurev.psych.57.102904.190143) [PubMed] [CrossRef] [Google Scholar] 25. Pulvermuller F. 2005. Brain mechanisms linking language and action. Nat. Rev. Neurosci. 6, 576–582. ( 10.1038/nrn1706) [PubMed] [CrossRef] [Google Scholar] 26. Mahon BZ, Caramazza A. 2008. A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. J. Physiol. Paris 102, 59–70. ( 10.1016/j.jphysparis.2008.03.004) [PubMed] [CrossRef] [Google Scholar] 27. Binder JR, Desai RH, Graves WW, Conant LL. 2009. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19, 2767–2796. ( 10.1093/cercor/bhp055) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 28. Visser M, Jefferies E, Embleton KV, Lambon Ralph MA. 2012. Both the middle temporal gyrus and the ventral anterior temporal area are crucial for multimodal semantic processing: distortion-corrected fMRI evidence for a double gradient of information convergence in the temporal lobes. J. Cogn. Neurosci. 24, 1766–1778. ( 10.1162/jocn_a_00244) [PubMed] [CrossRef] [Google Scholar] 29. Geschwind N, Quadfasel FA, Segarra JM. 1968. Isolation of the speech area. Neuropsychologia 6, 327–340. ( 10.1016/0028-3932(68)90005-5) [CrossRef] [Google Scholar] 30. Chertkow H, Bub D, Deaudon C, Whitehead V. 1997. On the status of object concepts in aphasia. Brain Lang. 58, 203–232. ( 10.1006/brln.1997.1771) [PubMed] [CrossRef] [Google Scholar] 31. Robson H, Sage K, Lambon Ralph MA. 2012. Wernicke's aphasia reflects a combination of acoustic-phonological and semantic control deficits: a case-series comparison of Wernicke's aphasia, semantic dementia and semantic aphasia. Neuropsychologia 50, 266–275. ( 10.1016/j.neuropsychologia.2011.11.021) [PubMed] [CrossRef] [Google Scholar] 32. Duncan J. 2010. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14, 172–179. ( 10.1016/j.tics.2010.01.004) [PubMed] [CrossRef] [Google Scholar] 33. Ogar JM, Baldo JV, Wilson SM, Brambati SM, Miller BL, Dronkers NF, Gorno-Tempini ML. 2011. Semantic dementia and persisting Wernicke's aphasia: linguistic and anatomical profiles. Brain Lang. 117, 28–33. ( 10.1016/j.bandl.2010.11.004) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 34. Geschwind N. 1972. Language and the brain. Sci. Am. 226, 76–83. ( 10.1038/scientificamerican0472-76) [PubMed] [CrossRef] [Google Scholar] 35. Binder JR, Desai RH. 2011. The neurobiology of semantic memory. Trends Cogn. Sci. 15, 527–536. ( 10.1016/j.tics.2011.10.001) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 36. Wang J, Conder JA, Blitzer DN, Shinkareva SV. 2010. Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies. Hum. Brain Map. 31, 1459–1468. ( 10.1002/hbm.20950) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 37. Schwartz MF, Kimberg DY, Walker GM, Brecher A, Faseyitan OK, Dell GS, Mirman D, Coslett HB. 2011. Neuroanatomical dissociation for taxonomic and thematic knowledge in the human brain. Proc. Natl Acad. Sci. USA 108, 8520–8524. ( 10.1073/pnas.1014935108) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 38. Bonner MF, Peelle JE, Cook PA, Grossman M. 2013. Heteromodal conceptual processing in the angular gyrus. Neuroimage 71, 175–186. ( 10.1016/j.neuroimage.2013.01.006) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 39. Seghier ML, Fagan E, Price CJ. 2010. Functional subdivisions in the left angular gyrus where the semantic system meets and diverges from the default network. J. Neurosci. 30, 16 809–16 817. ( 10.1523/jneurosci.3377-10.2010) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 40. Cabeza R, Ciaramelli E, Moscovitch M. 2012. Cognitive contributions of the ventral parietal cortex: an integrative theoretical account. Trends Cogn. Sci. 16, 338–352. ( 10.1016/j.tics.2012.04.008) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 41. Hodges JR, Patterson K. 2007. Semantic dementia: a unique clinicopathological syndrome. Lancet Neurol. 6, 1004–1014. ( 10.1016/S1474-4422(07)70266-1) [PubMed] [CrossRef] [Google Scholar] 42. Phan TG, Donnan GA, Wright PM, Reutens DC. 2005. A digital map of middle cerebral artery infarcts associated with middle cerebral artery trunk and branch occlusion. Stroke 36, 986–991. ( 10.1161/01.STR.0000163087.66828.e9) [PubMed] [CrossRef] [Google Scholar] 43. Visser M, Jefferies E, Lambon Ralph MA. 2010. Semantic processing in the anterior temporal lobes: a meta-analysis of the functional neuroimaging literature. J. Cogn. Neurosci. 22, 1083–1094. ( 10.1162/jocn.2009.21309) [PubMed] [CrossRef] [Google Scholar] 44. Butler CR, Brambati SM, Miller BL, Gorno-Tempini M-L. 2009. The neural correlates of verbal and nonverbal semantic processing deficits in neurodegenerative disease. Cogn. Behav. Neurol. 22, 73–80. ( 10.1097/WNN.0b013e318197925d) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 45. Galton CJ, Patterson K, Graham KS, Lambon Ralph MA, Williams G, Antoun N, Sahakian BJ, Hodges JR. 2001. Differing patterns of temporal atrophy in Alzheimer's disease and semantic dementia. Neurology 57, 216–225. ( 10.1212/WNL.57.2.216) [PubMed] [CrossRef] [Google Scholar] 46. Rohrer JD, Warren JD, Modat M, Ridgway GR, Douiri A, Rossor MN, Ourselin S, Fox NC. 2009. Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology 72, 1562–1569. ( 10.1212/WNL.0b013e3181a4124e) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 47. Patterson K, Nestor PJ, Rogers TT. 2007. Where do you know what you know? The representation of semantic knowledge in the human brain. Nat. Rev. Neurosci. 8, 976–987. ( 10.1038/nrn2277) [PubMed] [CrossRef] [Google Scholar] 48. Rogers TT, Lambon Ralph MA, Garrard P, Bozeat S, McClelland JL, Hodges JR, Patterson K. 2004. The structure and deterioration of semantic memory: a neuropsychological and computational investigation. Psychol. Rev. 111, 205–235. ( 10.1037/0033-295X.111.1.205) [PubMed] [CrossRef] [Google Scholar] 49. Warrington EK. 1975. The selective impairment of semantic memory. Q. J. Exp. Psychol. 27, 635–657. ( 10.1080/14640747508400525) [PubMed] [CrossRef] [Google Scholar] 50. Hodges JR, Graham N, Patterson K. 1995. Charting the progression of semantic dementia: implications for the organisation of semantic memory. Memory 3, 463–495. ( 10.1080/09658219508253161) [PubMed] [CrossRef] [Google Scholar] 51. Lambon Ralph MA, Patterson K. 2008. Generalisation and differentiation in semantic memory: insights from semantic dementia. Ann. NY Acad. Sci. 1124, 61–76. ( 10.1196/annals.1440.006) [PubMed] [CrossRef] [Google Scholar] 52. Bozeat S, Lambon Ralph MA, Patterson K, Garrard P, Hodges JR. 2000. Non-verbal semantic impairment in semantic dementia. Neuropsychologia 38, 1207–1215. ( 10.1016/S0028-3932(00)00034-8) [PubMed] [CrossRef] [Google Scholar] 53. Bozeat S, Lambon Ralph MA, Patterson K, Hodges JR. 2002. When objects lose their meaning: what happens to their use? Cogn. Affect. Behav. Neurosci. 2, 236–251. ( 10.3758/CABN.2.3.236) [PubMed] [CrossRef] [Google Scholar] 54. Coccia M, Bartolini M, Luzzi S, Provinciali L, Lambon Ralph MA. 2004. Semantic memory is an amodal, dynamic system: evidence from the interaction of naming and object use in semantic dementia. Cogn. Neuropsychol. 21, 513–527. ( 10.1080/02643290342000113) [PubMed] [CrossRef] [Google Scholar] 55. Lambon Ralph MA, McClelland JL, Patterson K, Galton CJ, Hodges JR. 2001. No right to speak? The relationship between object naming and semantic impairment: neuropsychological evidence and a computational model. J. Cogn. Neurosci. 13, 341–356. ( 10.1162/08989290151137395) [PubMed] [CrossRef] [Google Scholar] 56. Piwnica-Worms KE, Omar R, Hailstone JC, Warren JD. 2010. Flavour processing in semantic dementia. Cortex 46, 761–768. ( 10.1016/j.cortex.2009.07.002) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 57. Goll JC, Crutch SJ, Loo JHY, Rohrer JD, Frost C, Bamiou D-E, Warren JD. 2009. Non-verbal sound processing in the primary progressive aphasias. Brain 133, 272–285. ( 10.1093/brain/awp235) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 58. Binney RJ, Embleton KV, Jefferies E, Parker GJM, Lambon Ralph MA. 2010. The ventral and inferolateral aspects of the anterior temporal lobe are crucial in semantic memory: evidence from a novel direct comparison of distortion-corrected fMRI, rTMS, and semantic dementia. Cereb. Cortex 20, 2728–2738. ( 10.1093/cercor/bhq019) [PubMed] [CrossRef] [Google Scholar] 59. Marinkovic K, Dhond RP, Dale AM, Glessner M, Carr V, Halgren E. 2003. Spatiotemporal dynamics of modality-specific and supramodal word processing. Neuron 38, 487–497. ( 10.1016/S0896-6273(03)00197-1) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 60. Vandenberghe R, Price C, Wise R, Josephs O, Frackowiak RSJ. 1996. Functional-anatomy of a common semantic system for words and pictures. Nature 383, 254–256. ( 10.1038/383254a0) [PubMed] [CrossRef] [Google Scholar] 61. Visser M, Lambon Ralph MA. 2011. Differential contributions of bilateral ventral anterior temporal lobe and left anterior superior temporal gyrus to semantic processes. J. Cogn. Neurosci. 23, 3121–3131. ( 10.1162/jocn_a_00007) [PubMed] [CrossRef] [Google Scholar] 62. Lambon Ralph MA, Pobric G, Jefferies E. 2009. Conceptual knowledge is underpinned by the temporal pole bilaterally: convergent evidence from rTMS. Cereb. Cortex 19, 832–838. ( 10.1093/cercor/bhn131) [PubMed] [CrossRef] [Google Scholar] 63. Pobric GG, Jefferies E, Lambon Ralph MA. 2007. Anterior temporal lobes mediate semantic representation: mimicking semantic dementia by using rTMS in normal participants. Proc. Natl Acad. Sci. USA 104, 20 137–20 141. ( 10.1073/pnas.0707383104) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 64. Pobric G, Jefferies E, Lambon Ralph MA. 2010. Amodal semantic representations depend on both anterior temporal lobes: evidence from repetitive transcranial magnetic stimulation. Neuropsychologia 48, 1336–1342. ( 10.1016/j.neuropsychologia.2009.12.036) [PubMed] [CrossRef] [Google Scholar] 65. Pobric G, Jefferies E, Lambon Ralph MA. 2010. Category-specific versus category-general semantic impairment induced by transcranial magnetic stimulation. Curr. Biol. 20, 964–968. ( 10.1016/j.cub.2010.03.070) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 66. Rogers TT, Hocking J, Noppeney U, Mechelli A, Gorno-Tempini ML, Patterson K, Price CJ. 2006. Anterior temporal cortex and semantic memory: reconciling findings from neuropsychology and functional imaging. Cogn. Affect. Behav. Neurosci. 6, 201–213. ( 10.3758/CABN.6.3.201) [PubMed] [CrossRef] [Google Scholar] 67. Morán MA, Mufson EJ, Mesulam MM. 1987. Neural inputs into the temporopolar cortex of the rhesus monkey. J. Comp. Neurol. 256, 88–103. ( 10.1002/cne.902560108) [PubMed] [CrossRef] [Google Scholar] 68. Binney RJ, Parker GJM, Lambon Ralph MA. 2012. Convergent connectivity and graded specialization in the rostral human temporal lobe as revealed by diffusion-weighted imaging probabilistic tractography. J. Cogn. Neurosci. 24, 1998–2014. ( 10.1162/jocn_a_00263) [PubMed] [CrossRef] [Google Scholar] 69. Lambon Ralph MA, Patterson K, Garrard P, Hodges JR. 2003. Semantic dementia with category specificity: a comparative case-series study. Cogn. Neuropsychol. 20, 307–326. ( 10.1080/02643290244000301) [PubMed] [CrossRef] [Google Scholar] 70. Lambon Ralph MA, Lowe C, Rogers TT. 2007. Neural basis of category-specific semantic deficits for living things: evidence from semantic dementia, HSVE and a neural network model. Brain 130, 1127–1137. ( 10.1093/brain/awm025) [PubMed] [CrossRef] [Google Scholar] 71. Lambon Ralph MA, Howard D. 2000. Gogi aphasia or semantic dementia? Simulating and assessing poor verbal comprehension in a case of progressive fluent aphasia. Cogn. Neuropsychol. 17, 437–466. ( 10.1080/026432900410784) [PubMed] [CrossRef] [Google Scholar] 72. Lambon Ralph MA, Cipolotti L, Manes F, Patterson K. 2010. Taking both sides: do unilateral anterior temporal lobe lesions disrupt semantic memory? Brain 133, 3243–3255. ( 10.1093/brain/awq264). [PubMed] [CrossRef] [Google Scholar] 73. Lambon Ralph MA, Ehsan S, Baker GA, Rogers TT. 2012. Semantic memory is impaired in patients with unilateral anterior temporal lobe resection for temporal lobe epilepsy. Brain 135, 242–258. ( 10.1093/brain/awr325) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 74. Seidenberg M, Griffith R, Sabsevitz D, Moran M, Haltiner A, Bell B, Swanson S, Hammeke T, Hermann B. 2002. Recognition and identification of famous faces in patients with unilateral temporal lobe epilepsy. Neuropsychologia 40, 446–456. ( 10.1016/S0028-3932(01)00096-3) [PubMed] [CrossRef] [Google Scholar] 75. Funnell E. 1995. Objects and properties: a study of the breakdown of semantic memory. Memory 3, 497–518. ( 10.1080/09658219508253162) [PubMed] [CrossRef] [Google Scholar] 76. Lambon Ralph MA, Graham KS, Ellis AW, Hodges JR. 1998. Naming in semantic dementia: what matters? Neuropsychologia 36, 775–784. ( 10.1016/S0028-3932(97)00169-3) [PubMed] [CrossRef] [Google Scholar] 77. Jefferies E, Patterson K, Jones RW, Lambon Ralph MA. 2009. Comprehension of concrete and abstract words in semantic dementia. Neuropsychology 23, 492–499. ( 10.1037/a0015452) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 78. Woollams AM, Cooper-Pye E, Hodges JR, Patterson K. 2008. Anomia: a doubly typical signature of semantic dementia. Neuropsychologia 46, 2503–2514. ( 10.1016/j.neuropsychologia.2008.04.005) [PubMed] [CrossRef] [Google Scholar] 79. Butterworth B, Cappelletti M, Kopelman M. 2001. Category specificity in reading and writing: the case of number words. Nat. Neurosci. 4, 784–786. ( 10.1038/90484) [PubMed] [CrossRef] [Google Scholar] 80. Jefferies E, Patterson K, Jones RW, Bateman D, Lambon Ralph MA. 2004. A category-specific advantage for numbers in verbal short-term memory: evidence from semantic dementia. Neuropsychologia 42, 639–660. ( 10.1016/j.neuropsychologia.2003.10.002) [PubMed] [CrossRef] [Google Scholar] 81. Damasio H, Grabowski TJ, Tranel D, Hichwa RD, Damasio AR. 1996. A neural basis for lexical retrieval. Nature 380, 499–505. ( 10.1038/380499a0) [PubMed] [CrossRef] [Google Scholar] 82. Tranel D, Damasio H, Damasio AR. 1997. A neural basis for the retrieval of conceptual knowledge. Neuropsychologia 35, 1319–1327. ( 10.1016/S0028-3932(97)00085-7) [PubMed] [CrossRef] [Google Scholar] 83. Rogers TT, Patterson K. 2007. Object categorization: reversals and explanations of the basic-level advantage. J. Exp. Psychol. Gen. 136, 451–469. ( 10.1037/0096-3445.136.3.451) [PubMed] [CrossRef] [Google Scholar] 84. Mion M, et al. 2010. What the left and right anterior fusiform gyri tell us about semantic memory. Brain 133, 3256–3268. ( 10.1093/brain/awq272) [PubMed] [CrossRef] [Google Scholar] 85. Sharp DJ, Scott SK, Wise RJS. 2004. Retrieving meaning after temporal lobe infarction: the role of the basal language area. Ann. Neurol. 56, 836–846. ( 10.1002/ana.20294) [PubMed] [CrossRef] [Google Scholar] 86. Lambon Ralph MA, Sage K, Jones RW, Mayberry EJ. 2010. Coherent concepts are computed in the anterior temporal lobes. Proc. Natl Acad. Sci. USA 107, 2717–2722. ( 10.1073/pnas.0907307107) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 87. Rogers TT, McClelland JL. 2004. Semantic cognition: a parallel distributed processing approach. Cambridge, MA: The MIT Press. [Google Scholar] 88. Wittgenstein L. 2001. Philosophical investigations: the German text, with a revised English translation 50th anniversary commemorative edition. Oxford, UK: Wiley-Blackwell. [Google Scholar] 89. Murphy GL, Medin DL. 1985. The role of theories in conceptual coherence. Psychol. Rev. 92, 289–316. ( 10.1037/0033-295X.92.3.289) [PubMed] [CrossRef] [Google Scholar] 90. Smith EE, Medin DL. 1981. Categories and concepts. Cambridge, MA: Harvard University Press. [Google Scholar] 91. Rosch E. 1975. Cognitive representations of semantic categories. J. Exp. Psychol. Gen. 104, 192–233. ( 10.1037/0096-3445.104.3.192) [CrossRef] [Google Scholar] 92. Mayberry EJ, Sage K, Lambon Ralph MA. 2011. At the edge of semantic space: the breakdown of coherent concepts in semantic dementia is constrained by typicality and severity but not modality. J. Cogn. Neurosci. 23, 2240–2251. ( 10.1162/jocn.2010.21582) [PubMed] [CrossRef] [Google Scholar] 93. Ding S, Van Hoesen GW, Cassell MD, Poremba A. 2009. Parcellation of human temporal polar cortex: a combined analysis of multiple cytoarchitectonic, chemoarchitectonic, and pathological markers. J. Comp. Neurol. 514, 595–623. ( 10.1002/cne.22053) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 94. Plaut DC. 2002. Graded modality-specific specialization in semantics: a computational account of optic aphasia. Cogn. Neuropsychol. 19, 603–639. ( 10.1080/02643290244000112) [PubMed] [CrossRef] [Google Scholar] 95. Bi Y, Wei T, Wu C, Han Z, Jiang T, Caramazza A. 2011. The role of the left anterior temporal lobe in language processing revisited: evidence from an individual with ATL resection. Cortex 47, 575–587. ( 10.1016/j.cortex.2009.12.002) [PubMed] [CrossRef] [Google Scholar] 96. Brown S, Schafer EA. 1888. An investigation into the functions of the occipital and temporal lobes of the monkey's brain. Phil. Trans. R. Soc. Lond. B 179, 303–327. ( 10.1098/rstb.1888.0011) [CrossRef] [Google Scholar] 97. Klüver H, Bucy P. 1939. Preliminary analysis of functions of the temporal lobes in monkeys. Arch. Neurol. Psychiatry 42, 979–1000. ( 10.1001/archneurpsyc.1939.02270240017001) [CrossRef] [Google Scholar] 98. Terzian H, Dalle Ore G. 1955. Syndrome of Kluver–Bucy reproduced in man by bilateral removal of the temporal lobes. Neurology 5, 373–380. ( 10.1212/WNL.5.6.373) [PubMed] [CrossRef] [Google Scholar] 99. Schapiro AC, McClelland JL, Welbourne SR, Rogers TT, Lambon Ralph MA. 2013. Why bilateral damage is worse than unilateral damage to the brain. J. Cogn. Neurosci. 25, 2107–2123. [PubMed] [Google Scholar] 100. Snowden JS, Thompson JC, Neary D. 2004. Knowledge of famous faces and names in semantic dementia. Brain 127, 860–872. ( 10.1093/brain/awh099) [PubMed] [CrossRef] [Google Scholar] 101. Tranel D. 2009. The left temporal pole is important for retrieving words for unique concrete entities. Aphasiology 23, 867–884. ( 10.1080/02687030802586498) [PMC free article] [PubMed] [CrossRef] [Google Scholar] 102. Gainotti G. 2012. The format of conceptual representations disrupted in semantic dementia: a position paper. Cortex 48, 521–529. ( 10.1016/j.cortex.2011.06.019) [PubMed] [CrossRef] [Google Scholar] 103. McClelland JL, McNaughton BL, O'Reilly RC. 1995. Why there are complementary learning-systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419–457. ( 10.1037/0033-295X.102.3.419) [PubMed] [CrossRef] [Google Scholar] 104. Rumelhart DE, Todd PM. 1993. Learning and connectionist representations. In Attention and performance XIV: synergies in experimental psychology, artificial intelligence, and cognitive neuroscience (eds Meyer DE, Kornblum S.), pp. 3–30. Cambridge, MA: MIT Press. [Google Scholar] Articles from Philosophical Transactions of the Royal Society B: Biological Sciences are provided here courtesy of The Royal Society What does the word distributed in parallel distributed processing approach imply?What does the word distributed in parallel distributed processing approach imply? Information is distribution across many locations in the brain.
Where is knowledge located in the parallel distributed processing model quizlet?general knowledge is stored in sensory memory and working memory, rather than in long-term memory. One feature of the parallel distributed processing approach is called spontaneous generalization.
What is the current status of the parallel distributed processing approach?What is the current status of the parallel distributed processing approach? It has been applied to many cognitive processes, but it fails to acknowledge that some cognitive processes use serial processing. it allows us to draw inferences that extend beyond the information supplied in the original stimulus.
How are spontaneous generalization and default assignment different from each other in the parallel distributed processing approach?According to the parallel distributed processing model of semantic memory, how does spontaneous generalization differ from default assignment? In spontaneous generalization, we draw a conclusion about an entire category; in default assignment, we draw a conclusion about one member of a category.
|
In the name parallel distributed processing approach, the word distributed refers to
zusammenhängende Posts
Urheberrechte © © 2024 decemle Inc.
