Implicit memory: Effects of network size and interconnectivity on cued recall.

Nelson, Douglas L.; Bennett, David; Gee, Nancy R.; Schreiber, Thomas A.; McKinney, Vanesa M.

doi:10.1037/0278-7393.19.4.747

Cited by 80 publications

(110 citation statements)

References 48 publications

(82 reference statements)

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Small set size words were recalled more often than large set size words. However, it did not reveal the same pattern of recall with respect to connectivity shown by Nelson et al (1993). Words of lower connectivity were recalled higher than words of higher connectivity.…”

Section: Word Association Neural Networkmentioning

confidence: 58%

“…According to Nelson et al (1993), highly connective words are recalled more due to a stronger surge of activation surrounding the target word. The findings of our study challenge this theoretical explanation for the greater prevalence of highly connective words being recalled, as well as Nelson et al's perception of connectivity.…”

Section: Word Association Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

Nestor

Han

Niznikiewicz

et al. 2001

Biological Psychology

View full text Add to dashboard Cite

We view schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left (dominant) temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a disturbance in connectivity (associative strength) but not network size (number of associates) in patients with schizophrenia. Paralleling these findings are recent neural network simulation studies of the abnormal connectivity effect in schizophrenia through 'lesioning' network connection weights while holding constant network size. Supporting evidence at the level of biology are in vitro studies examining N-methyl-D-aspartate (NMDA) receptor antagonists on recurrent inhibition; simulations in neural populations with realistically modeled biophysical properties show NMDA antagonists produce a schizophrenialike disturbance in pattern association. We propose a similar failure of NMDA-mediated recurrent inhibition as a candidate biological substrate for attention and semantic anomalies of schizophrenia.

show abstract

Section: Word Association Neural Networkmentioning

confidence: 58%

Section: Word Association Neural Networkmentioning

confidence: 99%

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

Nestor

Han

Niznikiewicz

et al. 2001

Biological Psychology

View full text Add to dashboard Cite

show abstract

“…In all connectionist models of associative memory, words are represented as networks of interconnected nodes. Early so-called localist connectionist models assumed one-to-one representation between a word and a node (Nelson et al 1993). By contrast, distributed models of representation are now viewed as more cognitively and biologically plausible: instead of one word being represented by one node, various aspects of words (e.g., orthographic, semantic, phonological) are each represented by corresponding sets of nodes.…”

Section: A Connectionist Model Of Disturbed Associations In Schizophrmentioning

confidence: 99%

“…Nestor et al (1998) used a cued-recall word paradigm and norms derived from Nelson to attempt to identify the cognitive dynamics underlying schizophrenic associative disturbance. Schizophrenic patients and comparison subjects studied a list of to-be-remembered target words, and then were given a cued recall test in which both word targets and cues were equal in terms of connectivity and network size, as measured by the quantitative normative studies of Nelson et al (1992Nelson et al ( , 1993. These studies used the number of associates of a word to determine its network size and the degree of association both among these associates and between the target word and its associates to determine its network connectivity.…”

Section: A Connectionist Model Of Disturbed Associations In Schizophrmentioning

confidence: 99%

Cognitive dysfunction in schizophrenia: unifying basic research and clinical aspects

McCarley

Niznikiewicz

Salisbury

et al. 1999

European Archives of Psychiatry and Clinical Neurosciences

View full text Add to dashboard Cite

Seeking to unite psychological and biological approaches, this paper links cognitive and cellular hypotheses and data about thought and language abnormalities in schizophrenia. The common thread, it is proposed, is a dysregulated suppression of associations (at the behavioral and functional neural systems level), paralleled by abnormalities of inhibition at the cellular and molecular level, and by an abnormal anatomical substrate (reduced MRI gray matter volume) in areas subserving language.At the level of behavioral experiments and connectionist modeling, data suggest an abnormal semantic network connectivity (strength of associations) in schizophrenia, but not an abnormality of network size (number of associates). This connectivity abnormality is likely to be a preferential processing of the dominant (strongest) association, with the neglect of preceding contextual information.At the level of functional neural systems, the N400 event-related potential amplitude is used to index the extent of "search" for a semantic match to a word. In a short stimulus-onset-asynchrony condition, both schizophrenic and schizotypal personality disorder subjects showed, compared with controls, a reduced N400 amplitude to the target words that were related to cues, e.g. cat-dog, a result compatible with behavioral data. Other N400 data strongly and directly suggest that schizophrenics do not efficiently utilize context. At the level of anatomical system substrates, considerable MRI data indicate abnormalities in the temporal lobe structures that subserve language and verbal associations. Gray matter volume is reduced in the posterior portion of the dominant superior temporal gyrus in both chronic and first episode schizophrenics (but not in manic-depressive psychosis), with the magnitude of reduction correlating with the degree of thought disorder.At the level of in vitro cellular and molecular analysis, NMDA receptors on inhibitory neurons are much more sensitive to blockade than are excitatory projections. A resulting failure of recurrent inhibition may account for the psychotomimetic effects of such NMDA receptor blockers as ketamine and phencyclidine, and may also be present in schizophrenia, where an endogenous NMDA receptor blocker, NAAG, is increased, and where other abnormalities of recurrent inhibition may be present. A biophysical simulation of this circuit abnormality in a model of learned pattern recognition produced, because of the reduction in recurrent inhibition, aberrant spread of excitation, resulting in confusion of normally distinguishable patterns. We suggest the neural circuit failure of inhibition and consequent aberrant spread of activation may be the substrate for an inability to use context, with the behavioral and functional consequences just described. Furthermore, there is the possibility that the unbalanced excitation might lead to progressive, neurodegenerative changes in gray matter, marked by progressive volume reduction.

show abstract

“…Words and their corresponding nodes are assumed to be organized into networks, which may differ in size, as in number of associates, and in the strength of connections linking these associates. Activation spreads as a function of both size and associative strength of a network, with maximum activation and, hence, better recall for words of small, highly connected networks (Nelson et al, 1993). Connectionist models thus allow for the novel examination of how size and connectivity of word networks govern activation spread in associative memory of schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Associative memory in chronic schizophrenia: a computational model

Han

Nestor

Shenton

et al. 2003

Schizophrenia Research

View full text Add to dashboard Cite

We developed a computer model to simulate associative memory recall of patients with chronic schizophrenia. Model inputs consisted of words derived from normative data that differed in terms of connectivity and network size, with the former quantitatively represented by parametric weights and the latter by the specific number of word associates that formed a particular network. Previous behavioral studies of normal subjects indicated better recall for words of high connectivity-small network (HCSN), followed by low connectivity-small network (LCSN), high connectivity-large network (HCLN), and low connectivity-large network (LCLN). This pattern of recall differed from that observed in behavioral studies of schizophrenic patients, which showed better recall for high connectivity words, regardless of network size. Holding constant network size while manipulating connection weights effectively simulated this schizophrenic pattern of recall. That is, manipulation of parametric weights coupled with a slight increase in noise significantly and reliably elicited the response pattern of abnormal connectivity demonstrated in the prior behavioral study of patients with chronic schizophrenia. An increase in noise was a necessary, but insufficient step in modeling the response pattern of abnormal connectivity. These findings provide support for the use of computational models to investigate dynamics of associative word recall in patients with chronic schizophrenia.

show abstract

Implicit memory: Effects of network size and interconnectivity on cued recall.

Cited by 80 publications

References 48 publications

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

Cognitive dysfunction in schizophrenia: unifying basic research and clinical aspects

Associative memory in chronic schizophrenia: a computational model

Contact Info

Product

Resources

About