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Memory strength versus memory variability in visual change detection | SpringerLink

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In addition, according to the Symantec’s Internet Security Thread Report, These ‘free’ versions are pirated and frequently infected with. The model version containing all developments is referred to as change related studies, modelling exercises comparing free air CO2. Cerebral lateralization for language production and spatial attention and their relationships with manual preference strength (MPS) were assessed in a.
 
 

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Brain Res. The psychophysics of perceptual memory.

 

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LIs As strong left-handers are known to gather together the most 2. Regional LIs. We investigated whether there was a re- atypical language dominant subjects, we investigated whether the lationship between spatial attention and language production relationship between language and spatial functions could be due within ROIs.

In the duction Mazoyer et al. These strong aty- parietal lobe, rightward asymmetries were restricted to the pos- pical individuals were removed of the analysis and the above- terior parietal cortex, in the precuneus and along the parieto-oc- mentioned ANCOVA model was performed again at the hemi- cipital sulcus.

Leftward asymmetry was observed in the supple- spheric and regional levels. These regions were not part of the motor ROI used to 3. Results parse out irrelevant activity arising from hand-related responses during LBJ task.

Baseline contrast, except within ventral areas 3. Language including the occipito-temporal region, the posterior part of the On average, participants took 5. The average number of words per generated sentence was HLI values Table 1. Effect of control condition on language and spatial hemispheric p o0.

Post-hoc t-tests indicated that for language, 3. Asymmetrical brain patterns of language and spatial tasks contrasting a sentence production to a high-level control condition PRODLIST maximized the degree of leftward asymmetry as com- 3. Language production pared to baseline. Baseline showed large leftward asymmetries in the frontal vs. Rightward asymmetry was LBJ vs. Relationships between spatial attention, language production gular and supramarginal gyri.

Note that restricted rightward LIs, MPS and spatial bias asymmetries were located in the caudate nucleus, the anterior 3. Spatial attention and the distribution of the residuals followed the Gaussian law As shown in Fig.

As illustrated in Fig. The larger the lateral middle occipital gyrus MOG. Rightward asymmetry was HLI for spatial attention was lateralized to one hemisphere i. As before, this interaction revealed that only the sLH group Fig. Scatterplot of the relationship between hemispheric lateralization indices during line bisection judgment LBJ-HLI and behavioral spatial response bias.

Here again, only the sLH group showed a of spatial attention during line bisection. Finally, no effect of spatial response bias F language processing Mazoyer et al. Effect of strong atypical language right dominant subjects on gions in landmark tasks Badzakova-Trajkov et al. These 3. Using Prod-HLIo as a cut-off point, 10 rightward asymmetries interested regions belonging to the dorsal individuals 9 sLH and 1 MH were found to be strongly right-la- frontoparietal network controlling for spatial attention, and the teralized for language production.

Over these 10 subjects, the ventral attentional system involved in stimulus-driven reorienting mean LBJ-HLI was positive 9. Fink et al. In particular, the study of Rorden et al. Although cortex and MOG, and within the inferior frontal cortex. Within the ventral network, important asymmetries were 3.

Bush et al. In average, the subjects more frequently erroneously judged b. This response bias can be related to the behavioral pseu- 4. Pseudoneglect has been between language and spatial attention lateralization see Fig. A negative correlation has been previously observed Bowers and Heilman, Here, we show that the degree of between inferior frontal region for language and parietal region for cerebral lateralization during LBJ is correlated with the degree of landmark task in a sample of left-handers including left- and pseudoneglect.

Previous studies have found the association be- right-dominant language subjects Cai et al. The study of tween cerebral lateralization and spatial bias. For example, Badzakova-Trajkov et al. Interestingly, a close inspection of the scatterplot of the was associated with increased right hemispheric engagement of Fig.

Finally, Thiebaut de Schotten et al. To assess this effect, we reanalyzed their dataset gi- demonstrated, with diffusion tensor imaging DTI , a correlation ven in supporting information. Applying our MPS categorization to between the rightward asymmetry of the volume of the second their sample of participants, 35 individuals were then cate- branch of the superior longitudinal fasciculus SLFII connecting gorized as sLH, 64 as MH and 56 as sRH. We regressed the right- parietal and frontal regions and the pseudoneglect during the line parietal lobe LI by the left-frontal lobe LI, in function of MPS, and bisection Thiebaut de Schotten et al.

As shown by Cai et al. According to 4. Complementary but not associated in right- and mixed-handers the causal hypothesis, two strong predictions can be put forward. First, an individual with atypical right language dominance should The present study including left-handers and right- simultaneously manifest left spatial dominance. In other words, handers provided compelling evidence that the association be- the sole atypical pattern of HS consists in a mirror-reversed pat- tween language and spatial lateralization is only found in a group tern.

It was indeed the case in Cai’s study , since all atypi- of left-handers characterized by a strong manual preference. Ex- cally right-lateralized language left-handers subjects in frontal ROI cept for this group of sLH, language and spatial asymmetries were were atypically left-lateralized in parietal ROI during landmark not associated in the other subjects, including right-handers and task, while all except one typically left-lateralized for language subjects with a weak manual preference subjects out of were typically right-lateralized for spatial function.

Second, the subjects, see Fig. First of all, these results help to re- correlation between both lateralized functions should be due to concile discrepant results found in the literature concerning the mirror-reversed subjects. Interestingly, the observation of RH co-lateralization of et al. The absence of correlation between language and language and spatial functions in healthy participants raises the spatial lateralization is consistent with previous fTCD studies question of the hemispheric crowding hypothesis Teuber, , showing no association, including only right-handers Lust et al.

Thus, associated lateralization of language and spatial of the complementary cerebral organization Bryden, The functioning in the right hemisphere affects non-verbal abilities.

In potential sources of the left asymmetry for language processing healthy subjects, previous studies showed discrepant results. While it is accepted that the teralized pattern left for language and right for spatial performed left-hemisphere bias for language processing is a multifactorial better than people showing bilateral representation for one or trait determined by several genetic and non-genetic factors, it is either function or both functions lateralized to the same hemi- still unclear which genes and environmental factors determine sphere only when carrying out a dual-task.

Scatterplots of the relationship between frontal lateralization indices during line bisection judgment and sentence production plotted for the whole sample and each manual preference strength group.

The Prod-Frontal LI o cut-off point symbolized by a dashed line is given to indicate atypical rightward language lateralized subjects. However, the correlation that Further investigations are now needed to compare individual we observed in sLH does not imply causality. In addition, spatial lateralization does not give any information about the ex- our results suggest that, the proportion of atypical HS patterns istence of a causal relationship between language and spatial varied depending on the ROI in which laterality index were cor- functions.

The corpus callosum CC is the major support for related. One characteristic By contrast, when the correlation was performed within the of CC connectivity at the macroscopic level is its spatial organiza- frontal ROI, we found subjects exhibiting a RH co-lateralization tion, as it connects cortical regions in mirroring homotopic areas Fig. The transition of the division between hemispheres of complementary functions.

Here, we to the hemispheric patterns of cerebral organization. By contrast, in the majority of the popula- are removed. Interestingly, the fact that this interaction was still tion, this mechanism may have been active across development to present between LBJ-Occipital and SENT-Frontal but not within the set up a stable complementary pattern of functions and does not frontal lobe, suggest that the contribution of each region to the need to be at play anymore.

To further explore this hypothesis, the pattern of HS needs to be further explored. The causal perspective suggests causal mechanisms manual preference strength. Scatterplots of the relationship between occipital lateralization indices during line bisection judgment and frontal LIs during sentence production plotted for the whole sample and each manual preference strength group.

Conclusion Psychol. Bryden, M. Patterns of cerebral organization. Brain Lang. Trends Cognit. What can atypical language hemispheric specia- lization tell us about cognitive functions? Complementary hemispheric spe- ture investigations on inter-hemispheric organization using a cialization for language production and visuospatial attention. USA 4 , — Probabilistic explore the mechanisms that control cerebral lateralization.

Fur- topography of human corpus callosum using cytoarchitectural parcellation and thermore, the variability of the patterns of HS raises the question high angular resolution diffusion imaging tractography.

Brain Mapp. Brain activity during landmark and line cognitive performance. Cook, N. Homotopic callosal inhibition.

Brain and Lang. References Corballis, M. The evolution and genetics of cerebral asymmetry. B, Biol. Badzakova-Trajkov, G. Magical ideation, crea- Corbetta, M. Control of goal-directed and stimulus-driven tivity, handedness, and cerebral asymmetries: a combined behavioural and attention in the brain.

Neuropsychologia 49, — Dorst, J. Func- Badzakova-Trajkov, G. Cerebral tional transcranial doppler sonography and a spatial orientation paradigm asymmetries: complementary and independent processes.

Plos One 5 3 , identify the non-dominant hemisphere. Brain Cogn. Benwell, C. A rightward shift in the vi- Duecker, F. Hemispheric differences in the voluntary suospatial attention vector with healthy aging. Aging Neurosci. Pseudoneglect: effects of hemispace on a tactile Neuropsychologia 18 4—5 , — Dym, R. Is functional MR imaging as- Brooks, J. Representational pseudoneglect: a re- sessment of hemispheric language dominance as good as the wada test? Meta view. Fan, J. Choosing sides: the left and right of the normal brain.

Petit, L. Strong rightward lateralization of the dorsal attentional network in left- Freund, H. Line bisection judgments implicate right parietal cortex and handers with right sighting-eye: an evolutionary advantage. Neurology 54 6 , — Fink, G. The neural basis of vertical and Petit, L. Functional asymmetries revealed in visually guided saccades: NeuroImage 14 1 , S59—S Association between language and graphy.

Blood Flow. NeuroImage 59 2 , Phasic alerting of neglect Nature , healthy humans. Neurology 57 6 , — Gazzaniga, M. Cerebral specialization and interhemispheric communica- Rorden, C.

Disturbed line bisection is tion: does the corpus callosum enable the human condition? Brain: J. Brain Res. Revisiting Rosch, R.

Lateralised visual attention is un- human hemispheric specialization with neuroimaging. Neuropsychologia 50 5 , — Shulman, G. The evolution of hemispheric Right hemisphere dominance during spatial selective attention and target specialization. In: Bizzi, E. The Intelligent Systems 3. Academic, New York, pp.

Knecht, S. Determining the hemispheric dominance of spatial attention: Szczepanski, S. Shifting attentional priorities: control of spatial a comparison between ftcd and fmri. Karnath, H. The anatomy of spatial neglect. Neuropsychologia Teuber, H. Why two Brains?. In: Schmitt, F. Kosslyn, S. Seeing and imagining in the cerebral hemispheres: a compu- Thiebaut de Schotten, M.

A lateralized brain network for visuospatial atten- Lidzba, K. Neuropsychologia 44 7 , N. Automated anatomical labeling of activations in SPM using a — Lust, J. Without allowing for any changes in memory strength, the reduced-sensitivity model predicts decreases in respond-change probabilities, not increases.

The present version of the model tries to compensate for this difficulty by assuming increasing guess-change probabilities across the retention interval the g 2 parameter in Eq. However, this assumption then forces the model to incorrectly predict increasing respond-change probabilities for the D3 and Far probes, so the model yields very poor fits.

To summarize, using the sensitivity-based approach to modeling precision loss, our interim conclusions are that a there is a role of a zero-stimulus-information state i. However, we have yet to contrast sudden-death versus gradual-decay accounts of the results. For example, the losses in memory strength may be all-or-none reflecting sudden death or gradual the continuous M t parameters. To address the sudden-death versus gradual-precision-loss hypotheses, we fitted additional special-case models to the data.

Only the all-or-none memory parameters pmem and retention-based guessing parameters g 2 were allowed to vary across the retention interval. As shown in Table 1 , the AIC fits of this constant perceptual-confusability model were approximately the same as those of the full model and the BIC fits of the model were considerably better than those of the full model.

Thus, the data are consistent with the hypothesis that there were no continuous changes in precision or memory strength across the retention interval and that the results can be interpreted in terms of sudden death and guessing. However, we also fitted a model to the data that assumed a constant probability of using memory a fixed value of pmem in Eq. Figure 6c and d reveal that both special-case models are capable of providing good accounts of how the change-detection judgments varied with stimulus distance and the retention interval.

It appears that the present data are not sufficiently strong to sharply discriminate between the sudden-death and gradual precision-loss views. The best-fitting parameters from these two parsimonious models, averaged across subjects, are reported in Table 2. The best-fitting parameter estimates are easily interpretable.

Second, the criterion parameters k as well as the guessing factor g 1 increase systematically as objective change-probability increases. Second, the continuous memory strengths M t decrease in regular fashion across the retention interval.

Footnote 3 And third, the criterion parameters k and g 1 estimates increase as objective change-probability increases. Recall that the second set of models formalized decreasing precision across the retention interval in terms of increased variance of the memory representations rather than in terms of decreased settings of the sensitivity parameter.

We implemented versions of the variance-based models that were analogous to those discussed previously for the sensitivity-based approach.

We fitted this class of models by using computer-simulation techniques. This momentary distance would then be transformed to a similarity s via Eq. The predictions were obtained by using 10, simulations at each individual combination of the factors d , t , and cp. The resulting fit values are reported in Table 3. In brief, the pattern of model fits paralleled in most respects the results that we have already discussed for the sensitivity-based approach.

The versions of the models that did not make allowance for a zero-information state or that did not make allowance for any decreases in memory strength with the retention interval generally provided poor AIC fits to the data and sometimes even produced poor BIC fits. By comparison, parsimonious accounts of the data are again available using the two alternative modeling approaches. First, the model that assumes no changes in visual-memory confusability but in which there is sudden death across the retention interval fits well.

Second, the model that assumes no increases in sudden death but gradual decreases in visual-memory discriminability due to increased variance also fits well.

The best-fitting parameter estimates for those two models, averaged across subjects, are reported in Table 4. Again, the patterns of parameter estimates are easily interpretable. As expected, the criterion parameters k and g 1 estimates increase with increases in objective change probability.

Second, the memory strengths M t decrease systematically across the retention interval. And third, the criterion parameters and g 1 estimates increase as objective change-probability increases. In a nutshell, both the sudden-death and gradual-precision-loss models provide viable and easily interpretable accounts of the data. Our applications of the variability-based models motivate preliminary investigation of another fundamental issue, namely whether a pure variable -resources model can provide adequate fits to the data without requiring the assumption of a zero-information stimulus state that forces guessing.

As explained previously, according to variable-resources models, the variability of the memory representation may itself vary considerably across items. If minimal resources are devoted to an item, then the remembered value of that item may be quite distant from the true value. In our view, however, even if one makes allowance for the possibility of a remembered value that is highly distant from the original, it is unclear how such variable-resources models could account for performance in the present change-detection task without making allowance for a guessing state.

The issue is illustrated schematically in Fig. The to-be-remembered item has reference value zero and the big-change probe is illustrated far to the right. We illustrate the possibility of a highly dispersed memory representation for the original item in terms of a normal distribution with extreme variability.

The likelihood of such an occurrence seems extremely small. By contrast, models that make allowance for guessing from a zero-stimulus-information state do not have this difficulty. Schematic illustration of the low probability with which a specific value from a high-variability memory representation will be highly similar to a big-change Far test stimulus.

To pursue this line of argument, we formulated some rudimentary versions of variable-resources models that did not make allowance for a zero-stimulus-information guessing state but that did make allowance for a high-variance memory state. However, we also defined a high-variance parameter for cases in which the observer might develop a highly dispersed memory representation for an item.

Finally, we defined four high-variance-probability parameters, one for each retention interval. Each parameter reflected the probability that a high-variance memory representation was associated with an item at each retention interval. As was the case for our previously described variance-based models, to generate predictions, on each simulated trial a momentary distance d would be sampled from the relevant memory distribution.

The distance would then be transformed to a similarity see Eq. In a baseline version of the model, the continuous memory-strength parameters from Eq. As reported in Table 5 , these versions of the no-guessing variable-resource models provided poor fits to the data, in accord with the intuitions that we developed in Fig.

It remains an open question whether elaborated versions of such models might be developed that do allow one to achieve viable accounts of the present data. In summary, in the present VWM change-detection task, we provided model-based evidence that there are decreases in forms of memory strength that go beyond possible decreases in visual-memory precision with time.

By combining assumptions regarding individual-item strength and pairwise similarity, various versions of the present change-detection models were able to predict how respond-change judgments varied with the retention interval and test-probe distance. Models that did not make allowance for any changes in memory strength either continuous or all-or-none failed to account for these findings and provided poor quantitative fits Tables 1 and 2.

We should acknowledge that, rather than assuming that individual-item memory strength varies, an alternative approach to fitting the change-probability data would be to make allowance for varying response bias across the different retention intervals. In our view, however, such an explanation is post hoc. The hypothesis that individual-item memory strength decreases with the retention interval is strongly motivated by much past work in cognitive and perceptual psychology, and its translation to the domain of VWM seems compelling and natural.

A second finding is that adequate quantitative fits to the data relied on the assumption that a zero-stimulus-information state — an absence of memory — operated on some proportion of the trials. A natural interpretation, corroborated by the model fits, is that observers would rarely confuse a study item and a big-change probe if a memory trace for the study item still existed.

We also conducted preliminary tests of variable-resource models of VWM for the present paradigm. According to those models, the variability of the memory representation may itself vary considerably across trials and across different items from the memory set, in some cases showing extreme variability.

The key intuition is that although those models make allowance for the possibility that the observer may remember a value for a studied item that is highly distant from the true value of the studied item, the probability that the remembered value happens to be highly similar to any particular big-change probe is still low see Fig.

It remains to be seen if alternative versions of the variable-resources models than we tested here could account for the results. It is important to remind readers that although plots of the change-probability data suggested that the probability of the zero-information state increased with the retention interval, the data were not strong enough to rule out the possibility that the zero-information state had constant probability e.

Thus, the evidence for the zero-information state in the present task could reflect, at least in part, attentional and encoding limitations rather than memory limitations and sudden death.

In our view, the most likely possibility is that the zero-information state reflects both encoding and memory failures. Another limitation of the present study is that we were unable to sharply distinguish between gradual-decay versus sudden-death accounts of the results. Resolving this fundamental issue remains as a matter for future research.

Third, we examined VWM retention for only a single type of stimulus attribute in the present research, namely color. Although we expect that our findings involving reduced precision and memory strength will be observed across many such attributes, future research is needed to test for the generality of these effects.

Finally, our current account — namely that forgetting from VWM involves a combination of losses in both memory precision and memory strength — can be viewed as descriptive in nature. It is important that future work build bridges between our account and those framed at a more mechanistic level, including theories that emphasize varieties of interference e.

It is an open question whether these more mechanistic theories may capture the types of losses in memory precision and memory strength that appear to underlie the present VWM forgetting phenomena. Depending on the to-be-remembered item, the Far probe was on average either 5. For simplicity, we used the weighted distance 7. Spot checks of individual subject results revealed that the exact choice of Far distance within this range led to minuscule changes in model fits.

Recall also that these big-change same judgments cannot be attributed to spatial-position uncertainty because the Far probes are perceptually distant from the items at all spatial locations. In line with related work involving short-term memory search e. A similar relation is observed for the sensitivity-parameter estimates. Anderson, J. The adaptive character of thought. Hillsdale, NJ: Erlbaum. Google Scholar. Bays, P. The precision of visual working memory is set by allocation of a shared resource.

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Journal of Vision, 4 12 , — Zhang, W. Discrete fixed-resolution representations in visual working memory. Sudden death and gradual decay in visual working memory. Psychological Science, 20, — Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Robert M. Reprints and Permissions.

Memory strength versus memory variability in visual change detection. Atten Percept Psychophys 78, 78—93 Download citation. Published : 20 October Issue Date : January Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract Observers made change-detection judgments for colored squares in a paradigm that manipulated the retention interval, the magnitude of change, and objective change probability.

Variability versus strength We had three main motivations for testing the change-detection version of the task.

Full size image. Sudden death versus extreme variability The third motivation for our study was to conduct preliminary tests of a potential variable-resources account of the manner in which VWM declines with the retention interval e. Experiment We conducted a VWM change-detection task involving color stimuli. Method Subjects The subjects were five members of the Indiana University community who were paid for their participation.

Procedure On each trial, three colored squares were presented on the computer screen in random locations within the central rectangular region, subject to the constraint that the centers of each square were at least 60 pixels away. Modeling analyses Modeling framework The most general models that we use for fitting the data and interpreting the results assume that the change judgments arise from a mixture of two processes: one based on perceptual memory and one based on guessing.

Table 2 Mean values of best-fitting parameters for the sensitivity-based models Full size table. Table 4 Mean Values of best-fitting parameters for the variability-based models Full size table. General discussion Memory strength In summary, in the present VWM change-detection task, we provided model-based evidence that there are decreases in forms of memory strength that go beyond possible decreases in visual-memory precision with time.

Zero stimulus-information state versus extreme variability A second finding is that adequate quantitative fits to the data relied on the assumption that a zero-stimulus-information state — an absence of memory — operated on some proportion of the trials. Limitations and future research It is important to remind readers that although plots of the change-probability data suggested that the probability of the zero-information state increased with the retention interval, the data were not strong enough to rule out the possibility that the zero-information state had constant probability e.

Notes Depending on the to-be-remembered item, the Far probe was on average either 5. References Anderson, J. Google Scholar Bays, P. Article Google Scholar Ennis, D. Article Google Scholar Fougnie, D. Article Google Scholar Laming, D. Article Google Scholar Luce, R. Article Google Scholar Nosofsky, R.

 
 

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