TARGET BAND 7 PDF

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Target Band 7 Pdf

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Full size image Figure 3 Coefficients representing the independent relationships between classifier confidence for task-level decoding on the one hand, and for cue, target, and responses on the other.

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For clarity of presentation, the relationship with distractor classifier confidence was omitted here, which hovered around 0 throughout. Full size image Figure 4 Average classifier confidence, separately for switch vs. Full size image Figure 5 Histogram of correlations of task-contrast t-values across electrodes and frequency bands for all possible pairs of individual subjects i.

Full size image For some of the subsequent analyses e. For these analyses, we identified the average time point with the maximum decoding accuracy for each aspect see Fig. For the response aspect, the maximum decoding accuracy was towards the end of the ms interval.

Here, we simply averaged the period from — ms. In the Supplemental Information, we provide additional analyses to a rule out the effects of eye-movements on task decoding, b examine task-specific effects on the encoding of lower-level features, and c provide additional information about trial-by-trial predictive relationships between decoding accuracy and behavior.

Table 1 RTs and error rates as a function of task and switch contrast. Full size table The magnitude of RT switch costs was relatively small. This is likely due to two design factors in our paradigm. First, to allow blinks between trials we used a relatively long response-stimulus interval RSI that ranged between and ms.

Second, different from the more standard switching paradigm, where a central stimulus combines all task-relevant aspects, in the current paradigm target and distractors were spatially separated in order to allow decoding of target and distractor locations as task-related aspects.

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Given the spatial separation between targets and distractors, participants could use spatial attention to reduce or even eliminate stimulus-induced, between-task interference, which is critical for obtaining large task-switch costs Likely this is due to the fact that the left-versus-right orientation discrimination has a compatible stimulus-response mapping i.

Remarkably, task-level i. To ensure our conclusions regarding the decoding of the theoretically critical task feature are not compromised by multiple comparisons and faulty comparisons against chance 34 , 35 , we also conducted a cluster-based permutation test.

First, we generated a series of t-values via t-tests against the chance level We computed a cluster p-value under the permutation distribution of cluster-level statistics permutations , which defined clusters as significant if their size was larger than the 95th i.

However, we found that cue decoding accuracy i. This result is consistent with the view that cue representations are used to activate task- or attentional-set representations 5 , 27 and are less involved with actually regulating task-specific processes.

But how exactly are attentional sets instantiated?

As shown in Fig. Determining the relevance of representations Going beyond average activation trajectories, the trial-by-trial decoding approach allows determining at what point in the trial, which of the represented aspects drive performance. For this purpose, we entered logit-transformed classifier probabilities for the five aspects as simultaneous predictors into a linear mixed-effects model, predicting trial-by-trial RTs, with a random intercept and random coefficients for each subject using the lme4 package RTs were pre-whitened by removing any linear or quadratic trends across the blocks of the experiment.

The coefficients shown in Fig.

Note that negative coefficients imply that the greater the classifier confidence, the faster the RTs—indicating that classifier confidence can be interpreted as representational strength.

Consistent with the view that attentional sets, and not superficial cues, control lower-level representations, we found that cue-related activity is largely irrelevant for performance, a result that also holds up when cue decoding accuracy is entered as the sole predictor. In contrast, attentional-set information became highly predictive of RTs during the post-stimulus phase, suggesting that fluctuations in the quality of attentional-sets are a major source of trial-to-trial variability in performance.

Consistent with the parallel-activation account, attentional sets begin to predict RTs only once also the independent predictive power of stimulus and response information emerges. Moreover, the aspects that we do not expect to predict performance i. This result rules out the possibility that fast-RT trials are simply less noisy and therefore allow better decoding of any aspect.

We also looked at the degree to which the decoding accuracy for the different aspects is related to individual differences in RTs. In addition, Fig. With its small sample size, the current experiment was not originally designed to examine individual differences. Therefore, these analyses need to be treated as exploratory and require replication. Nevertheless, confidence in the results is strengthened by the fact that the relationships are strikingly robust and are, both in terms of involved features and their time course, highly consistent with the within-individual relationships.

Relationship between representations The notion that cues activate task-level control settings, which in turn bias stimulus and eventually response representations, leads to a straightforward prediction about the sequence in which different lower-level representations should be related to the strength of attentional settings.

As expected, early in the prestimulus phase, the strength of attentional sets was coupled with the strength of cue representation, likely indicating the activation of the attentional sets based on the cue 5.

Following stimulus onset, a correlation with the target location emerged and subsequently, a correlation with the response information. This pattern is again consistent with the parallel-activation account, where attentional sets coordinate lower-level representations in a concurrent manner see Fig. Effects of task switching In the results presented so far, we had ignored potential effects of trial-to-trial changes in tasks—typically of major interest in task-switching research 1.

In fact, our version of the task-switching paradigm was optimized towards EEG decoding analyses, not towards producing large switch effects i.

EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations

We constrained our analyses a-priori to the ms intervals centered around the peak of the activation trajectories for each feature see Fig. In addition, given the strong relationship between RTs and decoding accuracy for task, stimulus locations, and response, we also conducted a median split into fast and slow RT trials. The median-split was conducted within each subject, task, and switch condition; values were than averaged across tasks and subjects, but presented separately for no-switch and switch trials.

The dominant aspect in Fig. Thus, at the time of peak attentional-set activation, decoding of task-level information was less robust on switch than on no-switch trials, whereas information related to the competing task was more strongly expressed.

How abstract are attentional sets?

In our paradigm, attentional settings are confounded with attention to different visual features i. Thus, the results reported so far do not allow strong conclusions about the question to what degree the task-level decoding reflects representations of abstract task rules, or the engagement of task-correlated sensory or response-related features.

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In fact, as shown in the Supplemental Material, there were marked between-task differences in the time course of encoding target locations, with color decoding being stronger and earlier than orientation decoding. Arguably, even if task-level decoding is driven by such aspects they would still be the consequence of top-down, task-level representations and thus indirectly reflect the strength of top-down control through such abstract representations.

However, we can also conduct additional analyses to explore the abstractness of the information decoded on the level of tasks. Charles Sturt Library Service. City of Burnside. City of Gold Coast Libraries. City of Onkaparinga Libraries.

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Spatial multiplexing involves deploying multiple antennas at the transmitter and at the receiver. Independent streams can then be transmitted simultaneously from all the antennas. This technology, called MIMO as a branch of intelligent antenna , multiplies the base data rate by the smaller of the number of transmit antennas or the number of receive antennas. Apart from this, the reliability in transmitting high speed data in the fading channel can be improved by using more antennas at the transmitter or at the receiver.

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This is called transmit or receive diversity. The other category is closed-loop multiple antenna technologies, which require channel knowledge at the transmitter. Since 4G is a collection of wireless standards, the final form of a 4G device will constitute various standards.

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Target Band 7: IELTS Academic Module – How to Maximize Your Score

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