Neural Representations of Task-relevant and Task-irrelevant Features of Attended Objects

Chen, J; Scotti, Paul S.; Ew, Dowd; Jd, Golomb

doi:10.1101/2021.05.21.445168

Cited by 4 publications

(16 citation statements)

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“…To validate eIEM and demonstrate its practical advantages, we implemented standard IEM and eIEM across three real fMRI datasets. The three datasets 13,18,47 span the research topics of perception, attention, and memory. We demonstrate how eIEM improves flexibility (potential to exclude low-confidence trial reconstructions) and interpretability (reconstruction performance in terms of prediction error) while avoiding the methodological pitfalls discussed in the previous section.…”

Section: Resultsmentioning

confidence: 99%

“…We first validate eIEM and demonstrate its practical advantages by implementing both standard IEM and eIEM on three real fMRI datasets 43, 47, 48 spanning the research topics of perception, attention, and memory. We then use simulated reconstructions to illustrate additional methodological issues and limitations with standard IEM that eIEM addresses.…”

Section: Resultsmentioning

confidence: 99%

“…We performed analyses on two publicly available published datasets (Henderson, Vo, Chunharas, Sprague, & Serences, 2019; Rademaker, Chunharas, & Serences, 2019) and one unpublished dataset from our lab (Chen, Scotti, Dowd, & Golomb, 2021). Note that we only analyzed a subset of the data from each dataset, analyzing one or two conditions across three brain regions for the sake of simplicity.…”

Section: Methodsmentioning

confidence: 99%

“…eIEM replicated this pattern. In the Attention dataset 47 , we used both techniques to decode the attended orientation within a multi-item, multi-feature stimulus array, in the same three ROIs. The "standard" IEM procedure revealed significant decoding in V1 and V4, but not IPS.…”

Section: Validating Eiem On Real Fmri Datamentioning

confidence: 99%

“…analyzed V1, V4, and IPS regions of interest which were defined via retinotopic mapping protocols where participants viewed rotating wedges and bowtie stimuli (e.g.,Wandell et al, 2007) while performing a covert attention task of detecting contrast dimming on a row of the checkerboard for the rotating wedge stimulus. We applied IEMs (following the procedures outlined earlier) to the post-processed data conducted by the authors of the original paper: Single-trial activation estimates consisted of averaged z-scored BOLD signal of the 3rd and 4th TRs following stimulus presentation.For more methods information, please refer to the original paper.Attention dataset: Chen, Scotti, Dowd, & Golomb (2021)Data were previously collected in our lab for another study(Chen et al, 2021). In this experiment, seven participants completed a visual attention task.…”

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confidence: 99%

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An enhanced inverted encoding model for neural reconstructions

Scotti

Chen

Golomb

2021

Preprint

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Inverted encoding models have recently become popular as a method for decoding stimuli and investigating neural representations. Here we present a novel modification to inverted encoding models that improves the flexibility and interpretability of stimulus reconstructions, addresses some key issues inherent in the standard inverted encoding model procedure, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. The standard inverted encoding model approach estimates channel responses (or reconstructions), which are averaged and aligned across trials and then typically evaluated using a metric such as slope, amplitude, etc.). We discuss how this standard procedure can produce spurious results and other interpretation issues. Our modifications are not susceptible to these methodological issues and are further advantageous due to our decoding metric taking into account the choice of population-level tuning functions and employing a prediction error-based metric directly comparable across experiments. Our modifications also allow researchers to obtain trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and increase statistical power. We validate and demonstrate the improved utility of our modified inverted encoding model procedure across three real fMRI datasets, and additionally offer a Python package for easy implementation of our approach.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Validating Eiem On Real Fmri Datamentioning

confidence: 99%

mentioning

confidence: 99%

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An enhanced inverted encoding model for neural reconstructions

Scotti

Chen

Golomb

2021

Preprint

Self Cite

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The interplay of uncertainty, relevance and learning influences auditory categorization

Sheth

Collina

Piasini

et al. 2022

Preprint

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Auditory perception requires categorizing sound sequences, such as speech, into classes, such as syllables. Auditory categorization depends not only on the sequences' acoustic waveform, but also on the listener's sensory uncertainty, any individual sound's relevance to the task, and learning the temporal statistics of the acoustic environment. However, whether and how these factors interact to shape categorization is unknown. Here, we measured human participants' performance on a multi-tone categorization task. Task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, poor estimates of tones' task relevance or high sensory uncertainty adversely impacted category choice. Learning temporal statistics of sound category also affected decisions. The magnitude of this effect correlated inversely with participants' relevance estimates. These results demonstrate that humans differentially weigh sensory uncertainty, task relevance and learning, providing a novel understanding of sensory decision-making under real-life behavioral demands.

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Enhanced neural representation of reach target direction for high reward magnitude but not high target probability

Keane,

Reuter,

Manzone

et al. 2023

Preprint

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Many characteristics of goal-directed movements, such as their initiation time, initial direction, and speed, are influenced both by the details of previously executed movements (i.e. action history), and by the degree to which previous movements were rewarded or punished (i.e. reward history). In reinforcement learning terms, when movements are externally cued, action and reward history jointly define the probability and magnitude of positive/negative outcomes of available options, and therefore their pre-stimulus expected value. To dissociate which of these neurocomputational variables influence sensorimotor brain processing, we studied how reach behaviour and evoked brain responses are affected by independent manipulations of action and reward history. We found that movements were initiated earlier both for more frequently repeated targets and targets associated with higher reward magnitude, but only movements to highly rewarded targets had higher movement speeds. Classical visually-evoked encephalographic (EEG) potentials (P1/N1) were not affected by either reward magnitude or target probability. There were, however, amplified midline ERP responses at centroparietal electrodes for rewarded targets and movements compared to control, but no differences between more frequently presented targets and control. Critically, the spatial precision of decoded target locations extracted from a multivariate linear decoding model of EEG data was greater for target locations associated with higher reward magnitude than for control target locations (∼150-300ms after target presentation). Again, there were no differences in the precision of decoded target direction representations between more frequent target locations and control target locations. These data suggest that the expected reward magnitude associated with an action, rather than its long-run expected value, determines the precision of early sensorimotor processing.Significance StatementWe move more quickly and more accurately toward goals that we value more highly, and this is due partly to enhanced motor preparation. However, our expectations about the value of an action depend both on the probability of its requirement and the magnitude of the reward associated with it. Here we disentangled the influence of reward magnitude and probability on early sensorimotor processing via a multivariate linear decoding approach to extract target direction from scalp encephalograms. We found that the spatial precision of decoded target direction was greater for high reward targets but not for more probable targets. Thus, early sensorimotor processing is sharpened when the magnitude of reward associated with movement to a cued target is high.HighlightsThe direction of movement can be reliably decoded from the scalp EEG from ∼80ms after target presentation.The neural representation of movement direction is more precise for targets that are associated with high reward, but not for targets that are more probable.The magnitude of reward associated with movement to a presented target, rather than the long-run expected value of the movement, sharpens the spatial precision of early sensorimotor processing.

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Neural Representations of Task-relevant and Task-irrelevant Features of Attended Objects

Cited by 4 publications

References 47 publications

An enhanced inverted encoding model for neural reconstructions

An enhanced inverted encoding model for neural reconstructions

The interplay of uncertainty, relevance and learning influences auditory categorization

Enhanced neural representation of reach target direction for high reward magnitude but not high target probability

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