neuroblogsdaily.com Open in urlscan Pro
2a06:98c1:3120::3  Public Scan

Form analysis
0 forms found in the DOM

Text Content

Press "Enter" to skip to content
 * 
 * 

NeuroBlogs Daily

February 7, 2024

Open Access Brain Science, Lectures, & Podcasts

open menu
Back
 * Science
 * Lectures
 * Blog
 * Humanities
 * French & Spanish


SCIENCE

Do Love You Me? Failure to Notice Word Transpositions is Induced by Parallel
Word Processing Recent research has shown that readers may to fail notice word
transpositions during reading (e.g., the transposition of “fail” and “to” in
this sentence). Although this transposed word (TW) phenomenon was initially
taken as evidence that readers process multiple words in parallel, several
studies now show that TW-effects may also occur when words are presented
one-by-one. Critically however, in the majority of studies TW-effects are weaker
in serial presentation. Here we argue that while word position coding may to
some extent proceed post-lexically (allowing TW-effects to occur despite seeing
words one-by-one), stronger TW-effects in parallel presentation nonetheless
evidence a degree of parallel word processing. We additionally report an
experiment wherein a sample of Dutch participants (N = 34) made grammaticality
judgments about 4-word TW sentences (e.g., ‘the was man here’, ‘the went dog
away’) and ungrammatical control sentences (‘the man dog here’, ‘the was went
away’), whereby the four words were presented either serially or in parallel.
Ungrammaticality was decidedly more difficult to notice in the TW condition, but
only when words were presented in parallel. No effects were observed in the
serial presentation whatsoever. The present results bolster the notion that word
order is encoded with a degree of flexibility, and further provide
straightforward evidence for parallel word processing during reading.

Learning Through Prediction: A Case of Verb Bias Learning Linguistic prediction,
which emerges from acquired knowledge, is a pervasive process in language
comprehension. In language acquisition theories, prediction has also been
suggested as a key factor driving the implicit learning process. However, how
prediction develops as learning unfolds and how it, in turn, drives the learning
process remains unclear. This study examines the relationship between prediction
and learning, with a focus on three key questions: (1) whether learning leads to
prediction, (2) whether prediction motivates learning, and (3) whether
individuals’ prediction skills are stable across tasks. We first replicated the
malleability of verb bias in adults (Ryskin et al., 2017) and their ability to
predict using verb semantics (Nation et al., 2003). Beyond replications, our
results revealed that learners who successfully updated their verb biases showed
a higher proportion of first fixation to the instruments than to the animals
upon hearing an instrument-trained verb, indicating that individuals’ verb bias
predictions were modulated by the success of learning, and they were able to use
the newly learned verb bias knowledge to generate anticipatory eye movements
after training. To understand whether prediction might in turn motivate
learning, we found that the more divergent learners’ initial verb bias knowledge
was from the received training type, the greater the learning effects occurred,
linking prediction errors to learning outcomes. Finally, adults’ ability to
predict linguistic items based on verb information remained stable across
language tasks. Taken together, these results elucidate the dynamic interplay
between prediction and learning, providing empirical support for
prediction-based learning frameworks.

Conveying and Detecting Listening During Live Conversation Across all domains of
human social life, positive perceptions of conversational listening (i.e.,
feeling heard) predict well-being, professional success, and interpersonal
flourishing. However, a fundamental question remains: Are perceptions of
listening accurate? Prior research has not empirically tested the extent to
which humans can detect others’ cognitive engagement (attentiveness) during live
conversation. Across five studies (total N = 1,225), using a combination of
correlational and experimental methods, we find that perceivers struggle to
distinguish between attentive and inattentive conversational listening. Though
people’s listening fluctuated naturally throughout their conversations (people’s
minds wandered away from the conversation 24% of the time), they were able to
adjust their listening in line with instructions and incentives—by either
listening attentively, inattentively, or dividing their attention—and their
conversation partners struggled to detect these differences. Specifically,
speakers consistently overestimated their conversation partners’
attentiveness—often believing their partners were listening when they were not.
Our results suggest this overestimation is (at least partly) due to the largely
indistinguishable behavior of inattentive and attentive listeners. It appears
that people can (and do) divide their attention during conversation and
successfully feign attentiveness. Overestimating others’ attentiveness extended
to third-party observers who were not immersed in the conversation, listeners
who looked back on their own listening, and people interacting with partners who
could not hear their words (but were incentivized to act like they could). Our
work calls for a reexamination of a fundamental social behavior—listening—and
underscores the distinction between feeling heard and being heard during live
conversation.

Trained recurrent neural networks develop phase-locked limit cycles in a working
memory task Neural oscillations are ubiquitously observed in many brain areas.
One proposed functional role of these oscillations is that they serve as an
internal clock, or ‘frame of reference’. Information can be encoded by the
timing of neural activity relative to the phase of such oscillations. In line
with this hypothesis, there have been multiple empirical observations of
such phase codes in the brain. Here we ask: What kind of neural dynamics support
phase coding of information with neural oscillations? We tackled this question
by analyzing recurrent neural networks (RNNs) that were trained on a working
memory task. The networks were given access to an external reference oscillation
and tasked to produce an oscillation, such that the phase difference between the
reference and output oscillation maintains the identity of transient stimuli. We
found that networks converged to stable oscillatory dynamics. Reverse
engineering these networks revealed that each phase-coded memory corresponds to
a separate limit cycle attractor. We characterized how the stability of the
attractor dynamics depends on both reference oscillation amplitude and
frequency, properties that can be experimentally observed. To understand the
connectivity structures that underlie these dynamics, we showed that trained
networks can be described as two phase-coupled oscillators. Using this insight,
we condensed our trained networks to a reduced model consisting of two
functional modules: One that generates an oscillation and one that implements a
coupling function between the internal oscillation and external reference.

Neural correlates of object identity and reward outcome in the sensory
cortical-hippocampal hierarchy: coding of motivational information in perirhinal
cortex Neural circuits support behavioral adaptations by integrating sensory and
motor information with reward and error-driven learning signals, but it remains
poorly understood how these signals are distributed across different levels of
the corticohippocampal hierarchy. We trained rats on a multisensory
object-recognition task and compared visual and tactile responses of
simultaneously recorded neuronal ensembles in somatosensory cortex, secondary
visual cortex, perirhinal cortex, and hippocampus. The sensory regions primarily
represented unisensory information, whereas hippocampus was modulated by both
vision and touch. Surprisingly, the sensory cortices and the hippocampus coded
object-specific information, whereas the perirhinal cortex did not. Instead,
perirhinal cortical neurons signaled trial outcome upon reward-based feedback. A
majority of outcome-related perirhinal cells responded to a negative outcome
(reward omission), whereas a minority of other cells coded positive outcome
(reward delivery). Our results highlight a distributed neural coding of
multisensory variables in the cortico-hippocampal hierarchy. Notably, the
perirhinal cortex emerges as a crucial region for conveying motivational
outcomes, whereas distinct functions related to object identity are observed in
the sensory cortices and hippocampus.

GABAergic regulation of striatal spiny projection neurons depends upon their
activity state Synaptic transmission mediated by GABAA receptors (GABAARs) in
adult, principal striatal spiny projection neurons (SPNs) can suppress ongoing
spiking, but its effect on synaptic integration at subthreshold membrane
potentials is less well characterized, particularly those near the resting
down-state. To fill this gap, a combination of molecular, optogenetic, optical,
and electrophysiological approaches were used to study SPNs in mouse ex vivo
brain slices, and computational tools were used to model somatodendritic
synaptic integration. In perforated patch recordings, activation of GABAARs,
either by uncaging of GABA or by optogenetic stimulation of GABAergic synapses,
evoked currents with a reversal potential near −60 mV in both juvenile and adult
SPNs. Transcriptomic analysis and pharmacological work suggested that this
relatively positive GABAAR reversal potential was not attributable to NKCC1
expression, but rather to HCO3– permeability. Regardless, from down-state
potentials, optogenetic activation of dendritic GABAergic synapses depolarized
SPNs. This GABAAR-mediated depolarization summed with trailing ionotropic
glutamate receptor (iGluR) stimulation, promoting dendritic spikes and
increasing somatic depolarization. Simulations revealed that a diffuse dendritic
GABAergic input to SPNs effectively enhanced the response to dendritic iGluR
signaling and promoted dendritic spikes. Taken together, our results demonstrate
that GABAARs can work in concert with iGluRs to excite adult SPNs when they are
in the resting down-state, suggesting that their inhibitory role is limited to
brief periods near spike threshold. This state-dependence calls for a
reformulation for the role of intrastriatal GABAergic circuits.

The functional role of spatial anisotropies in ensemble perception The human
brain can rapidly represent sets of similar stimuli by their ensemble summary
statistics, like the average orientation or size. Classic models assume that
ensemble statistics are computed by integrating all elements with equal weight.
Challenging this view, here, we show that ensemble statistics are estimated by
combining parafoveal and foveal statistics in proportion to their reliability.
In a series of experiments, observers reproduced the average orientation of an
ensemble of stimuli under varying levels of visual uncertainty.

Computational characterization of the role of an attention schema in controlling
visuospatial attention How does the brain control attention? The Attention
Schema Theory suggests that the brain constructs an internal model of attention
for its control. However, it remains unclear under which circumstances an
attention schema is computationally useful, and whether it can emerge in a
learning system without hard-wiring it. To address these questions, we trained a
reinforcement learning agent with attention to track and catch a ball in a noisy
environment. Crucially, the agent had additional neural resources that it could
freely use. We asked under which conditions these additional resources develop
an attention schema to track attention. We found that the more uncertain the
agent was about the location of its attentional state, the more it benefited
from these additional resources, which developed an attention schema. Together,
these results indicate that an attention schema emerges in simple learning
systems where attention is both important and difficult to track.


>
NTS 278: Lori Holt PhD on Categorical Conception of Speech Sounds

Cortex Cast: Dr. Julia Harris on Sleep & Scents
Huberman Lab: Dr. Kay Tye on The Biology of Social Interactions and Emotions

JNP Micro Podcasts: Visual Strategy and Force-Steadiness in Older Adults

BigBrains: What our hands reveal about our thoughts, with Susan Goldin-Meadow (Ep. 128)

PBtS 746: Dr. Eric Skaar: Investigating the Intersection of Nutrition and Bacterial Infection and Pathogenesis

NeuroBlogs Daily

Open Access Brain Science, Lectures, & Podcasts

 * 
 * 


created by https://andyadkins.com

Blogged
 * Polyphenols, Mitochondria, Glial Cells & Neurodegenrative Pathway Studies
   (draft)
 * Week in Review

 * Polyphenols, Mitochondria, Glial Cells & Neurodegenrative Pathway Studies
   (draft)
 * Week in Review