Marlene Cohen
@marlenecohen.bsky.social
Neuroscientist at U Chicago
We’re excited about the connections in this work, between behavior, species, individual differences, brain areas, and neuronal mechanisms. We would love your feedback!
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
Behavioural and neuronal insights into multisensory combination of unpracticed cues.
Effective decision-making requires integrating multiple information sources, weighted by their reliability and context. While classic studies show near-optimal cue combination with well-learned signal...
www.biorxiv.org
October 30, 2025 at 10:35 PM
We’re excited about the connections in this work, between behavior, species, individual differences, brain areas, and neuronal mechanisms. We would love your feedback!
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
Keon is on the job market (ideally in Canada) & has incredibly exciting plans for his future lab. He will use neurophysiology-inspired psychophysics to study how perception and cognition differ across the lifespan and across neurotypical and neurodiverse people. Hire him! www.keonallen.com 8/
Keon S. Allen, PhD
www.keonallen.com
October 30, 2025 at 10:35 PM
Keon is on the job market (ideally in Canada) & has incredibly exciting plans for his future lab. He will use neurophysiology-inspired psychophysics to study how perception and cognition differ across the lifespan and across neurotypical and neurodiverse people. Hire him! www.keonallen.com 8/
This study reflects Keon’s pioneering spirit. He came to our visual neurophysiology lab from a background in psychology and haptic perception and built bridges between fields, from Bayesian models and online behavior to neuronal mechanisms of cue integration. 7/
October 30, 2025 at 10:35 PM
This study reflects Keon’s pioneering spirit. He came to our visual neurophysiology lab from a background in psychology and haptic perception and built bridges between fields, from Bayesian models and online behavior to neuronal mechanisms of cue integration. 7/
Together, these results suggest that the brain combines information differently within and across sensory modalities, perhaps from different circuitry in sensory & association areas. These distinctions seem to be conserved across species, and deviations could be diagnostic for brain differences. 6/
October 30, 2025 at 10:35 PM
Together, these results suggest that the brain combines information differently within and across sensory modalities, perhaps from different circuitry in sensory & association areas. These distinctions seem to be conserved across species, and deviations could be diagnostic for brain differences. 6/
Doug conducted parallel neurophysiological experiments in which a visual cue was combined with a causal manipulation.
Electrical microstimulation in visual cortex was integrated with sensory motion cues.
Stimulation in prefrontal cortex instead pushed choices toward winner-take-all. 5/
Electrical microstimulation in visual cortex was integrated with sensory motion cues.
Stimulation in prefrontal cortex instead pushed choices toward winner-take-all. 5/
October 30, 2025 at 10:35 PM
Doug conducted parallel neurophysiological experiments in which a visual cue was combined with a causal manipulation.
Electrical microstimulation in visual cortex was integrated with sensory motion cues.
Stimulation in prefrontal cortex instead pushed choices toward winner-take-all. 5/
Electrical microstimulation in visual cortex was integrated with sensory motion cues.
Stimulation in prefrontal cortex instead pushed choices toward winner-take-all. 5/
These strategies varied across people. Age and self-reported ADHD or Autism influenced which cues were judged most accurately and how they were integrated, suggesting that individual differences in multisensory combination may reflect broader cognitive or neural traits. 4/
October 30, 2025 at 10:35 PM
These strategies varied across people. Age and self-reported ADHD or Autism influenced which cues were judged most accurately and how they were integrated, suggesting that individual differences in multisensory combination may reflect broader cognitive or neural traits. 4/
Keon measured cue combination in large cohorts of neurodiverse participants who made judgments based on multiple cues.
People combined two visual cues nearly optimally. When vision and sound conflicted, behavior became winner-take-all, usually but not always favoring the more reliable cue. 3/
People combined two visual cues nearly optimally. When vision and sound conflicted, behavior became winner-take-all, usually but not always favoring the more reliable cue. 3/
October 30, 2025 at 10:35 PM
Keon measured cue combination in large cohorts of neurodiverse participants who made judgments based on multiple cues.
People combined two visual cues nearly optimally. When vision and sound conflicted, behavior became winner-take-all, usually but not always favoring the more reliable cue. 3/
People combined two visual cues nearly optimally. When vision and sound conflicted, behavior became winner-take-all, usually but not always favoring the more reliable cue. 3/
Many studies test how subjects combine information from well-practiced cues with feedback. But we often need to combine unfamiliar signals. For example, we might try to match what we see and hear when a new appliance beeps.
Keon and Doug Ruff asked how brains do that. 2/
Keon and Doug Ruff asked how brains do that. 2/
October 30, 2025 at 10:35 PM
Many studies test how subjects combine information from well-practiced cues with feedback. But we often need to combine unfamiliar signals. For example, we might try to match what we see and hear when a new appliance beeps.
Keon and Doug Ruff asked how brains do that. 2/
Keon and Doug Ruff asked how brains do that. 2/
I’m sorry that I won’t be in San Diego to join you. But I hope it’s wonderful, and a heartfelt thank you for everything you do for our community.
October 9, 2025 at 10:53 PM
I’m sorry that I won’t be in San Diego to join you. But I hope it’s wonderful, and a heartfelt thank you for everything you do for our community.
Thank you! Your work was definitely the inspiration for a lot of this.
September 23, 2025 at 8:13 PM
Thank you! Your work was definitely the inspiration for a lot of this.
Thanks, Hannah!
September 23, 2025 at 7:40 PM
Thanks, Hannah!
Ha! My student was the one who realized we should cite your paper. Maybe she should earn a second PhD in history...
September 23, 2025 at 6:25 PM
Ha! My student was the one who realized we should cite your paper. Maybe she should earn a second PhD in history...
Thank you, especially for the laugh!
September 23, 2025 at 4:40 PM
Thank you, especially for the laugh!
We are excited about potential applications of this work, from artificial intelligence to translational efforts to fix memory disorders. This highlights a central value of our field: using curiosity-driven science for broad impact. We’d love your feedback! doi.org/10.1101/2025.09.22.677855 /end
Neuronal signatures of successful one-shot memory in mid-level visual cortex
High-capacity, one-shot visual recognition memory challenges theories of learning and neural coding because it requires rapid, robust, and durable representations. Most studies have focused on the hip...
doi.org
September 23, 2025 at 3:09 PM
We are excited about potential applications of this work, from artificial intelligence to translational efforts to fix memory disorders. This highlights a central value of our field: using curiosity-driven science for broad impact. We’d love your feedback! doi.org/10.1101/2025.09.22.677855 /end
This is the first chapter of Grace’s thesis, and there is so much more to come. She is something special, and I am going to thoroughly enjoy seeing her take our field by storm. 9/
September 23, 2025 at 3:09 PM
This is the first chapter of Grace’s thesis, and there is so much more to come. She is something special, and I am going to thoroughly enjoy seeing her take our field by storm. 9/
These findings show that the building blocks of fast, high-capacity memory are present in mid-level visual cortex. Take-home: cognition is distributed. And stay tuned: Grace’s next papers will explore mechanisms by which these signals interact with the larger network and are disrupted in disease. 8/
September 23, 2025 at 3:09 PM
These findings show that the building blocks of fast, high-capacity memory are present in mid-level visual cortex. Take-home: cognition is distributed. And stay tuned: Grace’s next papers will explore mechanisms by which these signals interact with the larger network and are disrupted in disease. 8/
We also found faster response dynamics to familiar images, consistent with pattern completion. This means that after the first couple of image fragments, V4 already signaled the whole image (but only during successful memory). The hippocampus does this, but we were surprised to see it in V4. 🤯 7/
September 23, 2025 at 3:09 PM
We also found faster response dynamics to familiar images, consistent with pattern completion. This means that after the first couple of image fragments, V4 already signaled the whole image (but only during successful memory). The hippocampus does this, but we were surprised to see it in V4. 🤯 7/
We found all of these neuronal signatures in V4. But the only ones that reliably predicted behavior were related to how consistent population responses were during memory encoding and retrieval. More consistent responses = greater memory success. 6/
September 23, 2025 at 3:09 PM
We found all of these neuronal signatures in V4. But the only ones that reliably predicted behavior were related to how consistent population responses were during memory encoding and retrieval. More consistent responses = greater memory success. 6/
We looked for proposed neuronal signatures of memory, including:
• magnitude coding
• repetition suppression
• sparse coding
• population response consistency (=similar responses to novel and familiar images) 5/
• magnitude coding
• repetition suppression
• sparse coding
• population response consistency (=similar responses to novel and familiar images) 5/
September 23, 2025 at 3:09 PM
We looked for proposed neuronal signatures of memory, including:
• magnitude coding
• repetition suppression
• sparse coding
• population response consistency (=similar responses to novel and familiar images) 5/
• magnitude coding
• repetition suppression
• sparse coding
• population response consistency (=similar responses to novel and familiar images) 5/
Grace and awesome staff scientist Cheng Xue tested whether area V4 contains the signals that could support recognition memory. Their task revealed images bit by bit. This allowed us to analyze dynamics and increased difficulty so we could compare neuronal responses on correct vs error trials. 4/
September 23, 2025 at 3:09 PM
Grace and awesome staff scientist Cheng Xue tested whether area V4 contains the signals that could support recognition memory. Their task revealed images bit by bit. This allowed us to analyze dynamics and increased difficulty so we could compare neuronal responses on correct vs error trials. 4/
Most previous studies have focused on hippocampus and higher cortical areas. But behavioral work shows that memorability depends on visual features and recognition memory distinguishes even semantically similar images. Seems like a job for mid-level visual cortex. 3/
September 23, 2025 at 3:09 PM
Most previous studies have focused on hippocampus and higher cortical areas. But behavioral work shows that memorability depends on visual features and recognition memory distinguishes even semantically similar images. Seems like a job for mid-level visual cortex. 3/
When she was a rotation student, Grace DiRisio pointed out that visual recognition memory challenges all our neural coding theories because of its remarkable capacity. Linear codes work for low capacity functions e.g. discrimination & attention. Memory for thousands of images is another story. 2/
September 23, 2025 at 3:09 PM
When she was a rotation student, Grace DiRisio pointed out that visual recognition memory challenges all our neural coding theories because of its remarkable capacity. Linear codes work for low capacity functions e.g. discrimination & attention. Memory for thousands of images is another story. 2/