Anderson et al used scalp EEG recordings to decode the content of working memory and its quality. Subjects performed a orientation working memory task. Anderson et al found that the spatial distribution of alpha band power could be used to determine what orientation the subject was remembering and how precisely they were remembering it. Cool.
Matsushima and Tanaka compared neural correlates of spatial working memory for locations within the same hemifield or across hemifields. When the two remembered locations were in the same hemifield (right or left side of vision), the neural response in the prefrontal cortex was intermediate to the two cues presented alone. When the cues were across hemifields, the neural response was the same as the preferred cue presented alone. In other words, remembered locations within a hemifield seemed to be in competition with each other whereas locations across the hemifields seemed to be have no interaction at all. In yet other words, it was as if the (intact) monkeys had their brains split down the middle. The authors concluded local inhibitory interactions between cues within, but not across, hemifields.
This confirms Buschman et al (2011) who found that independent capacities for visual working memory in the right and left hemifields.
Further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
Gamma band oscillations are seen throughout the cortex and subcortex. Do they have a single or different functions? Bosman et al review the literature and conclude the latter but nonetheless point out that gamma likely rises from a cortical motif involving interactions between excitatory and inhibitory neurons. So, just as activity of individual neurons means different things in different brain areas so does gamma rhythms.
Does the prefrontal cortex (PFC) maintain the contents of working memory or does it direct the focus of attention? Lara and Wallis asked this question by training monkeys to perform a multi-color change detection task. Few PFC neurons encoded the color of the stimuli. Instead, the dominant signals were the spatial location of the item and the location of focal attention. This suggests that the PFC is more involved in directing attention than retaining information in working memory. Supporting this was increased power in alpha and theta power in the PFC, frequency bands associated with long-range neural communication.
Matsushima and Tanaka examined the neural correlates of spatial working memory for one vs two locations. When the two locations were in the same (right or left) hemifield, the level neural activity was intermediate between that elicited from either cue alone. By contrast, when the cues were presented in opposite hemifields, neural activity to each cue was as if the cue was presented alone. This lends support to other observations (e.g., Buschman et al 2011) that there are independent capacities for working memory in the right and left visual hemifields, as if the brain was split down the middle.
Further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
See lectures from the Cognitive Rhythms Collaborative conference on Rhythmic Dynamics and Cognition, which took place on June 4, 2013 at MIT.
Talks:
Elizabeth Buffalo: Neural Signals for Memory and Space in the Primate Medial Temporal Lobe
Earl K. Miller: Cognition is Rhythmic
Robert Knight: Oscillations and Human PFC
Peter Ulhaas: Neural Oscillations in Schizophrenia: Perspectives from MEG
Charles Schroeder: Neural Substrates of Temporal Prediction in Active Sensing
Peter Brown: Beta Oscillations in the Human Basal Ganglia
Christa van Dort: Optogenetic Activation of Cholinergic Neurons in the PPT Induces REM Sleep
Rosalyn Doran: Dynamic Causal Modeling and Neurophysiology
Liam Paninski: Statistical Neuroscience
Astrid Prinz: How do rhythmically active circuits “analyze” their own activity?
Recent studies have suggested that beta-band oscillatory synchrony plays a role in cognition. For example, different networks of neurons in the prefrontal cortex dynamically synchronize at beta as animals switch between two different task rules (Buschman et al., 2012) suggesting that beta synchrony is forming the neural ensembles for the rules. Different items simultaneously held in working memory line-up on different phases of beta/low-gamma oscillations, as if the brain is juggling the two items 30 times a second (Siegel et al., 2009). Hanslmayr et al disrupted these fine temporal relations by stimulating the human with beta-band TMS pulses. Beta stimulation of the left inferior frontal gyrus impaired memory formation while stimulation at other frequencies did not. There was a beta “echo” that outlasted the stimulation. Subjects with better beta entrainment showed more memory impairment. This lends support for the role of beta rhythms in cognition by showing a causal relationship between beta desynchrony and memory.
This paper:
Simon Hanslmayr, Jonas Matuschek, Marie-Christin Fellner, Entrainment of Prefrontal Beta Oscillations Induces an Endogenous Echo and Impairs Memory Formation, Current Biology, Available online 27 March 2014, ISSN 0960-9822
References
Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. View PDF
Siegel, M., Warden, M.R., and Miller, E.K. (2009) Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106: 21341-21346. View PDF » Read commentary by Vogel and Fukuda
Mike Hasselmo and colleagues examined how the brain generalizes and infers new behaviors from previous experience. They trained different styles of neural network models to learn context-dependent behaviors (i.e., the response to four stimuli, A B C D, mapped onto two different responses X Y differently in different contexts). There were previously unseen stimuli whose response could be inferred from the other stimuli. They analyzed a Deep Belief Network, a Multi-Layer Perceptron, and the combination of a Deep Belief Network with a Linear Perceptron. The combination of the Deep Belief Network with Linear Perceptron worked best.
A Deep Belief Network has multiple layers of hidden units with connections between, but not within, the layers.
Peelen and Kastner extend studies of attention in the lab (using simple, neutral displays) to the real world (complex, meaningful scenes). They discuss interactions between what and where templates shaped by object familiarity, scene context, and memory
Think you can multitask well? Watanabe and Funahasi show that task information signaled by neurons in the prefrontal cortex degrade when animals perform a competing, concurrent task.
An excellent review by Matt Shapiro and crew on an important topic. They discuss complementary roles and bidirectional interactions between the prefrontal cortex and hippocampus.
Working memory is limited in capacity. As you load more “stuff” into working memory, errors increase. Bays shows how this may happen. Errors with increasing working memory load may be due to decreased signal strength of spiking neurons. Humans can increase the precision of high priority stimuli in working memory at the expense of low priority stimuli. The reduction in drive to neurons representing high priority stimuli can explain this tradeoff.
Noudoost, Clark, and Moore deactivated the frontal eye fields (FEF) and recorded from visual cortical area V4. This disrupted saccades to targets but *increased* pre-saccade activity in V4. V4 neurons, however, showed reduced discrimination of the target stimulus. It seems that the FEF provides details about the saccade target to visual cortex.
The Oxford Handbook of Attention is a veritable who’s who of attention research. (Sorry that it costs $149 USD).
Check out the table of contents:
Part A: Introduction
1. Current landscape and historical context, Michael Posner
Part B: Theoretical Models of Attention
2. Feature integration and guided search, Jeremy Wolfe
3. Perceptual/Executive load theory, Polly Dalton and Nilli Lavie
4. A multi-level account of selective attention, Sabine Kastner and John Serences
5. Large-scale network model of control, Marsel Mesulam and Professor Anna Christina Nobre
6. Multiple-demand network and adaptive coding, Mark Stokes and John Duncan
Part C: Spatial Attention
7. Spatial covert attention: Perceptual Modulation, Marisa Carrasco
8. Spatial orienting and attentional capture, Jan Theeuwes
9. Neural systems of spatial attention (fMRI), Diane Beck and Sabine Kastner
10. The time course of spatial attention: Insights from event-related brain potentials,Martin Eimer
11. Neuronal Mechanisms of Spatial Attention in Visual Cerebral Cortex, Marlene Cohen and John Maunsell
12. Cellular mechanisms of attentional control: Frontal, Jacqueline Gottlieb
13. Neuronal mechanisms of attentional control: Frontal cortex, Kelsey L. Clark, Behrad Noudoost, and Robert J. Schafer and Professor Tirin Moore
14. Neural mechanisms of Spatial Attention in the Visual Thalamus, Yuri B. Saalmann and Sabine Kastner
15. Attentional Functions of the Superior Colliculus, Richard J. Krauzlis
16. Orienting attention: a crossmodal perspective, Charles Spence
17. Neuronal Dynamics and the Mechanistic Bases of Selective Attention, Charles E.Schroeder, Jose L. Herrero and Saskia Haegens
18. The neuropharmacology of attention, Trevor Robbins
19. Developing attention and self-regulation in childhood, Michael Posner
Part D: Non-spatial Attention
20. Feature- and object-based attentional modulation in the human visual system,Miranda Scolari, Edward F. Ester, and John Serences
21. Object- and feature-based attention: monkey physiology, Stefan Treue
22. The Role of Brain Oscillations In The Temporal Limits of Attention, Kimron Shapiro and Simon Hanslmayr
23. Dynamic Attention, Patrick Cavanagh, Lorella Battelli, and Alex O. Holcombe
24. Temporal orienting, Anna Christina Nobre
Part E: Interactions between Attention and Other Psychological Domains
25. Attention, Motivation, and Emotion, Luiz Pessoa
26. Attention and executive functions
27. Neural mechanisms for the executive control of attention, Earl K. Miller and Timothy J. Buschman
28. Memory and Attention, Brice A. Kuhl and Marvin M. Chun
29. Attention and decision-making, Christopher Summerfield and Tobias Egner
30. Attention and action, Heiner Deubel
Part F: Attention-related Disorders
31. Attention and awareness, Geraint Rees
32. Attention and Aging, Theodore P. Zanto & Adam Gazzaley
33. Unilateral Spatial Neglect, Guiseppe Vallar
34. Neurological disorders of attention, Sanjay Manohar, Valerie Bonnelle and Masud Husain
35. Balint’s syndrome and the Study of Attention, Lynn C. Robertson
36. Rehabilitation of Attention Functions, Ian H. Robertson and Redmond G O’Connell
Part G: Computational Models
37. Theory of visual attention, Claus Bundesen and Thomas Habekost
38. Bottom up and contextual effects, Laurent Itti and Ali Borji
39. Bayesian models, Angela Yu
Part H: Conclusions
40. Outlook and Future Directions, Anna Christina Nobre and Sabine Kastner
Rey et al recorded local field potentials and neuron spikes from the human medial temporal lobe during a recognition task. Single-neuron responses were preceded by a global increase in theta oscillations and a local and stimulus-specific increase in gamma oscillations. The LFPs responses were correlated with conscious recognition and neuron spiking was time-locked to the LFPs. They suggest that theta reflects a global recognition signal whereas phase-locked of neurons to gamma reflects activation of local circuits that represent the recognized stimulus.
Bahlmann et al studied the human prefrontal cortex using a task with two different types of stimuli (spatial vs language) and three levels of abstraction. They found a rostro-caudal organization based on level abstraction (more anterior = more abstract).
This review examines evidence for a neurobiological explanation of executive functions of working memory. We suggest that executive control stems from information about task rules acquired by mixed selective, adaptive coding, multifunction neurons in the prefrontal cortex. Their output dynamically links the cortical-wide networks needed to complete the task. The linking may occur via synchronizing of neural rhythms, which may explain why we have a limited capacity for simultaneous thought.
Is conscious perception continuous or discrete? Asplund et al use the attentional blink paradigm to demonstrate that conscious perception is discrete and quantal. Attention increases the probability that a representation will reach awareness.
We have argued that cognition is discrete and quantal because the backbone of neural communication used for cognition is oscillatory. For this discussion see:
- Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »
The modal model of working memory (WM) is that of sustained activity in the prefrontal cortex. Sreenivasan et al argue for a more complex model. High-fidelity WM representations are maintained in sensory cortex while the prefrontal cortex instead maintains representations of multiple goal-related variables. These PFC representations serve to bias stimulus-specific activity in sensory cortex.
Roy et al show that the activity of neurons in the prefrontal cortex (pFC) are linked to categorical decisions. Monkeys were trained to categorize a set of computer-generated images as “cats” vs “dogs”. Then, they were shown ambiguous images were centered on a category boundary, that is, they were a mix of 50% of cats and dogs and therefore had no category information. The monkeys guessed at their category membership. Activity to the same ambiguous image differed significantly, depending on the monkey’s decision about the image’s category. Thus, pFC activity reflects categorical decisions.
Cannon et al review the contributions of brain oscillations to neural computations and relate them to a variety of cognitive functions.
Corbetta and colleagues studied attention by recording from patients undergoing surgery for epilepsy. They found evidence for frequency-based attention mechanisms, in particular phase modulation at lower frequencies. Different types of attentional operations (holding vs shifting attention) were associated with synchrony at different frequencies.
Everybody agrees that we can only hold a few things in mind simultaneously. However, there is disagreement about why. One theory is that limited cognitive resources are flexible and spread among the items held in mind; the more items, the “thinner” the information about each. Another theory is more of a fixed limit model: Resources are allocated in a discrete fashion and there is a fixed number of items that can be held in mind. Ester et al provide evidence for the latter, fixed, model. Subjects monitored a number of locations and then asked details about one of the locations. The subject’s performance and neural data was best described by a fixed limit model.
Wolf Singer reviews recent work on cortical dynamics. He concludes that precise temporal coordination between neurons dynamically forms networks and provides a high-dimensional space for neural computations.
For further reading see:
- Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
- Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
- Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
- Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. View PDF
Everybody knows that we can only hold a limited number of things in mind simultaneously. Is this capacity limit due to a limited number of “slots” in working memory or due a limited resource pool that is divided among the items held in mind? We found evidence for both (Buschman et al, 2011). Now, Roggeman et al use computational modeling to provide further evidence for a hybrid model for capacity limits of working memory.
Further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »