Attention restores discrete items to visual short-term memory

Paper 1: Attention restores discrete items to visual short-term memory
by Murray AM, Nobre AC, Clark I, Cravo AM & Stokes MG
Psychological Science 24(4):550-6. (2013)
http://pss.sagepub.com/content/early/2013/02/22/0956797612457782

Paper 2: Top-down modulation: bridging selective attention and working memory
by Gazzaley A, Nobre AC
Trends Cogn Sci 16(2):129-35. (2012)
http://www.ncbi.nlm.nih.gov/pubmed/22209601

 

Paper 1:
When a memory is forgotten, is it lost forever? Our study shows that selective attention can restore forgotten items to visual short-term memory (VSTM). In our two experiments, all stimuli presented in a memory array were designed to be equally task relevant during encoding. During the retention interval, however, participants were sometimes given a cue predicting which of the memory items would be probed at the end of the delay. This shift in task relevance improved recall for that item. We found that this type of cuing improved recall for items that otherwise would have been irretrievable, providing critical evidence that attention can restore forgotten information to VSTM. Psychophysical modeling of memory performance has confirmed that restoration of information in VSTM increases the probability that the cued item is available for recall but does not improve the representational quality of the memory. We further suggest that attention can restore discrete items to VSTM.

 

Paper 2:
Selective attention, the ability to focus our cognitive resources on information relevant to our goals, influences working memory (WM) performance. Indeed, attention and working memory are increasingly viewed as overlapping constructs. Here, we review recent evidence from human neurophysiological studies demonstrating that top-down modulation serves as a common neural mechanism underlying these two cognitive operations. The core features include activity modulation in stimulus-selective sensory cortices with concurrent engagement of prefrontal and parietal control regions that function as sources of top-down signals. Notably, top-down modulation is engaged during both stimulus-present and stimulus-absent stages of WM tasks; that is, expectation of an ensuing stimulus to be remembered, selection and encoding of stimuli, maintenance of relevant information in mind and memory retrieval.

Presented by Mingbo/Angela on 5/3/16

Attentional modulation of object representations in working memory

Attentional modulation of object representations in working memory

by Pepsin & Nobre
Celeb Cortex, Vol 17(9), p: 2072-83. Nov 10 2006 | doi: 10.1093/cercor/bhl116

http://cercor.oxfordjournals.org/content/17/9/2072.long

We investigated the role of object-based attention in modulating the maintenance of faces and scenes held online in working memory (WM). Participants had to remember a face and a scene, while cues presented during the delay instructed them to orient their attention to one or the other item. Event-related functional magnetic resonance imaging revealed that orienting attention in WM modulated the activity in fusiform and parahippocampal gyri, involved in maintaining representations of faces and scenes respectively. Measures from complementary behavioral studies indicated that this increase in activity corresponded to improved WM performance. The results show that directed attention can modulate maintenance of specific representations in WM, and help define the interplay between the domains of attention and WM.

Presented by Andra on 4/12/16

Feeding and Reward Are Differentially Induced by Activating GABAergic Lateral Hypothalamic Projections to VTA

Feeding and Reward Are Differentially Induced by Activating GABAergic Lateral Hypothalamic Projections to VTA

by Barbano, Wang, Morales & Wise.
J Neurosci, Vol 36(10). pii: 2975-85. Mar 9 2016 | doi: 10.1523/JNEUROSCI.3799-15.2016.

https://www.ncbi.nlm.nih.gov/pubmed/?term=wise+morales+lateral+hypothalamus

Electrical stimulation of the lateral hypothalamus (LH) has two motivational effects: long trains of stimulation induce drive-like effects such as eating, and short trains are rewarding. It has not been clear whether a single set of activated fibers subserves the two effects. Previous optogenetic stimulation studies have confirmed that reinforcement and induction of feeding can each be induced by selective stimulation of GABAergic fibers originating in the bed nucleus of the LH and projecting to the ventral tegmental area (VTA). In the present study we determined the optimal stimulation parameters for each of the two optogenetically induced effects in food-sated mice. Stimulation-induced eating was strongest with 5 Hz and progressively weaker with 10 and 20 Hz. Stimulation-induced reward was strongest with 40 Hz and progressively weaker with lower or higher frequencies. Mean preferred duration for continuous 40 Hz stimulation was 61.6 s in a “real-time” place preference task; mean preferred duration for 5 Hz stimulation was 45.6 s. The differential effects of high- and low-frequency stimulation of this pathway seem most likely to be due to differential effects on downstream targets.

Presented by Mel on 4/5/16

Modeling Task State Representation by the Orbitofrontal Cortex with a Reservoir Network

Modeling Task State Representation by the Orbitofrontal Cortex with a Reservoir Network

by Zhenbo Cheng, Tianming Yang
Advances in Cognitive Neurodynamics (V), pp 625-631. |

http://link.springer.com/chapter/10.1007/978-981-10-0207-6_84

The theory of reinforcement learning has been used to explain how animals use rewards to adjust their behavior. It has been proposed that the orbitofrontal cortex (OFC) may provide task state information during learning, but the nature of its neural encoding is not clear. Here we propose a neural network model of the OFC using a reservoir network. The reservoir receives sensory inputs and reward inputs just as the OFC does. The connections within the reservoir are sparse and randomly assigned. The neurons in the reservoir exhibit heterogeneous and dynamical responses. The learning shapes its readout. We show the model reproduces the animals’ behavior in a classical reversal learning experiment. Neurons in the reservoir encode state information. We also make predictions based on the model that can be experimentally tested in the future.

Presented by Nico on 3/22/16

Dopamine regulates stimulus generalization in the human hippocampus

Dopamine regulates stimulus generalization in the human hippocampus

by Kahnt & Tobler
ELife, Vol 5. pii: e12678. Feb 2 2016 | doi: 10.7554/eLife.12678

http://elifesciences.org/content/5/e12678v1

The ability to generalize previously learned information to novel situations
is fundamental for adaptive behavior. However, too wide or too narrow
generalization is linked to neuropsychiatric disorders. Previous research
suggests that interactions between the dopaminergic system and the
hippocampus may play a role in generalization, but whether and how the
degree of generalization can be modulated via these pathways is currently
unknown. Here, we addressed this question in humans using pharmacology,
functional magnetic resonance imaging, and computational modeling. Blocking
dopamine D2-receptors (D2R) altered generalization behavior as revealed by
an increased kurtosis of the generalization gradient, and a decreased width
of model-derived generalization parameters. Moreover, D2R-blockade modulated
similarity-based responses in the hippocampus and decreased
midbrain-hippocampal connectivity, which in turn correlated with individual
differences in generalization. These results suggest that dopaminergic
activity in the hippocampus may relate to the degree of generalization and
highlight a potential target for treatment.

Presented by Yeon Soon on 3/1/16

Striatal dynamics explain duration judgments

Striatal dynamics explain duration judgments

by Gouvêa, Monteiro, Motiwala, Soares, Machens, Paton
ELife, Vol 4. pii: e11386. Dec 7 2015 | doi: 10.7554/eLife.11386.

http://www.ncbi.nlm.nih.gov/pubmed/26641377

The striatum is an input structure of the basal ganglia implicated in several time-dependent functions including reinforcement learning, decision making, and interval timing. To determine whether striatal ensembles drive subjects’ judgments of duration, we manipulated and recorded from striatal neurons in rats performing a duration categorization psychophysical task. We found that the dynamics of striatal neurons predicted duration judgments, and that simultaneously recorded ensembles could judge duration as well as the animal. Furthermore, striatal neurons were necessary for duration judgments, as muscimol infusions produced a specific impairment in animals’ duration sensitivity. Lastly, we show that time as encoded by striatal populations ran faster or slower when rats judged a duration as longer or shorter, respectively. These results demonstrate that the speed with which striatal population state changes supports the fundamental ability of animals to judge the passage of time.

Presented by Sarah DuBrow on 2/2/16

Reinforcement Learning with Selective Perception and Hidden State — part II

Reinforcement Learning with Selective Perception and Hidden State — part II

(PhD thesis by Andrew McCallum, 1995)

http://web.media.mit.edu/~tristan/Classes/MAS.945/Papers/Contextual/McCallum_Thesis.pdf

Presented by Angela Radulescu on 1/26/16

A scalable population code for time in the striatum

A scalable population code for time in the striatum 

by Mello, Soares, Paton
Current Biology, Volume 25, Issue 9, 4 May 2015, p1113–1122,

http://www.cell.com/current-biology/abstract/S0960-9822(15)00205-5

To guide behavior and learn from its consequences, the brain must represent time over many scales. Yet, the neural signals used to encode time in the seconds-to-minute range are not known. The striatum is a major input area of the basal ganglia associated with learning and motor function. Previous studies have also shown that the striatum is necessary for normal timing behavior. To address how striatal signals might be involved in timing, we recorded from striatal neurons in rats performing an interval timing task. We found that neurons fired at delays spanning tens of seconds and that this pattern of responding reflected the interaction between time and the animals’ ongoing sensorimotor state. Surprisingly, cells rescaled responses in time when intervals changed, indicating that striatal populations encoded relative time. Moreover, time estimates decoded from activity predicted timing behavior as animals adjusted to new intervals, and disrupting striatal function led to a decrease in timing performance. These results suggest that striatal activity forms a scalable population code for time, providing timing signals that animals use to guide their actions.

Presented by Angela Langdon on 1/19/16

Contrasting Roles for Orbitofrontal Cortex and Amygdala in Credit Assignment and Learning in Macaques

Contrasting Roles for Orbitofrontal Cortex and Amygdala in Credit Assignment and Learning in Macaques 

by Chau, Sallet, Papageorgiou, Noonan, Bell, Walton and Rushworth
Neuron, Volume 87, Issue 5, 2 September 2015, Pages 1106–1118

http://www.sciencedirect.com/science/article/pii/S0896627315007126

Recent studies have challenged the view that orbitofrontal cortex (OFC) and amygdala mediate flexible reward-guided behavior. We trained macaques to perform an object discrimination reversal task during fMRI sessions and identified a lateral OFC (lOFC) region in which activity predicted adaptive win-stay/lose-shift behavior. Amygdala and lOFC activity was more strongly coupled on lose-shift trials. However, lOFC-amygdala coupling was also modulated by the relevance of reward information in a manner consistent with a role in establishing how credit for reward should be assigned. Day-to-day fluctuations in signals and signal coupling were correlated with day-to-day fluctuation in performance. A second experiment confirmed the existence of signals for adaptive stay/shift behavior in lOFC and reflecting irrelevant reward in the amygdala in a probabilistic learning task. Our data demonstrate that OFC and amygdala each make unique contributions to flexible behavior and credit assignment.

Presented by Nico Schuck on 1/12/16

Reinforcement Learning with Selective Perception and Hidden State

Reinforcement Learning with Selective Perception and Hidden State

(PhD thesis by Andrew McCallum, 1995)

http://web.media.mit.edu/~tristan/Classes/MAS.945/Papers/Contextual/McCallum_Thesis.pdf

Presented by Angela Radulescu on 12/15/15