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|Symposia at the 2016 Annual Meeting|
SYMPOSIUM I: Model-based Cognitive NeuroscienceOrganized by THOMAS J. PALMERI, Vanderbilt University and BRANDON M. TURNER, The Ohio State University.
Friday, November 18, 10:00 a.m. - 12:05 p.m.
Location: Grand Ballroom
Cognitive modeling has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition, using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called model-based cognitive neuroscience, which can both inform model development and help interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states are used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions of behavior. This symposium highlights a number of successful approaches within the emerging field of model-based cognitive neuroscience.
Introduction to Model-based Cognitive Neuroscience. THOMAS J. PALMERI, Vanderbilt University and BRANDON TURNER, The Ohio State University.
What is model-based cognitive neuroscience? A brief overview of the approach and of the symposium is provided.
Approaches to Model-Based Cognitive Neuroscience: Bridging Levels of Understanding of Perceptual Decision Making. THOMAS J. PALMERI, Vanderbilt University.
Cognitive modeling and neuroscience have converged on well-known accumulation of evidence models (including the diffusion model, the linear ballistic accumulator model, race and counter models, and competing accumulator model) to explain the behavioral and neural dynamics of perceptual decision making. Building off a taxonomy of approaches to model-based cognitive neuroscience we recently outlined in a collaborative paper (Turner et al., in press), I will describe how we have use neural data to constrain cognitive models (such as accumulator models), how we use cognitive models to predict neural data, and how we connect abstract cognitive models with mechanisms taking places at the level of individual neurons and ensembles of neurons.
Joint Models of Neural and Behavioral Data. BRANDON TURNER, The Ohio State University.
The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. We present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach reveals interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. In this talk, we demonstrate the utility of our approach with applications to data fusion -- the integration of EEG, fMRI, and behavioral data -- and extensions to sparse representations of high dimensional data. We demonstrate that in both a generative and predictive sense, models that consider neural data perform better than those that do not.
Decision Threshold Dynamics in the Human Subcortex Measured with Ultra-high Resolution Magnetic Resonance Imaging. BIRTE U. FORSTMANN, University of Amsterdam.
Deciding between multiple courses of action often entails an increasing need to do something as time passes - a sense of urgency. This notion of urgency is not incorporated in standard theories of speeded decision-making that assume information is accumulated until a critical fixed threshold is reached. In two experiments, we investigated the behavioral and neural evidence for an ‘urgency signal’ in humans. Experiment 1 found that as the duration of the decision-making process increased, participants made a choice based on less evidence for the selected option. Experiment 2 replicated this finding, and additionally found that variability in this effect across participants covaried with activation in striatum. These results are extended by using ultra-high resolution 7Telsa magnetic resonance imaging (MRI) to zoom in the spatio-temporal dynamics of the urgency signal in the striatum. We conclude that the striatum plays a more general role in the decision-making process than previously reported.
Combining Space and Time in the Mind. JOHN R. ANDERSON, Carnegie Mellon University.
Many cognitive modeling efforts are concerned with when cognitive events occur in time and many cognitive neuroscience efforts are concerned with where things are happening in the brain. We have combined hidden semi-Markov models (HSMM) and multivariate pattern analysis (MVPA) to merge the information from both sources. I will describe how we have used HSMM-MVPA to both discover and test models of cognitive processes.
Tracking the Neural Dynamics of Conceptual Knowledge During Category Learning with Computational Model-based Neuroimaging. MICHAEL L. MACK, University of Texas, Austin.
Learning requires updating our knowledge to incorporate goal-relevant information. Computational models provide a formal account of how attention guides this process, with item representations shifting to reflect diagnostic features over learning. Neurally, such updating is hypothesized to rely on hippocampal memory processes; however, a direct link between memory mechanisms and attention-weighted representation has not been shown. Here, we combine computational modeling with fMRI to investigate the neural mechanisms of learning-based shifts in category representation. Participants performed two classification tasks that required different attentional strategies. We used a computational learning model, SUSTAIN, to quantify each participant’s attention-weighted knowledge representations. We found that neural representations in left anterior hippocampus correspond with model predictions of conceptual knowledge. Our method uniquely advances current cognitive neuroscience approaches to link neural measures and cognitive models. Leveraging model predictions of latent knowledge organization to constrain neuroimaging analysis enabled us to index the neural codes underlying concept formation.
The Neurocognitive Dynamics of Memory Search. SEAN M. POLYN, Vanderbilt University.
In the free-recall task, participants study a series of items, and are then asked to recall the items in whatever order they come to mind. The analysis of the identity, order, and latency of the remembered items provides fertile ground for the development of computational models of the cognitive processes engaged during memory search. These models describe a dynamic system in which executive processes construct and deploy a retrieval cue to probe memory structures in order to reactivate the details of recent past experience. These dynamic cognitive models provide a natural framework to characterize the functional properties of neural signals recorded during both study and recall periods. I will describe recent and ongoing work using the neurocognitive memory search (NCMS) modeling framework to specify models that link univariate and multivariate neural signals to specific cognitive processes and representations in the model. These models provide evidence for neural processes related to recall initiation, the temporal and semantic organization of memories, and the termination of search.
SYMPOSIUM II: Motivated Memory: Considering the Functional Role of MemoryOrganized by CHRISTOPHER R. MADAN, Boston College.
Friday, November 18, 1:30 p.m. - 3:30 p.m.
Location: Grand Ballroom
Memory does not serve as a veridical recording of prior experiences that can be played back, instead many factors can lead some experiences to be more memorable than others. This leads to an important consideration: What is the functional role of memory? From this perspective, some experiences are more valuable in informing future behavior and should be selectively prioritized, such as those that evoke reward- or emotion-related processes. Here we broadly consider these processes as effects of motivational salience on memory. To capture the breadth of this topic, research highlighted in this symposium spans a variety of research approaches, including fMRI, cognitive aging, sleep-related consolidation, and cross-cultural differences.
Reward Motivation Facilitates Hippocampal-Dependent Encoding and Consolidation. VISHNU P. MURTY, University of Pittsburgh.
Motivation has been shown to facilitate episodic memory. Animal models suggest that these memory enhancements emerge through interactions of the ventral tegmental area (VTA) and hippocampus both during and after encoding. I will present two fMRI studies detailing mechanisms guiding reward-motivated memory enhancements. In Study 1, I will show that rewarding contexts facilitate VTA hippocampal interactions resulting in enhanced hippocampal responses to salient, un-rewarded, events. Further, I will show that enhanced hippocampal responses is paralleled with increased memory for those salient events. In study 2, I will show that post-encoding changes in network connectivity of the VTA and hippocampus predict better long-term memory for reward-associated events. Critically, post-encoding VTA-hippocampal interactions specifically targeted sensory cortex that was associated with reward during encoding. These findings support a model by which VTA-hippocampal interactions enhance episodic memory for rewarding events by (1) enriching encoding and (2) selectively stabilizing reward memory following encoding.
Mechanisms of Motivational Modulation of Attention in Younger and Older Adults. JULIA SPANIOL, Ryerson University.
Motivational signals bias attention across the lifespan. Significant evidence suggests that aging is associated with an attentional positivity effect, but the mechanisms underlying this age-related shift are still poorly understood. In the present study, we examined the link between phasic arousal, linked to noradrenergic neuromodulation, and the impact of gain and loss motivation on attention. Younger adults (aged 18–34 years) and older adults (60–82 years) completed the Attention Network Test (ANT; Fan et al., 2002), modified to include gain and loss incentives. The behavioral alerting index served as a marker of phasic arousal efficiency. For younger adults, this marker correlated positively with the effect of both gain and loss incentives on ANT performance. In contrast, for older adults, the correlation held for gain incentives only, suggesting an age-related reduction in phasic arousal to loss signals. We discuss this finding in the context of Adaptive Gain Theory (Aston-Jones, 1994).
Preferential Consolidation of Emotional Components of Memory During a Nap is Preserved with Age. SARA E. ALGERS, University of Notre Dame.
Emotionally salient information is better remembered at the expense of less relevant details. Sleep increases the magnitude of this memory trade-off, preferentially preserving emotional components in young adults. Although both memory and sleep decline with age, little is known about whether their functional relationship changes. The current study compared changes in memory for negative and neutral components of scenes across a retention period containing an immediate or delayed nap versus wake. All subjects (18-64yrs) demonstrated the emotional memory trade-off effect. Interestingly, covarying for age, immediately napping led to the greatest increase in negative memory trade-off compared to both wake and delayed napping, indicating that sleep facilitated preferential consolidation of emotional components. There was a positive correlation between slow-wave sleep and negative object memory across all nap subjects, providing strong evidence that even as we age, sleep preserves salient information over less important details, despite general declines in memory and sleep.
Motivational Salience and Association-Memory: Positive Affect is Not Like the Others. CHRISTOPHER R. MADAN, Boston College.
Memory in daily life is not simply for occurrence of isolated information, but also for associations between different pieces of information. By using tasks such as paired-associate learning and cued recall to disentangle effects of item- and association-memory, previous research has demonstrated that negative affect, rewards, and motor-related information can all enhance memory for items, while simultaneously impairing memory for associations. Here we examined the influence of positive affect on item- and association-memory and found an enhancement of both memory for items and associations, relative to emotionally neutral information. This benefit of positive affect on association-memory was consistently demonstrated, revealing a different pattern than with equally arousing negative affect. These results provide strong evidence that positive information is processed differently than negative, and also differently than other types of motivationally salient information, such as rewarding or motor-related information.
Culture Motivates What is Remembered Accurately and Erroneously. ANGELA GUTCHESS,
SYMPOSIUM III: Language by Mouth and by HandOrganized by IRIS BERENT, Northeastern University and SUSAN GOLDIN-MEADOW,
University of Chicago.
SYMPOSIUM IV: The Evolutionary and Psychological Significance of PlayFrom the Psychonomic Society’s Leading Edge Workshop initiative.
In honor of Stanley J. Kuczaj, II