Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1311-0
A. Beukers, K. Norman, J. Cohen
We present a model of how working memory (WM) and episodic memory (EM) interact in the n-back task. Contrary to previous models in which information is actively maintained in WM, our model posits that information about previous stimuli is retained exclusively in EM. Unlike WM-based active maintenance, which has limited maintenance capacity, EM-based storage has unlimited storage capacity but is subject to proactive interference. Using the model we show that benchmark phenomena ordinarily attributed to use of a limited-capacity WM system (the set size effect and the lure interference effect) can also arise in a model with no such maintenance constraints.
{"title":"Working with Episodic Memory: The N-back Task","authors":"A. Beukers, K. Norman, J. Cohen","doi":"10.32470/ccn.2019.1311-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1311-0","url":null,"abstract":"We present a model of how working memory (WM) and episodic memory (EM) interact in the n-back task. Contrary to previous models in which information is actively maintained in WM, our model posits that information about previous stimuli is retained exclusively in EM. Unlike WM-based active maintenance, which has limited maintenance capacity, EM-based storage has unlimited storage capacity but is subject to proactive interference. Using the model we show that benchmark phenomena ordinarily attributed to use of a limited-capacity WM system (the set size effect and the lure interference effect) can also arise in a model with no such maintenance constraints.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132043755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1303-0
Kelsey R. McDonald, S. Huettel, John M. Pearson
Previous studies of strategic social interaction in game theory have predominantly used games with clearlydefined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. We have previously shown that it is possible to quantify the instantaneous dynamic coupling in strategic human game play when paired against both human and artificial opponents. Here, we apply this coupling model to human neuroimaging data. We observe that the rTPJ and dmPFC exhibit increased activation when playing against a human opponent compared to a computer opponent, both immediately before and after game play. Moreover, a network of regions frequently associated with social cognition, including the dlPFC and dmPFC, was found to correlate with player coupling metrics derived from our model for both human and computer opponents. These findings suggest that prefrontal cortex may play a role in tracking the relationship between oneself and other dynamic agents, regardless of whether those agents are perceived to be human.
{"title":"Bayesian nonparametric models characterize social sensitivity in a competitive dynamic game","authors":"Kelsey R. McDonald, S. Huettel, John M. Pearson","doi":"10.32470/ccn.2019.1303-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1303-0","url":null,"abstract":"Previous studies of strategic social interaction in game theory have predominantly used games with clearlydefined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. We have previously shown that it is possible to quantify the instantaneous dynamic coupling in strategic human game play when paired against both human and artificial opponents. Here, we apply this coupling model to human neuroimaging data. We observe that the rTPJ and dmPFC exhibit increased activation when playing against a human opponent compared to a computer opponent, both immediately before and after game play. Moreover, a network of regions frequently associated with social cognition, including the dlPFC and dmPFC, was found to correlate with player coupling metrics derived from our model for both human and computer opponents. These findings suggest that prefrontal cortex may play a role in tracking the relationship between oneself and other dynamic agents, regardless of whether those agents are perceived to be human.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130951097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1051-0
Tessa Rusch
Humans are distinctly skilled at cooperation. To successfully engage with others they apply Theory of Mind (ToM). Here, we investigate neuro-computational mechanisms underlying ToM during real-time dyadic coordination in a probabilistic social decision game. To effectively coordinate participants have to represent the surrounding they interacted in and simultaneously simulate their partner’s representation of the world. These cognitive computations are formalized with a decision framework that combines decision-making under uncertainty with intentional models of other agents. Using model-based EEG analyses, we identify oscillatory signals related to errors experienced by players when own expectations towards the surroundings are violated and simulations of errors experienced by the partner when the partner’s predictions fail. Consistent with previous studies, we find positive correlations between power in frontal delta and theta oscillations and experienced errors. Most strikingly, these signals are also found in relation to simulations of the partner’s error, at times when participants themselves experience no prediction error themselves. These findings unveil the neural signature of a crucial computational component of the mental model of a partner and demonstrate that the brain recruits similar mechanisms for simulation the decisions of others as for computing one’s own decision.
{"title":"Modelling models of other minds: a neuro-computational characterization of theory of mind processes during cooperative interaction","authors":"Tessa Rusch","doi":"10.32470/ccn.2019.1051-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1051-0","url":null,"abstract":"Humans are distinctly skilled at cooperation. To successfully engage with others they apply Theory of Mind (ToM). Here, we investigate neuro-computational mechanisms underlying ToM during real-time dyadic coordination in a probabilistic social decision game. To effectively coordinate participants have to represent the surrounding they interacted in and simultaneously simulate their partner’s representation of the world. These cognitive computations are formalized with a decision framework that combines decision-making under uncertainty with intentional models of other agents. Using model-based EEG analyses, we identify oscillatory signals related to errors experienced by players when own expectations towards the surroundings are violated and simulations of errors experienced by the partner when the partner’s predictions fail. Consistent with previous studies, we find positive correlations between power in frontal delta and theta oscillations and experienced errors. Most strikingly, these signals are also found in relation to simulations of the partner’s error, at times when participants themselves experience no prediction error themselves. These findings unveil the neural signature of a crucial computational component of the mental model of a partner and demonstrate that the brain recruits similar mechanisms for simulation the decisions of others as for computing one’s own decision.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131250503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1429-0
Santani Teng, Giovanni Fusco
Echolocating organisms ensonify their surroundings, then extract object and spatial information from the echoes. This behavior has been observed in some blind humans, but the computations underlying this strategy remain extremely poorly understood. Here we tracked the movements and echo emissions of an expert blind echolocator performing a target detection and localization task. We found that the precision of responses as well as target acquisition movements depended significantly on the size of the target and availability of active echo cues. The distribution of click directions suggested that the maximal energy of each click was always directed at the target. Our results pave the way toward characterizing human echolocation in the context of other active sensing behaviors, constraining the types of perceptual mechanisms mediating its behavior, and at a practical level, building a quantitative evidence base for optimizing therapeutic training interventions.
{"title":"Modeling echo-target acquisition in blind humans","authors":"Santani Teng, Giovanni Fusco","doi":"10.32470/ccn.2019.1429-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1429-0","url":null,"abstract":"Echolocating organisms ensonify their surroundings, then extract object and spatial information from the echoes. This behavior has been observed in some blind humans, but the computations underlying this strategy remain extremely poorly understood. Here we tracked the movements and echo emissions of an expert blind echolocator performing a target detection and localization task. We found that the precision of responses as well as target acquisition movements depended significantly on the size of the target and availability of active echo cues. The distribution of click directions suggested that the maximal energy of each click was always directed at the target. Our results pave the way toward characterizing human echolocation in the context of other active sensing behaviors, constraining the types of perceptual mechanisms mediating its behavior, and at a practical level, building a quantitative evidence base for optimizing therapeutic training interventions.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126244049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1210-0
Jessica Dully, David P. McGovern, S. Kelly, R. O’Connell
A feature common across sequential sampling models is that decisions are formed by accumulating sensory information up to an action-triggering bound. Aside from this central ingredient, numerous model variants exist that invoke distinct algorithmic elements and adaptations. A key area of disagreement has been whether decisions are achieved by integrating evidence 'perfectly', without the loss of already obtained information, or whether evidence accumulation is subject to 'leak' whereby older samples of information are discarded or lost as time passes. The present study used EEG to investigate a previously identified signal of human evidence accumulation (the centro-parietal positivity; CPP) for signatures of leak. Twenty-three participants completed a continuous random dot motion task with the goal of detecting periods of coherent upward motion. Within half of these coherent targets, a brief 200ms 'gap' of incoherent motion was inserted. Preliminary analyses indicate that these evidence gaps produced substantial reaction time delays and a corresponding deceleration in the build-up of the CPP. However, initial analyses do not identify a negative CPP slope during the gap which would be diagnostic of leak. Our data do not support the role of leak in evidence accumulation.
{"title":"Investigating the Presence of 'Leaky' Accumulation in a Human Evidence Integration Signal","authors":"Jessica Dully, David P. McGovern, S. Kelly, R. O’Connell","doi":"10.32470/ccn.2019.1210-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1210-0","url":null,"abstract":"A feature common across sequential sampling models is that decisions are formed by accumulating sensory information up to an action-triggering bound. Aside from this central ingredient, numerous model variants exist that invoke distinct algorithmic elements and adaptations. A key area of disagreement has been whether decisions are achieved by integrating evidence 'perfectly', without the loss of already obtained information, or whether evidence accumulation is subject to 'leak' whereby older samples of information are discarded or lost as time passes. The present study used EEG to investigate a previously identified signal of human evidence accumulation (the centro-parietal positivity; CPP) for signatures of leak. Twenty-three participants completed a continuous random dot motion task with the goal of detecting periods of coherent upward motion. Within half of these coherent targets, a brief 200ms 'gap' of incoherent motion was inserted. Preliminary analyses indicate that these evidence gaps produced substantial reaction time delays and a corresponding deceleration in the build-up of the CPP. However, initial analyses do not identify a negative CPP slope during the gap which would be diagnostic of leak. Our data do not support the role of leak in evidence accumulation.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126591317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1320-0
Huriye Atilgan, C. Murphy, A. Kwan
Learning from experience is essential to the optimization of behavior. In particular, we learn from past choices and outcomes to infer the predicted values of the actions to be taken. Then based on the values, we may select an informed choice. However, despite the many neural correlates identified, we still do not have a clear picture for how values are computed and translated into informed behavior. Here, we trained head-fixed mice to perform a two-armed bandit task. Animals based their decisions on past choices and reinforcements, consistent with having an internal representation of action values. To determine the causal contributions of the medial prefrontal cortex, we tested the animals before and after an excitotoxic lesion of the medial secondary motor cortex (M2). We found that unilateral M2 lesion led to side-specific effects on the animal’s ability to learn from past choices. To quantify the decision-making process, we fitted the animal’s choice behavior with Q-learning models to extract learning parameters such as learning rate, forgetting rate, and inverse temperature. Altogether, the results provide insights into the causal involvement of mouse mM2 in value-based decision making.
{"title":"The causal contributions of medial prefrontal cortex to value-based decisions in mice","authors":"Huriye Atilgan, C. Murphy, A. Kwan","doi":"10.32470/ccn.2019.1320-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1320-0","url":null,"abstract":"Learning from experience is essential to the optimization of behavior. In particular, we learn from past choices and outcomes to infer the predicted values of the actions to be taken. Then based on the values, we may select an informed choice. However, despite the many neural correlates identified, we still do not have a clear picture for how values are computed and translated into informed behavior. Here, we trained head-fixed mice to perform a two-armed bandit task. Animals based their decisions on past choices and reinforcements, consistent with having an internal representation of action values. To determine the causal contributions of the medial prefrontal cortex, we tested the animals before and after an excitotoxic lesion of the medial secondary motor cortex (M2). We found that unilateral M2 lesion led to side-specific effects on the animal’s ability to learn from past choices. To quantify the decision-making process, we fitted the animal’s choice behavior with Q-learning models to extract learning parameters such as learning rate, forgetting rate, and inverse temperature. Altogether, the results provide insights into the causal involvement of mouse mM2 in value-based decision making.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125729407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1058-0
H. Blank, C. Büchel
Perception is an active inference in which perceptual priors are combined with sensory input. It is still unclear how the precision of prior expectations is represented in the human brain. Prior precision could be represented with prior content itself in sensory regions. Alternatively, there could be distinct, specialized brain regions that represent precision separately from the content of the prior. Here, we used multivariate functional resonance imaging to test whether the precision of face priors can be measured together with expected face identity in facesensitive regions. During face anticipation, representations of expected face identity increased with prior precision in the face-sensitive anterior temporal lobe. In contrast, during face presentation, representations of face identity increased with surprise in the fusiform face area and the insula. Our findings suggest that precision of face priors is represented in higher-level face areas. These priors seem to influence the representation of face input in lower-level face regions and additional specialized brain regions which signal surprise to unexpected stimuli.
{"title":"Representation of face-prior precision","authors":"H. Blank, C. Büchel","doi":"10.32470/ccn.2019.1058-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1058-0","url":null,"abstract":"Perception is an active inference in which perceptual priors are combined with sensory input. It is still unclear how the precision of prior expectations is represented in the human brain. Prior precision could be represented with prior content itself in sensory regions. Alternatively, there could be distinct, specialized brain regions that represent precision separately from the content of the prior. Here, we used multivariate functional resonance imaging to test whether the precision of face priors can be measured together with expected face identity in facesensitive regions. During face anticipation, representations of expected face identity increased with prior precision in the face-sensitive anterior temporal lobe. In contrast, during face presentation, representations of face identity increased with surprise in the fusiform face area and the insula. Our findings suggest that precision of face priors is represented in higher-level face areas. These priors seem to influence the representation of face input in lower-level face regions and additional specialized brain regions which signal surprise to unexpected stimuli.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116047947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We analyze the close link between perception and memory. Our main hypothesis is that some of the main memory systems of the human brain, e.g., the episodic memory, the semantic memory, and to some degree also the working memory, are by-products of the need for humans to gradually extract more meaningful and more complex information from sensory inputs. Our model is an extension to the tensor memory approach. The key notions are index representations for entities, concepts, relationships and time instances, embeddings associated with the indices, a working memory layer, and a sensory memory layer. Perception and memory are realized as an interplay between the different layers. Our model is both competitive to other technical solutions and, as we argue, biologically plausible. Our experiments demonstrate that semantic memory can evolve from perception as a distinguishable functional module.
{"title":"A Model for Perception and Memory","authors":"Volker Tresp, Sahand Sharifzadeh, Dario Konopatzki","doi":"10.32470/ccn.2019.1264-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1264-0","url":null,"abstract":"We analyze the close link between perception and memory. Our main hypothesis is that some of the main memory systems of the human brain, e.g., the episodic memory, the semantic memory, and to some degree also the working memory, are by-products of the need for humans to gradually extract more meaningful and more complex information from sensory inputs. Our model is an extension to the tensor memory approach. The key notions are index representations for entities, concepts, relationships and time instances, embeddings associated with the indices, a working memory layer, and a sensory memory layer. Perception and memory are realized as an interplay between the different layers. Our model is both competitive to other technical solutions and, as we argue, biologically plausible. Our experiments demonstrate that semantic memory can evolve from perception as a distinguishable functional module.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"121 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113961537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1178-0
Sonia Poltoratski, Kendrick Norris Kay, K. Grill-Spector
While spatial information and biases have been consistently reported in high-level face regions, the functional contribution of this information toward face recognition behavior is unclear. Here, we propose that spatial integration of information plays a critical role in a hallmark phenomenon of face perception: holistic processing, or the tendency to process all features of a face concurrently rather than independently. We sought to gain insight into the neural basis of face recognition behavior by using a voxelwise encoding model of spatial selectivity to characterize the human face network using both typical face stimuli, and stimuli thought to disrupt normal face perception. We mapped population receptive fields (pRFs) using 3T fMRI in 6 participants using upright as well as inverted faces, which are thought to disrupt holistic processing. Compared to upright faces, inverted faces yielded substantial differences in measured pRF size, position, and amplitude. Further, these differences increased in magnitude along the face network hierarchy, from IOGto pFusand mFus-faces. These data suggest that pRFs in high-level regions reflect complex stimulusdependent neural computations that underlie variations in recognition performance.
{"title":"Using population receptive field models to elucidate spatial integration in high-level visual cortex","authors":"Sonia Poltoratski, Kendrick Norris Kay, K. Grill-Spector","doi":"10.32470/ccn.2019.1178-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1178-0","url":null,"abstract":"While spatial information and biases have been consistently reported in high-level face regions, the functional contribution of this information toward face recognition behavior is unclear. Here, we propose that spatial integration of information plays a critical role in a hallmark phenomenon of face perception: holistic processing, or the tendency to process all features of a face concurrently rather than independently. We sought to gain insight into the neural basis of face recognition behavior by using a voxelwise encoding model of spatial selectivity to characterize the human face network using both typical face stimuli, and stimuli thought to disrupt normal face perception. We mapped population receptive fields (pRFs) using 3T fMRI in 6 participants using upright as well as inverted faces, which are thought to disrupt holistic processing. Compared to upright faces, inverted faces yielded substantial differences in measured pRF size, position, and amplitude. Further, these differences increased in magnitude along the face network hierarchy, from IOGto pFusand mFus-faces. These data suggest that pRFs in high-level regions reflect complex stimulusdependent neural computations that underlie variations in recognition performance.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123764045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.32470/ccn.2019.1316-0
Johanni Brea, W. Gerstner
Birds of the crow family are well known for their complex cognition. In laboratory experiments it has been observed that jays adapt food caching strategies to anticipated needs and rely on a memory of the what, where and when of previous caching events for cache recovery. While this behaviour is well studied, little is known about the algorithms and neural processes that produce this behaviour. We present a computational model and propose a neural implementation of food caching behaviour. Our model features latent hunger variables for motivational control, an associative memory for snapshots of the sensory states during caching events, a system memory consolidation for flexible decoding of the age of a memory, a stimulus-driven retrieval mechanism, and rewardmodulated update of retrieval and caching policies during inspection of caches. We show that our model is in quantitative agreement with the results of 22 behavioural experiments. Our methodology of a formalization of experimental protocols via a domain-specific language is transferable to other domains and may serve as a tool to design new experiments and foster collaboration between experimentalists and theoreticians. Our model is an example of a structured reinforcement learning algorithm that could have evolved in species that operate in partially observable environments.
{"title":"A Memory-Augmented Reinforcement Learning Model of Food Caching Behaviour in Birds","authors":"Johanni Brea, W. Gerstner","doi":"10.32470/ccn.2019.1316-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1316-0","url":null,"abstract":"Birds of the crow family are well known for their complex cognition. In laboratory experiments it has been observed that jays adapt food caching strategies to anticipated needs and rely on a memory of the what, where and when of previous caching events for cache recovery. While this behaviour is well studied, little is known about the algorithms and neural processes that produce this behaviour. We present a computational model and propose a neural implementation of food caching behaviour. Our model features latent hunger variables for motivational control, an associative memory for snapshots of the sensory states during caching events, a system memory consolidation for flexible decoding of the age of a memory, a stimulus-driven retrieval mechanism, and rewardmodulated update of retrieval and caching policies during inspection of caches. We show that our model is in quantitative agreement with the results of 22 behavioural experiments. Our methodology of a formalization of experimental protocols via a domain-specific language is transferable to other domains and may serve as a tool to design new experiments and foster collaboration between experimentalists and theoreticians. Our model is an example of a structured reinforcement learning algorithm that could have evolved in species that operate in partially observable environments.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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