CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference最新文献_第3页

Belief dynamics extraction. 信念动力学提取。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2019-07-01

Arun Kumar, Zhengwei Wu, Xaq Pitkow, Paul Schrater

{"title":"Belief dynamics extraction.","authors":"Arun Kumar, Zhengwei Wu, Xaq Pitkow, Paul Schrater","doi":"","DOIUrl":"","url":null,"abstract":"Animal behavior is not driven simply by its current observations, but is strongly influenced by internal states. Estimating the structure of these internal states is crucial for understanding the neural basis of behavior. In principle, internal states can be estimated by inverting behavior models, as in inverse model-based Reinforcement Learning. However, this requires careful parameterization and risks model-mismatch to the animal. Here we take a data-driven approach to infer latent states directly from observations of behavior, using a partially observable switching semi-Markov process. This process has two elements critical for capturing animal behavior: it captures non-exponential distribution of times between observations, and transitions between latent states depend on the animal's actions, features that require more complex non-markovian models to represent. To demonstrate the utility of our approach, we apply it to the observations of a simulated optimal agent performing a foraging task, and find that latent dynamics extracted by the model has correspondences with the belief dynamics of the agent. Finally, we apply our model to identify latent states in the behaviors of monkey performing a foraging task, and find clusters of latent states that identify periods of time consistent with expectant waiting. This data-driven behavioral model will be valuable for inferring latent cognitive states, and thereby for measuring neural representations of those states.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2019 ","pages":"2058-2064"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754614/pdf/nihms-1654209.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39103239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How do infants start learning object names in a sea of clutter? 婴儿是如何在杂乱的海洋中开始学习物体名称的?

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2019-07-01

Hadar Karmazyn Raz, Drew H Abney, David Crandall, Chen Yu, Linda B Smith

{"title":"How do infants start learning object names in a sea of clutter?","authors":"Hadar Karmazyn Raz, Drew H Abney, David Crandall, Chen Yu, Linda B Smith","doi":"","DOIUrl":"","url":null,"abstract":"Infants are powerful learners. A large corpus of experimental paradigms demonstrate that infants readily learn distributional cues of name-object co-occurrences. But infants' natural learning environment is cluttered: every heard word has multiple competing referents in view. Here we ask how infants start learning name-object co-occurrences in naturalistic learning environments that are cluttered and where there is much visual ambiguity. The framework presented in this paper integrates a naturalistic behavioral study and an application of a machine learning model. Our behavioral findings suggest that in order to start learning object names, infants and their parents consistently select a set of a few objects to play with during a set amount of time. What emerges is a frequency distribution of a few toys that approximates a Zipfian frequency distribution of objects for learning. We find that a machine learning model trained with a Zipf-like distribution of these object images outperformed the model trained with a uniform distribution. Overall, these findings suggest that to overcome referential ambiguity in clutter, infants may be selecting just a few toys allowing them to learn many distributional cues about a few name-object pairs.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2019 ","pages":"521-526"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903936/pdf/nihms-1673092.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25406160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Synergy of Passive and Active Learning Modes in Adaptive Perceptual Learning. 被动和主动学习模式在适应性知觉学习中的协同作用。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2019-07-01

Everett Mettler, Austin S Phillips, Christine M Massey, Timothy Burke, Patrick Garrigan, Philip J Kellman

{"title":"The Synergy of Passive and Active Learning Modes in Adaptive Perceptual Learning.","authors":"Everett Mettler, Austin S Phillips, Christine M Massey, Timothy Burke, Patrick Garrigan, Philip J Kellman","doi":"","DOIUrl":"","url":null,"abstract":"Adaptive learning systems that generate spacing intervals based on learner performance enhance learning efficiency and retention (Mettler, Massey & Kellman, 2016). Recent research in factual learning suggests that initial blocks of passive trials, where learners observe correct answers without overtly responding, produce greater learning than passive or active trials alone (Mettler, Massey, Burke, Garrigan & Kellman, 2018). Here we tested whether this passive + active advantage generalizes beyond factual learning to perceptual learning. Participants studied and classified images of butterfly genera using either: 1) Passive Only presentations, 2) Passive Initial Blocks followed by active, adaptive scheduling, 3) Passive Initial Category Exemplar followed by active, adaptive scheduling, or 4) Active Only learning. We found an advantage for combinations of active and passive presentations over Passive Only or Active Only presentations. Passive trials presented in initial blocks showed the best performance, paralleling earlier findings in factual learning. Combining active and passive learning produces greater learning gains than either alone, and these effects occur for diverse forms of learning, including perceptual learning.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2019 ","pages":"2351-2357"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138178174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Meta-Analysis of Infants' Mispronunciation Sensitivity Development. 婴儿发音错误敏感性发展的Meta分析。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2018-07-01

Katie Von Holzen, Christina Bergmann

引用次数: 0

A Case of Divergent Predictions Made by Delta and Decay Rule Learning Models. Delta和衰减规则学习模型的不同预测案例。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2018-07-01

Darrell A Worthy, A Ross Otto, Astin C Cornwall, Hilary J Don, Tyler Davis

{"title":"A Case of Divergent Predictions Made by Delta and Decay Rule Learning Models.","authors":"Darrell A Worthy, A Ross Otto, Astin C Cornwall, Hilary J Don, Tyler Davis","doi":"","DOIUrl":"","url":null,"abstract":"The Delta and Decay rules are two learning rules used to update expected values in reinforcement learning (RL) models. The delta rule learns average rewards, whereas the decay rule learns cumulative rewards for each option. Participants learned to select between pairs of options that had reward probabilities of .65 (option A) versus .35 (option B) or .75 (option C) versus .25 (option D) on separate trials in a binary-outcome choice task. Crucially, during training there were twice as AB trials as CD trials, therefore participants experienced more cumulative reward from option A even though option C had a higher average reward rate (.75 versus .65). Participants then decided between novel combinations of options (e.g, A versus C). The Decay model predicted more A choices, but the Delta model predicted more C choices, because those respective options had higher cumulative versus average reward values. Results were more in line with the Decay model's predictions. This suggests that people may retrieve memories of cumulative reward to compute expected value instead of learning average rewards for each option.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2018 ","pages":"1175-1180"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086699/pdf/nihms-997021.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38941524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Semantic Fluency Data as Search on a Semantic Network. 语义流畅性数据的语义网络搜索建模。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2017-07-01

Jeffrey C Zemla, Joseph L Austerweil

引用次数: 0

Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks. 无监督深度神经网络中音位分类的分布学习分析。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2016-08-01

Okko Räsänen, Tasha Nagamine, Nima Mesgarani

{"title":"Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks.","authors":"Okko Räsänen, Tasha Nagamine, Nima Mesgarani","doi":"","DOIUrl":"","url":null,"abstract":"Infants' speech perception adapts to the phonemic categories of their native language, a process assumed to be driven by the distributional properties of speech. This study investigates whether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable of learning phoneme-like representations of speech in an unsupervised manner. We trained DNNs with unlabeled and labeled speech and analyzed the activations of each layer with respect to the phones in the input segments. The analyses reveal that the emergence of phonemic invariance in DNNs is dependent on the availability of phonemic labeling of the input during the training. No increased phonemic selectivity of the hidden layers was observed in the purely unsupervised networks despite successful learning of low-dimensional representations for speech. This suggests that additional learning constraints or more sophisticated models are needed to account for the emergence of phone-like categories in distributional learning operating on natural speech.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2016 ","pages":"1757-1762"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5775908/pdf/nihms850015.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35759267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical Word Learning is a Continuous Process: Evidence from the Human Simulation Paradigm. 统计词汇学习是一个持续的过程：来自人类模拟范例的证据

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2015-07-01

Yayun Zhang, Daniel Yurovsky, Chen Yu

引用次数: 0

Visual-motor coordination in natural reaching of young children and adults. 幼儿和成人自然伸手时的视觉运动协调。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2015-07-01

John M Franchak, Chen Yu

引用次数: 0

Linking Joint Attention with Hand-Eye Coordination - A Sensorimotor Approach to Understanding Child-Parent Social Interaction. 将共同注意力与手眼协调联系起来--用感觉运动方法理解儿童与父母的社交互动。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2015-07-01

Chen Yu, Linda B Smith

{"title":"Linking Joint Attention with Hand-Eye Coordination - A Sensorimotor Approach to Understanding Child-Parent Social Interaction.","authors":"Chen Yu, Linda B Smith","doi":"","DOIUrl":"","url":null,"abstract":"An understanding of human collaboration requires a level of analysis that concentrates on sensorimotor behaviors in which the behaviors of social partners continually adjust to and influence each other. A suite of individual differences in partners' ability to both read the social cues of others and to send effective behavioral cues to others create dyad differences in joint attention and joint action. The present paper shows that infant and dyad differences in hand-eye coordination predict dyad differences in joint attention. In the study reported here, 51 toddlers and their parents wore head-mounted eye-trackers as they played together with objects. This method allowed us to track the gaze direction of each participant to determine when they attended to the same object. We found that physically active toddlers align their looking behavior with their parent, and achieve a high proportion of time spent jointly attending to the same object in toy play. However, joint attention bouts in toy play don't depend on gaze following but rather on the coordination of gaze with hand actions on objects. Both infants and parents attend to their partner's object manipulations and in so doing fixate the object visually attended by their partner. Thus, the present results provide evidence for another pathway to joint attention - hand following instead of gaze following. Moreover, dyad differences in joint attention are associated with dyad differences in hand following, and specifically parents' and infants' manual activities on objects and the within- and between-partner coordination of hands and eyes during parent-infant interactions. In particular, infants' manual actions on objects play a critical role in organizing parent-infant joint attention to an object.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"2015 ","pages":"2763-2768"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722468/pdf/nihms776008.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35241784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0