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

Devaluation of Unchosen Options: A Bayesian Account of the Provenance and Maintenance of Overly Optimistic Expectations. 未选择选项的贬值:对过度乐观预期的起源和维持的贝叶斯解释。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2020-07-01

Corey Yishan Zhou, Dalin Guo, Angela J Yu

{"title":"Devaluation of Unchosen Options: A Bayesian Account of the Provenance and Maintenance of Overly Optimistic Expectations.","authors":"Corey Yishan Zhou, Dalin Guo, Angela J Yu","doi":"","DOIUrl":"","url":null,"abstract":"Humans frequently overestimate the likelihood of desirable events while underestimating the likelihood of undesirable ones: a phenomenon known as unrealistic optimism. Previously, it was suggested that unrealistic optimism arises from asymmetric belief updating, with a relatively reduced coding of undesirable information. Prior studies have shown that a reinforcement learning (RL) model with asymmetric learning rates (greater for a positive prediction error than a negative prediction error) could account for unrealistic optimism in a bandit task, in particular the tendency of human subjects to persistently choosing a single option when there are multiple equally good options. Here, we propose an alternative explanation of such persistent behavior, by modeling human behavior using a Bayesian hidden Markov model, the Dynamic Belief Model (DBM). We find that DBM captures human choice behavior better than the previously proposed asymmetric RL model. Whereas asymmetric RL attains a measure of optimism by giving better-than-expected outcomes higher learning weights compared to worse-than-expected outcomes, DBM does so by progressively devaluing the unchosen options, thus placing a greater emphasis on choice history independent of reward outcome (e.g. an oft-chosen option might continue to be preferred even if it has not been particularly rewarding), which has broadly been shown to underlie sequential effects in a variety of behavioral settings. Moreover, previous work showed that the devaluation of unchosen options in DBM helps to compensate for a default assumption of environmental non-stationarity, thus allowing the decision-maker to both be more adaptive in changing environments and still obtain near-optimal performance in stationary environments. Thus, the current work suggests both a novel rationale and mechanism for persistent behavior in bandit tasks.","PeriodicalId":72634,"journal":{"name":"CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference","volume":"42 ","pages":"1682-1688"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39281170","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

Sheer Time Spent Expecting or Maintaining a Representation Facilitates Subsequent Retrieval during Sentence Processing. 在句子处理过程中，期望或保持表示所花费的时间有助于后续检索。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2020-07-01

Hossein Karimi, Michele Diaz, Eva Wittenberg

引用次数: 0

Memory integration into visual perception in infancy, childhood, and adulthood. 婴儿期、儿童期和成年期记忆与视觉知觉的整合。

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Pub Date : 2020-07-01

Sagi Jaffe-Dax, Christine Potter, Tiffany Leung, Casey Lew-Williams, Lauren L Emberson

引用次数: 0

The everyday statistics of objects and their names: How word learning gets its start. 物体及其名称的日常统计:单词学习是如何开始的。

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

Elizabeth M Clerkin, Linda B Smith

引用次数: 0

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