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Ali H, Gilani SO, Waris A, Shah UH, Khattak MAK, Khan MJ, Afzal N. Memorability-based multimedia analytics for robotic interestingness prediction system using trimmed Q-learning algorithm. Sci Rep 2023; 13:19799. [PMID: 37957144 PMCID: PMC10643645 DOI: 10.1038/s41598-023-44553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/10/2023] [Indexed: 11/15/2023] Open
Abstract
Mobile robots are increasingly employed in today's environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user's environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique's efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%.
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Affiliation(s)
- Hasnain Ali
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology, Robotics & AI, Islamabad, 44000, Pakistan.
| | - Syed Omer Gilani
- Department of Electrical, Computer, and Biomedical Engineering, Abu Dhabi University, Abu Dhabi, UAE
| | - Asim Waris
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology, Biomedical Engineering & Sciences, Islamabad, 44000, Pakistan
| | - Umer Hameed Shah
- Department of Mechanical Engineering and Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, UAE.
| | | | - Muhammad Jawad Khan
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology, Robotics & AI, Islamabad, 44000, Pakistan
| | - Namra Afzal
- Department of Biomedical Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
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2
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Hoshi A, Hirayama Y, Saito F, Ishiguro T, Suetani H, Kitajo K. Spatiotemporal consistency of neural responses to repeatedly presented video stimuli accounts for population preferences. Sci Rep 2023; 13:5532. [PMID: 37015982 PMCID: PMC10073227 DOI: 10.1038/s41598-023-31751-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 03/16/2023] [Indexed: 04/06/2023] Open
Abstract
Population preferences for video advertisements vary across short video clips. What underlies these differences? Repeatedly watching a video clip may produce a consistent spatiotemporal pattern of neural activity that is dependent on the individual and the stimulus. Moreover, such consistency may be associated with the degree of engagement and memory of individual viewers. Since the population preferences are associated with the engagement and memory of the individual viewers, the consistency observed in a smaller group of viewers can be a predictor of population preferences. To test the hypothesis, we measured the degree of inter-trial consistency in participants' electroencephalographic (EEG) responses to repeatedly presented television commercials. We observed consistency in the neural activity patterns across repetitive views and found that the similarity in the spatiotemporal patterns of neural responses while viewing popular television commercials predicts population preferences obtained from a large audience. Moreover, a regression model that used two datasets, including two separate groups of participants viewing different stimulus sets, showed good predictive performance in a leave-one-out cross-validation. These findings suggest that universal spatiotemporal patterns in EEG responses can account for population-level human behaviours.
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Affiliation(s)
- Ayaka Hoshi
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- KIRIN Central Research Institute, Research & Development Division, Kirin Holdings Company, Limited, 26-1-12-12 Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Yuya Hirayama
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Fumihiro Saito
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Tatsuji Ishiguro
- KIRIN Central Research Institute, Research & Development Division, Kirin Holdings Company, Limited, 26-1-12-12 Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Hiromichi Suetani
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Faculty of Science and Technology, Oita University, 700 Dannoharu, Oita, 870-1192, Japan
| | - Keiichi Kitajo
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, 38 Nishigonaka, Myodaiji, Okazaki, Aichi, 444-8585, Japan.
- Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan.
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Kyle-Davidson C, Zhou EY, Walther DB, Bors AG, Evans KK. Characterising and dissecting human perception of scene complexity. Cognition 2023; 231:105319. [PMID: 36399902 DOI: 10.1016/j.cognition.2022.105319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 11/17/2022]
Abstract
Humans can effortlessly assess the complexity of the visual stimuli they encounter. However, our understanding of how we do this, and the relevant factors that result in our perception of scene complexity remain unclear; especially for the natural scenes in which we are constantly immersed. We introduce several new datasets to further understanding of human perception of scene complexity. Our first dataset (VISC-C) contains 800 scenes and 800 corresponding two-dimensional complexity annotations gathered from human observers, allowing exploration for how complexity perception varies across a scene. Our second dataset, (VISC-CI) consists of inverted scenes (reflection on the horizontal axis) with corresponding complexity maps, collected from human observers. Inverting images in this fashion is associated with destruction of semantic scene characteristics when viewed by humans, and hence allows analysis of the impact of semantics on perceptual complexity. We analysed perceptual complexity from both a single-score and a two-dimensional perspective, by evaluating a set of calculable and observable perceptual features based upon grounded psychological research (clutter, symmetry, entropy and openness). We considered these factors' relationship to complexity via hierarchical regressions analyses, tested the efficacy of various neural models against our datasets, and validated our perceptual features against a large and varied complexity dataset consisting of nearly 5000 images. Our results indicate that both global image properties and semantic features are important for complexity perception. We further verified this by combining identified perceptual features with the output of a neural network predictor capable of extracting semantics, and found that we could increase the amount of explained human variance in complexity beyond that of low-level measures alone. Finally, we dissect our best performing prediction network, determining that artificial neurons learn to extract both global image properties and semantic details from scenes for complexity prediction. Based on our experimental results, we propose the "dual information" framework of complexity perception, hypothesising that humans rely on both low-level image features and high-level semantic content to evaluate the complexity of images.
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Affiliation(s)
| | | | - Dirk B Walther
- University of Toronto, Department of Psychology, Toronto ON, M5S 1A1, Canada
| | - Adrian G Bors
- University of York, Department of Computer Science, York, YO10 5GH, UK
| | - Karla K Evans
- University of York, Department of Psychology, York, YO10 5DD, UK
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Abstract
Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% ([Formula: see text], [Formula: see text]). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% ([Formula: see text], [Formula: see text]). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.
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5
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Visual consequent stimulus complexity affects performance in audiovisual associative learning. Sci Rep 2022; 12:17793. [PMID: 36272988 PMCID: PMC9587981 DOI: 10.1038/s41598-022-22880-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/20/2022] [Indexed: 01/19/2023] Open
Abstract
In associative learning (AL), cues and/or outcome events are coupled together. AL is typically tested in visual learning paradigms. Recently, our group developed various AL tests based on the Rutgers Acquired Equivalence Test (RAET), both visual and audiovisual, keeping the structure and logic of RAET but with different stimuli. In this study, 55 volunteers were tested in two of our audiovisual tests, SoundFace (SF) and SoundPolygon (SP). The antecedent stimuli in both tests are sounds, and the consequent stimuli are images. The consequents in SF are cartoon faces, while in SP, they are simple geometric shapes. The aim was to test how the complexity of the applied consequent stimuli influences performance regarding the various aspects of learning the tests assess (stimulus pair learning, retrieval, and generalization of the previously learned associations to new but predictable stimulus pairs). In SP, behavioral performance was significantly poorer than in SF, and the reaction times were significantly longer, for all phases of the test. The results suggest that audiovisual associative learning is significantly influenced by the complexity of the consequent stimuli.
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Rybář M, Daly I. Neural decoding of semantic concepts: A systematic literature review. J Neural Eng 2022; 19. [PMID: 35344941 DOI: 10.1088/1741-2552/ac619a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/27/2022] [Indexed: 11/12/2022]
Abstract
Objective Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding. Approach We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity. Results Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area. Significance Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.
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Affiliation(s)
- Milan Rybář
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ian Daly
- University of Essex, School of Computer Science and Electronic Engineering, Wivenhoe Park, Colchester, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Eördegh G, Tót K, Kelemen A, Kiss Á, Bodosi B, Hegedűs A, Lazsádi A, Hertelendy Á, Kéri S, Nagy A. The influence of stimulus complexity on the effectiveness of visual associative learning. Neuroscience 2022; 487:26-34. [PMID: 35122873 DOI: 10.1016/j.neuroscience.2022.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 10/19/2022]
Abstract
Visually guided equivalence learning is a special type of associative learning, which can be evaluated using the Rutgers Acquired Equivalence Test (RAET) among other tests. RAET applies complex stimuli (faces and colored fish) between which the test subjects build associations. The complexity of these stimuli offers the test subject several clues that might ease association learning. To reduce the number of such clues, we developed an equivalence learning test (Polygon), which is structured as RAET but uses simple grayscale geometric shapes instead of faces and colored fish. In this study, we compared the psychophysical performances of the same healthy volunteers in both RAET and Polygon test. Equivalence learning, which is a basal ganglia-associated form of learning, appears to be strongly influenced by the complexity of the visual stimuli. The simple geometric shapes were associated with poor performance as compared to faces and fish. However, the difference in stimulus complexity did not affect performance in the retrieval and transfer parts of the test phase, which are assumed to be mediated by the hippocampi.
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Affiliation(s)
- Gabriella Eördegh
- Faculty of Health Sciences and Social Studies, University of Szeged, Szeged, Hungary
| | - Kálmán Tót
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - András Kelemen
- Department of Applied Informatics, University of Szeged, Szeged, Hungary
| | - Ádám Kiss
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Balázs Bodosi
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - András Hegedűs
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Anna Lazsádi
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Ábel Hertelendy
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Szabolcs Kéri
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Attila Nagy
- Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary.
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8
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Ruiz-Rizzo AL, Pruitt PJ, Finke K, Müller HJ, Damoiseaux JS. Lower-Resolution Retrieval of Scenes in Older Adults With Subjective Cognitive Decline. Arch Clin Neuropsychol 2021; 37:408-422. [PMID: 34342647 PMCID: PMC8865194 DOI: 10.1093/arclin/acab061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/09/2021] [Accepted: 07/05/2021] [Indexed: 11/21/2022] Open
Abstract
Objective Scenes with more perceptual detail can help detect subtle memory deficits more than scenes with less detail. Here, we investigated whether older adults with subjective cognitive decline (SCD) show less brain activation and more memory deficits to scenes with more (vs. scenes with less) perceptual detail compared to controls (CON). Method In 37 healthy older adults (SCD: 16), we measured blood oxygenation level-dependent-functional magnetic resonance imaging during encoding and behavioral performance during retrieval. Results During encoding, higher activation to scenes with more (vs. less) perceptual detail in the parahippocampal place area predicted better memory performance in SCD and CON. During retrieval, superior performance for new scenes with more (vs. less) perceptual detail was significantly more pronounced in CON than inSCD. Conclusions Together, these results suggest a present, but attenuated benefit from perceptual detail for memory retrieval in SCD. Memory complaints in SCD might, thus, refer to a decreased availability of perceptual detail of previously encoded stimuli.
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Affiliation(s)
- Adriana L Ruiz-Rizzo
- Department of Psychology, General and Experimental Psychology Unit, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Patrick J Pruitt
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Kathrin Finke
- Department of Psychology, General and Experimental Psychology Unit, Ludwig-Maximilians-Universität München, Munich, Germany.,Hans-Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Hermann J Müller
- Department of Psychology, General and Experimental Psychology Unit, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jessica S Damoiseaux
- Institute of Gerontology, Wayne State University, Detroit, MI, USA.,Department of Psychology, Wayne State University, Detroit, MI, USA
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King ML, Groen IIA, Steel A, Kravitz DJ, Baker CI. Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images. Neuroimage 2019; 197:368-382. [PMID: 31054350 PMCID: PMC6591094 DOI: 10.1016/j.neuroimage.2019.04.079] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 03/26/2019] [Accepted: 04/29/2019] [Indexed: 12/20/2022] Open
Abstract
Numerous factors have been reported to underlie the representation of complex images in high-level human visual cortex, including categories (e.g. faces, objects, scenes), animacy, and real-world size, but the extent to which this organization reflects behavioral judgments of real-world stimuli is unclear. Here, we compared representations derived from explicit behavioral similarity judgments and ultra-high field (7T) fMRI of human visual cortex for multiple exemplars of a diverse set of naturalistic images from 48 object and scene categories. While there was a significant correlation between similarity judgments and fMRI responses, there were striking differences between the two representational spaces. Behavioral judgements primarily revealed a coarse division between man-made (including humans) and natural (including animals) images, with clear groupings of conceptually-related categories (e.g. transportation, animals), while these conceptual groupings were largely absent in the fMRI representations. Instead, fMRI responses primarily seemed to reflect a separation of both human and non-human faces/bodies from all other categories. Further, comparison of the behavioral and fMRI representational spaces with those derived from the layers of a deep neural network (DNN) showed a strong correspondence with behavior in the top-most layer and with fMRI in the mid-level layers. These results suggest a complex relationship between localized responses in high-level visual cortex and behavioral similarity judgments - each domain reflects different properties of the images, and responses in high-level visual cortex may correspond to intermediate stages of processing between basic visual features and the conceptual categories that dominate the behavioral response.
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Affiliation(s)
- Marcie L King
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA; Department of Psychological and Brain Sciences, University of Iowa, W311 Seashore Hall, Iowa City, IA, 52242, USA
| | - Iris I A Groen
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA; Department of Psychology, New York University, 6 Washington Place, New York, NY, 10003, USA
| | - Adam Steel
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dwight J Kravitz
- Department of Psychology, George Washington University, 2125 G St. NW, Washington, DC, 20008, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
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Leal SL, Ferguson LA, Harrison TM, Jagust WJ. Development of a mnemonic discrimination task using naturalistic stimuli with applications to aging and preclinical Alzheimer's disease. ACTA ACUST UNITED AC 2019; 26:219-228. [PMID: 31209116 PMCID: PMC6581010 DOI: 10.1101/lm.048967.118] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/30/2019] [Indexed: 11/24/2022]
Abstract
Most tasks test memory within the same day, however, most forgetting occurs after 24 h. Further, testing memory for simple words or objects does not mimic real-world memory experiences. We designed a memory task showing participants video clips of everyday kinds of experiences, including positive, negative, and neutral stimuli, and tested memory immediately and 24 h later. During the memory test, we included repeated and similar stimuli to tax both target recognition and lure discrimination ability. Participants' memory was worse after 24 h, especially the ability to discriminate similar stimuli. Emotional videos were better remembered when tested immediately, however, after 24 h we find gist versus detail trade-offs in emotional forgetting. We also applied this paradigm to a sample of cognitively normal older adults that also underwent amyloid and tau PET imaging. We found that older adults performed worse on the task compared to young adults. While both young and older adults showed similar patterns of forgetting of repeated emotional and neutral clips, older adults showed preserved neutral compared to emotional discrimination after 24 h. Further, lure discrimination performance correlated with medial temporal lobe tau in older adults with preclinical Alzheimer's disease. These results suggest factors such as time between encoding and retrieval, emotion, and similarity influence memory performance and should be considered when examining memory performance for an accurate picture of memory function and dysfunction.
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Affiliation(s)
- Stephanie L Leal
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA
| | - Lorena A Ferguson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720, USA.,Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
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Güçlütürk Y, van Lier R. Decomposing Complexity Preferences for Music. Front Psychol 2019; 10:674. [PMID: 31001167 PMCID: PMC6457315 DOI: 10.3389/fpsyg.2019.00674] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/11/2019] [Indexed: 11/29/2022] Open
Abstract
Recently, we demonstrated complexity as a major factor for explaining individual differences in visual preferences for abstract digital art. We have shown that participants could best be separated into two groups based on their liking ratings for abstract digital art comprising geometric patterns: one group with a preference for complex visual patterns and another group with a preference for simple visual patterns. In the present study, building up on these results, we extended our investigations for complexity preferences from highly controlled visual stimuli to ecologically valid stimuli in the auditory modality. Similar to visual preferences, we showed that music preferences are highly influenced by stimulus complexity. We demonstrated this by clustering a large number of participants based on their liking ratings for song excerpts from various musical genres. Our results show that, based on their liking ratings, participants can best be separated into two groups: one group with a preference for more complex songs and another group with a preference for simpler songs. Finally, we considered various demographic and personal characteristics to explore differences between the groups, and reported that at least for the current data set age and gender to be significant factors separating the two groups.
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Affiliation(s)
- Yaǧmur Güçlütürk
- Artificial Cognitive Systems Lab, Cognitive Artificial Intelligence Department, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Rob van Lier
- Perception and Awareness Lab, Cognitive Psychology Department, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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12
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Groen IIA, Jahfari S, Seijdel N, Ghebreab S, Lamme VAF, Scholte HS. Scene complexity modulates degree of feedback activity during object detection in natural scenes. PLoS Comput Biol 2018; 14:e1006690. [PMID: 30596644 PMCID: PMC6329519 DOI: 10.1371/journal.pcbi.1006690] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 01/11/2019] [Accepted: 12/01/2018] [Indexed: 02/06/2023] Open
Abstract
Selective brain responses to objects arise within a few hundreds of milliseconds of neural processing, suggesting that visual object recognition is mediated by rapid feed-forward activations. Yet disruption of neural responses in early visual cortex beyond feed-forward processing stages affects object recognition performance. Here, we unite these discrepant findings by reporting that object recognition involves enhanced feedback activity (recurrent processing within early visual cortex) when target objects are embedded in natural scenes that are characterized by high complexity. Human participants performed an animal target detection task on natural scenes with low, medium or high complexity as determined by a computational model of low-level contrast statistics. Three converging lines of evidence indicate that feedback was selectively enhanced for high complexity scenes. First, functional magnetic resonance imaging (fMRI) activity in early visual cortex (V1) was enhanced for target objects in scenes with high, but not low or medium complexity. Second, event-related potentials (ERPs) evoked by target objects were selectively enhanced at feedback stages of visual processing (from ~220 ms onwards) for high complexity scenes only. Third, behavioral performance for high complexity scenes deteriorated when participants were pressed for time and thus less able to incorporate the feedback activity. Modeling of the reaction time distributions using drift diffusion revealed that object information accumulated more slowly for high complexity scenes, with evidence accumulation being coupled to trial-to-trial variation in the EEG feedback response. Together, these results suggest that while feed-forward activity may suffice to recognize isolated objects, the brain employs recurrent processing more adaptively in naturalistic settings, using minimal feedback for simple scenes and increasing feedback for complex scenes.
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Affiliation(s)
- Iris I. A. Groen
- New York University, Department of Psychology, New York, New York, United States of America
| | - Sara Jahfari
- Spinoza Centre for Neuroimaging, Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, The Netherlands
- University of Amsterdam, Department of Psychology, Section Brain and Cognition, Amsterdam, The Netherlands
| | - Noor Seijdel
- University of Amsterdam, Department of Psychology, Section Brain and Cognition, Amsterdam, The Netherlands
| | - Sennay Ghebreab
- University of Amsterdam, Department of Psychology, Section Brain and Cognition, Amsterdam, The Netherlands
- University of Amsterdam, Department of Informatics, Intelligent Systems Lab, Amsterdam, The Netherlands
| | - Victor A. F. Lamme
- University of Amsterdam, Department of Psychology, Section Brain and Cognition, Amsterdam, The Netherlands
| | - H. Steven Scholte
- University of Amsterdam, Department of Psychology, Section Brain and Cognition, Amsterdam, The Netherlands
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14
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