1
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Ficco L, Li C, Kaufmann JM, Schweinberger SR, Kovács GZ. Investigating the neural effects of typicality and predictability for face and object stimuli. PLoS One 2024; 19:e0293781. [PMID: 38776350 PMCID: PMC11111078 DOI: 10.1371/journal.pone.0293781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/08/2024] [Indexed: 05/24/2024] Open
Abstract
The brain calibrates itself based on the past stimulus diet, which makes frequently observed stimuli appear as typical (as opposed to uncommon stimuli, which appear as distinctive). Based on predictive processing theory, the brain should be more "prepared" for typical exemplars, because these contain information that has been encountered frequently, allowing it to economically represent items of that category. Thus, one could ask whether predictability and typicality of visual stimuli interact, or rather act in an additive manner. We adapted the design by Egner and colleagues (2010), who used cues to induce expectations about stimulus category (face vs. chair) occurrence during an orthogonal inversion detection task. We measured BOLD responses with fMRI in 35 participants. First, distinctive stimuli always elicited stronger responses than typical ones in all ROIs, and our whole-brain directional contrasts for the effects of typicality and distinctiveness converge with previous findings. Second and importantly, we could not replicate the interaction between category and predictability reported by Egner et al. (2010), which casts doubt on whether cueing designs are ideal to elicit reliable predictability effects. Third, likely as a consequence of the lack of predictability effects, we found no interaction between predictability and typicality in any of the four tested regions (bilateral fusiform face areas, lateral occipital complexes) when considering both categories, nor in the whole brain. We discuss the issue of replicability in neuroscience and sketch an agenda for how future studies might address the same question.
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Affiliation(s)
- Linda Ficco
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
- International Max-Planck Research School for the Science of Human History, Jena, Germany
| | - Chenglin Li
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jürgen M. Kaufmann
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
| | - Stefan R. Schweinberger
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
- International Max-Planck Research School for the Science of Human History, Jena, Germany
| | - Gyula Z. Kovács
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
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2
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Delhaye E, Coco MI, Bahri MA, Raposo A. Typicality in the brain during semantic and episodic memory decisions. Neuropsychologia 2023; 184:108529. [PMID: 36898662 DOI: 10.1016/j.neuropsychologia.2023.108529] [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: 03/28/2022] [Revised: 11/25/2022] [Accepted: 03/02/2023] [Indexed: 03/11/2023]
Abstract
Concept typicality is a key semantic dimension supporting the categorical organization of items based on their features, such that typical items share more features with other members of their category than atypical items, which are more distinctive. Typicality effects manifest in better accuracy and faster response times during categorization tasks, but higher performance for atypical items in episodic memory tasks, due to their distinctiveness. At a neural level, typicality has been linked to the anterior temporal lobe (ATL) and the inferior frontal gyrus (IFG) in semantic decision tasks, but patterns of brain activity during episodic memory tasks remain to be understood. We investigated the neural correlates of typicality in semantic and episodic memory to determine the brain regions associated with semantic typicality and uncover effects arising when items are reinstated during retrieval. In an fMRI study, 26 healthy young subjects first performed a category verification task on words representing typical and atypical concepts (encoding), and then completed a recognition memory task (retrieval). In line with previous literature, we observed higher accuracy and faster response times for typical items in the category verification task, while atypical items were better recognized in the episodic memory task. During category verification, univariate analyses revealed a greater involvement of the angular gyrus for typical items and the inferior frontal gyrus for atypical items. During the correct recognition of old items, regions belonging to the core recollection network were activated. We then compared the similarity of the representations from encoding to retrieval (ERS) using Representation Similarity Analyses. Results showed that typical items were reinstated more than atypical ones in several regions including the left precuneus and left anterior temporal lobe (ATL). This suggests that the correct retrieval of typical items requires finer-grained processing, evidenced by greater item-specific reinstatement, which is needed to resolve their confusability with other members of the category due to their higher feature similarity. Our findings confirm the centrality of the ATL in the processing of typicality while extending it to memory retrieval.
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Affiliation(s)
- Emma Delhaye
- GIGA-CRC IVI, Liege University, Belgium; CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Portugal.
| | - Moreno I Coco
- CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Portugal; Department of Psychology, Sapienza, University of Rome, Italy; IRCCS Santa Lucia, Rome, Italy
| | | | - Ana Raposo
- CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Portugal
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3
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Labek K, Sittenberger E, Kienhöfer V, Rabl L, Messina I, Schurz M, Stingl JC, Viviani R. The gradient model of brain organization in decisions involving “empathy for pain”. Cereb Cortex 2022; 33:5839-5850. [PMID: 36537039 DOI: 10.1093/cercor/bhac464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 12/24/2022] Open
Abstract
Abstract
Influential models of cortical organization propose a close relationship between heteromodal association areas and highly connected hubs in the default mode network. The “gradient model” of cortical organization proposes a close relationship between these areas and highly connected hubs in the default mode network, a set of cortical areas deactivated by demanding tasks. Here, we used a decision-making task and representational similarity analysis with classic “empathy for pain” stimuli to probe the relationship between high-level representations of imminent pain in others and these areas. High-level representations were colocalized with task deactivations or the transitions from activations to deactivations. These loci belonged to 2 groups: those that loaded on the high end of the principal cortical gradient and were associated by meta-analytic decoding with the default mode network, and those that appeared to accompany functional repurposing of somatosensory cortex in the presence of visual stimuli. These findings suggest that task deactivations may set out cortical areas that host high-level representations. We anticipate that an increased understanding of the cortical correlates of high-level representations may improve neurobiological models of social interactions and psychopathology.
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Affiliation(s)
- Karin Labek
- University of Innsbruck Institute of Psychology, , Universitätsstraße 5-7, 6020 Innsbruck , Austria
| | - Elisa Sittenberger
- University of Ulm Psychiatry and Psychotherapy Clinic III, , Leimgrubenweg 12, 89075 Ulm , Germany
| | - Valerie Kienhöfer
- University of Innsbruck Institute of Psychology, , Universitätsstraße 5-7, 6020 Innsbruck , Austria
- University of Ulm Psychiatry and Psychotherapy Clinic III, , Leimgrubenweg 12, 89075 Ulm , Germany
| | - Luna Rabl
- University of Innsbruck Institute of Psychology, , Universitätsstraße 5-7, 6020 Innsbruck , Austria
- University of Ulm Psychiatry and Psychotherapy Clinic III, , Leimgrubenweg 12, 89075 Ulm , Germany
| | - Irene Messina
- University of Ulm Psychiatry and Psychotherapy Clinic III, , Leimgrubenweg 12, 89075 Ulm , Germany
- Scienze e Tecniche Psicologiche,Universitas Mercatorum , Piazza Mattei 10, 00186 Rome , Italy
| | - Matthias Schurz
- University of Innsbruck Institute of Psychology, , Universitätsstraße 5-7, 6020 Innsbruck , Austria
- University of Innsbruck Digital Science Center (DiSC), , Innrain 15, 6020 Innsbruck , Austria
| | - Julia C Stingl
- University Clinic Aachen Clinical Pharmacology, , Wendlingweg 2, 52074 Aachen , Germany
| | - Roberto Viviani
- University of Innsbruck Institute of Psychology, , Universitätsstraße 5-7, 6020 Innsbruck , Austria
- University of Ulm Psychiatry and Psychotherapy Clinic III, , Leimgrubenweg 12, 89075 Ulm , Germany
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4
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Sun M, Hu L, Xin X, Zhang X. Neural Hierarchy of Color Categorization: From Prototype Encoding to Boundary Encoding. Front Neurosci 2021; 15:679627. [PMID: 34349615 PMCID: PMC8327959 DOI: 10.3389/fnins.2021.679627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/16/2021] [Indexed: 11/13/2022] Open
Abstract
A long-standing debate exists on how our brain assigns the fine-grained perceptual representation of color into discrete color categories. Recent functional magnetic resonance imaging (fMRI) studies have identified several regions as the candidate loci of color categorization, including the visual cortex, language-related areas, and non-language-related frontal regions, but the evidence is mixed. Distinct from most studies that emphasized the representational differences between color categories, the current study focused on the variability among members within a category (e.g., category prototypes and boundaries) to reveal category encoding in the brain. We compared and modeled brain activities evoked by color stimuli with varying distances from the category boundary in an active categorization task. The frontal areas, including the inferior and middle frontal gyri, medial superior frontal cortices, and insular cortices, showed larger responses for colors near the category boundary than those far from the boundary. In addition, the visual cortex encodes both within-category variability and cross-category differences. The left V1 in the calcarine showed greater responses to colors at the category center than to those far from the boundary, and the bilateral V4 showed enhanced responses for colors at the category center as well as colors around the boundary. The additional representational similarity analyses (RSA) revealed that the bilateral insulae and V4a carried information about cross-category differences, as cross-category colors exhibited larger dissimilarities in brain patterns than within-category colors. Our study suggested a hierarchically organized network in the human brain during active color categorization, with frontal (both lateral and medial) areas supporting domain-general decisional processes and the visual cortex encoding category structure and differences, likely due to top-down modulation.
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Affiliation(s)
- Mengdan Sun
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Luming Hu
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, National Demonstration Center for Experimental Psychology Education, Beijing Normal University, Beijing, China
| | - Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Xuemin Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, National Demonstration Center for Experimental Psychology Education, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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5
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Hennings AC, Lewis-Peacock JA, Dunsmoor JE. Emotional learning retroactively enhances item memory but distorts source attribution. Learn Mem 2021; 28:178-186. [PMID: 34011514 PMCID: PMC8139636 DOI: 10.1101/lm.053371.120] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 03/24/2021] [Indexed: 01/06/2023]
Abstract
An adaptive memory system should prioritize information surrounding a powerful learning event that may prove useful for predicting future meaningful events. The behavioral tagging hypothesis provides a mechanistic framework to interpret how weak experiences persist as durable memories through temporal association with a strong experience. Memories are composed of multiple elements, and different mnemonic aspects of the same experience may be uniquely affected by mechanisms that retroactively modulate a weakly encoded memory. Here, we investigated how emotional learning affects item and source memory for related events encoded close in time. Participants encoded trial-unique category exemplars before, during, and after Pavlovian fear conditioning. Selective retroactive enhancements in 24-h item memory were accompanied by a bias to misattribute items to the temporal context of fear conditioning. The strength of this source memory bias correlated with participants' retroactive item memory enhancement, and source misattribution to the emotional context predicted whether items were remembered overall. In the framework of behavioral tagging: Memory attribution was biased to the temporal context of the stronger event that provided the putative source of memory stabilization for the weaker event. We additionally found that fear conditioning selectively and retroactively enhanced stimulus typicality ratings for related items, and that stimulus typicality also predicted overall item memory. Collectively, these results provide new evidence that items related to emotional learning are misattributed to the temporal context of the emotional event and judged to be more representative of their semantic category. Both processes may facilitate memory retrieval for related events encoded close in time.
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Affiliation(s)
- Augustin C Hennings
- Institute for Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, Department of Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
| | - Jarrod A Lewis-Peacock
- Institute for Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, Department of Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, Austin, Texas 78712, USA
| | - Joseph E Dunsmoor
- Institute for Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, Department of Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, Austin, Texas 78712, USA
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6
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Li R, Perrachione TK, Tourville JA, Kiran S. Representation of semantic typicality in brain activation in healthy adults and individuals with aphasia: A multi-voxel pattern analysis. Neuropsychologia 2021; 158:107893. [PMID: 34022187 DOI: 10.1016/j.neuropsychologia.2021.107893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 03/31/2021] [Accepted: 05/13/2021] [Indexed: 11/28/2022]
Abstract
This study aimed to investigate brain regions that show different activation patterns between semantically typical and atypical items in both healthy adults and individuals with aphasia (PWA). Eighteen neurologically healthy adults and twenty-one PWA participated in an fMRI semantic feature verification task that included typical and atypical stimuli from five different semantic categories. A whole-brain searchlight multi-voxel pattern analysis (MVPA) was conducted to classify brain activation patterns between typical and atypical conditions in each participant group separately. Behavioral responses were faster and more accurate for typical vs. atypical items across both groups. The searchlight MVPA identified two significant clusters in healthy adults: left middle occipital gyrus and right calcarine cortex, but no significant clusters were found in PWA. A follow-up analysis in PWA revealed a significant association between neural classification of semantic typicality in the left middle occipital gyrus and reaction times in the fMRI task. When the typicality effect was examined for each semantic category at the univariate level, significance was identified in the visual cortex for fruits in both groups of participants. These findings suggest that semantic typicality was modulated in the visual cortex in healthy individuals, but to a lesser extent in the same region in PWA.
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Affiliation(s)
- Ran Li
- Department of Speech, Language & Hearing Sciences, Boston University, Boston, MA, USA.
| | - Tyler K Perrachione
- Department of Speech, Language & Hearing Sciences, Boston University, Boston, MA, USA
| | - Jason A Tourville
- Department of Speech, Language & Hearing Sciences, Boston University, Boston, MA, USA
| | - Swathi Kiran
- Department of Speech, Language & Hearing Sciences, Boston University, Boston, MA, USA
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7
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Kelley TD, McNeely DA, Serra MJ, Davis T. Delayed Judgments of Learning Are Associated With Activation of Information From Past Experiences: A Neurobiological Examination. Psychol Sci 2020; 32:96-108. [PMID: 33275057 DOI: 10.1177/0956797620958004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Research in metacognition suggests that the information people use to predict their memory performance can vary depending on the contexts in which they make their predictions. For example, if people judge their memories after a delay from initial encoding, they may be more likely to use retrieved information about the past encoding experience than if they judged memories immediately after encoding. Although this seems intuitive, past behavioral and neuroimaging work has not tested whether delayed memory judgments are more strongly coupled with information about past experiences than immediate memory judgments. We scanned participants using functional MRI while they encoded paired associates and made predictions about their future memory performance either immediately after encoding or after a delay. Consistent with the hypothesis that people use retrieved information about past experiences to inform delayed memory judgments, our results showed that activation patterns associated with past experience were more strongly coupled with delayed memory judgments than with immediate ones.
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Affiliation(s)
| | | | | | - Tyler Davis
- Department of Psychological Sciences, Texas Tech University
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8
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Bowman CR, Iwashita T, Zeithamova D. Tracking prototype and exemplar representations in the brain across learning. eLife 2020; 9:59360. [PMID: 33241999 PMCID: PMC7746231 DOI: 10.7554/elife.59360] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 11/26/2020] [Indexed: 11/13/2022] Open
Abstract
There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.
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Affiliation(s)
- Caitlin R Bowman
- Department of Psychology, University of Oregon, Eugene, United States.,Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, United States
| | - Takako Iwashita
- Department of Psychology, University of Oregon, Eugene, United States
| | - Dagmar Zeithamova
- Department of Psychology, University of Oregon, Eugene, United States
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9
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Categorical Biases in Human Occipitoparietal Cortex. J Neurosci 2019; 40:917-931. [PMID: 31862856 DOI: 10.1523/jneurosci.2700-19.2019] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 12/03/2019] [Indexed: 12/25/2022] Open
Abstract
Categorization allows organisms to generalize existing knowledge to novel stimuli and to discriminate between physically similar yet conceptually different stimuli. Humans, nonhuman primates, and rodents can readily learn arbitrary categories defined by low-level visual features, and learning distorts perceptual sensitivity for category-defining features such that differences between physically similar yet categorically distinct exemplars are enhanced, whereas differences between equally similar but categorically identical stimuli are reduced. We report a possible basis for these distortions in human occipitoparietal cortex. In three experiments, we used an inverted encoding model to recover population-level representations of stimuli from multivoxel and multielectrode patterns of human brain activity while human participants (both sexes) classified continuous stimulus sets into discrete groups. In each experiment, reconstructed representations of to-be-categorized stimuli were systematically biased toward the center of the appropriate category. These biases were largest for exemplars near a category boundary, predicted participants' overt category judgments, emerged shortly after stimulus onset, and could not be explained by mechanisms of response selection or motor preparation. Collectively, our findings suggest that category learning can influence processing at the earliest stages of cortical visual processing.SIGNIFICANCE STATEMENT Category learning enhances perceptual sensitivity for physically similar yet categorically different stimuli. We report a possible mechanism for these changes in human occipitoparietal cortex. In three experiments, we used an inverted encoding model to recover population-level representations of stimuli from multivariate patterns in occipitoparietal cortex while participants categorized sets of continuous stimuli into discrete groups. The recovered representations were systematically biased by category membership, with larger biases for exemplars adjacent to a category boundary. These results suggest that mechanisms of categorization shape information processing at the earliest stages of the visual system.
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10
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Abstract
One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex elicited from viewing a robin similar to a sparrow? One common assumption in fMRI analysis is that neural similarity is described by Pearson correlation. However, there are a host of other possibilities, including Minkowski and Mahalanobis measures, with each differing in its mathematical, theoretical, and neural computational assumptions. Moreover, the operable measures may vary across brain regions and tasks. Here, we evaluated which of several competing similarity measures best captured neural similarity. Our technique uses a decoding approach to assess the information present in a brain region, and the similarity measures that best correspond to the classifier’s confusion matrix are preferred. Across two published fMRI datasets, we found the preferred neural similarity measures were common across brain regions but differed across tasks. Moreover, Pearson correlation was consistently surpassed by alternatives.
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Affiliation(s)
- S Bobadilla-Suarez
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.,The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
| | - C Ahlheim
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.,The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
| | - A Mehrotra
- Department of Geography, University College London, Gower Street, London, WC1E 6BT UK.,The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
| | - A Panos
- Department of Statistical Science, University College London, Gower Street, London, WC1E 6BT UK.,The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
| | - B C Love
- Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.,The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
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11
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Frank LE, Bowman CR, Zeithamova D. Differential Functional Connectivity along the Long Axis of the Hippocampus Aligns with Differential Role in Memory Specificity and Generalization. J Cogn Neurosci 2019; 31:1958-1975. [PMID: 31397613 PMCID: PMC8080992 DOI: 10.1162/jocn_a_01457] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The hippocampus contributes to both remembering specific events and generalization across events. Recent work suggests that information may be represented along the longitudinal axis of the hippocampus at varied levels of specificity: detailed representations in the posterior hippocampus and generalized representations in the anterior hippocampus. Similar distinctions are thought to exist within neocortex, with lateral prefrontal and lateral parietal regions supporting memory specificity and ventromedial prefrontal and lateral temporal cortices supporting generalized memory. Here, we tested whether functional connectivity of anterior and posterior hippocampus with cortical memory regions is consistent with these proposed dissociations. We predicted greater connectivity of anterior hippocampus with putative generalization regions and posterior hippocampus with putative memory specificity regions. Furthermore, we tested whether differences in connectivity are stable under varying levels of task engagement. Participants learned to categorize a set of stimuli outside the scanner, followed by an fMRI session that included a rest scan, passive viewing runs, and category generalization task runs. Analyses revealed stronger connectivity of ventromedial pFC to anterior hippocampus and of angular gyrus and inferior frontal gyrus to posterior hippocampus. These differences remained relatively stable across the three phases (rest, passive viewing, category generalization). Whole-brain analyses further revealed widespread cortical connectivity with both anterior and posterior hippocampus, with relatively little overlap. These results contribute to our understanding of functional organization along the long axis of the hippocampus and suggest that distinct hippocampal-cortical connections are one mechanism by which the hippocampus represents both individual experiences and generalized knowledge.
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12
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Stillesjö S, Nyberg L, Wirebring LK. Building Memory Representations for Exemplar-Based Judgment: A Role for Ventral Precuneus. Front Hum Neurosci 2019; 13:228. [PMID: 31379536 PMCID: PMC6646524 DOI: 10.3389/fnhum.2019.00228] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 06/21/2019] [Indexed: 01/13/2023] Open
Abstract
The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. Ventral precuneus is implicated in various episodic memory processes, notably such that increased activity during learning in this region as well as in the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) have been linked to retrieval success. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning revealed higher activity in ventral precuneus, the bilateral MTL, and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building, storing, and being implicated in retrieving memory representations for judgment.
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Affiliation(s)
- Sara Stillesjö
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Linnea Karlsson Wirebring
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Psychology, Umeå University, Umeå, Sweden
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13
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Saxe AM, McClelland JL, Ganguli S. A mathematical theory of semantic development in deep neural networks. Proc Natl Acad Sci U S A 2019; 116:11537-11546. [PMID: 31101713 PMCID: PMC6561300 DOI: 10.1073/pnas.1820226116] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: What are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep-learning dynamics to give rise to these regularities.
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Affiliation(s)
- Andrew M Saxe
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom;
| | | | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA 94305
- Google Brain, Google, Mountain View, CA 94043
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14
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Ritchie JB, Kaplan DM, Klein C. Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience. THE BRITISH JOURNAL FOR THE PHILOSOPHY OF SCIENCE 2019; 70:581-607. [PMID: 31086423 PMCID: PMC6505581 DOI: 10.1093/bjps/axx023] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Since its introduction, multivariate pattern analysis (MVPA), or 'neural decoding', has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder's dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis?2.2The informational benefits of multivariate pattern analysis3Why the Decoder's Dictum Is False 3.1We don't know what information is decoded3.2The theoretical basis for the dictum3.3Undermining the theoretical basis4Objections and Replies 4.1Does anyone really believe the dictum?4.2Good decoding is not enough4.3Predicting behaviour is not enough5Moving beyond the Dictum6Conclusion.
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Affiliation(s)
| | | | - Colin Klein
- Department of Philosophy, Macquarie University, Sydney, Australia
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Kuznetsova N, Verkhodanova V. Phonetic Realisation and Phonemic Categorisation of the Final Reduced Corner Vowels in the Finnic Languages of Ingria. PHONETICA 2019; 76:201-233. [PMID: 31112960 DOI: 10.1159/000494927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 10/29/2018] [Indexed: 06/09/2023]
Abstract
Individual variability in sound change was explored at three stages of final vowel reduction and loss in the endangered Finnic varieties of Ingria (subdialects of Ingrian, Votic and Ingrian Finnish). The correlation between the realisation of reduced vowels and their phonemic categorisation by speakers was studied. The correlated results showed that if V was pronounced >70%, its starting loss was not yet perceived, apart from certain frequent elements, but after >70% loss, V was not perceived any more. A split of 50/50 between V and loss in production correlated with the same split in categorisation. At the beginning of a sound change, production is, therefore, more innovative, but after reanalysis, categorisation becomes more innovative and leads the change. The vowel a was the most innovative in terms of loss, u/o were the most conservative, and i was in the middle, while consonantal palatalisation was more salient than labialisation. These differences are based on acoustics, articulation and perception.
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Affiliation(s)
- Natalia Kuznetsova
- Institute for Linguistic Studies, Department of the Languages of Russia, Russian Academy of Sciences, St. Petersburg, Russian Federation,
- Dipartimento di Lingue e Letterature straniere e Culture moderne, Universita degli Studi di Torino, Turin, Italy,
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Ritchie JB, Op de Beeck H. A Varying Role for Abstraction in Models of Category Learning Constructed from Neural Representations in Early Visual Cortex. J Cogn Neurosci 2019; 31:155-173. [DOI: 10.1162/jocn_a_01339] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The human capacity for visual categorization is core to how we make sense of the visible world. Although a substantive body of research in cognitive neuroscience has localized this capacity to regions of human visual cortex, relatively few studies have investigated the role of abstraction in how representations for novel object categories are constructed from the neural representation of stimulus dimensions. Using human fMRI coupled with formal modeling of observer behavior, we assess a wide range of categorization models that vary in their level of abstraction from collections of subprototypes to representations of individual exemplars. The category learning tasks range from simple linear and unidimensional category rules to complex crisscross rules that require a nonlinear combination of multiple dimensions. We show that models based on neural responses in primary visual cortex favor a variable, but often limited, extent of abstraction in the construction of representations for novel categories, which differ in degree across tasks and individuals.
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Wirebring LK, Stillesjö S, Eriksson J, Juslin P, Nyberg L. A Similarity-Based Process for Human Judgment in the Parietal Cortex. Front Hum Neurosci 2018; 12:481. [PMID: 30631267 PMCID: PMC6315133 DOI: 10.3389/fnhum.2018.00481] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 11/16/2018] [Indexed: 11/24/2022] Open
Abstract
One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments (n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment.
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Affiliation(s)
- Linnea Karlsson Wirebring
- Department of Psychology, Umeå University, Umeå, Sweden
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Sara Stillesjö
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Johan Eriksson
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Peter Juslin
- Department of Psychology, Uppsala University, Uppsala, Sweden
| | - Lars Nyberg
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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Bauer AJ, Just MA. Brain reading and behavioral methods provide complementary perspectives on the representation of concepts. Neuroimage 2018; 186:794-805. [PMID: 30458304 DOI: 10.1016/j.neuroimage.2018.11.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 10/31/2018] [Accepted: 11/16/2018] [Indexed: 10/27/2022] Open
Abstract
The advent of brain reading techniques has enabled new approaches to the study of concept representation, based on the analysis of multivoxel activation patterns evoked by the contemplation of individual concepts such as animal concepts. The present fMRI study characterized the representation of 30 animal concepts. Dimensionality reduction of the multivoxel activation patterns underlying the individual animal concepts indicated that the semantic building blocks of the brain's representations of the animals corresponded to intrinsic animal properties (e.g. fierceness, intelligence, size). These findings were compared to behavioral studies of concept representation, which have typically collected pairwise similarity ratings between two concepts (e.g. Henley, 1969). Behavioral similarity judgments, by contrast, indicated that the animals were organized into taxonomically defined groups (e.g. canine, feline, equine). The difference in the results between the brain reading and behavioral approaches might derive from differences in cognitive processing during judging similarities versus contemplating one animal at a time. Brain reading approaches may have an advantage in describing thoughts about an individual concept, owing to the ability to decode brain activation patterns elicited by the brief consideration of a single concept (e.g. word reading) without a complex cognitive or behavioral task (e.g. similarity judgments). On the other hand, some behavioral tasks may tend to evoke a concept from numerous perspectives, yielding a representation of the breadth and sophistication of the concept knowledge. These results suggest that neural and behavioral measures offer complementary perspectives that together characterize the content and structure of concept representations.
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Affiliation(s)
- Andrew James Bauer
- Sidney Smith Hall, Dept. of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada.
| | - Marcel Adam Just
- Center for Cognitive Brain Imaging, Baker Hall, Dept. of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
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Ghosts in machine learning for cognitive neuroscience: Moving from data to theory. Neuroimage 2018; 180:88-100. [DOI: 10.1016/j.neuroimage.2017.08.019] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 07/17/2017] [Accepted: 08/04/2017] [Indexed: 12/17/2022] Open
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O'Bryan SR, Worthy DA, Livesey EJ, Davis T. Model-based fMRI reveals dissimilarity processes underlying base rate neglect. eLife 2018; 7:36395. [PMID: 30074478 PMCID: PMC6108825 DOI: 10.7554/elife.36395] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 08/01/2018] [Indexed: 11/13/2022] Open
Abstract
Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have posited that it arises either from associative similarity-based mechanisms or from dissimilarity-based processes that may depend on higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.
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Affiliation(s)
- Sean R O'Bryan
- Department of Psychological Sciences, Texas Tech University, Lubbock, United States
| | - Darrell A Worthy
- Department of Psychology, Texas A&M University, College Station, United States
| | - Evan J Livesey
- School of Psychology, University of Sydney, Sydney, Australia
| | - Tyler Davis
- Department of Psychological Sciences, Texas Tech University, Lubbock, United States
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21
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Shehzad Z, McCarthy G. Category representations in the brain are both discretely localized and widely distributed. J Neurophysiol 2018; 119:2256-2264. [PMID: 29537922 PMCID: PMC6032110 DOI: 10.1152/jn.00912.2017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/05/2018] [Accepted: 03/05/2018] [Indexed: 11/22/2022] Open
Abstract
Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.
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Affiliation(s)
- Zarrar Shehzad
- Department of Psychology, Yale University , New Haven, Connecticut
| | - Gregory McCarthy
- Department of Psychology, Yale University , New Haven, Connecticut
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O'Bryan SR, Walden E, Serra MJ, Davis T. Rule activation and ventromedial prefrontal engagement support accurate stopping in self-paced learning. Neuroimage 2018; 172:415-426. [PMID: 29410293 DOI: 10.1016/j.neuroimage.2018.01.084] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 01/26/2018] [Accepted: 01/30/2018] [Indexed: 10/18/2022] Open
Abstract
When weighing evidence for a decision, individuals are continually faced with the choice of whether to gather more information or act on what has already been learned. The present experiment employed a self-paced category learning task and fMRI to examine the neural mechanisms underlying stopping of information search and how they contribute to choice accuracy. Participants learned to classify triads of face, object, and scene cues into one of two categories using a rule based on one of the stimulus dimensions. After each trial, participants were given the option to explicitly solve the rule or continue learning. Representational similarity analysis (RSA) was used to examine activation of rule-relevant information on trials leading up to a decision to solve the rule. We found that activation of rule-relevant information increased leading up to participants' stopping decisions. Stopping was associated with widespread activation that included medial prefrontal cortex and visual association areas. Engagement of ventromedial prefrontal cortex (vmPFC) was associated with accurate stopping, and activation in this region was functionally coupled with signal in dorsolateral prefrontal cortex (dlPFC). Results suggest that activating rule information when deciding whether to stop an information search increases choice accuracy, and that the response profile of vmPFC during such decisions may provide an index of effective learning.
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Affiliation(s)
- Sean R O'Bryan
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX 79409, USA.
| | - Eric Walden
- Rawls College of Business, Texas Tech University, Lubbock, TX 79409, USA
| | - Michael J Serra
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX 79409, USA
| | - Tyler Davis
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX 79409, USA
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Paniukov D, Davis T. The evaluative role of rostrolateral prefrontal cortex in rule-based category learning. Neuroimage 2018; 166:19-31. [DOI: 10.1016/j.neuroimage.2017.10.057] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 09/18/2017] [Accepted: 10/25/2017] [Indexed: 01/28/2023] Open
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Braunlich K, Liu Z, Seger CA. Occipitotemporal Category Representations Are Sensitive to Abstract Category Boundaries Defined by Generalization Demands. J Neurosci 2017; 37:7631-7642. [PMID: 28674173 PMCID: PMC6596645 DOI: 10.1523/jneurosci.3825-16.2017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 06/20/2017] [Accepted: 06/27/2017] [Indexed: 11/21/2022] Open
Abstract
Categorization involves organizing perceptual information so as to maximize differences along dimensions that predict class membership while minimizing differences along dimensions that do not. In the current experiment, we investigated how neural representations reflecting learned category structure vary according to generalization demands. We asked male and female human participants to switch between two rules when determining whether stimuli should be considered members of a single known category. When categorizing according to the "strict" rule, participants were required to limit generalization to make fine-grained distinctions between stimuli and the category prototype. When categorizing according to the "lax" rule, participants were required to generalize category knowledge to highly atypical category members. As expected, frontoparietal regions were primarily sensitive to decisional demands (i.e., the distance of each stimulus from the active category boundary), whereas occipitotemporal representations were primarily sensitive to stimulus typicality (i.e., the similarity between each exemplar and the category prototype). Interestingly, occipitotemporal representations of stimulus typicality differed between rules. While decoding models were able to predict unseen data when trained and tested on the same rule, they were unable to do so when trained and tested on different rules. We additionally found that the discriminability of the multivariate signal negatively covaried with distance from the active category boundary. Thus, whereas many accounts of occipitotemporal cortex emphasize its important role in transforming visual information to accentuate learned category structure, our results highlight the flexible nature of these representations with regards to transient decisional demands.SIGNIFICANCE STATEMENT Occipitotemporal representations are known to reflect category structure and are often assumed to be largely invariant with regards to transient decisional demands. We found that representations of equivalent stimuli differed between strict and lax generalization rules, and that the discriminability of these representations increased as distance from abstract category boundaries decreased. Our results therefore indicate that occipitotemporal representations are flexibly modulated by abstract decisional factors.
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Affiliation(s)
- Kurt Braunlich
- Department of Experimental Psychology, University College London, London WC1E 6BT, United Kingdom, and
- Department of Psychology and Program in Molecular, Cellular, and Integrative Neurosciences, Colorado State University, Fort Collins, Colorado 80523
| | - Zhiya Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, PR China,
| | - Carol A Seger
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, PR China,
- Department of Psychology and Program in Molecular, Cellular, and Integrative Neurosciences, Colorado State University, Fort Collins, Colorado 80523
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Wasserman EA, Chakroff A, Saxe R, Young L. Illuminating the conceptual structure of the space of moral violations with searchlight representational similarity analysis. Neuroimage 2017; 159:371-387. [PMID: 28743459 DOI: 10.1016/j.neuroimage.2017.07.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/26/2017] [Accepted: 07/19/2017] [Indexed: 11/29/2022] Open
Abstract
Characterizing how representations of moral violations are organized, cognitively and neurally, is central to understanding how people conceive and judge them. Past work has identified brain regions that represent morally relevant features and distinguish moral domains, but has not yet advanced a broader account of where and on what basis neural representations of moral violations are organized. With searchlight representational similarity analysis, we investigate where category membership drives similarity in neural patterns during moral judgment of violations from two key moral domains: Harm and Purity. Representations converge across domains in a network of regions resembling the mentalizing network. However, Harm and Purity violation representations respectively converge in different regions: precuneus (PC) and left inferior frontal gyrus (LIFG). Examining substructure within moral domains, Harm violations converge in PC regardless of subdomain (physical harms, psychological harms), while Purity subdomains (pathogen-related violations, sex-related violations) converge in distinct sets of regions - mirroring a dissociation observed in principal-component analysis of behavioral data. Further, we find initial evidence for representation of morally relevant features within these two domain-encoding regions. The present analyses offer a case study for understanding how organization within the complex conceptual space of moral violations is reflected in the organization of neural patterns across the cortex.
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Affiliation(s)
- E A Wasserman
- Dept. of Psychology, Boston College, Chestnut Hill, MA, United States.
| | - A Chakroff
- Dept. of Psychology, Boston College, Chestnut Hill, MA, United States
| | - R Saxe
- Dept. of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - L Young
- Dept. of Psychology, Boston College, Chestnut Hill, MA, United States
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Davis T, Goldwater M, Giron J. From Concrete Examples to Abstract Relations: The Rostrolateral Prefrontal Cortex Integrates Novel Examples into Relational Categories. Cereb Cortex 2017; 27:2652-2670. [PMID: 27130661 DOI: 10.1093/cercor/bhw099] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrating novel examples during relational categorization whereas other PFC regions support more general decision functions.
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Affiliation(s)
- Tyler Davis
- Department of Psychological Sciences, Texas Tech University, Lubbock, TX 79403, USA
| | - Micah Goldwater
- School of Psychology, University of Sydney, Sydney, New South Wales 2006, Australia
| | - Josue Giron
- School of Psychology, University of Sydney, Sydney, New South Wales 2006, Australia
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Morton NW, Polyn SM. Beta-band activity represents the recent past during episodic encoding. Neuroimage 2017; 147:692-702. [DOI: 10.1016/j.neuroimage.2016.12.049] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 11/22/2016] [Accepted: 12/18/2016] [Indexed: 10/20/2022] Open
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Hammer R, Sloutsky V. Visual Category Learning Results in Rapid Changes in Brain Activation Reflecting Sensitivity to the Category Relation between Perceived Objects and to Decision Correctness. J Cogn Neurosci 2016; 28:1804-1819. [DOI: 10.1162/jocn_a_01008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Abstract
Little is known about the time scales in which sensitivity to novel category identity may become evident in visual and executive cortices in visual category learning (VCL) tasks and the nature of such changes in brain activation. We used fMRI to investigate the processing of category information and trial-by-trial feedback information. In each VCL task, stimuli differed in three feature dimensions. In each trial, either two same-category stimuli or two different-categories stimuli were presented. The participant had to learn which feature dimension was relevant for categorization based on the feedback that followed each categorization decision. We contrasted between same-category stimuli trials and different-category trials and between correct and incorrect categorization decision trials. In each trial, brain activation in the visual stimuli processing phase was modeled separately from activation during the later feedback processing phase. We found activation in the lateral occipital complex, indicating sensitivity to the category relation between stimuli, to be evident in VCL within only few learning trials. Specifically, greater lateral occipital complex activation was evident when same-category stimuli were presented than when different-category stimuli were presented. In the feedback processing phase, greater activation in both executive and visual cortices was evident primarily after “misdetections” of same-category stimuli. Implications regarding the contribution of different learning trials to VCL, and the respective role of key brain regions, at the onset of VCL, are discussed.
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Milton F, Bealing P, Carpenter KL, Bennattayallah A, Wills AJ. The Neural Correlates of Similarity- and Rule-based Generalization. J Cogn Neurosci 2016; 29:150-166. [PMID: 27575389 DOI: 10.1162/jocn_a_01024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405-415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks-Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.
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Constantinescu AO, O’Reilly JX, Behrens TEJ. Organizing conceptual knowledge in humans with a gridlike code. Science 2016; 352:1464-1468. [PMID: 27313047 PMCID: PMC5248972 DOI: 10.1126/science.aaf0941] [Citation(s) in RCA: 395] [Impact Index Per Article: 49.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 05/17/2016] [Indexed: 12/20/2022]
Abstract
It has been hypothesized that the brain organizes concepts into a mental map, allowing conceptual relationships to be navigated in a manner similar to that of space. Grid cells use a hexagonally symmetric code to organize spatial representations and are the likely source of a precise hexagonal symmetry in the functional magnetic resonance imaging signal. Humans navigating conceptual two-dimensional knowledge showed the same hexagonal signal in a set of brain regions markedly similar to those activated during spatial navigation. This gridlike signal is consistent across sessions acquired within an hour and more than a week apart. Our findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.
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Affiliation(s)
- Alexandra O. Constantinescu
- Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
| | - Jill X. O’Reilly
- Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, 9 South Parks Road, Oxford OX1 3UD, UK
- Donders Institute, Radboud University, Nijmegen, The Netherlands
| | - Timothy E. J. Behrens
- Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
- Donders Institute, Radboud University, Nijmegen, The Netherlands
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Iordan MC, Greene MR, Beck DM, Fei-Fei L. Typicality sharpens category representations in object-selective cortex. Neuroimage 2016; 134:170-179. [PMID: 27079531 DOI: 10.1016/j.neuroimage.2016.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 03/12/2016] [Accepted: 04/05/2016] [Indexed: 11/18/2022] Open
Abstract
The purpose of categorization is to identify generalizable classes of objects whose members can be treated equivalently. Within a category, however, some exemplars are more representative of that concept than others. Despite long-standing behavioral effects, little is known about how typicality influences the neural representation of real-world objects from the same category. Using fMRI, we showed participants 64 subordinate object categories (exemplars) grouped into 8 basic categories. Typicality for each exemplar was assessed behaviorally and we used several multi-voxel pattern analyses to characterize how typicality affects the pattern of responses elicited in early visual and object-selective areas: V1, V2, V3v, hV4, LOC. We found that in LOC, but not in early areas, typical exemplars elicited activity more similar to the central category tendency and created sharper category boundaries than less typical exemplars, suggesting that typicality enhances within-category similarity and between-category dissimilarity. Additionally, we uncovered a brain region (cIPL) where category boundaries favor less typical categories. Our results suggest that typicality may constitute a previously unexplored principle of organization for intra-category neural structure and, furthermore, that this representation is not directly reflected in image features describing natural input, but rather built by the visual system at an intermediate processing stage.
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Affiliation(s)
| | - Michelle R Greene
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
| | - Diane M Beck
- Beckman Institute and Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Li Fei-Fei
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
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Poldrack RA, Farah MJ. Progress and challenges in probing the human brain. Nature 2015; 526:371-9. [DOI: 10.1038/nature15692] [Citation(s) in RCA: 167] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Accepted: 09/04/2015] [Indexed: 01/20/2023]
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Global neural pattern similarity as a common basis for categorization and recognition memory. J Neurosci 2014; 34:7472-84. [PMID: 24872552 DOI: 10.1523/jneurosci.3376-13.2014] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels.
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Park CH, Chang WH, Lee M, Kwon GH, Kim L, Kim ST, Kim YH. Predicting the performance of motor imagery in stroke patients: multivariate pattern analysis of functional MRI data. Neurorehabil Neural Repair 2014; 29:247-54. [PMID: 25055835 DOI: 10.1177/1545968314543308] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND In a brain-computer interface for stroke rehabilitation, motor imagery is a preferred means for providing a gateway to an effector action or behavior. However, stroke patients often exhibit failure to comply with motor imagery, and therefore their motor imagery performance is highly variable. OBJECTIVE We sought to identify motor cortical areas responsible for motor imagery performance in stroke patients, specifically by using a multivariate pattern analysis of functional magnetic resonance imaging data. METHODS We adopted an imaginary finger tapping task in which motor imagery performance could be monitored for 12 chronic stroke patients with subcortical infarcts and 12 age- and sex-matched healthy controls. We identified the typical activation pattern elicited for motor imagery in healthy controls, as computed over the voxels within each searchlight in the motor cortex. Then we measured the similarity of each individual's activation pattern to the typical activation pattern. RESULTS In terms of activation levels, the stroke patients showed no activation in the ipsilesional primary motor cortex (M1); in terms of activation patterns, they showed lower similarity to the typical activation pattern in the area than the healthy controls. Furthermore, the stroke patients were better able to perform motor imagery if their activation patterns in the bilateral supplementary motor areas and ipsilesional M1 were close to the typical activation pattern. CONCLUSIONS These findings suggest functional roles of the motor cortical areas for compliance with motor imagery in stroke, which can be applied to the implementation of motor imagery-based brain-computer interface for stroke rehabilitation.
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Affiliation(s)
| | - Won Hyuk Chang
- Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Minji Lee
- Samsung Advanced Institute for Health Sciences and Technology, Seoul, Korea
| | - Gyu Hyun Kwon
- Korea Institute of Science and Technology, Seoul, Korea
| | - Laehyun Kim
- Korea Institute of Science and Technology, Seoul, Korea
| | - Sung Tae Kim
- Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yun-Hee Kim
- Sungkyunkwan University School of Medicine, Seoul, Korea Samsung Advanced Institute for Health Sciences and Technology, Seoul, Korea
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What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. Neuroimage 2014; 97:271-83. [PMID: 24768930 DOI: 10.1016/j.neuroimage.2014.04.037] [Citation(s) in RCA: 182] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 03/31/2014] [Accepted: 04/15/2014] [Indexed: 11/21/2022] Open
Abstract
Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results.
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Kriegeskorte N, Kievit RA. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn Sci 2013; 17:401-12. [PMID: 23876494 PMCID: PMC3730178 DOI: 10.1016/j.tics.2013.06.007] [Citation(s) in RCA: 458] [Impact Index Per Article: 41.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Revised: 06/06/2013] [Accepted: 06/12/2013] [Indexed: 01/08/2023]
Abstract
Representational geometry is a framework that enables us to relate brain, computation, and cognition. Representations in brains and models can be characterized by representational distance matrices. Distance matrices can be readily compared to test computational models. We review recent insights into perception, cognition, memory, and action and discuss current challenges.
The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure.
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