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Carlson, T.

Publications and source records attributed to Carlson, T..

12 recordsLinked to original sources
bioRxiv2024

Positive serial dependence in ratings of food images for appeal and calories

Food is fundamental to survival and our brains are highly attuned to rapidly process food stimuli. Neural signals show foods can be discriminated as edible or inedible as early as 85 ms after stimulus onset1, distinguished as processed or unprocessed beginning at 130 ms2 and as high or low density from 165 ms3. Recent evidence revealed specialised processing of food stimuli in the ventral visual pathway4-6, an area that underlies perception of faces and other important objects. For many visual objects, present perception can be biased towards recent perceptual history (known as serial dependence7,8). We examined serial dependence for food in two large samples (n>300) that rated sequences of food images for either appeal or calories. Calorie ratings by males and females agreed closely but appeal ratings were higher in males. High calorie ratings were associated with high appeal, especially in males. Serial analyses testing if current trial ratings were influenced by the previous one showed both appeal and calorie ratings exhibited clear positive dependences (i.e., a high preceding rating increased current trial ratings). The serial effect for appeal was roughly twice that for calories and males showed a greater serial effect than females for both ratings. Serial amplitude was larger in those who reported a longer elapsed time since they last ate and was larger in the BMI>25 group compared to BMI<25. These findings square with recently found food selectively in visual temporal cortex, reveal a new mechanism influencing food decision-making and suggest a new sensory-level component that could complement cognitive strategies in diet intervention.

neuroscience↗
bioRxiv2023

Mapping the Dynamics of Visual Feature Coding: Insights into Perception and Integration

The basic computations performed in the human early visual cortex are the foundation for visual perception. While we know a lot about these computations, a key missing piece is how the coding of visual features relates to our perception of the environment. To investigate visual feature coding, interactions, and their relationship to human perception, we investigated neural responses and perceptual similarity judgements to a large set of visual stimuli that varied parametrically along four feature dimensions. We measured neural responses using electroencephalography (N=16) to 256 grating stimuli that varied in orientation, spatial frequency, contrast, and colour. We then mapped the response profiles of the neural coding of each visual feature and their interactions, and related these to independently obtained behavioural judgements of stimulus similarity. The results confirmed fundamental principles of feature coding in the visual system, such that all four features were processed simultaneously but differed in their dynamics, and there was distinctive conjunction coding for different combinations of features in the neural responses. Importantly, modelling of the behaviour revealed that every stimulus feature contributed to perceptual judgements, despite the untargeted nature of the behavioural task. Further, the relationship between neural coding and behaviour was evident from initial processing stages, signifying that the fundamental features, not just their interactions, contribute to perception. This study highlights the importance of understanding how feature coding progresses through the visual hierarchy and the relationship between different stages of processing and perception. Author summaryThis study investigates the foundational computations in the human early visual cortex that underlie visual perception. Using electroencephalography (EEG), neural responses to 256 grating stimuli with variations in orientation, spatial frequency, contrast, and colour were measured in 16 participants. We explored how the neural coding of these visual features and their interactions relate to perceptual similarity judgments. Results confirmed simultaneous processing of all four features with distinct dynamics, revealing conjunction coding for various feature combinations. Behavioural modelling demonstrated that each stimulus feature contributes to perceptual judgments. This work emphasizes the significance of understanding feature coding progression through the visual hierarchy and its relationship to perception from early processing stages.

neuroscience↗
bioRxiv2020

Temporal codes provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the "mean" neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability which is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional "mean" activity. Here we compare the information content of 31 variability-sensitive features against the "mean" of activity, using three independent highly-varied datasets. In whole-trial decoding, the classical event-related potential (ERP) components of "P2a" and "P2b" provided information comparable to those provided by "Original Magnitude Data (OMD)" and "Wavelet Coefficients (WC)", the two most informative variability-sensitive features. In time-resolved decoding, the "OMD" and "WC" outperformed all the other features (including "mean"), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in Theta frequency band, previously suggested to support feed-forward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously e.g. phase, amplitude and frequency rather than "mean" per se. In our active categorization dataset, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

neuroscience↗
bioRxiv2020

Decoding Predictions and Violations of Object Position and Category in Time-resolved EEG

Classic models of predictive coding propose that sensory systems use information retained from prior experience to predict current sensory input. Any mismatch between predicted and current input (prediction error) is then fed forward up the hierarchy leading to a revision of the prediction. We tested this hypothesis in the domain of object vision using a combination of multivariate pattern analysis and time-resolved electroencephalography. We presented participants with sequences of images that stepped around fixation in a predictable order. On the majority of presentations, the images conformed to a consistent pattern of position order and object category order, however, on a subset of presentations the last image in the sequence violated the established pattern by either violating the predicted category or position of the object. Contrary to classic predictive coding when decoding position and category we found no differences in decoding accuracy between predictable and violation conditions. However, consistent with recent extensions of predictive coding, exploratory analyses showed that a greater proportion of predictions was made to the forthcoming position in the sequence than to either the previous position or the position behind the previous position suggesting that the visual system actively anticipates future input as opposed to just inferring current input.

neuroscience↗
bioRxiv2019

Stimulus-specific information flow across the canonical cortical microcircuit

The vast majority of mammalian neocortex consists of a stereotypical microcircuit, the canonical cortical microcircuit (CCM), consisting of a granular input layer, positioned between superficial and deep layers. Due to this uniform layout, neuronal activation tends to follow a similar laminar sequence, with unique information extracted at each step. For example, the primate primary visual cortex (V1) combines the two eyes signals, extracts stimulus orientation and modulates its activity depending on stimulus history. Several theories have been proposed on when and where these processes happen within the CCMs laminar activation sequence, but it has been methodologically challenging to test these hypotheses. Here, we use time-resolved multivariate pattern analysis (MVPA) to decode information regarding the eye-of-origin, stimulus orientation and stimulus repetition from simultaneously measured spiking responses across V1s laminar microcircuit. We find that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1, consistent with the notion that two eyes signals are combined within the upper layers. Conversely, orientation information was transient and equally pronounced across the microcircuit, in line with the idea that this information is relayed to other areas for further processing. Moreover, when stimuli were repeated, information regarding orientation was enhanced at the expense of eye-of origin information, suggesting that V1 modulates information flow to optimize specific stimulus dimensions. Taken together, these findings provide empirical evidence that adjudicates between long-standing hypotheses and reveals how information transfer within the CCM supports unique cortical functions.\n\nSignificance StatementDespite the brains daunting complexity, there are common organizing principles across brain areas. For example, neocortical activation follows a stereotypical pattern that spreads from input layers towards layers above and below. While this activation pattern is well known, it has been challenging to ascertain how unique types of information are extracted within this common sequence in different brain areas. Here we use machine learning to track the flow of stimulus-specific information across the layers of visual cortex. We found that information regarding several separate stimulus dimensions was routed uniquely within the common activation sequence in a manner that confirmed prior model predictions. This finding demonstrates how differences in information flow within the stereotypical neocortical activation sequence shape area-specific functions.

neuroscience↗
bioRxiv2019

A humanness dimension to visual object coding in the brain

Neuroimaging studies investigating human object recognition have largely focused on a relatively small number of object categories, in particular, faces, bodies, scenes, and vehicles. More recent studies have taken a broader focus, investigating hypothesised dichotomies, for example animate versus inanimate, and continuous feature dimensions, such as biologically similarity. These studies typically have used stimuli that are clearly identified as animate or inanimate, neglecting objects that may not fit into this dichotomy. We generated a novel stimulus set including standard objects and objects that blur the animate-inanimate dichotomy, for example robots and toy animals. We used MEG time-series decoding to study the brains emerging representation of these objects. Our analysis examined contemporary models of object coding such as dichotomous animacy, as well as several new higher order models that take into account an objects capacity for agency (i.e. its ability to move voluntarily) and capacity to experience the world. We show that early brain responses are best accounted for by low-level visual similarity of the objects; and shortly thereafter, higher order models of agency/experience best explained the brains representation of the stimuli. Strikingly, a model of human-similarity provided the best account for the brains representation after an initial perceptual processing phase. Our findings provide evidence for a new dimension of object coding in the human brain - one that has a \"human-centric\" focus.

neuroscience↗
bioRxiv2019

Yellow strawberries and red bananas: The influence of object-colour knowledge on emerging object representations in the brain

The ability to rapidly and accurately recognise complex objects is a crucial function of the human visual system. To recognise an object, we need to bind incoming visual features such as colour and form together into cohesive neural representations and integrate these with our pre-existing knowledge about the world. For some objects, typical colour is a central feature for recognition; for example, a banana is typically yellow. Here, we applied multivariate pattern analysis on time-resolved neuroimaging (magnetoencephalography) data to examine how object-colour knowledge affects emerging object representations over time. Our results from 20 participants (11 female) show that the typicality of object-colour combinations influences object representations, although not at the initial stages of object and colour processing. We find evidence that colour decoding peaks later for atypical object-colour combinations in comparison to typical object-colour combinations, illustrating the interplay between processing incoming object features and stored object-knowledge. Taken together, these results provide new insights into the integration of incoming visual information with existing conceptual object knowledge. Significance StatementTo recognise objects, we have to be able to bind object features such as colour and shape into one coherent representation and compare it to stored object knowledge. The magnetoencephalography data presented here provide novel insights about the integration of incoming visual information with our knowledge about the world. Using colour as a model to understand the interaction between seeing and knowing, we show that there is a unique pattern of brain activity for congruently coloured objects (e.g., a yellow banana) relative to incongruently coloured objects (e.g., a red banana). This effect of object-colour knowledge only occurs after single object features are processed, demonstrating that conceptual knowledge is accessed relatively late in the visual processing hierarchy.

neuroscience↗
bioRxiv2018

Open-Source Food: Nutrition, Toxicology, and Availability of Wild Edible Greens in the East Bay

SignificanceForaged leafy greens are consumed around the globe, including in urban areas, and may play a larger role when food is scarce or expensive. It is thus important to assess the safety and nutritional value of wild greens foraged in urban environments.\n\nMethodsField observations, soil tests, and nutritional and toxicology tests on plant tissue were conducted for three sites, each roughly 9 square blocks, in disadvantaged neighborhoods in the East San Francisco Bay Area in 2014-2015. The sites included mixed-use areas and areas with high vehicle traffic.\n\nResultsEdible wild greens were abundant, even during record droughts. Soil at some survey sites had elevated concentrations of lead and cadmium, but tissue tests suggest that rinsed greens of the tested species are safe to eat. Daily consumption of standard servings comprise less than the EPA reference doses of lead, cadmium, and other heavy metals. Pesticides, glyphosate, and PCBs were below detection limits.\n\nThe nutrient density of 6 abundant species compared favorably to that of the most nutritious domesticated leafy greens.\n\nConclusionsWild edible greens harvested in industrial, mixed-use, and high-traffic urban areas in the San Francisco East Bay area are abundant and highly nutritious. Even grown in soils with elevated levels of heavy metals, tested species were safe to eat after rinsing in tap water. This does not mean that all edible greens growing in contaminated soil are safe to eat--tests on more species, in more locations, and over a broader range of soil chemistry are needed to determine what is generally safe and what is not. But it does suggest that wild greens could contribute to nutrition, food security, and sustainability in urban ecosystems. Current laws, regulations, and public-health guidance that forbid or discourage foraging on public lands, including urban areas, should be revisited.

ecology↗
bioRxiv2018

Seeing versus Knowing: The Temporal Dynamics of Real and Implied Colour Processing in the Human Brain

Colour is a defining feature of many objects, playing a crucial role in our ability to rapidly recognise things in the world around us and make categorical distinctions. For example, colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries. That means our representation of many objects includes key colour-related information. The question addressed here is whether the neural representation activated by knowing that something is red is the same as that activated when we actually see something red, particularly in regard to timing. We addressed this question using neural timeseries (magnetoencephalography, MEG) data to contrast real colour perception and implied object colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain activation patterns evoked by colour accessed via real colour perception and implied colour activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of these processes. Male and female human participants (N=18) viewed isoluminant red and green shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked by real colour perception is similar to implied colour activation, but that this pattern is instantiated at a later time. These results suggest that a common colour representation can be triggered by activating object representations from memory and perceiving colours.

neuroscience↗
bioRxiv2018

Decoding digits and dice with Magnetoencephalography: Evidence for a shared representation of magnitude

Numerical format describes the way magnitude is conveyed, for example as a digit ( 3) or Roman Numeral ( III). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography (MEG) offers an opportunity to record brain activity with high temporal resolution. Multivariate Pattern Analysis (MVPA) applied to MEG data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can crossdecode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests similar representation of these numerical symbols. Additionally, results from a time-generalisation analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

neuroscience↗
bioRxiv2018

Finding decodable information that is read out in behaviour

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

neuroscience↗