Biology subjects

Soto, F. A.

Publications and source records attributed to Soto, F. A..

7 recordsLinked to original sources

bioRxiv2026

What group averages conceal: functional heterogeneity in human eyeblink habituation

In animal learning research, learning is often represented by plotting a behavioral measure as a function of training trials. A particularly clear case is habituation, a basic form of learning in which repeated presentation of a stimulus produces a decrement in responding. Although retention tests provide the strongest basis for evaluating durable habituation once short-lived performance effects have dissipated, the pattern of response change across stimulus repetitions, or habituation curve, remains theoretically and empirically relevant because it is used to characterize determinants of habituation, individual and clinical profiles, and functional forms, including linear, curvilinear, asymptotic, and mixed incremental-decremental patterns of responding. However, group averaged curves may conceal substantial individual heterogeneity. Here, we analyzed archived human eyeblink habituation data from 157 participants to ask whether the curve shape selected for the group average reflects the curve shapes observed at the individual level. Five candidate functions were fitted separately to each participant and to the corresponding group average. No single function characterized most individuals. More importantly, the model selected for the group average differed from the most frequent individual model in all four groups. When data were pooled across groups, the average favored a dual-process form, a shape that matched the individual plurality in none of them. Simulation analyses showed that averaging heterogeneous individual trajectories can itself produce a group curve that favors a more complex model. Our findings show that group averaged habituation curves should not be treated as direct descriptions of the typical individual trajectory.

animal behavior and cognition↗

bioRxiv2023

Towards a mechanistic understanding of the role of error monitoring and memory in social anxiety

Cognitive models state social anxiety (SA) involves biased cognitive processing that impacts what is learned and remembered within social situations, leading to the maintenance of SA. Neuroscience work links SA to enhanced error monitoring, reflected in error-related neural responses arising from mediofrontal cortex (MFC). Yet, the role of error monitoring in SA remains unclear, as it is unknown whether error monitoring can drive changes in memory, biasing what is learned or remembered about social situations. Thus, we developed a novel paradigm to investigate the role of error-related MFC theta oscillations (associated with error monitoring) and memory biases in SA. EEG was collected while participants completed a novel Face-Flanker task, involving presentation of task-unrelated, trial-unique faces behind target/flanker arrows on each trial. A subsequent incidental memory assessment evaluated memory biases for error events. Severity of SA symptoms were associated with greater error-related theta synchrony over MFC, as well as between MFC and sensory cortex. SA was positively associated with memory biases for error events. Consistent with a mechanistic role in biased cognitive processing, greater error-related MFC-sensory theta synchrony during the Face-Flanker predicted subsequent memory biases for error events. Our findings suggest high SA individuals exhibit memory biases for error events, and that this behavioral phenomenon may be driven by error-related MFC-sensory theta synchrony associated with error monitoring. Moreover, results demonstrate the potential of a novel paradigm to elucidate mechanisms underlying relations between error monitoring and SA.

neuroscience↗

bioRxiv2020

Changes within neural population codes can be inferred from psychophysical threshold studies

The use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via inverted encoding modeling. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. However, computational work aimed at determining to what extent different mechanisms can be differentiated using psychophysics is lacking. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study, chosen because they have been proposed in the previous literature as mechanisms for improved task performance (e.g., due to attention or learning): specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling, and provide strong constraints on inferences based on the latter. We use our results to provide recommendations for interested researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.

neuroscience↗

bioRxiv2020

Invariance of neural representations cannot be validly inferred from neuroimaging decoding studies: Empirical and computational evidence

Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features?). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory provides two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations. Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results. Author SummaryMany research questions in sensory neuroscience involve determining whether the representation of a stimulus property is invariant or specific to a change in stimulus context (e.g., translation-invariant object representation; configural representation of face features). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging research has studied invariance using operational tests, among which the most widely used in recent years is cross-classification. We provide evidence from a functional MRI study, simulations, and theoretical results supporting two insights regarding such tests: (1) tests that seek to provide evidence for invariance (like cross-classification) have an inflated false positive rate, but (2) using complementary tests that seek evidence for context-specificity leads to more valid conclusions.

neuroscience↗

bioRxiv2019

Visual search mimics configural processing in human causal learning

Theories of learning distinguish between elemental and configural stimulus processing depending on whether stimuli are processed independently or as whole configurations. Evidence for elemental processing comes from findings of summation in animals where a compound of two dissimilar stimuli is deemed to be more predictive than each stimulus alone, whereas configural processing is supported by experiments employing similar stimuli in which summation is not found. However, in humans the summation effect is robust and impervious to similarity manipulations. In three experiments in human predictive learning, we show that summation can be obliterated when partially reinforced cues are added to the summands in training and test. This lack of summation only holds when the partially reinforced cues are similar to the reinforced cues (Experiment 1) and seems to depend on participants sampling only the most salient cue in each trial (Experiments 2a and 2b) in a sequential visual search process. Instead of attributing our and others instances of lack of summation to the customary idea of configural processing, we offer a formal sub-sampling rule that might be applied to situations in which the stimuli are hard to parse from each other.

animal behavior and cognition↗

bioRxiv2018

Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data

Many research questions in visual perception involve determining whether stimulus properties are represented and processed independently. In visual neuroscience, there is great interest in determining whether important object dimensions are represented independently in the brain. For example, theories of face recognition have proposed either completely or partially independent processing of identity and emotional expression. Unfortunately, most previous research has only vaguely defined what is meant by \"independence,\" which hinders its precise quantification and testing. This article develops a new quantitative framework that links signal detection theory from psychophysics and encoding models from computational neuroscience, focusing on a special form of independence defined in the psychophysics literature: perceptual separability. The new theory allowed us, for the first time, to precisely define separability of neural representations and to theoretically link behavioral and brain measures of separability. The framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach. In particular, the theory identifies exactly what valid inferences can be made about independent encoding of stimulus dimensions from the results of multivariate analyses of neuroimaging data and psychophysical studies. In addition, commonly used operational tests of independence are re-interpreted within this new theoretical framework, providing insights on their correct use and interpretation. Finally, we apply this new framework to the study of separability of brain representations of face identity and emotional expression (neutral/sad) in a human fMRI study with male and female participants.\n\nAuthor SummaryA common question in vision research is whether certain stimulus properties, like face identity and expression, are represented and processed independently. We develop a theoretical framework that allowed us, for the first time, to link behavioral and brain measures of independence. Unlike previous approaches, our framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach in the study of independence. This allows to identify what kind of inferences can be made about brain representations from multivariate analyses of neuroimaging data or psychophysical studies. We apply this framework to the study of independent processing of face identity and expression.

neuroscience↗

bioRxiv2017

The role of stimulus similarity on the summation effect in human causal learning

Several contemporary models of associative learning anticipate that the higher responding to a compound of two cues separately trained with a common outcome than to each of the cues alone -a summation effect-is modulated by the similarity between the cues forming the compound. Here, we explored this hypothesis in a series of causal learning experiments with humans. Participants were presented with two visual cues that separately predicted a common outcome and later asked for the outcome predicted by the compound of the two cues. Importantly, the cues similarity was varied between groups through changes in shape, spatial position, color, configuration and rotation. In variance with the predictions of these models, we observed similar and strong levels of summation in both groups across all manipulations of similarity (Experiments 1-5). The summation effect was significantly reduced by manipulations intended to impact assumptions about the causal independence of the cues forming the compound, but this reduction was independent of stimulus similarity (Experiment 6). These results are problematic for similarity-based models and can be more readily explained by rational approaches to causal learning.

animal behavior and cognition↗