Logistic analysis of choice data: A primer

A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking

Value-guided decisions are at the core of reinforcement learning and neuroeconomics, yet the basic computations they require remain poorly understood at the mechanistic level. For instance, how does the brain implement the multiplication of reward magnitude by probability to yield an expected value? Where within a neural circuit is the indifference point for comparing reward types encoded? How do learned values generalize to novel options? Here, we introduce a biologically plausible model that adheres to Dale's law and is trained on five choice tasks, offering potential answers to these questions. The model captures key neurophysiological observations from the orbitofrontal cortex of monkeys and generalizes to novel offer values. Using a single network model to solve diverse tasks, we identified compositional neural representations-quantified via task variance analysis and corroborated by curriculum learning. This work provides testable predictions that probe the neural basis of decision making and its disruption in neuropsychiatric disorders.

Laminar Architecture of a Decision Circuit in Orbitofrontal Cortex

During economic choice, different neurons in orbitofrontal cortex (OFC) encode individual offer values, the binary choice outcome, and the chosen value. Previous work suggests that these cell groups form a decision circuit, but the anatomical organization of this circuit is poorly understood. Using calcium imaging, we recorded from layer 2/3 (L2/3) and layer 5 (L5) of mice choosing between juice flavors. Decision variables were differentially represented across layers: juice-specific offer values and their spatial configuration were predominant in L2/3, while spatial offer values, chosen side, and chosen value were predominant in L5. Within each layer, functional cell groups were organized in clusters. The temporal dynamics of neural signals in the two layers indicated a combination of feed-forward and feed-back processes, and pointed to L5 as the locus for winner-take-all value comparison. These results reveal that economic decisions rely on a complex architecture distributed across layers of OFC.

Gustatory cortex neurons perform reliability-dependent integration of multisensory flavor inputs

Flavor is the quintessential multisensory experience, combining gustatory, retronasal olfactory, and texture qualities to inform food perception and consumption behavior. However, the computations that govern multisensory integration of flavor components and their underlying neural mechanisms remain elusive. Here, we use rats as a model system to test the hypothesis that taste and smell components of flavor are integrated in a reliability-dependent manner to inform hedonic judgments and that this computation is performed by neurons in the primary taste cortex. Using a series of two-bottle preference tests, we demonstrate that hedonic judgments of taste + smell mixtures are a weighted average of the component judgments, and that the weight of the components depends on their relative reliability. Using extracellular recordings of single-neuron spiking and local field potential activity in combination with decoding analysis, we reveal a correlate of this computation in gustatory cortex (GC). GC neurons weigh bimodal taste and smell inputs based on their reliability, with more reliable inputs contributing more strongly to taste + smell mixture responses. Input reliability was associated with less variable responses and stronger network-level synchronization in the gamma band. Together, our findings establish a quantitative framework for understanding hedonic multisensory flavor judgments and identify the neural computations that underlie them.

Deconstructing Delay Discounting in Human Cocaine Addiction Using Computational Modeling and Neuroimaging

BACKGROUND

A preference for sooner-smaller over later-larger rewards, known as delay discounting, is a candidate transdiagnostic marker of waiting impulsivity and a research domain criterion. While abnormal discounting rates have been associated with many psychiatric diagnoses and abnormal brain structure, the underlying neuropsychological processes remain largely unknown. Here, we deconstruct delay discounting into choice and rate processes by testing different computational models and investigate their associations with white matter tracts.

METHODS

Patients with cocaine use disorder (CUD) (n = 107) and healthy participants (n = 81) completed the Monetary Choice Questionnaire. We computed their discounting rate using the well-known Kirby method, as well as logistic regression, single-subject Bayesian, and full hierarchical Bayesian models. In Bayesian models, we also included a choice sharpness parameter. Seventy patients with CUD and 69 healthy participants also underwent diffusion tensor imaging tractography to quantify streamlines that connect the executive control and valuation brain networks.

RESULTS

Patients with CUD showed significantly higher discounting rates and lower choice sharpness, suggesting greater indifference in their choices. Importantly, the full Bayesian model had the greatest reliability for parameter recovery when compared to the Kirby and logistic regression methods. Using Bayesian estimates, we found that white matter streamlines that connect the executive control network with the nucleus accumbens predicted the discounting rate in healthy participants but not in patients with CUD.

CONCLUSIONS

We demonstrated that measuring delay discounting and choice sharpness directly with a novel computational model explained impulsive discounting choices in patients with CUD better than standard hyperbolic discounting. Our findings highlight a distinct neuropsychological phenotype of impulsive discounting, which may be generalizable to other patient groups.

Kinematic coding: Measuring information in naturalistic behaviour

Recent years have seen an explosion of interest in naturalistic behaviour and in machine learning tools for automatically tracking it. However, questions about what to measure, how to measure it, and how to relate naturalistic behaviour to neural activity and cognitive processes remain unresolved. In this Perspective, we propose a general experimental and computational framework - kinematic coding - for measuring how information about cognitive states is encoded in structured patterns of behaviour and how this information is read out by others during social interactions. This framework enables the design of new experiments and the generation of testable hypotheses that link behaviour, cognition, and neural activity at the single-trial level. Researchers can employ this framework to identify single-subject, single-trial encoding and readout computations and address meaningful questions about how information encoded in bodily motion is transmitted and communicated.

The representation of decision variables in orbitofrontal cortex is longitudinally stable

The computation and comparison of subjective values underlying economic choices rely on the orbitofrontal cortex (OFC). In this area, distinct groups of neurons encode the value of individual options, the binary choice outcome, and the chosen value. These variables capture both the choice input and the choice output, suggesting that the cell groups found in the OFC constitute the building blocks of a decision circuit. Here, we show that this neural circuit is longitudinally stable. Using two-photon calcium imaging, we record from the OFC of mice engaged in a juice-choice task. Imaging of individual cells continues for up to 40 weeks. For each cell and each session pair, we compare activity profiles using cosine similarity, and we assess whether the neuron encodes the same variable in both sessions. We find a high degree of stability and a modest representational drift. Quantitative estimates indicate that this drift would not randomize the circuit within the animal's lifetime.

Sensory choices as logistic classification

Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement it by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.

Multiattribute Decision-making in Macaques Relies on Direct Attribute Comparisons

In value-based decisions, there are frequently multiple attributes, such as cost, quality, or quantity, that contribute to the overall goodness of an option. Because one option may not be better in all attributes at once, the decision process should include a means of weighing relevant attributes. Most decision-making models solve this problem by computing an integrated value, or utility, for each option from a weighted combination of attributes. However, behavioral anomalies in decision-making, such as context effects, indicate that other attribute-specific computations might be taking place. Here, we tested whether rhesus macaques show evidence of attribute-specific processing in a value-based decision-making task. Monkeys made a series of decisions involving choice options comprising a sweetness and probability attribute. Each attribute was represented by a separate bar with one of two mappings between bar size and the magnitude of the attribute (i.e., bigger = better or bigger = worse). We found that translating across different mappings produced selective impairments in decision-making. Choices were less accurate and preferences were more variable when like attributes differed in mapping, suggesting that preventing monkeys from easily making direct attribute comparisons resulted in less accurate choice behavior. This was not the case when mappings of unalike attributes within the same option were different. Likewise, gaze patterns favored transitions between like attributes over transitions between unalike attributes of the same option, so that like attributes were sampled sequentially to support within-attribute comparisons. Together, these data demonstrate that value-based decisions rely, at least in part, on directly comparing like attributes of multiattribute options.

Neuronal Activity in the Gustatory Cortex during Economic Choice

An economic choice entails computing and comparing the values of individual offers. Offer values are represented in the orbitofrontal cortex (OFC)-an area that participates in value comparison-but it is unknown where offer values are computed in the first place. One possibility is that this computation takes place in OFC. Alternatively, offer values might be computed upstream of OFC. For choices between edible goods, a primary candidate is the gustatory region of the anterior insula (gustatory cortex, GC). Here we recorded from the GC of male rhesus monkeys choosing between different juice types. As a population, neurons in GC represented the flavor, the quantity, and the subjective value of the juice chosen by the animal. These variables were represented by distinct groups of cells and with different time courses. Specifically, chosen value signals emerged shortly after offer presentation, while neurons encoding the chosen juice and the chosen quantity peaked after juice delivery. Surprisingly, neurons in GC did not represent individual offer values in a systematic way. In a computational sense, the variables encoded in GC follow the process of value comparison. Thus our results argue against the hypothesis that offer values are computed in GC. At the same time, signals representing the subjective value of the expected reward indicate that responses in GC are not purely sensory. Thus neuronal responses in GC appear consummatory in nature.

Sensory choices as logistic classification

Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

The Representation of Decision Variables in Orbitofrontal Cortex is Longitudinally Stable

The computation and comparison of subjective values underlying economic choices rely on the orbitofrontal cortex (OFC). In this area, distinct groups of neurons encode the value of individual options, the binary choice outcome, and the chosen value. These variables capture both the input and the output of the choice process, suggesting that the cell groups found in OFC constitute the building blocks of a decision circuit. Here we show that this neural circuit is longitudinally stable. Using two-photon calcium imaging, we recorded from mice choosing between different juice flavors. Recordings of individual cells continued for up to 20 weeks. For each cell and each pair of sessions, we compared the activity profiles using cosine similarity, and we assessed whether the cell encoded the same variable in both sessions. These analyses revealed a high degree of stability and a modest representational drift. A quantitative estimate indicated this drift would not randomize the circuit within the animal's lifetime.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

BACKGROUND

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making.

MATERIALS AND METHODS

We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively).

RESULTS

The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC.

CONCLUSIONS

The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

Is this worth the trouble? Strategic conflict management in Tonkean macaques

Conflict management entails preventing and repairing damages resulting from social conflicts. While previous research has emphasized post-conflict actions like reconciliation, the understanding of how primates weigh the costs and benefits of conflict remains limited. Uncovering this hidden but fundamental aspect of conflict management requires addressing actively avoided social conflicts. In a study involving semi-free ranging Tonkean macaques, individuals were presented with social dilemmas: displacing a peer to access a preferred juice reward or opting for a peer-free but less preferred one to avoid conflict. The results showed that subjects attributed a cost to the social conflict and did not demonstrate a systematic drive to dominate. Decision modeling revealed integration of peer hierarchy and reward subjective value, with subjects' own social rank impacting the balance between these social and economic dimensions. Overall, this research highlights how primates strategically address group cohesion and peacekeeping, sometimes at the expense of personal preferences.

Neuronal origins of reduced accuracy and biases in economic choices under sequential offers

Economic choices are characterized by a variety of biases. Understanding their origins is a long-term goal for neuroeconomics, but progress on this front has been limited. Here, we examined choice biases observed when two goods are offered sequentially. In the experiments, rhesus monkeys chose between different juices offered simultaneously or in sequence. Choices under sequential offers were less accurate (higher variability). They were also biased in favor of the second offer (order bias) and in favor of the preferred juice (preference bias). Analysis of neuronal activity recorded in the orbitofrontal cortex revealed that these phenomena emerged at different computational stages. Lower choice accuracy reflected weaker offer value signals (valuation stage), the order bias emerged during value comparison (decision stage), and the preference bias emerged late in the trial (post-comparison). By neuronal measures, each phenomenon reduced the value obtained on average in each trial and was thus costly to the monkey.