Contribution of cognitive psychology and neuropsychology

The emotional function is a crucial factor in understanding human behaviour, decision-making processes, social interactions and all in all mental health. To begin with, it’s important to define what emotional function is. It’s a combination of several processes by which individuals express, recognise and regulate their emotions. Emotions are intense feelings which are appearing as a reaction to outward stimuli such as changes in the environment or social interactions. There are 6 basic emotions: anger, fear, disgust, surprise, sadness and happiness (Andrews, 2016). Also, it’s important to outdraw 3 components of Emotional function: autonomic arousal, which happens involuntarily triggered by emotions as a physiological response (Andrews, 2016); emotions can be categorised as positive and negative effects which include both internal feelings and external expressions (Lazarus, 1991); State vs. Mood component- moods are more stable over time and enduring when states are transient and situational. Such emotional disorders as anxiety, for instance, illustrate how these states could be pathological (Andrews, 2016).

Indeed the importance of the emotional function cannot be denied. If to look at it from the evolutionary point of view it has an important role in increasing individual survival and social communication. According to Darwin’s Theory of Emotions (1872), emotions are inherited behavioural patterns that have evolved to enhance survival. For example, the function of raised eyebrows and widened eyes, while being in a state of fear, is the increased visual sharpness which is a helpful mechanism of threat detection. Also, Darwin argued on the example of blind children who blushed when felt shame that emotions are innate or inherited and are not learned by individuals. Thus, making it crucially important for studying to understand human behaviour. This essay will specifically focus on exploring the contribution that cognitive psychology and cognitive neuropsychology have made to the study of emotional function.

When behaviourism was a dominant approach brain functions have been viewed as more simple stimuli-reaction mechanisms. For example, the James-Lange theory claims that emotions result from psychological reactions to events. Another theory of this time is Cannon-Bard (1927), which proposed that emotions and psychological responses happen simultaneously and independently in response to stimuli, with the thalamus playing a central role in emotional generation. Later research has shown that the thalamus is not the sole centre for emotional processing. Studies using functional magnetic resonance imaging (fMRI) revealed that the limbic system, including the amygdala, prefrontal cortex and insula, plays a crucial role in emotional generation and regulation (Phan et al., 2002).

For example, the latest meta-analysis by Berboth and Morawetz (2021) examined the neural underpinnings of specific brain regions involved in emotional functions across 15 neuroimaging studies. They performed a coordinate-based meta-analysis using the activation likelihood estimation (ALE) algorithm on studies which research the connectivity between the amygdala and other regions involved in emotion regulation through psychophysiological interaction (PPI) analysis. Results showed that during emotion regulation, connectivity between the amygdala and the left ventrolateral prefrontal cortex was identified in PPI studies. This suggests that reappraisal, as a specific strategy of emotion regulation, influences how these brain regions communicate during the process. Additionally, they have found convergent connectivity between the amygdala and the right dorsolateral prefrontal cortex, the left ventrolateral prefrontal cortex, and the dorsomedial prefrontal cortex during the analysis of the functional interaction of these brain parts during the process of down-regulation of emotions. These findings show the neurally-derived models of emotion regulation and highlight the dynamic of interactions between systems responsible for generating and regulating emotions.

Such advanced tools as fMRI and PET showed how complex the brain is. In contrast to the previous view of the brain as a simple stimuli-reaction mechanism, fMRI and PET allowed scientists to examine the hierarchical organisation of the brain, showing how different layers of neural circuits predict sensory inputs at various levels of abstraction (Friston, 2005). These findings refined models like dual-process theory, which posits that both automatic and controlled processes are involved in emotional regulation (Thompson, 2009). Moreover, dysfunction in dual-processing can cause different psychological disorders. For example, there have been studies that show how impaired prefrontal regulation (analytic process) can lead to an overactive amygdala response (heuristic process). This contributes to the development of anxiety and mood disorders, additionally, this can lead to cognitive biases and delusions (Bronstein et al., 2019).

Studying the dysregulation mechanism of effective regulation could be beneficial for uncovering the nature of associated psychological disorders and thus give a better understanding of effective cognitive and pharmacological treatment. Many forms of psychopathology connect with failures in emotional regulation processes, which can lead to the development of various issues from distress to self-destructive behaviours (Ochsner and Gross, 2005).

Cognitive neuroscience has contributed to neuropsychological accounts by elucidating the neural mechanisms underlying emotional function. For instance, lesion studies have shown that damage to the prefrontal cortex impairs emotional regulation, supporting its role in top-down control of emotions. One of the notable examples is – the case of Phineas Gage, which involves a railway worker who survived severe brain damage that dramatically changed his personality and behaviour (Harlow, 1868).

However, limitations such as small sample sizes and the complexity of isolating specific neural correlates highlight the need for further research. (Andrews, 2016)

More recent research, specifically on neural correlations of emotional regulation, found that the interaction between the amygdala and prefrontal cortex is critical for effective emotional regulation. This has been possible to uncover with the use of fMRI technology, which highlights how advancement in brain imaging techniques is deepening our understanding of the underlying mechanisms (Pessoa, 2020). Another notable example is research by Torrisi et al. (2018), where they explored in depth 2 interconnected parts of the brain: the bed nucleus of the stria terminalis (BNST) and the central amygdala (CeA) of the extended amygdala, which is responsible for mediating responses to sustained, unpredictable threats. In their study, they examined the changes in the connectivity of these parts during sustained anticipation of electric shock. According to the results BNST and CeA become less coupled with ventromedial prefrontal cortex cingulate, and nucleus accumbens, at the same time they become more coupled with the thalamus, under threat. These findings suggest that by examining specific roles and interactions of CeA and BNST it’s possible to see their contribution to the anxious state and its maintenance.

It’s without a doubt, that combination of cognitive psychology, cognitive neuropsychology, and cognitive neuroscience is crucial for a comprehensive understanding of emotional function. Cognitive psychology provides theoretical frameworks, neuropsychology highlights the insights through lesion and behavioural studies, while neuroscience shows the underlying mechanism of it all. Emotional function is multidimensional and complex, it requires interdisciplinary research. Future research should explain other emotion regulation strategies, understand individual differences and develop targeted interventions for emotional dysregulation. This will further enrich the understanding and treatment of psychological disorders.

References

Andrewes, D. (2016). Chapter 8: Emotional and Social Dysfunction. In Neuropsychology:

From Theory to Practice. Routledge: Dawson Books.

Berboth, S., & Morawetz, C. (2021). Amygdala-prefrontal connectivity during emotion

regulation: A meta-analysis of psychophysiological interactions. Neuropsychologia,

153. https://doi.org/10.1016/j.neuropsychologia.2021.107767.

Bronstein, M. V., Pennycook, G., Joormann, J., Corlett, P. R., & Cannon, T. D. (2019). Dual-

process theory, conflict processing, and delusional belief. Clinical psychology review,

72, 101748. https://doi.org/10.1016/j.cpr.2019.101748

Cannon, W. B. (1927). The James-Lange theory of emotions: a critical examination and an

alternative theory. The American Journal of Psychology, 39, 106–124.

https://doi.org/10.2307/1415404

Darwin, C. (1872). The expression of the emotions in man and animals. John Murray.

https://shorturl.at/NPw4O

Harlow, J. M. (1868). Recovery from the Passage of an Iron Bar through the Head.

Publications of the Massachusetts Medical Society, 2, 327-347.

Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal

Society B: Biological Sciences, 360(1456), 815-836.7

Lazarus, R.S. (1991). Progress on a cognitive–motorational relational theory of emotion.

American Psychologist 46, 819–34. https://doi.org/10.1037/0003-066X.46.8.819

Ochsner, K., Gross, J. (2005). The cognitive control of emotion. Trends in Cognitive

Sciences, 9(5), 242-249. https://doi.org/10.1016/j.tics.2005.03.010

Pessoa, L. (2020). The Cognitive-Emotional Brain: From Interactions to Integration. MIT

Press. https://doi.org/10.7551/mitpress/9780262019569.003.0001

Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional Neuroanatomy of

Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fMRI.

NeuroImage, 16(2), 331-348. https://doi.org/10.1006/nimg.2002.1087

Thompson, V. A. (2009). Dual-process theories: A metacognitive perspective. In J. St. B. T.

Evans & K. Frankish (Eds.), In two minds: Dual processes and beyond, 171-195.

Oxford University Press.

https://doi.org/10.1093/acprof:oso/9780199230167.003.0008

Torrisi, S., Gorka, A. X., Gonzalez-Castillo, J., O’Connell, K., Balderston, N., Grillon, C., &

Ernst, M. (2018). Extended amygdala connectivity changes during sustained shock

anticipation. Translational psychiatry, 8(1), 33. https://doi.org/10.1038/s41398-017-

0074-6

What is the evidence that supports the idea that measures of individual differences can predict human behaviour?

This discussion will critically examine the evidence of the predictive power of individual differences to predict behaviours. 

To begin with, research has shown consistently that personality traits, particularly those from the Five-Factor Model (e.g., conscientiousness, neuroticism), are useful in predicting behaviour. For example, conscientiousness is linked to job performance, academic success, and better care of overall health, while neuroticism is associated with negative mental health outcomes (Maltby et al., 2023). Moreover, trait theorists claim that finding out the source traits of a person by testing to which extent a person possesses surface traits, will allow them to predict an individual’s behaviour. They view traits as stable characteristics, which allows future behaviour prediction (Cervone & Pervin, 2013). 

The major critique of using personality traits to predict behaviour is the context dependency of behaviour. For example, Geukes et al. (2017) show that personality traits can predict behaviour to some extent, but it has limitations due to significant variability depending on the context. Similarly, Lievens et al. (2018) highlight the importance of recognising substantial intraindividual variability in behaviour across different situations and for the most accurate result, both between and within-person trait variability should be measured. 

However, individual differences observed in behaviour are not merely psychological constructs but have a physiological basis. For instance, neuroimaging studies indicate that structural differences in the brain are linked to behavioural and cognitive abilities differences. Particularly, MRI studies showed that inter-individual variability in cognitive functions like memory, motor control, perception, and ability to introspect can be predicted from the structure of grey and white matter. Researchers stated that the differences in strengths of white matter tract connectivity allow higher or lower speed of information transfer across the brain’s regions, which can be linked with inter-individual differences in human behaviour. This has been studied using the Diffusion Tensor Imaging technique. Moreover, after conducting experiments, researchers stated that inter-individual variability in the ability to correct and quickly choose the response during visual stimulus tests correlates with the grey matter density of the pre-supplementary motor area. (Kanai & Rees, 2011).

On the other hand, critics argue that the relationship between brain structure and behaviour is more complex due to non-linear and multifunctional brain structure. One of the examples is the brain’s plasticity ability, which lets behavioural differences shape and reshape brain structure, at the same time brain structure can also influence behaviour (Pessoa, 2014).

In conclusion, the evidence suggests that individual differences, such as personality traits and brain structure, play a significant role in predicting behaviour. For example, the Five-Factor Model has been shown to accurately predict future important life outcomes such as job performance, and overall health. Additionally, neuroimaging studies provided a physiological basis, linking structural brain differences to behavioural and cognitive abilities variations.

However, the complexity of the non-linear nature of the brain and context dependency highlights the importance of developing an approach which will incorporate both psychological and physiological factors, when measuring individual differences. 

Reference List

Cervone D. & Pervin L. A. (2013). Personality: theory and research (Twelfth). Wiley.

Geukes, K., Nestler, S., Hutteman, R., Küfner, A., & Back, M. (2017). Trait personality and state variability: Predicting individual differences in within- and cross-context fluctuations in affect, self-evaluations, and behavior in everyday life. Journal of Research in Personality, 69, 124-138. https://doi.org/10.1016/J.JRP.2016.06.003.

Kanai, R., Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Reviews Neuroscience, 12, 231–242 (2011). https://shorturl.at/UsEKc

Lievens, F., Lang, J., Fruyt, F., Corstjens, J., Vijver, M., & Bledow, R. (2018). The Predictive Power of People’s Intraindividual Variability Across Situations: Implementing Whole Trait Theory in Assessment. Journal of Applied Psychology, 103, 753–771. https://doi.org/10.1037/apl0000280.

Maltby, J., Day, L., & Macaskill, A. (2023). Personality, Individual Differences (5th ed.). Pearson International Content. https://essexonline.vitalsource.com/books/9781292726960

Pessoa, L. (2014). Understanding brain networks and brain organization. Physics of Life Reviews, 11(3), 400-435. https://doi.org/10.1016/j.plrev.2014.03.005

Three “Eras” of Mental Processes Study: Evolution Through Biological Insights. 

How the biological study of mental processes has contributed to the development of psychology as a discipline? To better answer the question, this essay will illustrate how the study of mental processes has evolved through three significant phases: before Biological studies, this “era” focuses on the early philosophical and introspective approaches; the Stimulus-reaction period, which is the “era” of behaviourism and early neurophysiological models, which characterised the brain as a stimulus-response machine; Predictive Processing, the current “era”, which provides a more integrated and dynamic understanding of mental processes as proactive, prediction-based processes.

Brain’s function and structure across time.

Before going into a detailed exploration of the study of mental processes across the history of psychology, it’s important to look at the evolution of understanding brain functions and structures across the mentioned periods; to see the profound changes in the conceptualisation of the brain in psychology and neuroscience.

Before Biological contribution: In the beginning mind and body were mostly studied by philosophers. The understanding of the brain function and structure, of that time, was rudimentary and relayed on philosophical speculations. The anatomical knowledge was as well limited and the brain’s importance was overlooked. Aristotle for instance thought that the heart has a more crucial role and is the primary organ of sensation (Aristotle, 350 BCE). Another example of when the brain’s fun actions were viewed through metaphysical concepts, is the Humoral theory, which suggests that bodily fluids influenced behaviour and temperament (Hippocrates, 400 BCE). Later in the 17th century, Descartes proposed a new theory “Mind-Body Dualism”, where he distinctly separates the nature of the mind and the nature of the body, arguing that one can exist without another. Although he assigns a function of consciousness and reason to the brain.

Stimulus-Reaction Era: shaped by the rise of behaviourism and early neuroscience, the understanding of the brain shifted towards more empirical and anatomical forms. Which led to a clearer understanding of the brain’s structure and functions. One of the most significant findings was Broca’s discovery of the speech production centre in the brain, known as Broca’s area, which linked specific brain areas to cognitive function (Broca, 1861). This was the beginning of a new field- neurophysiology. Later Wernickle (1874) developed even further the brain-behaviour relationship, by identifying the brain’s area responsible for language comprehension. During the same period, the brain’s function was understood as a stimulus-response mechanism, (where specific inputs led to certain outputs.) This era was dominated by the behaviourists’ perspective that all behaviours could be understood as reflexes conditioned by environmental stimuli (Watson, 1913; Pavlov 1927).

The predictive processing era views the brain as an active participant that doesn’t just passively respond to the external world but proactively simulates and predicts the environment. The distinction of understanding brain structure in this era is neuroimaging technologies such as fMRI and PET scans which allowed to examine hierarchical organisation of the brain, showing how different layers of neural circuits predict sensory inputs at various levels of abstraction (Friston, 2005). The brain’s function is understood as a continuous prediction process, to minimise the error between its predictions and sensory inputs and by adjusting its predictions, shapes cognitive functions. As well as, construct and maintain perceptual reality (Clark, 2013). Unlike earlier theories which often separated mind and body, the modern approach emphasises the inseparability of cognitive processes from their biological bases, aligning psychology more closely with biological science.

This section compares how mental processes were understood and studied across different eras, examining each historical period through various psychological fields.

Perception and Cognition.

First era: In the evolution of psychological theories, early introspective methods by Descartes (1637) and Locke (1690) gave subjective insights but lacked empirical accuracy. Associationism was introduced by Hume (1739) and experimental approaches by Ebbinghaus (1913) began to systematise the study of memory and learning, by observing how subjects recall and connect ideas. The biggest flaw of these methods was introspection’s subjectivity. Wundt challenged that drawback by stressing the importance of the use of experimental methods to increase precision (Wundt, 1910). Developing on the idea Titchener, (1896) founded structuralism but was criticized for missing psychological holistic aspects. Later, Wertheimer, (1923) developed Gestalt psychology and switched the focus onto the observable behaviours and perception of the whole, which showed more objective insight into cognitive processes. During the Stimulus-Reaction era, behaviourism was a dominant theory during which the emphasis was on observable behaviours. In comparison to the previous era, this era is characterised by a more objective framework, including controlled laboratory settings as the main setting for behavioural analysis. Main methods such as Pavlov’s (1927) classical conditioning and Skinner’s (1938) operant conditioning defined brain function as a direct stimulus-response mechanism. However, a strong focus solely on the behaviour part was criticised for neglecting the brain’s complex cognitive and neurological underpinnings. Later Lashley’s (1929) lesion studies and Thompson’s (1986) investigations into neural plasticity challenged this limitation. They have expanded the understanding of the brain’s role and revealed complex synaptic changes and neural pathways, which are critical in learning and memory processes.

Despite the main focus of this era being on observable behaviour, it sets the groundwork for the cognitive-behaviour revolution. Clinicians started to recognise that to change or understand behaviours they need to take into consideration cognitive processes which co-occur with behaviours. This realisation led to the foundation of cognitive-behavioural therapy (CBT) (Beck, 1976).

Predictive Processing Era: This era has transformed the understanding of perception and cognition. For instance, Karl Friston (2005) and his influential research “The Free Energy Principle” showed how the brain simultaneously reduces surprises making predictions based on internal models and updating them using sensory input, thus giving the brain an active role in the perception processes. Additionally, his development of dynamic causal modelling shows the transition from previous approaches to connectivity, by using an explicit generative model which measures brain responses in their non-linear causal architecture (Friston et al., 2003). Moreover, he brought sophisticated statistical tools to neuroscience, enabling a detailed examination of how the brain minimises prediction errors. It could require more careful and resourceful interpretation to avoid misrepresenting complex brain functions. Nonetheless, he fundamentally challenged traditional stimulus-response models. (Friston, 2009). Studies like those by Rauss and Pourtois (2013) utilize fMRI and EEG to observe how top-down predictions influence sensory processing, showcasing the application of predictive models in real-time brain activity analysis. Andy Clark’s integration of Bayesian inference into psychological theory (Clark, 2013) offers a robust framework for understanding perception as an active, inference-driven process, in comparison to the past passive role in linear perception systems. While these methodologies provide profound insights into the brain’s predictive mechanisms, they also demand high computational resources and sophisticated data analysis skills, which can be a barrier to broader application. Additionally, the reliance on statistical modelling to infer neural processes requires assumptions that may oversimplify the underlying biological realities.

Clinical psychology.

First era: The Dominating theory of this period was Freudian psychoanalysis, which emphasised internal conflicts, childhood experiences, and the unconscious mind as determinants of psychopathology (Freud, 1923). Freud introduced talk therapy and psychoanalytical techniques in clinical settings (Freud, 1900). The methodology of that time included mostly theoretical and qualitative case studies, which lacked empirical factors and were hard to generalise.

Stimulus-reaction era: One of the main shifts was the application of behaviourist principles in clinical settings. For example, Wolpe (1958) developed a systematic desensitisation method, using classical conditioning to treat anxiety disorders. By his method, participants would practice relaxation techniques while being gradually exposed to fear- inducing stimuli. The method aims to recondition the patient’s response. Another notable example of the development of behaviour modification therapies is: that techniques such as token economies and contingency management were used in various settings, including hospitals and schools, to modify behaviours by manipulating reinforcements and punishments (Ayllon & Azrin, 1968).

This era also is signified by research on brain lesions, which provided a deeper understanding of the neural mechanisms underlying behaviour. For instance, studies by Milner et al., (1968) on patient H.M. demonstrated the role of the medial temporal lobe in memory formation, which changed clinical approaches to amnesia and other cognitive deficits. Additionally, studies on animals with induced lesions revealed critical brain areas involved in emotional responses, such as the amygdala and prefrontal cortex (Murray et al., 2022).

Another crucial contribution to clinical psychology was the discovery of neurotransmitters and their roles in mood and behaviour. For example, the identification of serotonin’s role in depression led to the development of selective serotonin reuptake inhibitors (SSRIs), revolutionizing the treatment of mood disorders (Wong et al., 2005). This biological perspective integrated pharmacological treatments with behavioural therapies, offering a more holistic approach to mental health.

Predictive Processing Era: This era significantly expanded the current understanding of various psychological disorders and their manifestation in the brain structure and function. One notable example is how Predictive Processing theories can explain the mechanisms behind delusions and hallucinations in schizophrenia. According to this, the symptoms appear due to impaired prediction error signalling within the brain, leading to an inability to distinguish between internally generated and external stimuli (Corlett et al. 2007). This has led to new approaches in psychosocial interventions that focus on enhancing the brain’s ability to form accurate predictions.

Another key contribution is in the treatment of Depression and Anxiety. Ramos-Grille et al. (2021) demonstrate a new outlook on the understanding of mood disorders. By using the Predictive Processing framework, he was able to examine in patients with depression how maladaptive predictive models could lead to persistent negative biases. Moreover, to correct cognitive distortions, he reinterpreted cognitive-behavioural treatment strategies as methods for updating those distorted brain predictions.

A similar approach was suggested for OCD interventions. Studies by Voon and colleagues (2015) in that area have shown that repetitive behaviours in OCD may stem from an over-reliance on prediction error minimisation strategies that inaccurately signal a need for corrective action. This approach makes it possible to recalibrate the brain’s predictive models to reduce compulsive behaviours.

Another groundbreaking discovery in clinical settings using the Predictive Processing model was explaining the perceptual peculiarities, such as hypersensitivities and attention to detail in Autism Spectrum Disorders (ASD). Pellicano and Burr’s (2012) research has shown that overwhelming sensory experiences in ASD are the result of the atypical predictive processes where sensory input is under-predicted. This led to the development of a therapeutic approach which modulates sensory prediction mechanisms. The integration of Predictive Processing into clinical psychology further developed neuroimaging tools to assess how therapy influences brain predictions. Such techniques as real-time fMRI and EEG neuro-feedback help clinicians to observe how therapeutic interventions influence brain activity patterns, and to choose more effective treatment (Zotev et al., 2014; Perronnet et al., 2017). These techniques not only help with a better understanding of undergoing processes of the brain predictions but also integrate advanced theories into everyday clinical practice.

Conclusion.

The biological study of mental processes had a great impact on the development and structuring of the field of psychology. It significantly changed the understanding of the brain-behaviour relationship and provided a robust empirical foundation. During the pre-biological era, psychological theories were mostly speculative and philosophical. Moreover, the scientists of that time were relying on introspective methods that lacked empirical foundation. This changed significantly during biological study development at the beginning of the stimulus-reaction era. Pioneering work by researchers like Ivan Pavlov and B.F. Skinner introduced systematic experimental methods to study behaviour, while neurophysiological discoveries by scientists such as Paul Broca and Karl Wernicke linked specific brain areas to cognitive functions, thus laying the groundwork for neuropsychology. Neuropsychology’s rapid development started with the predictive processing era. During this scientists further developed the understanding of mental processes. According to a new model the brain doesn’t have a passive role anymore as it was viewed in previous eras, it has an active role in predicting and interpreting sensory inputs and at the same time maintaining a complex framework of reality. One of the biggest contributions in the field was Karl Friston’s Free Energy Principle and advancements in neuroimaging technologies, such as fMRI and EEG, revolutionised an outlook on the brain’s hierarchical organisation and its predictive mechanisms. What is more, this era has also seen significant developments in clinical psychology, where predictive models have been applied to understand and treat various mental disorders. Indeed, the gap between theoretical models and clinical practice has been minimised by implementing real-time neuroimaging techniques in clinical settings. This allowed clinicians to be more accurate in assessing various disorders like schizophrenia, depression, OCD, and ASD. As well as it helped to tailor therapeutic interventions to the individual neurological needs of patients.

In conclusion, modern psychology has come a long way from speculative science to science grounded in empirical research. It would not be possible without biological study. This assay shows how intertwined the two fields are. The development of biological research has deepened the understanding of the connection between the human brain, its functions, structure, emotions, behaviours and reactions. Another important aspect is the significant development in the efficiency of therapeutic interventions and methodology. This highlights the inseparable nature of psychology and neuroscience. The future advancement in biological psychology will enrich the field and improve mental health care practices even more.

Reference List

Friston, K. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273-1302. https://doi.org/10.1016/S1053-8119(03)00202-7 

Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815-836. https://doi.org/10.1098/rstb.2005.1622 

Friston, K. (2009). The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences, 13(7), 293-301. https://shorturl.at/fdWMN 

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Lashley, K. S. (1929). Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain. University of Chicago Press. https://psycnet.apa.org/doi/10.1037/10017- 000 

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Perronnet, L., Lécuyer, A., Mano, M., Bannier, E., Lotte, F., Clerc, M., et al. (2017). Unimodal Versus Multimodal EEG-fMRI Neurofeedback of a Motor Imagery Task. Frontiers in Human Neuroscience, 11, 193. https://doi.org/10.3389/fnhum.2017.00193 

Ramos-Grille, I., Weyant, J., Wormwood, J. B., Robles, M., Vallès, V., Camprodon, J. A., & Chanes, L. (2022). Predictive processing in depression: Increased prediction error following negative valence contexts and influence of recent mood-congruent yet irrelevant experiences. Journal of affective disorders, 311, 8–16. https://doi.org/10.1016/j.jad.2022.05.030 

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How the structural integrity of the amygdala-prefrontal pathway predicts trait anxiety?

For the purpose of exploring a relationship between the structure/function of the human nervous system and emotion and/or behaviour, have been chosen the article “The Structural Integrity of an Amygdala–Prefrontal Pathway Predicts Trait Anxiety” by Kim M., and Whalen P. Their research aimed to explore the strategies of combining fMRI with DTI to identify the differences in structural pathways that predict behaviour outcomes. These two neuroimaging techniques allowed researchers to examine the biological basis of anxiety by comparing related structural and functional aspects of the brain, thus identifying how the structural integrity of the amygdala-prefrontal pathway predicts trait anxiety.

In this particular case, 20 healthy participants have been chosen to go through the series of tests. First, they were shown 36 images with fearful and neutral faces in random order. During this test participants have been scanned using functional magnetic resonance imaging (fMRI) to assess the amygdala’s activation in response to fearful versus neutral faces. This helps to understand the amygdala’s role in processing fear and anxiety. After the process, individuals were asked to fill out self-report cards where they needed to rate the valence and arousal levels of faces they’d seen and complete a questionnaire for assessing anxiety and depression levels. What is more, the diffusion tensor imaging (DTI) technique was employed to measure the structural integrity of white matter pathways that connect the amygdala and prefrontal cortex.

Findings showed that participants rated fearful faces to be more arousing and fearful than neutral faces. Moreover, DTI results showed a correlation between the structural integrity of the amygdala-prefrontal pathway (as measured by FA values) and levels of trait anxiety, rather than a direct correlation between amygdala responses to fearful faces and FA values. This indicates that stronger structural connectivity, suggested by higher FA values, is associated with lower levels of trait anxiety, highlighting the importance of structural integrity in anxiety. 

FMRI data showed how individual differences in amygdala reactivity are related to trait anxiety. This approach provided an outlook on the importance of both the structure and function of brain pathways in forming emotional responses and behaviours related to anxiety. FMRI and other functional neuroimaging techniques have been used and advocated for as useful methodologies to understand how different regions of the brain are connected (Henson, 2005).  

This study demonstrates a direct relationship between the structural integrity of the amygdala-prefrontal pathway and trait anxiety, revealing how brain structure influences emotional regulation and behaviour.  

Increased fractional anisotropy values indicate higher structural connectivity which correlates with lower levels of trait anxiety. This suggests that the brain’s physical connections play a crucial role in how individuals perceive and respond to fear, underlining a biological basis for emotional responses. Similar findings can be seen in an earlier paper by LeDoux (1998) on the amygdala’s role in fear processing, where he showed how structural variations in brain pathways can affect emotional and behavioural outcomes.

Resources:

Henson, R. (2005). What can functional neuroimaging tell the experimental psychologist? Quarterly Journal of Experimental Psychology, 58A(2), 193-233.

Kim, M. J., & Whalen, P. J. (2009). The Structural Integrity of an Amygdala–Prefrontal Pathway Predicts Trait Anxiety. Journal of Neuroscience, 29(37), 11614-11618. https://www.jneurosci.org/content/jneuro/29/37/11614.full.pdf

LeDoux, J. (1998). The Emotional Brain: The Mysterious Underpinnings of Emotional Life. Simon & Schuster. https://books.google.rs/books?id=7EJN5I8sk2wC&printsec=frontcover&hl=sr#v=onepage&q&f=false

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How the biological study of mental processes has contributed to the development of psychology as a discipline?

Introduction

To better answer the question, this post will illustrate how the study of mental processes has evolved through three significant phases: before Biological studies, this “era” focuses on the early philosophical and introspective approaches; the Stimulus-reaction period, this is the “era” of behaviourism and early neurophysiological models, which characterised brain as a stimulus-response machine; Predictive Processing, the current “era”, which provides a more integrated and dynamic understanding of mental processes as proactive, prediction-based processes.

Brain’s function and structure across the time

Before going into a detailed exploration of the study of mental processes across the history of psychology, it’s important to look at the evolution of understanding brain functions and structures across the mentioned periods; to see the profound changes in the conceptualisation of the brain in psychology and neuroscience. 

Before Biological contribution: in this era, the understanding of the brain function and structure was rudimentary and relayed on philosophical speculations. The anatomical knowledge was limited and the brain’s importance was overlooked. Aristotle for instance thought that the heart has a more crucial role and is the primary organ of sensation (Aristotle, 350 BCE). The brain’s function, for example was often explained through metaphysical concepts, like the Humoral theory, which suggests that bodily fluids influenced behaviour and temperament (Hippocrates, 400 BCE). Later in the 17th century, Descartes proposed a new theory “Mind-Body Dualism”, where he distinctly separates the nature of the mind and the nature of the body, arguing that one can exist without another. Although he assigns a function of consciousness and reason to the brain. 

Stimulus-Reaction Era: shaped by the rise of behaviourism and early neuroscience, the understanding of the brain shifted towards more empirical and anatomical forms. Which led to a clearer understanding of the brain’s structure and functions. One of the most significant findings was Broca’s discovery of the speech production centre in the brain, known as Broca’s area, which linked specific brain areas to cognitive function (Broca, 1861). This was the beginning of a new field- neurophysiology. Later Wernickle (1874) developed even further the brain-behaviour relationship, by identifying the brain’s area responsible for language comprehension. During the same period, the brain’s function was understood as a stimulus-response mechanism, (where specific inputs led to certain outputs.) This era was dominated by the behaviourists’ perspective that all behaviours could be understood as reflexes conditioned by environmental stimuli (Watson, 1913; Pavlov 1927).

The Predictive Processing Era: views the brain as an active participant that doesn’t just passively respond to the external world but proactively simulates and predicts the environment. The distinction of understanding brain structure in this era is neuroimaging technologies such as fMRI and PET scans which allowed to examine hierarchical organisation of the brain, showing how different layers of neural circuits predict sensory inputs at various levels of abstraction (Friston, 2005). The brain’s function is understood as a continuous prediction process, to minimise the error between its predictions and sensory inputs and by adjusting its predictions, shapes cognitive functions. As well as, construct and maintain perceptual reality (Clark, 2013). Unlike earlier theories which often separated mind and body, the modern approach emphasises the inseparability of cognitive processes from their biological bases, aligning psychology more closely with biological science.

References

  • Aristotle. (circa 350 BCE). De Anima (On the Soul).
  •  Broca, P. (1861). Remarks on the seat of the faculty of articulated language, following an observation of aphemia (loss of speech). Bulletin de la Société Anatomique.
  • Clark, A. (2013). “Whatever next? Predictive brains, situated agents, and the future of cognitive science.” Behavioral and Brain Sciences.
  • Friston, K. (2005). “A theory of cortical responses.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.
  • Hippocrates. (460-370 BCE). On the Sacred Disease.
  • Pavlov, I.P. (1927). Conditioned Reflexes. London: Oxford University Press.
  • Watson, J.B. (1913). “Psychology as the behaviorist views it.” Psychological Review
  • Wernicke, C. (1874). Der aphasische Symptomencomplex: Eine psychologische Studie auf anatomischer Basis. Cohn & Weigert.

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