The proposed two-tiered model makes at least two valuable contributions to life history theory. First, the model distinguishes between the effects of energetic stress, which are primarily mediated through biological pathways and slow the pace of sexual development and reproduction, and those of ambient cues to extrinsic mortality (EM), which are mediated mainly through psychological processes (e.g., perception, interpretation, internalization, etc.) and accelerate sexual development and reproduction. Second, the model outlines how energetic stress and EM cues can act together, hierarchically before puberty and dualistically after puberty, to produce “mixed” life history profiles that include both “fast” and “slow” life history traits.
The model goes some way towards elucidating the theoretical and empirical contradictions that impeded the development of the field. However, it is limited by design to EM levels (harshness), excluding variability in EM (unpredictability) (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009), which is a distinct environmental stressor that garners increasing research attention (e.g., Maranges et al., Reference Maranges, Timbs, Psihogios and Haddad2025). My commentary focuses on the possible challenges of integrating unpredictability into the two-tiered model. I do not engage with the competing hypotheses regarding the role of unpredictability in predicting life history outcomes. Instead, I focus on the ability to distinguish between harshness and unpredictability effects in each tier of the model, considering the different mediating processes at play.
Incorporating unpredictability into the first tier of the model should be relatively straightforward because the physiological outcomes of energetic conditions closely track their environmental antecedents. For example, the temporal statistical distribution of resource availability should be reflected in the temporal statistical distribution of caloric intake. Similarly, the temporal statistical distribution of pathogen load should be reflected in the temporal statistical distribution of immune responses. Because these embodied distributions of bioenergetic stress are reflective of both the degree of harshness (low mean) and the degree of unpredictability (high variance, low autocorrelation) in the local environment, harsh and unpredictable energetic stress should leave distinct physiological footprints, allowing for specialized developmental adaptations (Frankenhuis et al., Reference Frankenhuis, Nettle and Dall2019). However, relying only on physiological markers may not account for cyclical elements of the environment (e.g., seasons), conflating predictable and unpredictable changes in energetic stress. An accurate assessment of harshness and unpredictability in energetic stress might also require cognitive mediation, perhaps through statistical learning mechanisms (Frankenhuis et al., Reference Frankenhuis, Gergely and Watson2013; Young et al., Reference Young, Frankenhuis and Ellis2020).
In contrast, ambient cues to harshness and unpredictability need not reflect the temporal or spatial distribution of EM in the environment. Ambient cues to high EM levels (harshness) encompass both prolonged exposures to mortality stress (e.g., living in a war zone) and one-time exposures to extreme events (e.g., the death of a sibling). Similarly, ambient cues to high EM variability (unpredictability) encompass both actual variability in EM (e.g., frequent changes in resource availability) and one-time exposures to major changes in one’s close environment (e.g., parental separation). Physiological stress responses alone cannot capture the fitness-relevant information encapsulated in these ambient cues. It is primarily up to psychological mechanisms to extract this information and use it to adaptively calibrate life history outcomes. For the effects of harshness and unpredictability to be dissociable, these psychological mechanisms should elicit different responses to ambient cues for harshness and unpredictability (Szepsenwol, Reference Szepsenwol2022).
However, identifying such specific psychological mediators is still a work in progress. Generally, EM cues should increase threat perceptions and lower the expected lifespan (Canetti-Nisim et al., Reference Canetti-Nisim, Halperin, Sharvit and Hobfoll2009; Chisholm et al., Reference Chisholm, Quinlivan, Petersen and Coall2005), while unpredictability cues should induce perceptions that future outcomes and relationships are unreliable (an unpredictability schema, insecure attachment; Cabeza de Baca et al., Reference Cabeza de Baca, Barnett and Ellis2016; Ross & Hill, Reference Ross and Hill2002; Szepsenwol & Simpson, Reference Szepsenwol and Simpson2019). Indeed, studies have found that exposure to ambient cues to unpredictability in childhood is associated with insecure and anxious attachment, indicating lower trust in relationships (Barbaro & Shackelford, Reference Barbaro and Shackelford2019; French et al., Reference French, Whitley, Altgelt and Meltzer2020; Szepsenwol et al., Reference Szepsenwol, Simpson, Griskevicius and Raby2015). Moreover, individuals recently exposed to unpredictability cues were found to hold stronger unpredictability beliefs, perceiving their physical and social environment as less reliable (Szepsenwol et al., Reference Szepsenwol, Shai, Zamir and Simpson2021). However, in the same study, exposure to morbidity and mortality cues (e.g., close others getting seriously ill) was also uniquely related to stronger unpredictability beliefs, with a similar strength of association. It is not surprising that being reminded of the possibility of death makes life seem less predictable, suggesting that some harshness cues could double as unpredictability cues. Indeed, studies have found that ambient cues that should signal high EM levels (e.g., poor neighborhood quality, bullying victimization, exposure to violence) are associated with stronger unpredictability schemas, even when some ambient cues to unpredictability (e.g., family instability) do not (Deitzer et al., Reference Deitzer, Frankenhuis, Jacobsen, Ribeaud, Eisner, Feinberg and van Gelder2025; Proffitt Leyva & Hill, Reference Proffitt Leyva and Hill2018). Attachment anxiety, often linked to unpredictable childhoods (Szepsenwol & Simpson, Reference Szepsenwol, Simpson, Thompson, Simpson and Berlin2021), can also develop in response to the loss of an attachment figure (e.g., parent, spouse), whether this loss was a result of a breakup, an unpredictability cue, or death, a mortality cue (Mikulincer & Shaver, Reference Mikulincer and Shaver2022).
These findings suggest that while ambient cues to harshness and unpredictability represent different categories of environmental stress, they share some of the same psychological mediators. These mediators are not limited to cognitive schemas and attachment representations. Both childhood unpredictability and childhood exposure to morbidity and mortality are associated with heightened emotional responses to mortality threats (Cohen et al., Reference Cohen, Haimov and Szepsenwol2024; Haimov et al., Reference Haimov, Szepsenwol and Cohen2022). Dysregulated emotion, in turn, mediates behavioral outcomes associated with fast life histories, such as relationship and parenting difficulties (Szepsenwol et al., Reference Szepsenwol, Simpson, Griskevicius, Zamir, Young, Shoshani and Doron2022; Zreik et al., Reference Zreik, Haimov and Szepsenwol2025). Therefore, the behavioral effects of ambient cues to high EM levels could be conflated with those of high EM variability, posing a challenge to incorporating the harshness–unpredictability framework into the two-tiered model. To be clear, we should not discard research on ambient cues to EM. Their effects on life history outcomes have been documented extensively. Instead, we should acknowledge that ambient cues are messy and can signal multiple things simultaneously. At the psychological level, ambient cues to harshness and unpredictability may not always be dissociable, making it harder to distinguish between adaptations to harshness and adaptations to unpredictability.
The proposed two-tiered model makes at least two valuable contributions to life history theory. First, the model distinguishes between the effects of energetic stress, which are primarily mediated through biological pathways and slow the pace of sexual development and reproduction, and those of ambient cues to extrinsic mortality (EM), which are mediated mainly through psychological processes (e.g., perception, interpretation, internalization, etc.) and accelerate sexual development and reproduction. Second, the model outlines how energetic stress and EM cues can act together, hierarchically before puberty and dualistically after puberty, to produce “mixed” life history profiles that include both “fast” and “slow” life history traits.
The model goes some way towards elucidating the theoretical and empirical contradictions that impeded the development of the field. However, it is limited by design to EM levels (harshness), excluding variability in EM (unpredictability) (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009), which is a distinct environmental stressor that garners increasing research attention (e.g., Maranges et al., Reference Maranges, Timbs, Psihogios and Haddad2025). My commentary focuses on the possible challenges of integrating unpredictability into the two-tiered model. I do not engage with the competing hypotheses regarding the role of unpredictability in predicting life history outcomes. Instead, I focus on the ability to distinguish between harshness and unpredictability effects in each tier of the model, considering the different mediating processes at play.
Incorporating unpredictability into the first tier of the model should be relatively straightforward because the physiological outcomes of energetic conditions closely track their environmental antecedents. For example, the temporal statistical distribution of resource availability should be reflected in the temporal statistical distribution of caloric intake. Similarly, the temporal statistical distribution of pathogen load should be reflected in the temporal statistical distribution of immune responses. Because these embodied distributions of bioenergetic stress are reflective of both the degree of harshness (low mean) and the degree of unpredictability (high variance, low autocorrelation) in the local environment, harsh and unpredictable energetic stress should leave distinct physiological footprints, allowing for specialized developmental adaptations (Frankenhuis et al., Reference Frankenhuis, Nettle and Dall2019). However, relying only on physiological markers may not account for cyclical elements of the environment (e.g., seasons), conflating predictable and unpredictable changes in energetic stress. An accurate assessment of harshness and unpredictability in energetic stress might also require cognitive mediation, perhaps through statistical learning mechanisms (Frankenhuis et al., Reference Frankenhuis, Gergely and Watson2013; Young et al., Reference Young, Frankenhuis and Ellis2020).
In contrast, ambient cues to harshness and unpredictability need not reflect the temporal or spatial distribution of EM in the environment. Ambient cues to high EM levels (harshness) encompass both prolonged exposures to mortality stress (e.g., living in a war zone) and one-time exposures to extreme events (e.g., the death of a sibling). Similarly, ambient cues to high EM variability (unpredictability) encompass both actual variability in EM (e.g., frequent changes in resource availability) and one-time exposures to major changes in one’s close environment (e.g., parental separation). Physiological stress responses alone cannot capture the fitness-relevant information encapsulated in these ambient cues. It is primarily up to psychological mechanisms to extract this information and use it to adaptively calibrate life history outcomes. For the effects of harshness and unpredictability to be dissociable, these psychological mechanisms should elicit different responses to ambient cues for harshness and unpredictability (Szepsenwol, Reference Szepsenwol2022).
However, identifying such specific psychological mediators is still a work in progress. Generally, EM cues should increase threat perceptions and lower the expected lifespan (Canetti-Nisim et al., Reference Canetti-Nisim, Halperin, Sharvit and Hobfoll2009; Chisholm et al., Reference Chisholm, Quinlivan, Petersen and Coall2005), while unpredictability cues should induce perceptions that future outcomes and relationships are unreliable (an unpredictability schema, insecure attachment; Cabeza de Baca et al., Reference Cabeza de Baca, Barnett and Ellis2016; Ross & Hill, Reference Ross and Hill2002; Szepsenwol & Simpson, Reference Szepsenwol and Simpson2019). Indeed, studies have found that exposure to ambient cues to unpredictability in childhood is associated with insecure and anxious attachment, indicating lower trust in relationships (Barbaro & Shackelford, Reference Barbaro and Shackelford2019; French et al., Reference French, Whitley, Altgelt and Meltzer2020; Szepsenwol et al., Reference Szepsenwol, Simpson, Griskevicius and Raby2015). Moreover, individuals recently exposed to unpredictability cues were found to hold stronger unpredictability beliefs, perceiving their physical and social environment as less reliable (Szepsenwol et al., Reference Szepsenwol, Shai, Zamir and Simpson2021). However, in the same study, exposure to morbidity and mortality cues (e.g., close others getting seriously ill) was also uniquely related to stronger unpredictability beliefs, with a similar strength of association. It is not surprising that being reminded of the possibility of death makes life seem less predictable, suggesting that some harshness cues could double as unpredictability cues. Indeed, studies have found that ambient cues that should signal high EM levels (e.g., poor neighborhood quality, bullying victimization, exposure to violence) are associated with stronger unpredictability schemas, even when some ambient cues to unpredictability (e.g., family instability) do not (Deitzer et al., Reference Deitzer, Frankenhuis, Jacobsen, Ribeaud, Eisner, Feinberg and van Gelder2025; Proffitt Leyva & Hill, Reference Proffitt Leyva and Hill2018). Attachment anxiety, often linked to unpredictable childhoods (Szepsenwol & Simpson, Reference Szepsenwol, Simpson, Thompson, Simpson and Berlin2021), can also develop in response to the loss of an attachment figure (e.g., parent, spouse), whether this loss was a result of a breakup, an unpredictability cue, or death, a mortality cue (Mikulincer & Shaver, Reference Mikulincer and Shaver2022).
These findings suggest that while ambient cues to harshness and unpredictability represent different categories of environmental stress, they share some of the same psychological mediators. These mediators are not limited to cognitive schemas and attachment representations. Both childhood unpredictability and childhood exposure to morbidity and mortality are associated with heightened emotional responses to mortality threats (Cohen et al., Reference Cohen, Haimov and Szepsenwol2024; Haimov et al., Reference Haimov, Szepsenwol and Cohen2022). Dysregulated emotion, in turn, mediates behavioral outcomes associated with fast life histories, such as relationship and parenting difficulties (Szepsenwol et al., Reference Szepsenwol, Simpson, Griskevicius, Zamir, Young, Shoshani and Doron2022; Zreik et al., Reference Zreik, Haimov and Szepsenwol2025). Therefore, the behavioral effects of ambient cues to high EM levels could be conflated with those of high EM variability, posing a challenge to incorporating the harshness–unpredictability framework into the two-tiered model. To be clear, we should not discard research on ambient cues to EM. Their effects on life history outcomes have been documented extensively. Instead, we should acknowledge that ambient cues are messy and can signal multiple things simultaneously. At the psychological level, ambient cues to harshness and unpredictability may not always be dissociable, making it harder to distinguish between adaptations to harshness and adaptations to unpredictability.
Funding statement
This work was supported by the Israel Science Foundation (grant number 1197/21).
Competing interests
The author declares none.